System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving user-entered interest and preference information; means for collecting and organizing data relating to local tourist attractions and commercial establishments; means for suggesting tourist attractions and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; means for confirming the user's visit; and means for distributing rewards to the user and stores based on the confirmed visit.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] While the internet and travel guides are overflowing with information, much of it is general and often doesn't match the specific interests and needs of individual targets. This can make it difficult for tourists and local residents to find the right information. Foreign tourists also face language barriers and have difficulty taking advantage of special offers and incentives at their destinations. A system is needed to resolve these issues, allowing tourists and local residents to maximize the appeal of their local areas while also achieving mutual benefits with the businesses they visit. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including: means for receiving interest and preference information input by a user; means for collecting and organizing data on local tourist attractions and commercial facilities; means for suggesting tourist attractions and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; means for the user to confirm a visit; and means for distributing rewards to the user and the stores based on the confirmed visit. Furthermore, the system includes means for tagging the information input by the user and the collected data on tourist attractions and commercial facilities in multiple languages, and by using a generative AI model to make optimized suggestions of tourist attractions and stores based on the user's interest and preference information and the collected data, it becomes possible to provide optimal suggestions tailored to individual needs, as well as convenience and benefits at the visited destinations.

[0006] "User" refers to an individual who uses this system to receive suggestions for tourist spots and commercial facilities.

[0007] "Interest and preference information" refers to data that users input based on their own interests and preferences, and is used to suggest tourist spots and stores.

[0008] "Data" refers to information about local tourist attractions and commercial facilities that is collected and organized to generate the proposals.

[0009] "Means" refers to a method or apparatus for achieving a particular function.

[0010] "Suggestions" refers to a list of tourist attractions and commercial facilities generated based on the user's interests and preferences.

[0011] "Coupon" refers to a code or certificate offering discounts or special offers that can be used at proposed tourist attractions or commercial establishments.

[0012] "Visit confirmation" refers to the process by which the system verifies that the user has actually visited the suggested tourist attractions and commercial facilities.

[0013] "Rewards" refers to incentives and benefits that users and stores receive by using the system.

[0014] "Multilingual" refers to the ability of a system to function in multiple languages.

[0015] "Generative AI Model" refers to the artificial intelligence algorithm used to generate optimal recommendations based on user interests, preferences, and collected data.

[0016] "System" refers to a set of programs and hardware including the aforementioned means for providing suggestions and rewards for tourist destinations and commercial facilities. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a system that proposes local tourist attractions and stores optimized for a specific user and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[0039] The first major component of this system is a means for users to input personal information such as their interests, preferences, planned areas to visit, and length of stay. Users can easily input this information using a smartphone or web app. For example, if a user is interested in "food tours" and "historical buildings," inputting this information makes it possible to extract information relevant to them from other general information.

[0040] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[0041] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, and collected data. The AI ​​model analyzes user input and a vast data set to create personalized recommendations. For example, a user who wants to go on a food tour will be prioritized to see information about affiliated restaurants and cafes.

[0042] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or web app and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0043] When a user actually visits a suggested tourist attraction or store, the visit is confirmed: the user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0044] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0045] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and historical buildings. He then enters that he plans to visit Kyoto. Based on this, the server collects data on Kyoto's food spots and historical buildings, and uses that information to suggest the best tourist spots and stores for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[0046] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests and preferences, and by confirming visits and distributing rewards.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The user enters interest and preference information.

[0050] User: Accesses a smartphone or web app and registers or logs in.

[0051] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[0052] Terminal: Sends the entered information to the server.

[0053] Step 2:

[0054] Store basic information and information about your interests and preferences.

[0055] Server: Creates a user profile based on the received user basic information and interest / preference information.

[0056] Server: Stores the profile in a database.

[0057] Step 3:

[0058] Collect and organize data on local tourist attractions and commercial facilities.

[0059] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[0060] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[0061] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[0062] Step 4:

[0063] Generate suggestions based on user requests.

[0064] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[0065] Terminal: Sends the entered conditions to the server.

[0066] Server: Uses generative AI models to match user profiles with current requests and generate the best list of tourist attractions and stores.

[0067] Server: Sends the generated list to the user's device.

[0068] Step 5:

[0069] View the suggested results.

[0070] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[0071] Step 6:

[0072] Generate a coupon.

[0073] Server: Generates coupon codes for each suggested tourist attraction and store.

[0074] Server: Sends the coupon code to the user's device.

[0075] Step 7:

[0076] View coupons.

[0077] On your device: Display the coupon code in the user's app.

[0078] User: Use coupons at suggested tourist spots and stores.

[0079] Step 8:

[0080] Confirm your visit.

[0081] User: Visits the suggested tourist spot or store and checks in using the app.

[0082] Terminal: Sends check-in information to the server.

[0083] Server: Checks the user's location and check-in data, and records the visit.

[0084] Step 9:

[0085] Distribute rewards.

[0086] Server: After confirming the visit, the user is given points or a coupon to use next time.

[0087] Server: Provide incentives to proposed tourist destinations and stores.

[0088] Step 10:

[0089] Notify compensation information.

[0090] Terminal: Notifies the user of the points or coupons awarded.

[0091] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] Personalized travel plans are important for modern travelers, but existing systems do not adequately suggest optimal tourist destinations and stores based on users' individual interests and preferences. Furthermore, there is a lack of collaboration with stores and tourist destinations, and systems for providing coupons, confirming visits, and distributing rewards do not function efficiently, resulting in low satisfaction for both users and stores. Another issue is the lack of multilingual support, which limits the global use of tourist information.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes: means for receiving information about interests and preferences input by a user; means for collecting and organizing data on local tourist attractions and commercial facilities; means for suggesting tourist attractions and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; means for the user to confirm a visit; means for distributing rewards to the user and the facilities based on the confirmed visit; means for inputting the user's input information and the data as prompts into a generative AI model to create personalized recommendations; and means for tagging the collected data in multiple languages. This enables optimal recommendations of tourist attractions and stores based on the user's individual interests and preferences, and efficient confirmation of visits to the suggested tourist attractions and stores and distribution of rewards. Furthermore, multilingual support enables the use of global tourist information.

[0097] "User" refers to an individual who uses this system to receive suggestions of tourist spots and stores based on their interests and preferences.

[0098] "Information about interests and preferences" refers to information entered by the user about personal interests and preferences such as food tours and historical buildings, as well as information about areas to be visited and length of stay.

[0099] "Region" refers to the particular geographic area that a user plans to visit.

[0100] "Tourist destination" refers to a tourist spot or sightseeing spot that users intend to visit.

[0101] "Commercial facilities" refer to facilities that carry out commercial activities such as stores and restaurants.

[0102] "Data" refers to all information handled by the system, including information about tourist destinations and commercial facilities, as well as users' personal information.

[0103] "Generative AI models" refer to AI algorithms or models that generate recommendations for tourist destinations and stores based on user input and collected data.

[0104] A "prompt" refers to a string of characters containing commands or instructions that are input into a generative AI model.

[0105] "Coupon" refers to a code or ticket containing discounts or benefits that can be used by the user at suggested tourist spots or commercial facilities.

[0106] "Visit confirmation" refers to the means or actions to confirm that a user has actually visited a suggested tourist spot or store.

[0107] "Rewards" refers to points, coupons, incentives, etc. provided to users and affiliated facilities based on confirmation of visits.

[0108] "Multilingual tag" refers to identification information that is assigned to collected data to support multiple languages.

[0109] This invention is a system that suggests tourist spots and commercial facilities for users when traveling based on their interests and preferences, provides coupons, and distributes rewards after confirming their visit. This system is realized using a server, terminals, and a generative AI model.

[0110] Entering user information

[0111] Users input information such as their interests and preferences, areas they plan to visit, and the length of their stay via their smartphone or web app. This information is sent from the device to the server and stored in a database. For example, if a user is interested in "eating around" and "historical buildings," they can select these and input that they plan to visit Kyoto.

[0112] Data collection and organization

[0113] The server collects tourist attraction and store data from the internet, public databases, and partner companies. This includes tourist attraction locations, opening hours, photos, user reviews, and more. Python scripts and the Pandas library are used to consolidate and clean the data, tag it in multiple languages, and store it in a MySQL database. For example, the server retrieves and organizes tourist attraction data from a local tourist association's API.

[0114] Suggestions for users

[0115] The server inputs a prompt to a generative AI model, such as GPT-4, based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Area visited: Kyoto, Length of stay: 3 days." The generative AI model generates personalized suggestions based on this prompt, and the server sends the results in JSON format to the user's device.

[0116] Generate and offer coupons

[0117] The server connects with the suggested tourist attractions and stores to generate a unique coupon code, which is then sent to the user's device as a push notification. For example, the user can receive a coupon for a "free coffee" and use it at the store.

