system

Abstract

An object of the system according to the embodiment is to seamlessly perform necessary information collection, evaluation, and sharing of experience during a trip.SOLUTION: A system includes an information collection part, an evaluation part, a word-of-mouth registration part, a stamp rally part, an album part, an SNS cooperation part, and a settlement cooperation part. The information collection unit collects various types of information using the generated AI. The evaluation unit evaluates the information collected by the information collection unit. The word-of-mouth registering unit registers a word of mouth about a place visited by the traveler or a service experienced by the traveler. The stamp rally unit collects stamps according to the places visited by the traveler. The album section organizes photos and videos taken by the traveler. The SNS cooperation unit allows travelers to share travel experiences through the SNS. The payment federation allows travelers to make on-site payments.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the collection of information, evaluations, and sharing of experiences required during travel were dispersed, making it difficult to utilize data seamlessly.

[0005] The system according to the embodiment aims to seamlessly collect and evaluate the information required during travel, and share experiences. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an evaluation unit, a review registration unit, a stamp rally unit, an album unit, an SNS linkage unit, and a payment linkage unit. The information collection unit collects various information using a generation AI. The evaluation unit evaluates the information collected by the information collection unit. The review registration unit registers reviews about places visited by travelers or services experienced by travelers. The stamp rally unit collects stamps according to places visited by travelers. The album unit organizes photos and videos taken by travelers. The SNS linkage unit allows travelers to share their travel experiences via SNS. The payment linkage unit allows travelers to make payments on-site. [Effects of the Invention]

[0007] The system according to the embodiment allows users to seamlessly collect and evaluate information necessary for traveling and share experiences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The travel support system according to the embodiment of the present invention is a system that uses a generative AI to collect and evaluate various information and provide optimal information so that travelers can flexibly respond to changes in plans while on the go. This allows the travel support system to flexibly respond to changes in plans while on the go, enabling travelers to have a more enjoyable trip.

[0029] A travel support system according to an embodiment includes an information collection unit, an evaluation unit, a review registration unit, a stamp rally unit, an album unit, a social media linkage unit, and a payment linkage unit. The information collection unit collects various information using a generation AI. For example, if a traveler is looking for a recommended local restaurant, the information collection unit collects the latest reviews from social media and the web and lists highly rated restaurants. The evaluation unit evaluates the information collected by the information collection unit. For example, the evaluation unit analyzes the collected reviews and evaluates the information as useful to the traveler. The review registration unit registers reviews about places visited by the traveler and services experienced by the traveler. For example, if a traveler registers a review saying, "The food at this restaurant was delicious," the review registration unit analyzes the review and evaluates it as useful information for other travelers. The stamp rally unit collects stamps according to the places visited by the traveler. For example, when a traveler visits a tourist spot, stamps are automatically added via a smartphone app. The album unit organizes photos and videos taken by the traveler. For example, when a traveler uploads photos to the app, the shooting locations are automatically displayed on a map. The SNS linking unit allows travelers to share their travel experiences through SNS. For example, when a traveler posts on SNS about places they have visited or services they have experienced, the post is automatically linked to the app. The payment linking unit allows travelers to make payments on-site. For example, after a traveler has eaten at a restaurant, they can make a QR code payment through the app. This allows the travel support system according to the embodiment to flexibly accommodate changes in plans on-site, making for a more enjoyable trip.

[0030] The information gathering unit learns the traveler's past behavioral history and preferences, and can provide individually optimized information. For example, the information gathering unit collects data on places the traveler has visited in the past and services they have used, and the generation AI learns the traveler's preferences based on that data. For example, it can suggest the best restaurant for the traveler based on the ratings of restaurants they have visited in the past and the types of food they have. This allows it to provide the traveler with the best information.

[0031] The information gathering unit updates information in real time while the traveler is at their current location, allowing them to provide the most up-to-date information. For example, while the traveler is at their current location, the generation AI gathers the latest reviews in real time and provides them to the traveler. For example, if a traveler is looking for a restaurant at their current location, the generation AI will suggest the most suitable restaurant based on the latest reviews. This allows the traveler to be provided with the most up-to-date information.