[0118] Confirmation of visit

[0119] When a user visits a suggested tourist spot or store, they tap the "Check-in" button in the app. The smartphone acquires the user's location information and sends it to the server. The server records the received check-in information in a database.

[0120] Reward Distribution

[0121] The server checks the user's visit history and calculates rewards based on a point system. For example, if a user visits five tourist spots, they will receive a discount coupon that can be used next time. In addition, partner stores will automatically receive incentives based on the number of visitors.

[0122] This system allows users to enjoy personalized travel plans, use coupons during their trip, and earn rewards after their visit, while businesses benefit from increased visitor numbers and improved customer satisfaction.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] Users access the site from their smartphones or web apps and enter information such as their interests and preferences, the areas they plan to visit, and the length of their stay. Specifically, they enter the categories of interest (e.g., "food tours" or "historical buildings"), the areas they plan to visit (e.g., "Kyoto"), and the length of their stay (e.g., "3 days"). The device compiles this information and sends it to the server. The input data includes the interest categories, areas to visit, and length of stay, and when it is sent to the server, it becomes the base data for the next step.

[0126] Step 2:

[0127] The server collects data on tourist attractions and commercial facilities from the Internet, public databases, and partner companies. Specifically, it obtains information such as tourist attraction locations, opening hours, photos, and user reviews from the tourist association's API and partner company databases. The collected data includes basic information about tourist attractions and user reviews. The server uses Python scripts and the Pandas library to consolidate and clean the data, add multilingual tags, and store it in a MySQL database. The input data is the collected raw data, and the output data is the organized and consolidated tourist attraction data.

[0128] Step 3:

[0129] The server inputs a prompt to the generative AI model based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Areas visited: Kyoto, Length of stay: 3 days." The generative AI model (e.g., GPT-4) generates personalized suggestions based on this prompt. The input data is the user's profile information and collected tourist destination data, and the output data is a personalized list of tourist destinations and commercial facilities. The server sends the generated suggestions in JSON format to the user's device.

[0130] Step 4:

[0131] The server connects with the suggested tourist attractions and commercial facilities and generates a unique coupon code. Specifically, it uses a Python library to generate the coupon code, creating a code such as "COFFEEFREE_12345." The generated coupon code is sent to the user's device as a push notification. The input data is information about the suggested tourist attractions and commercial facilities, and the output data is the generated coupon code. The user can check the coupon code within the app, which is then displayed on the screen.

[0132] Step 5:

[0133] When a user visits a suggested tourist spot or commercial facility, they tap the "Check-in" button in the app. Specifically, the user's smartphone acquires location information, and once the visit is confirmed, the information is sent to the server. The input data is the user's current location information, and the output data is a check-in record. The server records this in a database.

[0134] Step 6:

[0135] The server checks the user's visit history and calculates rewards. For example, if a user visits multiple tourist attractions, points and coupons that can be used next time are awarded according to the number of visits. Specifically, if a user visits five tourist attractions, a discount coupon that can be used next time is generated and sent. Incentives are automatically awarded to affiliated facilities according to the number of visitors. The input data is check-in records, and the output data is points and coupons awarded to users, as well as incentives for affiliated facilities.

[0136] (Application example 1)

[0137] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] Conventional systems for recommending tourist spots and commercial facilities can recommend tourist spots and stores based on a user's interests and preferences, but they require the user to actually visit the locations, which requires time and effort and costs. It is also difficult to confirm visits and distribute rewards in real time. Furthermore, optimizing traffic flow and improving the efficiency of reward systems are also issues. To solve these problems, a system is needed that allows users to virtually experience sightseeing and commercial facility visits and receive rewards without actually visiting the locations.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0140] In this invention, the server includes means for receiving interest and preference information input by a user, means for collecting and organizing data on local tourist spots and commercial facilities, and means for suggesting tourist spots and stores based on the user's interest and preference information and the data. This allows a user to visit tourist spots and stores in a virtual space using a virtual reality device and receive rewards based on their visits.

[0141] The system also includes a means for generating coupons for suggested tourist attractions and stores, a means for users to confirm their virtual visits, and a means for distributing rewards to users and stores based on the confirmed visits. It also includes a means for tagging user-entered information and collected data about tourist attractions and commercial facilities in multiple languages, and a means for optimizing recommendations for tourist attractions and stores using a generative AI model based on the user's interests, preferences, and collected data. This improves the efficiency and satisfaction of users' sightseeing and shopping experiences, while simultaneously benefiting stores.

[0142] The "means for receiving information on interests and preferences input by the user" is an interface through which the user inputs and receives information on his or her interests, preferences, and places to visit.

[0143] "Means for collecting and organizing data on local tourist destinations and commercial facilities" refers to a system that automatically collects and organizes information on tourist destinations and commercial facilities from the Internet, public databases, etc.

[0144] "Means for suggesting tourist spots and stores based on the user's interests and preferences and the data" refers to an algorithm or engine that selects the most suitable tourist spots and stores from collected data based on the interests and preferences entered by the user and suggests them to the user.

[0145] The "means for generating coupons for suggested tourist spots and stores" is a system for generating coupons including discounts and special offers for tourist spots and stores that the user plans to visit and providing them to the user.

[0146] "Means for users to confirm their visit" refers to a method for users to confirm that they have actually visited the suggested tourist spots or stores, such as using a check-in function.

[0147] The "means for distributing rewards to users and stores based on confirmed visits" is a system for distributing points or coupons that can be used next time to users and stores based on when a user completes a visit.

[0148] "Means for visiting tourist spots and stores in a virtual space using a virtual reality device" refers to technology and devices that allow users to visit and experience tourist spots and stores in a virtual space using a virtual reality device.

[0149] A "means for distributing rewards to users based on their visits in a virtual space" is a system or method for distributing rewards to users based on their performance when they complete a visit in a virtual space.

[0150] The present invention relates to a system that suggests tourist spots and shops based on a user's preferences and interests, enables the user to visit those places in a virtual space using a virtual reality device, and further confirms the visit and distributes rewards.

[0151] System program implementation

[0152] First, the server receives information about interests and preferences entered by the user. Users enter information about their interests, preferences, and areas they plan to visit through their smartphones or web apps. This information is stored in a cloud database (e.g., Firebase).

[0153] The server then collects and organizes data on local tourist attractions and commercial facilities from the Internet and public databases. The specific software used here is a database management system (e.g., MySQL, PostgreSQL). The collected data includes tourist attraction locations, opening hours, photos, user reviews, etc.

[0154] The server then suggests tourist spots and shops based on the user's preferences and collected data. It uses a generative AI model (e.g., GPT-4) to analyze the user's preferences and related data and generate optimal suggestions. It uses prompts such as:

[0155] User preferences: Foodie, Historical buildings

[0156] Planned visit area: Kyoto

[0157] Please suggest recommended tourist spots and stores.

[0158] Based on the proposed results, the server enables sightseeing in a virtual space using a virtual reality device. Specifically, it generates a virtual tour using a VR head-mounted display (e.g., Oculus Rift S). It uses a VR library (e.g., vrpy) to build a virtual space, allowing users to visit tourist spots and stores within it.

[0159] The server also generates coupons that users can use for the suggested tourist spots and stores, such as a "free cup of coffee" coupon.

[0160] When a user completes a visit within the virtual space, the server verifies the visit using a checkpoint function that records that the user has arrived at a specific point within the virtual reality space.

[0161] Once the visit is confirmed, the server distributes rewards to the user and the store. The user receives points or coupons that can be used next time, and the store receives incentives.

[0162] Specific examples

[0163] For example, if a user is interested in "food tours" and "historical buildings," the system will suggest the most suitable tourist spots and stores in a virtual space based on this. A user planning to visit Kyoto can use a VR device to virtually visit Kyoto's tourist spots and enjoy checking information. In this case, the AI ​​model will suggest the most suitable tourist spots and stores by using the following prompt sentence:

[0164] User preferences: Foodie, Historical buildings

[0165] Planned visit area: Kyoto

[0166] Please suggest recommended tourist spots and stores.

[0167] This invention allows users to enjoy a virtual sightseeing experience before actually visiting, enabling efficient planning. Furthermore, coupons and rewards offered during the on-site visit provide benefits to both users and stores.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] The user uses a device to input information about their interests, preferences, and planned visit locations. The input data is stored in a cloud database (e.g., Firebase). The inputs are the user's interests and preferences (e.g., food tours, historical buildings) and the planned visit location (e.g., Kyoto). The output is stored in Firebase.

[0171] Step 2:

[0172] The server collects data about local tourist attractions and businesses from the internet and public databases. The collected data includes location, opening hours, photos, user reviews, etc. This data is stored in a database management system (e.g., MySQL, PostgreSQL). As input, the URLs or API endpoints of the tourist attractions and businesses are used. As output, the collected data is stored in the database.