[0032] The review registration unit can refer to the traveler's past review history and provide consistent ratings. For example, the review registration unit collects the review history posted by the traveler in the past, and the generation AI provides consistent ratings based on that data. For example, a new restaurant is rated based on reviews of restaurants that have been highly rated in the past. This allows for consistency in review ratings.

[0033] The review registration unit can evaluate the reliability of reviews and prioritize the display of highly reliable reviews. For example, the review registration unit analyzes the poster's past review history and ratings so that the generation AI can evaluate the reliability of the reviews. For example, reviews by posters who have received high ratings in the past are prioritized for display. This allows highly reliable reviews to be prioritized for display.

[0034] The stamp rally club can provide special rewards or benefits to travelers depending on their progress in the stamp rally. For example, if a traveler visits a specific tourist spot and collects stamps, the stamp rally club can provide special rewards or benefits. For example, a free guided tour or a discount coupon can be provided to travelers who collect stamps. This allows the club to provide special rewards or benefits depending on their progress in the stamp rally.

[0035] The stamp rally club allows travelers to share their stamp rally completion status on social media, encouraging competition with other travelers. The stamp rally club provides a function that allows travelers to share their stamp rally completion status on social media. For example, travelers can post the tourist spots they have visited and the stamps they have collected on social media, and compete with other travelers. This allows travelers to share their stamp rally completion status on social media, encouraging competition with other travelers.

[0036] The album section can automatically tag photos and videos taken by travelers, making them easier to search. The album section, for example, provides a function for automatically tagging photos and videos taken by travelers. For example, it analyzes the content of photos and videos and automatically assigns relevant tags. This makes it easier to search for photos and videos taken by travelers.

[0037] The album section can automatically edit photos and videos taken by travelers to create professional albums. The album section provides a function for automatically editing photos and videos taken by travelers to create professional albums. For example, it analyzes the content of photos and videos and automatically applies optimal layouts and effects. This allows photos and videos taken by travelers to be edited into professional albums.

[0038] The SNS linkage unit can automatically analyze the content posted by travelers on SNS and provide related information. For example, the SNS linkage unit can automatically analyze the content posted by travelers on SNS and provide information on related tourist spots and activities. For example, it can analyze photos and comments posted by travelers on SNS and suggest related tourist spots. This makes it possible to analyze the content posted by travelers on SNS and provide related information.

[0039] The SNS linking unit can automatically organize the content posted by travelers on SNS and create a feed based on the travelers' interests. The SNS linking unit can, for example, automatically organize the content posted by travelers on SNS and create a feed based on the travelers' interests. For example, it can analyze photos and comments posted by travelers in the past and display related information in the feed. This makes it possible to organize the content posted by travelers on SNS and create a feed based on the travelers' interests.

[0040] The payment linkage unit can learn from a traveler's past purchase history and provide individually optimized discount coupons. For example, the payment linkage unit collects a traveler's past purchase history, and the generation AI provides individually optimized discount coupons based on that data. For example, relevant discount coupons are provided based on products or services purchased in the past. This makes it possible to learn from a traveler's past purchase history and provide individually optimized discount coupons.

[0041] The payment linking unit can provide discount coupons for the nearest store in real time based on the traveler's current location. For example, the generation AI provides discount coupons for the nearest store in real time based on the traveler's current location. For example, if a traveler is searching for a restaurant in their current location, a discount coupon for the nearest restaurant is provided. This makes it possible to provide discount coupons for the nearest store in real time based on the traveler's current location.

[0042] The payment integration unit automatically translates discount coupons to suit the traveler's language and culture, making it possible to accommodate travelers from different cultures. For example, the generation AI automatically translates discount coupons into the traveler's language, making it possible to accommodate travelers from different cultures. For example, a discount coupon in Japanese can be translated into English and provided. This allows the discount coupon to be automatically translated to suit the traveler's language and culture, making it possible to accommodate travelers from different cultures.

[0043] The payment linkage unit can provide health-conscious discount coupons based on the traveler's health condition and dietary restrictions. For example, the payment linkage unit takes into account the traveler's health condition and the generation AI provides health-conscious discount coupons. For example, if a traveler has an allergy, a discount coupon for a restaurant that caters to allergies is provided. This makes it possible to provide health-conscious discount coupons based on the traveler's health condition and dietary restrictions.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The travel assistance system also includes a voice assistant unit, which provides a function that allows travelers to ask questions and give instructions by voice. For example, if a traveler asks, "What are some recommended restaurants nearby?", the voice assistant unit will use generative AI to suggest the best restaurant based on the latest reviews. The voice assistant unit can also provide information about the history and culture of the places the traveler visits by voice. This allows travelers to obtain information without using their hands, resulting in a more convenient travel experience.

[0046] The travel support system also includes a translation unit, which provides real-time translation functionality to help travelers understand the local language. For example, when a traveler wants to read a menu at a local restaurant, they can take a photo of the menu using their smartphone camera, and the translation unit will translate the menu into the traveler's native language. In addition, when a traveler communicates with local people, the voice translation function can be used to translate the conversation in real time. This enables smooth communication across language barriers and reduces stress for travelers.

[0047] The travel assistance system also includes an emergency contact section, which provides a function that allows travelers to receive prompt assistance in the event of an emergency. For example, if a traveler has an accident or falls ill, the emergency contact section will provide information on local emergency contacts and medical institutions. In addition, if a traveler gets lost, the emergency contact section can use GPS to pinpoint their current location and provide information on the nearest police station or tourist information center. This allows travelers to enjoy their trip with peace of mind.

[0048] The travel support system further includes an eco-tour suggestion unit, which provides a function to suggest environmentally friendly tourist spots and activities. For example, if a traveler is looking for a nature reserve or eco-friendly accommodation, the eco-tour suggestion unit uses generative AI to suggest the most suitable location. The eco-tour suggestion unit can also provide information on environmental conservation activities and workshops that travelers can participate in. This allows travelers to enjoy environmentally friendly travel.

[0049] The travel assistance system also includes a health management unit. The health management unit monitors the traveler's health status and provides appropriate advice. For example, if a traveler is tired after a long trip, the health management unit can suggest relaxing stretches and exercises. If a traveler has specific dietary restrictions, the health management unit can also suggest restaurants and menus that accommodate those restrictions. This allows travelers to enjoy their trip while maintaining their health.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information gathering section uses the generation AI to collect various information. For example, if a traveler is looking for a recommended local restaurant, the system will collect the latest reviews from social media and the web and create a list of highly rated restaurants. Step 2: The evaluation unit evaluates the information collected by the information collection unit. For example, it analyzes the collected word-of-mouth information and evaluates the information that is useful to travelers. Step 3: The review registration unit registers reviews about places that travelers have visited or services that they have experienced. For example, if a traveler registers a review saying, "The food at this restaurant was delicious," the review registration unit analyzes the review and evaluates it as useful information for other travelers. Step 4: The stamp rally club collects stamps based on the places that travelers visit. For example, when travelers visit tourist spots, stamps are automatically added via a smartphone app. Step 5: The album section organizes photos and videos taken by travelers. For example, when a traveler uploads a photo to the app, the location where the photo was taken will automatically be displayed on a map. Step 6: The SNS linking unit allows travelers to share their travel experiences through SNS. For example, when a traveler posts about the places they visited or the services they experienced on SNS, the post is automatically linked to the app. Step 7: The payment integration unit allows travelers to make payments locally. For example, after eating at a restaurant, travelers can make QR code payments through the app.

[0052] (Example 2) The travel support system according to the embodiment of the present invention is a system that uses a generative AI to collect and evaluate various information and provide optimal information so that travelers can flexibly respond to changes in plans while on the go. This allows the travel support system to flexibly respond to changes in plans while on the go, enabling travelers to have a more enjoyable trip.