[0173] Step 3:

[0174] The server uses a generative AI model (e.g., GPT-4) to create suggestions for tourist attractions and stores based on the user's input information and collected data. Specifically, the server sends the following prompt to GPT-4 and receives the results. The inputs used are the user's interests and preferences, as well as information about places they plan to visit. The output is the tourist attraction and store suggestions generated by the AI ​​model.

[0175] User preferences: Foodie, Historical buildings

[0176] Planned visit area: Kyoto

[0177] Please suggest recommended tourist spots and stores.

[0178] Step 4:

[0179] Based on the generated suggestions, the server generates a sightseeing experience in a virtual space using a virtual reality device (e.g., Oculus Rift S). Specifically, it uses a VR library (e.g., vrpy) to build a virtual space in which users can visit tourist attractions and shops. The generated information on the suggested tourist attractions and shops is used as input. The output is a virtual space that can be visited by users.

[0180] Step 5:

[0181] The server generates coupons for tourist attractions and stores in the virtual space. The generated coupons are sent to the user's device and can be used by the user. The input is information about the suggested tourist attractions and stores. The output is the generated coupons that are provided to the user.

[0182] Step 6:

[0183] When a user completes a visit in the virtual space, the device sends the visit information to the server, which verifies the visit information and stores it in a database. The input includes the user's visit information, and the output is the verified visit information.

[0184] Step 7:

[0185] The server distributes reward points and coupons to users based on the confirmed visits. In addition, incentives are also provided to suggested stores. The input is the confirmed visit information. The output is the distribution of rewards to users and stores.

[0186] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0187] The present invention relates to a system that combines interest and preference information input by a user with an emotion engine that recognizes emotions in real time to make more personalized suggestions about tourist spots and commercial facilities, and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[0188] The first major component of this system is a means for users to input personal information such as their interests and preferences, areas they plan to visit, and the length of their stay. Users can easily input this information using a smartphone or web application. For example, if a user is interested in "eating out" and "historical buildings," inputting this information will enable the system to provide optimal sightseeing suggestions to the user.

[0189] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[0190] Another feature of the present invention is the introduction of an emotion engine that recognizes the user's emotional state in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state (e.g., joy, surprise, sadness, stress, etc.). This emotion information, along with the user's interests and preferences, is used to suggest tourist spots and commercial facilities.

[0191] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, emotional state, and collected data. The AI ​​model analyzes all user inputs and real-time collected emotional information, and creates personalized recommendations based on that information. For example, if the emotion engine detects that the user is "stressed," it may suggest a relaxing cafe or a quiet park.

[0192] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0193] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0194] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0195] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and visiting historical buildings. He then enters that he plans to visit Kyoto. The emotion engine then analyzes Yamada's emotional state and determines that he is currently seeking relaxation. Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses this information to suggest tourist spots and stores that are ideal for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[0196] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests, preferences, and emotional information, and by confirming visits and distributing rewards.

[0197] The processing flow will be explained below.

[0198] Step 1:

[0199] The user enters interest and preference information.

[0200] User: Accesses a smartphone or web app and registers or logs in.

[0201] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[0202] Terminal: Sends the entered information to the server.

[0203] Step 2:

[0204] Store basic information and information about your interests and preferences.

[0205] Server: Creates a user profile based on the received user basic information and interest / preference information.

[0206] Server: Stores the profile in a database.

[0207] Step 3:

[0208] Collect and organize data on local tourist attractions and commercial facilities.

[0209] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[0210] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[0211] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[0212] Step 4:

[0213] An emotion engine is used to collect user emotion information.

[0214] Device: Uses the smartphone's camera and microphone to record the user's facial expressions and voice in real time.

[0215] On the device: An emotion engine is used to analyze the user's current emotional state (e.g., joy, surprise, sadness, stress, etc.).

[0216] Terminal: Sends the analysis results to the server.

[0217] Step 5:

[0218] Generate suggestions based on user requests.

[0219] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[0220] Terminal: Sends the entered conditions to the server.

[0221] Server: Matches user profile, current needs, and sentiment information and uses generative AI models to generate the best list of tourist attractions and stores.

[0222] Server: Sends the generated list to the user's device.

[0223] Step 6:

[0224] View the suggested results.

[0225] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[0226] Step 7:

[0227] Generate a coupon.

[0228] Server: Generates coupon codes for each suggested tourist attraction and store.

[0229] Server: Sends the coupon code to the user's device.

[0230] Step 8:

[0231] View coupons.

[0232] On your device: Display the coupon code in the user's app.

[0233] User: Use coupons at suggested tourist spots and stores.

[0234] Step 9:

[0235] Confirm your visit.

[0236] User: Visits the suggested tourist spot or store and checks in using the app.

[0237] Terminal: Sends check-in information to the server.

[0238] Server: Checks the user's location and check-in data, and records the visit.

[0239] Step 10:

[0240] Distribute rewards.

[0241] Server: After confirming the visit, the user is given points or a coupon to use next time.

[0242] Server: Provide incentives to proposed tourist destinations and stores.

[0243] Step 11:

[0244] Notify compensation information.

[0245] Terminal: Notifies the user of the points or coupons awarded.

[0246] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[0247] Example 2

[0248] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] In the modern tourism industry, there is a demand for personalized recommendations of tourist spots and shops that take into account a user's individual interests and emotional state. However, existing systems struggle to analyze a user's emotional information in real time and provide optimal recommendations to the user. Furthermore, there are issues with the fairness and efficiency of distributing rewards for suggested tourist spots and shops. This can lead to low satisfaction for both users and shops.

[0250] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving interest and preference information input by the user, a means for collecting and organizing data on local tourist attractions and commercial facilities, a means for collecting and analyzing emotional information in real time, a means for generating coupons for suggested tourist attractions and stores, a means for the user to confirm a visit, and a means for distributing rewards to the user and the facility based on the confirmed visit. This makes it possible to suggest optimal tourist attractions and stores based on the user's individual interest and preference information and real-time emotional information. Furthermore, the satisfaction of both the user and the store can be increased by confirming the visit and distributing rewards.

[0251] "User" refers to an individual who uses this system.

[0252] "Interest and preference information" is information that indicates a user's particular tastes and interests, including information related to sightseeing and leisure.

[0253] "Data" refers to a comprehensive range of information about tourist attractions and commercial facilities, including location, opening hours, photos, user reviews, etc.

[0254] "Emotion information" is information that indicates the user's emotional state, collected in real time from the user's facial expressions and tone of voice, and includes joy, surprise, sadness, stress, and the like.

[0255] "Coupon" means a code or ticket offering discounts or special offers that can be used at the proposed tourist attractions or stores.

[0256] "Visit confirmation" refers to the process of the system confirming that the user has actually visited the suggested tourist attractions and stores.

[0257] "Rewards" refers to incentives, points, and other benefits distributed to users and stores based on confirmed visits.

[0258] "Server" refers to the computer system that performs centralized data processing and proposal generation for this System.

[0259] A "multilingual tag" refers to an identifier that uniformly organizes information provided in various languages, allowing users to access the information across language barriers.

[0260] "Generative AI model" refers to an artificial intelligence modeling technology that suggests optimal tourist spots and stores based on a user's interests, preferences, emotional information, and collected data.

[0261] MODE FOR CARRYING OUT THE INVENTION

[0262] This invention is a system that combines information on the user's interests and preferences with an emotion engine that recognizes emotions in real time to suggest more personalized tourist spots and commercial facilities, and distributes rewards to both the user and the stores.

[0263] This system can generate suggestions based on personal information entered by users using their smartphones or web applications, such as their interests and preferences, areas they plan to visit, and length of stay. Next, data on local tourist attractions and commercial facilities is automatically collected, integrated, and organized by the server from the Internet, public databases of local governments, and partner companies. This creates a database containing information such as the locations of tourist attractions and stores, their opening hours, photos, and user reviews. Furthermore, this data is tagged in multiple languages, making it possible to provide information across language barriers.

[0264] Another feature of the present invention is that the device has an emotion engine that uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state. This allows emotional information to be collected in real time and used to suggest tourist spots and commercial facilities, along with the user's interests and preferences.

[0265] The server then uses a generative AI model to generate optimal recommendations for tourist spots and commercial facilities based on the user's input and collected emotional information. The AI ​​model analyzes all of the user's input and the emotional information collected in real time, and creates personalized recommendations based on that. For example, if the emotional engine detects that the user's emotional state is "stressed," it may suggest a relaxing cafe or a quiet park.

[0266] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0267] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0268] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0269] Specific examples

[0270] Consider a case where a traveler uses this system. The traveler accesses the app and registers that they are interested in eating out and visiting historical buildings. They then enter Kyoto as the area they plan to visit and set the length of their stay. The emotion engine analyzes the traveler's emotional state and determines that they are currently seeking "relaxation." Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses that information to suggest tourist spots and stores that are ideal for the traveler. For example, it suggests quiet and relaxing cafes. Coupons that can be used at the suggested cafes are issued, and the traveler uses them to visit various locations. Once the visit is confirmed, rewards are distributed to the traveler and the store.