[0053] A travel support system according to an embodiment includes an information collection unit, an evaluation unit, a review registration unit, a stamp rally unit, an album unit, a social media linkage unit, and a payment linkage unit. The information collection unit collects various information using a generation AI. For example, if a traveler is looking for a recommended local restaurant, the information collection unit collects the latest reviews from social media and the web and lists highly rated restaurants. The evaluation unit evaluates the information collected by the information collection unit. For example, the evaluation unit analyzes the collected reviews and evaluates the information as useful to the traveler. The review registration unit registers reviews about places visited by the traveler and services experienced by the traveler. For example, if a traveler registers a review saying, "The food at this restaurant was delicious," the review registration unit analyzes the review and evaluates it as useful information for other travelers. The stamp rally unit collects stamps according to the places visited by the traveler. For example, when a traveler visits a tourist spot, stamps are automatically added via a smartphone app. The album unit organizes photos and videos taken by the traveler. For example, when a traveler uploads photos to the app, the shooting locations are automatically displayed on a map. The SNS linking unit allows travelers to share their travel experiences through SNS. For example, when a traveler posts on SNS about places they have visited or services they have experienced, the post is automatically linked to the app. The payment linking unit allows travelers to make payments on-site. For example, after a traveler has eaten at a restaurant, they can make a QR code payment through the app. This allows the travel support system according to the embodiment to flexibly accommodate changes in plans on-site, making for a more enjoyable trip.

[0054] The information gathering unit learns the traveler's past behavioral history and preferences, and can provide individually optimized information. For example, the information gathering unit collects data on places the traveler has visited in the past and services they have used, and the generation AI learns the traveler's preferences based on that data. For example, it can suggest the best restaurant for the traveler based on the ratings of restaurants they have visited in the past and the types of food they have. This allows it to provide the traveler with the best information.

[0055] The information gathering unit updates information in real time while the traveler is at their current location, allowing them to provide the most up-to-date information. For example, while the traveler is at their current location, the generation AI gathers the latest reviews in real time and provides them to the traveler. For example, if a traveler is looking for a restaurant at their current location, the generation AI will suggest the most suitable restaurant based on the latest reviews. This allows the traveler to be provided with the most up-to-date information.

[0056] The information collection unit uses the emotion estimation function to analyze the traveler's current emotional state and prioritize providing information that matches that emotion. For example, to analyze the traveler's current emotional state, the generation AI analyzes the traveler's facial expressions and voice. For example, if the traveler is tired, the information collection unit can suggest tourist spots and cafes where they can relax. This allows the information provided to the traveler to match their emotions.

[0057] The review registration unit can refer to the traveler's past review history and provide consistent ratings. For example, the review registration unit collects the review history posted by the traveler in the past, and the generation AI provides consistent ratings based on that data. For example, a new restaurant is rated based on reviews of restaurants that have been highly rated in the past. This allows for consistency in review ratings.

[0058] The review registration unit can evaluate the reliability of reviews and prioritize the display of highly reliable reviews. For example, the review registration unit analyzes the poster's past review history and ratings so that the generation AI can evaluate the reliability of the reviews. For example, reviews by posters who have received high ratings in the past are prioritized for display. This allows highly reliable reviews to be prioritized for display.

[0059] The word-of-mouth registration unit can use the emotion estimation function to analyze the emotional tone of the word-of-mouth and preferentially display positive word-of-mouth. The word-of-mouth registration unit, for example, uses the emotion estimation function to analyze the emotional tone of the word-of-mouth. For example, word-of-mouth with a strong positive emotion can be preferentially displayed. This makes it possible to preferentially display positive word-of-mouth.

[0060] The stamp rally club can provide special rewards or benefits to travelers depending on their progress in the stamp rally. For example, if a traveler visits a specific tourist spot and collects stamps, the stamp rally club can provide special rewards or benefits. For example, a free guided tour or a discount coupon can be provided to travelers who collect stamps. This allows the club to provide special rewards or benefits depending on their progress in the stamp rally.