[0271] Prompt Sentence Examples

[0272] "I'm interested in food tours and historical architecture, and I'm planning to visit Kyoto. Can you suggest some relaxing tourist spots?"

[0273] This system proposes optimal tourist spots and commercial facilities based on the user's interests, preferences, and real-time emotional information, and distributes rewards for those visits, thereby benefiting both the user and the store.

[0274] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0275] System program processing flow

[0276] Step 1:

[0277] The user collects input information. Using a smartphone or web application, the user inputs personal information such as interests, preferences, areas to visit, and length of stay. This input information is sent to the server via the device. For example, a user may input information such as "interested in food tours" and "historical buildings," "plan to visit Kyoto," and "stay for three days."

[0278] Input: Interests and preferences, areas to visit, length of stay, etc.

[0279] Output: User information data sent to the server

[0280] Step 2:

[0281] This service collects and organizes data on tourist destinations and commercial facilities. The server automatically collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies. This collected data is integrated and centralized, with information such as location, opening hours, photos, and user reviews. In addition, multilingual tags are added, making it possible to provide information across language barriers.

[0282] Input: Internet, public databases of local governments, data from partner companies

[0283] Output: Integrated and organized tourist destination and commercial facility data

[0284] Step 3:

[0285] The device analyzes the user's emotional state in real time. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice, and the emotion engine evaluates the user's current emotional state. This emotional information is sent to the server in real time. For example, if the device detects that the user's facial expression is relaxed, it will determine that the user is "relaxed."

[0286] Input: User's facial expressions and tone of voice data

[0287] Output: Real-time evaluated emotion information

[0288] Step 4:

[0289] The server generates recommendations. Using a generative AI model, the server generates recommendations for optimal tourist spots and commercial facilities based on the user's input and emotional information. The recommendations are personalized by analyzing the user's interests, preferences, emotional state, and collected data. For example, if the user is looking to relax, the server will suggest "quiet cafes" or "peaceful parks."

[0290] Input: User information data, emotion information, organized tourist destination and commercial facility data

[0291] Output: Proposals for the best tourist spots and commercial facilities for the user

[0292] Step 5:

[0293] Generate and provide coupons. The server generates coupon codes for each suggested tourist spot and store, and creates coupons to provide to users. Users can check the coupons on their smartphones or web applications and use them locally. For example, a coupon for "free coffee" may be generated.

[0294] Input: Information on tourist spots and commercial facilities suggested to the user

[0295] Output: Generated coupon code

[0296] Step 6:

[0297] Confirm the user's visit. The user actually visits the suggested tourist spots and stores and checks in using their smartphone. This check-in information is sent to the server, and the user's visit is confirmed.

[0298] Input: User visit confirmation information (check-in)

[0299] Output: Visit history data recorded on the server

[0300] Step 7:

[0301] Rewards are distributed. The server checks the user's visit history and based on that, gives the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits a specific tourist spot, a discount coupon that can be used on the next trip can be distributed based on that history.

[0302] Input: User's visit history data

[0303] Output: Rewards (points or coupons) distributed to users and stores

[0304] The above is the specific processing flow of the program for this system.

[0305] (Application example 2)

[0306] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] Currently, there is a demand for recommendations for tourist destinations and commercial facilities that match the interests and preferences of diverse users. However, there is no system yet that can also recognize a user's emotional state in real time and make optimal recommendations based on the results. As a result, there is a lack of more personalized recommendations, and improving the user experience is an issue. In addition, there is no sufficient system for users to check in and receive rewards when they actually visit, and there are also issues with linking with the commercial facilities to which the recommendations are made. As a result, a situation has arisen in which neither users nor commercial facilities are able to reap sufficient benefits.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0309] In this invention, the server includes means for receiving interest and preference information input by the user, means for collecting and organizing data on local tourist facilities and commercial facilities, means for suggesting tourist facilities and commercial facilities based on the user's interest and preference information and data, means for recognizing the user's emotional state in real time, means for generating coupons for the suggested tourist facilities and commercial facilities, means for the user to confirm their visit, and means for distributing rewards to the user and commercial facilities based on the confirmed visit. This makes it possible to suggest optimal tourist facilities and commercial facilities that take into account the user's interest and preference information as well as their emotional state, thereby improving the user experience. Furthermore, the visit confirmation and reward distribution can increase benefits for both the user and the commercial facilities.

[0310] "Interest and Preference Information" is data that describes a user's interests and preferences, such as specific product categories, experiences, or activities.

[0311] "Tourist and commercial facilities" refers to places that offer services and goods to tourists and consumers. This includes tourist attractions, shops, restaurants, cafes, etc.

[0312] An "emotional state" refers to the emotion a user is feeling at a particular moment, such as happiness, surprise, sadness, or stress.

[0313] "Suggestion" refers to the system's act of recommending appropriate tourist facilities or commercial facilities to users.

[0314] "Coupon" refers to a code or voucher that provides a User with discounts or benefits that can be used at suggested tourist or commercial facilities.

[0315] "Visit confirmation" refers to the act of confirming that a user has actually visited a suggested tourist attraction or commercial facility.

[0316] "Rewards" refers to points, coupons that can be used next time, incentives, etc. that are provided to users and commercial facilities based on confirmation of their visit.

[0317] This invention is a system that suggests tourist attractions and commercial facilities based on the user's input of interest and preference information and emotional state recognized in real time, and distributes rewards to both the user and the commercial facilities. The system includes the following means.

[0318] First, smartphones and web applications are used to collect information about users' interests and preferences. Users enter information such as their interests and preferences, areas they plan to visit, and the length of their stay into a specific application. For example, if a user enters that they are interested in "cafes" and "relaxation," subsequent suggestions will be made based on this information.

[0319] Next, data on local tourist and commercial facilities is collected and organized. The server uses a cloud API to automatically collect data from the internet, public databases of local governments, and partner companies. This integrates a variety of information, such as the locations, opening hours, photos, and user reviews of tourist and commercial facilities. The collected data is also tagged in multiple languages, making it possible to provide information across language barriers.

[0320] Furthermore, it uses an emotion engine that recognizes the user's emotional state in real time. It uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice to assess their current emotional state. This emotional information is then used together with the user's interests and preferences.

[0321] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on the user's profile, current needs, and emotional information. For example, if the emotion engine detects that the user is feeling "fatigue," it will suggest cafes and stores with a relaxing effect.

[0322] For this proposed facility, the server generates a coupon code containing discounts and special offers and provides it to the user. The user can check this on their smartphone or web application and use it locally. An example of the coupon content is "free coffee at the cafe."

[0323] When a user actually visits a suggested tourist attraction or commercial facility, they can easily check in on their smartphone. This information is sent to the server, and a record of the visit is kept. Finally, based on the confirmed visit, the server distributes rewards to the user and the commercial facility. For example, if a user visits five suggested destinations and checks in at all, they will receive a discount coupon for their next trip.

[0324] As a concrete example, consider a scenario where a user uses the app to register their interest in "clothes" and "cafes," and then puts on smart glasses and goes shopping. If the emotion engine recognizes that the user's current emotion is "fatigue," the suggested store will be a cafe with a relaxation effect. When the user visits the suggested cafe and checks in, the result is recorded and the user will receive a coupon for their next visit.

[0325] An example of a prompt is as follows:

[0326] "The product category the user is interested in is 'clothing' and their emotional state is 'fatigue'. Their current location is 'Chuo-ku, Kyoto City'. Based on this information, please suggest cafes and shops where they can relax."

[0327] This system will enable users to receive more personalized suggestions, improving the user experience, and will also benefit both users and commercial establishments by encouraging them to visit the site and distributing rewards.

[0328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0329] Step 1:

[0330] Users enter their interests, preferences, areas they plan to visit, and length of stay into a smartphone or web application.

[0331] Input: Interests and preferences, planned areas to visit, length of stay

[0332] Output: User profile information

[0333] Specific operation: The user launches the application and enters the product categories, tourist spots, and length of stay they are interested in into the text boxes. The input information is sent to the server and saved as individual profile information.

[0334] Step 2:

[0335] The server collects data on local tourist and commercial facilities from the Internet, public databases of local governments, and affiliated companies.

[0336] Input: Area to visit

[0337] Output: Facility database

[0338] Specific operation: The server makes an API request based on the area to be visited and collects information on tourist facilities and commercial facilities in the area. The collected data is integrated, organized, and stored in a database.

[0339] Step 3:

[0340] The server uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice, and uses an emotion engine to evaluate their current emotional state.

[0341] Input: Camera video, audio data

[0342] Output: Emotion evaluation result

[0343] How it works: The device's camera and microphone capture the user's facial expressions and voice in real time, and the data is sent to the emotion engine, which analyzes it and evaluates the user's emotional state.