[0061] The stamp rally club allows travelers to share their stamp rally completion status on social media, encouraging competition with other travelers. The stamp rally club provides a function that allows travelers to share their stamp rally completion status on social media. For example, travelers can post the tourist spots they have visited and the stamps they have collected on social media, and compete with other travelers. This allows travelers to share their stamp rally completion status on social media, encouraging competition with other travelers.

[0062] The stamp rally unit uses the emotion estimation function to suggest stamp rallies that correspond to the emotional state of the traveler, thereby enticing the traveler's interest. The stamp rally unit, for example, uses the emotion estimation function to suggest stamp rallies that correspond to the emotional state of the traveler. For example, if the traveler is excited, an active stamp rally is suggested. This makes it possible to suggest stamp rallies that correspond to the emotional state of the traveler and enticing the traveler's interest.

[0063] The album section can automatically tag photos and videos taken by travelers, making them easier to search. The album section, for example, provides a function for automatically tagging photos and videos taken by travelers. For example, it analyzes the content of photos and videos and automatically assigns relevant tags. This makes it easier to search for photos and videos taken by travelers.

[0064] The album section can automatically edit photos and videos taken by travelers to create professional albums. The album section provides a function for automatically editing photos and videos taken by travelers to create professional albums. For example, it analyzes the content of photos and videos and automatically applies optimal layouts and effects. This allows photos and videos taken by travelers to be edited into professional albums.

[0065] The album unit uses the emotion estimation function to suggest photos and videos that correspond to the emotional state of the traveler, thereby garnering the traveler's interest. The album unit, for example, uses the emotion estimation function to suggest photos and videos that correspond to the emotional state of the traveler. For example, if the traveler is excited, active photos and videos are suggested. In this way, photos and videos that correspond to the emotional state of the traveler are suggested, thereby garnering the traveler's interest.

[0066] The SNS linkage unit can automatically analyze the content posted by travelers on SNS and provide related information. For example, the SNS linkage unit can automatically analyze the content posted by travelers on SNS and provide information on related tourist spots and activities. For example, it can analyze photos and comments posted by travelers on SNS and suggest related tourist spots. This makes it possible to analyze the content posted by travelers on SNS and provide related information.

[0067] The SNS linking unit can automatically organize the content posted by travelers on SNS and create a feed based on the travelers' interests. The SNS linking unit can, for example, automatically organize the content posted by travelers on SNS and create a feed based on the travelers' interests. For example, it can analyze photos and comments posted by travelers in the past and display related information in the feed. This makes it possible to organize the content posted by travelers on SNS and create a feed based on the travelers' interests.

[0068] The SNS linking unit uses the emotion estimation function to suggest SNS posts according to the emotional state of the traveler, thereby garnering the traveler's interest. The SNS linking unit, for example, uses the emotion estimation function to suggest SNS posts according to the emotional state of the traveler. For example, if the traveler is excited, active posts are suggested. This makes it possible to suggest SNS posts according to the emotional state of the traveler and garner the traveler's interest.

[0069] The payment linkage unit can learn from a traveler's past purchase history and provide individually optimized discount coupons. For example, the payment linkage unit collects a traveler's past purchase history, and the generation AI provides individually optimized discount coupons based on that data. For example, relevant discount coupons are provided based on products or services purchased in the past. This makes it possible to learn from a traveler's past purchase history and provide individually optimized discount coupons.

[0070] The payment linking unit can provide discount coupons for the nearest store in real time based on the traveler's current location. For example, the generation AI provides discount coupons for the nearest store in real time based on the traveler's current location. For example, if a traveler is searching for a restaurant in their current location, a discount coupon for the nearest restaurant is provided. This makes it possible to provide discount coupons for the nearest store in real time based on the traveler's current location.