[0344] Step 4:

[0345] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on user profile information, emotion evaluation results, and a facility database.

[0346] Input: User profile information, emotion evaluation results, facility database

[0347] Output: Optimized suggestion list

[0348] Specific operation: The server inputs a prompt statement (e.g., "The product category the user is interested in is 'clothing,' and their emotional state is 'fatigue.' Their current location is 'Chuo Ward, Kyoto City.' Based on this information, please suggest cafes and shops where they can relax.") into the generative AI model and generates an optimal list of suggestions.

[0349] Step 5:

[0350] The server generates coupons for the suggested tourist and commercial facilities and provides them to users via smartphones or web applications.

[0351] Input: Optimized suggestion list

[0352] Output: Coupon code

[0353] Specific operation: The server calls the coupon generation API for each facility on the proposal list, associates the generated coupon code with the user profile, and saves it. The user can then check the coupon information on the application.

[0354] Step 6:

[0355] Users visit the suggested tourist attractions and commercial facilities and check in on their smartphones.

[0356] Input: Visit information, check-in information

[0357] Output: Visit confirmation data

[0358] Specific operation: A user checks in to a facility using the application, and the information is sent to the server. The server updates the visit confirmation data and records it as a visit history.

[0359] Step 7:

[0360] The server distributes rewards to users and commercial establishments based on the verified visit data.

[0361] Input: Visit confirmation data

[0362] Output: Reward (points, coupons for next use, etc.)

[0363] Specific operation: The server analyzes the visit confirmation data and distributes rewards to users and affiliated commercial facilities who meet the conditions. Users are given points and coupons that can be used next time, and commercial facilities are provided with incentives.

[0364] 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.

[0365] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0366] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 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.

[0370] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0371] 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.

[0372] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0373] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0374] 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.

[0375] 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.

[0376] 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 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.

[0377] 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.

[0378] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0379] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0380] The present invention relates to a system that proposes local tourist attractions and stores optimized for a specific user and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[0381] The first major component of this system is a means for users to input personal information such as their interests, preferences, planned areas to visit, and length of stay. Users can easily input this information using a smartphone or web app. For example, if a user is interested in "food tours" and "historical buildings," inputting this information makes it possible to extract information relevant to them from other general information.

[0382] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[0383] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, and collected data. The AI ​​model analyzes user input and a vast data set to create personalized recommendations. For example, a user who wants to go on a food tour will be prioritized to see information about affiliated restaurants and cafes.

[0384] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or web app and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0385] When a user actually visits a suggested tourist attraction or store, the visit is confirmed: the user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0386] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0387] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and historical buildings. He then enters that he plans to visit Kyoto. Based on this, the server collects data on Kyoto's food spots and historical buildings, and uses that information to suggest the best tourist spots and stores for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[0388] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests and preferences, and by confirming visits and distributing rewards.

[0389] The processing flow will be explained below.

[0390] Step 1:

[0391] The user enters interest and preference information.

[0392] User: Accesses a smartphone or web app and registers or logs in.

[0393] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[0394] Terminal: Sends the entered information to the server.

[0395] Step 2:

[0396] Store basic information and information about your interests and preferences.

[0397] Server: Creates a user profile based on the received user basic information and interest / preference information.

[0398] Server: Stores the profile in a database.

[0399] Step 3:

[0400] Collect and organize data on local tourist attractions and commercial facilities.

[0401] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[0402] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[0403] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[0404] Step 4:

[0405] Generate suggestions based on user requests.

[0406] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[0407] Terminal: Sends the entered conditions to the server.

[0408] Server: Uses generative AI models to match user profiles with current requests and generate the best list of tourist attractions and stores.

[0409] Server: Sends the generated list to the user's device.

[0410] Step 5:

[0411] View the suggested results.

[0412] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[0413] Step 6:

[0414] Generate a coupon.

[0415] Server: Generates coupon codes for each suggested tourist attraction and store.

[0416] Server: Sends the coupon code to the user's device.

[0417] Step 7:

[0418] View coupons.

[0419] On your device: Display the coupon code in the user's app.

[0420] User: Use coupons at suggested tourist spots and stores.

[0421] Step 8:

[0422] Confirm your visit.

[0423] User: Visits the suggested tourist spot or store and checks in using the app.

[0424] Terminal: Sends check-in information to the server.

[0425] Server: Checks the user's location and check-in data, and records the visit.

[0426] Step 9:

[0427] Distribute rewards.

[0428] Server: After confirming the visit, the user is given points or a coupon to use next time.

[0429] Server: Provide incentives to proposed tourist destinations and stores.

[0430] Step 10:

[0431] Notify compensation information.

[0432] Terminal: Notifies the user of the points or coupons awarded.

[0433] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[0434] Example 1

[0435] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0436] Personalized travel plans are important for modern travelers, but existing systems do not adequately suggest optimal tourist destinations and stores based on users' individual interests and preferences. Furthermore, there is a lack of collaboration with stores and tourist destinations, and systems for providing coupons, confirming visits, and distributing rewards do not function efficiently, resulting in low satisfaction for both users and stores. Another issue is the lack of multilingual support, which limits the global use of tourist information.

[0437] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0438] In this invention, the server includes: means for receiving information about interests and preferences input by a user; means for collecting and organizing data on local tourist attractions and commercial facilities; means for suggesting tourist attractions and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; means for the user to confirm a visit; means for distributing rewards to the user and the facilities based on the confirmed visit; means for inputting the user's input information and the data as prompts into a generative AI model to create personalized recommendations; and means for tagging the collected data in multiple languages. This enables optimal recommendations of tourist attractions and stores based on the user's individual interests and preferences, and efficient confirmation of visits to the suggested tourist attractions and stores and distribution of rewards. Furthermore, multilingual support enables the use of global tourist information.

[0439] "User" refers to an individual who uses this system to receive suggestions of tourist spots and stores based on their interests and preferences.

[0440] "Information about interests and preferences" refers to information entered by the user about personal interests and preferences such as food tours and historical buildings, as well as information about areas to be visited and length of stay.

[0441] "Region" refers to the particular geographic area that a user plans to visit.

[0442] "Tourist destination" refers to a tourist spot or sightseeing spot that users intend to visit.

[0443] "Commercial facilities" refer to facilities that carry out commercial activities such as stores and restaurants.

[0444] "Data" refers to all information handled by the system, including information about tourist destinations and commercial facilities, as well as users' personal information.

[0445] "Generative AI models" refer to AI algorithms or models that generate recommendations for tourist destinations and stores based on user input and collected data.

[0446] A "prompt" refers to a string of characters containing commands or instructions that are input into a generative AI model.

[0447] "Coupon" refers to a code or ticket containing discounts or benefits that can be used by the user at suggested tourist spots or commercial facilities.

[0448] "Visit confirmation" refers to the means or actions to confirm that a user has actually visited a suggested tourist spot or store.

[0449] "Rewards" refers to points, coupons, incentives, etc. provided to users and affiliated facilities based on confirmation of visits.

[0450] "Multilingual tag" refers to identification information that is assigned to collected data to support multiple languages.

[0451] This invention is a system that suggests tourist spots and commercial facilities for users when traveling based on their interests and preferences, provides coupons, and distributes rewards after confirming their visit. This system is realized using a server, terminals, and a generative AI model.

[0452] Entering user information

[0453] Users input information such as their interests and preferences, areas they plan to visit, and the length of their stay via their smartphone or web app. This information is sent from the device to the server and stored in a database. For example, if a user is interested in "eating around" and "historical buildings," they can select these and input that they plan to visit Kyoto.

[0454] Data collection and organization

[0455] The server collects tourist attraction and store data from the internet, public databases, and partner companies. This includes tourist attraction locations, opening hours, photos, user reviews, and more. Python scripts and the Pandas library are used to consolidate and clean the data, tag it in multiple languages, and store it in a MySQL database. For example, the server retrieves and organizes tourist attraction data from a local tourist association's API.

[0456] Suggestions for users

[0457] The server inputs a prompt to a generative AI model, such as GPT-4, based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Area visited: Kyoto, Length of stay: 3 days." The generative AI model generates personalized suggestions based on this prompt, and the server sends the results in JSON format to the user's device.

[0458] Generate and offer coupons

[0459] The server connects with the suggested tourist attractions and stores to generate a unique coupon code, which is then sent to the user's device as a push notification. For example, the user can receive a coupon for a "free coffee" and use it at the store.

[0460] Confirmation of visit

[0461] When a user visits a suggested tourist spot or store, they tap the "Check-in" button in the app. The smartphone acquires the user's location information and sends it to the server. The server records the received check-in information in a database.

[0462] Reward Distribution

[0463] The server checks the user's visit history and calculates rewards based on a point system. For example, if a user visits five tourist spots, they will receive a discount coupon that can be used next time. In addition, partner stores will automatically receive incentives based on the number of visitors.