[0071] The payment linking unit uses the emotion estimation function to suggest discount coupons according to the emotional state of the traveler, thereby garnering the traveler's interest. The payment linking unit, for example, uses the emotion estimation function to suggest discount coupons according to the emotional state of the traveler. For example, if the traveler is excited, discount coupons for active activities are suggested. In this way, discount coupons according to the emotional state of the traveler are suggested, thereby garnering the traveler's interest.

[0072] The payment integration unit automatically translates discount coupons to suit the traveler's language and culture, making it possible to accommodate travelers from different cultures. For example, the generation AI automatically translates discount coupons into the traveler's language, making it possible to accommodate travelers from different cultures. For example, a discount coupon in Japanese can be translated into English and provided. This allows the discount coupon to be automatically translated to suit the traveler's language and culture, making it possible to accommodate travelers from different cultures.

[0073] The payment linkage unit can provide health-conscious discount coupons based on the traveler's health condition and dietary restrictions. For example, the payment linkage unit takes into account the traveler's health condition and the generation AI provides health-conscious discount coupons. For example, if a traveler has an allergy, a discount coupon for a restaurant that caters to allergies is provided. This makes it possible to provide health-conscious discount coupons based on the traveler's health condition and dietary restrictions.

[0074] The payment linkage unit uses the emotion estimation function to predict discount coupons that travelers are likely to be interested in, thereby drawing out new interests that travelers may not be aware of. The payment linkage unit, for example, uses the emotion estimation function to predict discount coupons that travelers are likely to be interested in. For example, new discount coupons may be suggested based on discount coupons for tourist spots and activities that travelers have shown interest in in the past. This makes it possible to predict discount coupons that travelers are likely to be interested in, thereby drawing out new interests that travelers may not be aware of.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The travel assistance system also includes a voice assistant unit, which provides a function that allows travelers to ask questions and give instructions by voice. For example, if a traveler asks, "What are some recommended restaurants nearby?", the voice assistant unit will use generative AI to suggest the best restaurant based on the latest reviews. The voice assistant unit can also provide information about the history and culture of the places the traveler visits by voice. This allows travelers to obtain information without using their hands, resulting in a more convenient travel experience.

[0077] The travel support system also includes a translation unit, which provides real-time translation functionality to help travelers understand the local language. For example, when a traveler wants to read a menu at a local restaurant, they can take a photo of the menu using their smartphone camera, and the translation unit will translate the menu into the traveler's native language. In addition, when a traveler communicates with local people, the voice translation function can be used to translate the conversation in real time. This enables smooth communication across language barriers and reduces stress for travelers.

[0078] The travel assistance system also includes an emergency contact section, which provides a function that allows travelers to receive prompt assistance in the event of an emergency. For example, if a traveler has an accident or falls ill, the emergency contact section will provide information on local emergency contacts and medical institutions. In addition, if a traveler gets lost, the emergency contact section can use GPS to pinpoint their current location and provide information on the nearest police station or tourist information center. This allows travelers to enjoy their trip with peace of mind.

[0079] The travel support system further includes an eco-tour suggestion unit, which provides a function to suggest environmentally friendly tourist spots and activities. For example, if a traveler is looking for a nature reserve or eco-friendly accommodation, the eco-tour suggestion unit uses generative AI to suggest the most suitable location. The eco-tour suggestion unit can also provide information on environmental conservation activities and workshops that travelers can participate in. This allows travelers to enjoy environmentally friendly travel.

[0080] The travel assistance system also includes a health management unit. The health management unit monitors the traveler's health status and provides appropriate advice. For example, if a traveler is tired after a long trip, the health management unit can suggest relaxing stretches and exercises. If a traveler has specific dietary restrictions, the health management unit can also suggest restaurants and menus that accommodate those restrictions. This allows travelers to enjoy their trip while maintaining their health.

[0081] The travel assistance system can also use the emotion estimation function to provide a music playlist based on the traveler's emotional state. For example, if a traveler wants to relax, the emotion estimation function can be used to suggest relaxing music. Alternatively, if a traveler is excited, the system can suggest energetic music. This allows the traveler to enjoy music that suits their emotional state, further enriching their travel experience.

[0082] The travel support system can also use the emotion estimation function to suggest tourist spots based on the traveler's emotional state. For example, if a traveler is tired, it can suggest relaxing tourist spots. If a traveler is excited, it can also suggest active tourist spots. This allows travelers to enjoy tourist spots that suit their emotional state, further enriching their travel experience.

[0083] The travel assistance system can further use the emotion estimation function to suggest restaurants based on the traveler's emotional state. For example, if a traveler wants to relax, it can suggest a restaurant with a quiet and calm atmosphere. On the other hand, if a traveler is excited, it can suggest a lively restaurant. This allows travelers to enjoy restaurants that suit their emotional state, enriching their travel experience.

[0084] The travel support system can also use emotion estimation to suggest activities based on the traveler's emotional state. For example, if a traveler wants to relax, it can suggest relaxing activities such as yoga or spa. If a traveler is excited, it can also suggest adventure sports or nightlife. This allows travelers to enjoy activities that suit their emotional state, further enriching their travel experience.

[0085] The travel support system can also use the emotion estimation function to make shopping suggestions based on the traveler's emotional state. For example, if a traveler wants to relax, it can suggest shopping areas with a quiet and calm atmosphere. On the other hand, if a traveler is excited, it can suggest lively shopping malls and markets. This allows travelers to enjoy shopping according to their emotional state, enriching their travel experience.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The information gathering section uses the generation AI to collect various information. For example, if a traveler is looking for a recommended local restaurant, the system will collect the latest reviews from social media and the web and create a list of highly rated restaurants. Step 2: The evaluation unit evaluates the information collected by the information collection unit. For example, it analyzes the collected word-of-mouth information and evaluates the information that is useful to travelers. Step 3: The review registration unit registers reviews about places that travelers have visited or services that they have experienced. For example, if a traveler registers a review saying, "The food at this restaurant was delicious," the review registration unit analyzes the review and evaluates it as useful information for other travelers. Step 4: The stamp rally club collects stamps based on the places that travelers visit. For example, when travelers visit tourist spots, stamps are automatically added via a smartphone app. Step 5: The album section organizes photos and videos taken by travelers. For example, when a traveler uploads a photo to the app, the location where the photo was taken will automatically be displayed on a map. Step 6: The SNS linking unit allows travelers to share their travel experiences through SNS. For example, when a traveler posts about the places they visited or the services they experienced on SNS, the post is automatically linked to the app. Step 7: The payment integration unit allows travelers to make payments locally. For example, after eating at a restaurant, travelers can make QR code payments through the app.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[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 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.

[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 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).

[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] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 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. An information collection unit that uses generation AI to collect various information; an evaluation unit that evaluates the information collected by the information collection unit; A review registration section where travelers register reviews about places they have visited or services they have experienced; A stamp rally section where travelers collect stamps according to the places they visit, An album section for organizing photos and videos taken by travelers, The SNS Collaboration Department allows travelers to share their travel experiences through social media, A payment linking unit through which travelers make payments locally. A system characterized by:

2. The information collecting unit Learn about the traveler's past behavioral history and preferences to provide individually optimized information 2. The system of claim 1.

3. The review registration unit Review the traveler's past review history to provide a consistent rating 2. The system of claim 1.

4. The stamp rally section Offering special rewards or benefits to the traveler depending on the progress of the stamp rally 2. The system of claim 1.

5. The album section includes: The photos and videos taken by the traveler are automatically tagged to make them easier to search.

2. The system of claim 1.

6. The SNS linking unit is Automatically analyze the content posted by the traveler on the SNS and provide the relevant information 2. The system of claim 1.

7. The payment linking unit Learn the traveler's past purchase history and provide individually optimized discount coupons 2. The system of claim 1.

8. The information collecting unit Analyze the traveler's current emotional state and provide information that matches that emotion preferentially 2. The system of claim 1.

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