[0464] This system allows users to enjoy personalized travel plans, use coupons during their trip, and earn rewards after their visit, while businesses benefit from increased visitor numbers and improved customer satisfaction.

[0465] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0466] Step 1:

[0467] Users access the site from their smartphones or web apps and enter information such as their interests and preferences, the areas they plan to visit, and the length of their stay. Specifically, they enter the categories of interest (e.g., "food tours" or "historical buildings"), the areas they plan to visit (e.g., "Kyoto"), and the length of their stay (e.g., "3 days"). The device compiles this information and sends it to the server. The input data includes the interest categories, areas to visit, and length of stay, and when it is sent to the server, it becomes the base data for the next step.

[0468] Step 2:

[0469] The server collects data on tourist attractions and commercial facilities from the Internet, public databases, and partner companies. Specifically, it obtains information such as tourist attraction locations, opening hours, photos, and user reviews from the tourist association's API and partner company databases. The collected data includes basic information about tourist attractions and user reviews. The server uses Python scripts and the Pandas library to consolidate and clean the data, add multilingual tags, and store it in a MySQL database. The input data is the collected raw data, and the output data is the organized and consolidated tourist attraction data.

[0470] Step 3:

[0471] The server inputs a prompt to the generative AI model based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Areas visited: Kyoto, Length of stay: 3 days." The generative AI model (e.g., GPT-4) generates personalized suggestions based on this prompt. The input data is the user's profile information and collected tourist destination data, and the output data is a personalized list of tourist destinations and commercial facilities. The server sends the generated suggestions in JSON format to the user's device.

[0472] Step 4:

[0473] The server connects with the suggested tourist attractions and commercial facilities and generates a unique coupon code. Specifically, it uses a Python library to generate the coupon code, creating a code such as "COFFEEFREE_12345." The generated coupon code is sent to the user's device as a push notification. The input data is information about the suggested tourist attractions and commercial facilities, and the output data is the generated coupon code. The user can check the coupon code within the app, which is then displayed on the screen.

[0474] Step 5:

[0475] When a user visits a suggested tourist spot or commercial facility, they tap the "Check-in" button in the app. Specifically, the user's smartphone acquires location information, and once the visit is confirmed, the information is sent to the server. The input data is the user's current location information, and the output data is a check-in record. The server records this in a database.

[0476] Step 6:

[0477] The server checks the user's visit history and calculates rewards. For example, if a user visits multiple tourist attractions, points and coupons that can be used next time are awarded according to the number of visits. Specifically, if a user visits five tourist attractions, a discount coupon that can be used next time is generated and sent. Incentives are automatically awarded to affiliated facilities according to the number of visitors. The input data is check-in records, and the output data is points and coupons awarded to users, as well as incentives for affiliated facilities.

[0478] (Application example 1)

[0479] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0480] Conventional systems for recommending tourist spots and commercial facilities can recommend tourist spots and stores based on a user's interests and preferences, but they require the user to actually visit the locations, which requires time and effort and costs. It is also difficult to confirm visits and distribute rewards in real time. Furthermore, optimizing traffic flow and improving the efficiency of reward systems are also issues. To solve these problems, a system is needed that allows users to virtually experience sightseeing and commercial facility visits and receive rewards without actually visiting the locations.

[0481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0482] In this invention, the server includes means for receiving interest and preference information input by a user, means for collecting and organizing data on local tourist spots and commercial facilities, and means for suggesting tourist spots and stores based on the user's interest and preference information and the data. This allows a user to visit tourist spots and stores in a virtual space using a virtual reality device and receive rewards based on their visits.

[0483] The system also includes a means for generating coupons for suggested tourist attractions and stores, a means for users to confirm their virtual visits, and a means for distributing rewards to users and stores based on the confirmed visits. It also includes a means for tagging user-entered information and collected data about tourist attractions and commercial facilities in multiple languages, and a means for optimizing recommendations for tourist attractions and stores using a generative AI model based on the user's interests, preferences, and collected data. This improves the efficiency and satisfaction of users' sightseeing and shopping experiences, while simultaneously benefiting stores.

[0484] The "means for receiving information on interests and preferences input by the user" is an interface through which the user inputs and receives information on his or her interests, preferences, and places to visit.

[0485] "Means for collecting and organizing data on local tourist destinations and commercial facilities" refers to a system that automatically collects and organizes information on tourist destinations and commercial facilities from the Internet, public databases, etc.

[0486] "Means for suggesting tourist spots and stores based on the user's interests and preferences and the data" refers to an algorithm or engine that selects the most suitable tourist spots and stores from collected data based on the interests and preferences entered by the user and suggests them to the user.

[0487] The "means for generating coupons for suggested tourist spots and stores" is a system for generating coupons including discounts and special offers for tourist spots and stores that the user plans to visit and providing them to the user.

[0488] "Means for users to confirm their visit" refers to a method for users to confirm that they have actually visited the suggested tourist spots or stores, such as using a check-in function.

[0489] The "means for distributing rewards to users and stores based on confirmed visits" is a system for distributing points or coupons that can be used next time to users and stores based on when a user completes a visit.

[0490] "Means for visiting tourist spots and stores in a virtual space using a virtual reality device" refers to technology and devices that allow users to visit and experience tourist spots and stores in a virtual space using a virtual reality device.

[0491] A "means for distributing rewards to users based on their visits in a virtual space" is a system or method for distributing rewards to users based on their performance when they complete a visit in a virtual space.

[0492] The present invention relates to a system that suggests tourist spots and shops based on a user's preferences and interests, enables the user to visit those places in a virtual space using a virtual reality device, and further confirms the visit and distributes rewards.

[0493] System program implementation

[0494] First, the server receives information about interests and preferences entered by the user. Users enter information about their interests, preferences, and areas they plan to visit through their smartphones or web apps. This information is stored in a cloud database (e.g., Firebase).

[0495] The server then collects and organizes data on local tourist attractions and commercial facilities from the Internet and public databases. The specific software used here is a database management system (e.g., MySQL, PostgreSQL). The collected data includes tourist attraction locations, opening hours, photos, user reviews, etc.

[0496] The server then suggests tourist spots and shops based on the user's preferences and collected data. It uses a generative AI model (e.g., GPT-4) to analyze the user's preferences and related data and generate optimal suggestions. It uses prompts such as:

[0497] User preferences: Foodie, Historical buildings

[0498] Planned visit area: Kyoto

[0499] Please suggest recommended tourist spots and stores.

[0500] Based on the proposed results, the server enables sightseeing in a virtual space using a virtual reality device. Specifically, it generates a virtual tour using a VR head-mounted display (e.g., Oculus Rift S). It uses a VR library (e.g., vrpy) to build a virtual space, allowing users to visit tourist spots and stores within it.

[0501] The server also generates coupons that users can use for the suggested tourist spots and stores, such as a "free cup of coffee" coupon.

[0502] When a user completes a visit within the virtual space, the server verifies the visit using a checkpoint function that records that the user has arrived at a specific point within the virtual reality space.

[0503] Once the visit is confirmed, the server distributes rewards to the user and the store. The user receives points or coupons that can be used next time, and the store receives incentives.

[0504] Specific examples

[0505] For example, if a user is interested in "food tours" and "historical buildings," the system will suggest the most suitable tourist spots and stores in a virtual space based on this. A user planning to visit Kyoto can use a VR device to virtually visit Kyoto's tourist spots and enjoy checking information. In this case, the AI ​​model will suggest the most suitable tourist spots and stores by using the following prompt sentence:

[0506] User preferences: Foodie, Historical buildings

[0507] Planned visit area: Kyoto

[0508] Please suggest recommended tourist spots and stores.

[0509] This invention allows users to enjoy a virtual sightseeing experience before actually visiting, enabling efficient planning. Furthermore, coupons and rewards offered during the on-site visit provide benefits to both users and stores.

[0510] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0511] Step 1:

[0512] The user uses a device to input information about their interests, preferences, and planned visit locations. The input data is stored in a cloud database (e.g., Firebase). The inputs are the user's interests and preferences (e.g., food tours, historical buildings) and the planned visit location (e.g., Kyoto). The output is stored in Firebase.

[0513] Step 2:

[0514] The server collects data about local tourist attractions and businesses from the internet and public databases. The collected data includes location, opening hours, photos, user reviews, etc. This data is stored in a database management system (e.g., MySQL, PostgreSQL). As input, the URLs or API endpoints of the tourist attractions and businesses are used. As output, the collected data is stored in the database.

[0515] Step 3:

[0516] The server uses a generative AI model (e.g., GPT-4) to create suggestions for tourist attractions and stores based on the user's input information and collected data. Specifically, the server sends the following prompt to GPT-4 and receives the results. The inputs used are the user's interests and preferences, as well as information about places they plan to visit. The output is the tourist attraction and store suggestions generated by the AI ​​model.

[0517] User preferences: Foodie, Historical buildings

[0518] Planned visit area: Kyoto

[0519] Please suggest recommended tourist spots and stores.

[0520] Step 4:

[0521] Based on the generated suggestions, the server generates a sightseeing experience in a virtual space using a virtual reality device (e.g., Oculus Rift S). Specifically, it uses a VR library (e.g., vrpy) to build a virtual space in which users can visit tourist attractions and shops. The generated information on the suggested tourist attractions and shops is used as input. The output is a virtual space that can be visited by users.

[0522] Step 5:

[0523] The server generates coupons for tourist attractions and stores in the virtual space. The generated coupons are sent to the user's device and can be used by the user. The input is information about the suggested tourist attractions and stores. The output is the generated coupons that are provided to the user.

[0524] Step 6:

[0525] When a user completes a visit in the virtual space, the device sends the visit information to the server, which verifies the visit information and stores it in a database. The input includes the user's visit information, and the output is the verified visit information.

[0526] Step 7:

[0527] The server distributes reward points and coupons to users based on the confirmed visits. In addition, incentives are also provided to suggested stores. The input is the confirmed visit information. The output is the distribution of rewards to users and stores.

[0528] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0529] The present invention relates to a system that combines interest and preference information input by a user with an emotion engine that recognizes emotions in real time to make more personalized suggestions about tourist spots and commercial facilities, and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[0530] The first major component of this system is a means for users to input personal information such as their interests and preferences, areas they plan to visit, and the length of their stay. Users can easily input this information using a smartphone or web application. For example, if a user is interested in "eating out" and "historical buildings," inputting this information will enable the system to provide optimal sightseeing suggestions to the user.

[0531] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[0532] Another feature of the present invention is the introduction of an emotion engine that recognizes the user's emotional state in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state (e.g., joy, surprise, sadness, stress, etc.). This emotion information, along with the user's interests and preferences, is used to suggest tourist spots and commercial facilities.

[0533] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, emotional state, and collected data. The AI ​​model analyzes all user inputs and real-time collected emotional information, and creates personalized recommendations based on that information. For example, if the emotion engine detects that the user is "stressed," it may suggest a relaxing cafe or a quiet park.

[0534] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0535] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0536] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0537] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and visiting historical buildings. He then enters that he plans to visit Kyoto. The emotion engine then analyzes Yamada's emotional state and determines that he is currently seeking relaxation. Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses this information to suggest tourist spots and stores that are ideal for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[0538] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests, preferences, and emotional information, and by confirming visits and distributing rewards.

[0539] The processing flow will be explained below.

[0540] Step 1:

[0541] The user enters interest and preference information.

[0542] User: Accesses a smartphone or web app and registers or logs in.

[0543] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[0544] Terminal: Sends the entered information to the server.

[0545] Step 2:

[0546] Store basic information and information about your interests and preferences.

[0547] Server: Creates a user profile based on the received user basic information and interest / preference information.

[0548] Server: Stores the profile in a database.

[0549] Step 3:

[0550] Collect and organize data on local tourist attractions and commercial facilities.

[0551] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[0552] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[0553] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[0554] Step 4:

[0555] An emotion engine is used to collect user emotion information.

[0556] Device: Uses the smartphone's camera and microphone to record the user's facial expressions and voice in real time.

[0557] On the device: An emotion engine is used to analyze the user's current emotional state (e.g., joy, surprise, sadness, stress, etc.).

[0558] Terminal: Sends the analysis results to the server.

[0559] Step 5:

[0560] Generate suggestions based on user requests.

[0561] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[0562] Terminal: Sends the entered conditions to the server.

[0563] Server: Matches user profile, current needs, and sentiment information and uses generative AI models to generate the best list of tourist attractions and stores.

[0564] Server: Sends the generated list to the user's device.

[0565] Step 6:

[0566] View the suggested results.

[0567] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[0568] Step 7:

[0569] Generate a coupon.

[0570] Server: Generates coupon codes for each suggested tourist attraction and store.

[0571] Server: Sends the coupon code to the user's device.

[0572] Step 8:

[0573] View coupons.

[0574] On your device: Display the coupon code in the user's app.

[0575] User: Use coupons at suggested tourist spots and stores.

[0576] Step 9:

[0577] Confirm your visit.

[0578] User: Visits the suggested tourist spot or store and checks in using the app.

[0579] Terminal: Sends check-in information to the server.

[0580] Server: Checks the user's location and check-in data, and records the visit.

[0581] Step 10:

[0582] Distribute rewards.

[0583] Server: After confirming the visit, the user is given points or a coupon to use next time.

[0584] Server: Provide incentives to proposed tourist destinations and stores.

[0585] Step 11:

[0586] Notify compensation information.

[0587] Terminal: Notifies the user of the points or coupons awarded.

[0588] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[0589] Example 2

[0590] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0591] In the modern tourism industry, there is a demand for personalized recommendations of tourist spots and shops that take into account a user's individual interests and emotional state. However, existing systems struggle to analyze a user's emotional information in real time and provide optimal recommendations to the user. Furthermore, there are issues with the fairness and efficiency of distributing rewards for suggested tourist spots and shops. This can lead to low satisfaction for both users and shops.

[0592] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving interest and preference information input by the user, a means for collecting and organizing data on local tourist attractions and commercial facilities, a means for collecting and analyzing emotional information in real time, a means for generating coupons for suggested tourist attractions and stores, a means for the user to confirm a visit, and a means for distributing rewards to the user and the facility based on the confirmed visit. This makes it possible to suggest optimal tourist attractions and stores based on the user's individual interest and preference information and real-time emotional information. Furthermore, the satisfaction of both the user and the store can be increased by confirming the visit and distributing rewards.

[0593] "User" refers to an individual who uses this system.

[0594] "Interest and preference information" is information that indicates a user's particular tastes and interests, including information related to sightseeing and leisure.

[0595] "Data" refers to a comprehensive range of information about tourist attractions and commercial facilities, including location, opening hours, photos, user reviews, etc.

[0596] "Emotion information" is information that indicates the user's emotional state, collected in real time from the user's facial expressions and tone of voice, and includes joy, surprise, sadness, stress, and the like.

[0597] "Coupon" means a code or ticket offering discounts or special offers that can be used at the proposed tourist attractions or stores.

[0598] "Visit confirmation" refers to the process of the system confirming that the user has actually visited the suggested tourist attractions and stores.

[0599] "Rewards" refers to incentives, points, and other benefits distributed to users and stores based on confirmed visits.

[0600] "Server" refers to the computer system that performs centralized data processing and proposal generation for this System.

[0601] A "multilingual tag" refers to an identifier that uniformly organizes information provided in various languages, allowing users to access the information across language barriers.

[0602] "Generative AI model" refers to an artificial intelligence modeling technology that suggests optimal tourist spots and stores based on a user's interests, preferences, emotional information, and collected data.

[0603] MODE FOR CARRYING OUT THE INVENTION

[0604] This invention is a system that combines information on the user's interests and preferences with an emotion engine that recognizes emotions in real time to suggest more personalized tourist spots and commercial facilities, and distributes rewards to both the user and the stores.

[0605] This system can generate suggestions based on personal information entered by users using their smartphones or web applications, such as their interests and preferences, areas they plan to visit, and length of stay. Next, data on local tourist attractions and commercial facilities is automatically collected, integrated, and organized by the server from the Internet, public databases of local governments, and partner companies. This creates a database containing information such as the locations of tourist attractions and stores, their opening hours, photos, and user reviews. Furthermore, this data is tagged in multiple languages, making it possible to provide information across language barriers.

[0606] Another feature of the present invention is that the device has an emotion engine that uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state. This allows emotional information to be collected in real time and used to suggest tourist spots and commercial facilities, along with the user's interests and preferences.

[0607] The server then uses a generative AI model to generate optimal recommendations for tourist spots and commercial facilities based on the user's input and collected emotional information. The AI ​​model analyzes all of the user's input and the emotional information collected in real time, and creates personalized recommendations based on that. For example, if the emotional engine detects that the user's emotional state is "stressed," it may suggest a relaxing cafe or a quiet park.

[0608] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0609] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0610] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0611] Specific examples

[0612] Consider a case where a traveler uses this system. The traveler accesses the app and registers that they are interested in eating out and visiting historical buildings. They then enter Kyoto as the area they plan to visit and set the length of their stay. The emotion engine analyzes the traveler's emotional state and determines that they are currently seeking "relaxation." Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses that information to suggest tourist spots and stores that are ideal for the traveler. For example, it suggests quiet and relaxing cafes. Coupons that can be used at the suggested cafes are issued, and the traveler uses them to visit various locations. Once the visit is confirmed, rewards are distributed to the traveler and the store.

[0613] Prompt Sentence Examples

[0614] "I'm interested in food tours and historical architecture, and I'm planning to visit Kyoto. Can you suggest some relaxing tourist spots?"

[0615] This system proposes optimal tourist spots and commercial facilities based on the user's interests, preferences, and real-time emotional information, and distributes rewards for those visits, thereby benefiting both the user and the store.

[0616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0617] System program processing flow

[0618] Step 1:

[0619] The user collects input information. Using a smartphone or web application, the user inputs personal information such as interests, preferences, areas to visit, and length of stay. This input information is sent to the server via the device. For example, a user may input information such as "interested in food tours" and "historical buildings," "plan to visit Kyoto," and "stay for three days."

[0620] Input: Interests and preferences, areas to visit, length of stay, etc.

[0621] Output: User information data sent to the server

[0622] Step 2:

[0623] This service collects and organizes data on tourist destinations and commercial facilities. The server automatically collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies. This collected data is integrated and centralized, with information such as location, opening hours, photos, and user reviews. In addition, multilingual tags are added, making it possible to provide information across language barriers.

[0624] Input: Internet, public databases of local governments, data from partner companies

[0625] Output: Integrated and organized tourist destination and commercial facility data

[0626] Step 3:

[0627] The device analyzes the user's emotional state in real time. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice, and the emotion engine evaluates the user's current emotional state. This emotional information is sent to the server in real time. For example, if the device detects that the user's facial expression is relaxed, it will determine that the user is "relaxed."

[0628] Input: User's facial expressions and tone of voice data

[0629] Output: Real-time evaluated emotion information

[0630] Step 4:

[0631] The server generates recommendations. Using a generative AI model, the server generates recommendations for optimal tourist spots and commercial facilities based on the user's input and emotional information. The recommendations are personalized by analyzing the user's interests, preferences, emotional state, and collected data. For example, if the user is looking to relax, the server will suggest "quiet cafes" or "peaceful parks."

[0632] Input: User information data, emotion information, organized tourist destination and commercial facility data

[0633] Output: Proposals for the best tourist spots and commercial facilities for the user

[0634] Step 5:

[0635] Generate and provide coupons. The server generates coupon codes for each suggested tourist spot and store, and creates coupons to provide to users. Users can check the coupons on their smartphones or web applications and use them locally. For example, a coupon for "free coffee" may be generated.

[0636] Input: Information on tourist spots and commercial facilities suggested to the user

[0637] Output: Generated coupon code

[0638] Step 6:

[0639] Confirm the user's visit. The user actually visits the suggested tourist spots and stores and checks in using their smartphone. This check-in information is sent to the server, and the user's visit is confirmed.

[0640] Input: User visit confirmation information (check-in)

[0641] Output: Visit history data recorded on the server

[0642] Step 7:

[0643] Rewards are distributed. The server checks the user's visit history and based on that, gives the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits a specific tourist spot, a discount coupon that can be used on the next trip can be distributed based on that history.

[0644] Input: User's visit history data

[0645] Output: Rewards (points or coupons) distributed to users and stores

[0646] The above is the specific processing flow of the program for this system.

[0647] (Application example 2)

[0648] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] Currently, there is a demand for recommendations for tourist destinations and commercial facilities that match the interests and preferences of diverse users. However, there is no system yet that can also recognize a user's emotional state in real time and make optimal recommendations based on the results. As a result, there is a lack of more personalized recommendations, and improving the user experience is an issue. In addition, there is no sufficient system for users to check in and receive rewards when they actually visit, and there are also issues with linking with the commercial facilities to which the recommendations are made. As a result, a situation has arisen in which neither users nor commercial facilities are able to reap sufficient benefits.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0651] In this invention, the server includes means for receiving interest and preference information input by the user, means for collecting and organizing data on local tourist facilities and commercial facilities, means for suggesting tourist facilities and commercial facilities based on the user's interest and preference information and data, means for recognizing the user's emotional state in real time, means for generating coupons for the suggested tourist facilities and commercial facilities, means for the user to confirm their visit, and means for distributing rewards to the user and commercial facilities based on the confirmed visit. This makes it possible to suggest optimal tourist facilities and commercial facilities that take into account the user's interest and preference information as well as their emotional state, thereby improving the user experience. Furthermore, the visit confirmation and reward distribution can increase benefits for both the user and the commercial facilities.

[0652] "Interest and Preference Information" is data that describes a user's interests and preferences, such as specific product categories, experiences, or activities.

[0653] "Tourist and commercial facilities" refers to places that offer services and goods to tourists and consumers. This includes tourist attractions, shops, restaurants, cafes, etc.

[0654] An "emotional state" refers to the emotion a user is feeling at a particular moment, such as happiness, surprise, sadness, or stress.

[0655] "Suggestion" refers to the system's act of recommending appropriate tourist facilities or commercial facilities to users.

[0656] "Coupon" refers to a code or voucher that provides a User with discounts or benefits that can be used at suggested tourist or commercial facilities.

[0657] "Visit confirmation" refers to the act of confirming that a user has actually visited a suggested tourist attraction or commercial facility.

[0658] "Rewards" refers to points, coupons that can be used next time, incentives, etc. that are provided to users and commercial facilities based on confirmation of their visit.

[0659] This invention is a system that suggests tourist attractions and commercial facilities based on the user's input of interest and preference information and emotional state recognized in real time, and distributes rewards to both the user and the commercial facilities. The system includes the following means.

[0660] First, smartphones and web applications are used to collect information about users' interests and preferences. Users enter information such as their interests and preferences, areas they plan to visit, and the length of their stay into a specific application. For example, if a user enters that they are interested in "cafes" and "relaxation," subsequent suggestions will be made based on this information.

[0661] Next, data on local tourist and commercial facilities is collected and organized. The server uses a cloud API to automatically collect data from the internet, public databases of local governments, and partner companies. This integrates a variety of information, such as the locations, opening hours, photos, and user reviews of tourist and commercial facilities. The collected data is also tagged in multiple languages, making it possible to provide information across language barriers.

[0662] Furthermore, it uses an emotion engine that recognizes the user's emotional state in real time. It uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice to assess their current emotional state. This emotional information is then used together with the user's interests and preferences.

[0663] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on the user's profile, current needs, and emotional information. For example, if the emotion engine detects that the user is feeling "fatigue," it will suggest cafes and stores with a relaxing effect.

[0664] For this proposed facility, the server generates a coupon code containing discounts and special offers and provides it to the user. The user can check this on their smartphone or web application and use it locally. An example of the coupon content is "free coffee at the cafe."

[0665] When a user actually visits a suggested tourist attraction or commercial facility, they can easily check in on their smartphone. This information is sent to the server, and a record of the visit is kept. Finally, based on the confirmed visit, the server distributes rewards to the user and the commercial facility. For example, if a user visits five suggested destinations and checks in at all, they will receive a discount coupon for their next trip.

[0666] As a concrete example, consider a scenario where a user uses the app to register their interest in "clothes" and "cafes," and then puts on smart glasses and goes shopping. If the emotion engine recognizes that the user's current emotion is "fatigue," the suggested store will be a cafe with a relaxation effect. When the user visits the suggested cafe and checks in, the result is recorded and the user will receive a coupon for their next visit.

[0667] An example of a prompt is as follows:

[0668] "The product category the user is interested in is 'clothing' and their emotional state is 'fatigue'. Their current location is 'Chuo-ku, Kyoto City'. Based on this information, please suggest cafes and shops where they can relax."

[0669] This system will enable users to receive more personalized suggestions, improving the user experience, and will also benefit both users and commercial establishments by encouraging them to visit the site and distributing rewards.

[0670] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0671] Step 1:

[0672] Users enter their interests, preferences, areas they plan to visit, and length of stay into a smartphone or web application.

[0673] Input: Interests and preferences, planned areas to visit, length of stay

[0674] Output: User profile information

[0675] Specific operation: The user launches the application and enters the product categories, tourist spots, and length of stay they are interested in into the text boxes. The input information is sent to the server and saved as individual profile information.

[0676] Step 2:

[0677] The server collects data on local tourist and commercial facilities from the Internet, public databases of local governments, and affiliated companies.

[0678] Input: Area to visit

[0679] Output: Facility database

[0680] Specific operation: The server makes an API request based on the area to be visited and collects information on tourist facilities and commercial facilities in the area. The collected data is integrated, organized, and stored in a database.

[0681] Step 3:

[0682] The server uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice, and uses an emotion engine to evaluate their current emotional state.

[0683] Input: Camera video, audio data

[0684] Output: Emotion evaluation result

[0685] How it works: The device's camera and microphone capture the user's facial expressions and voice in real time, and the data is sent to the emotion engine, which analyzes it and evaluates the user's emotional state.

[0686] Step 4:

[0687] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on user profile information, emotion evaluation results, and a facility database.

[0688] Input: User profile information, emotion evaluation results, facility database

[0689] Output: Optimized suggestion list

[0690] Specific operation: The server inputs a prompt statement (e.g., "The product category the user is interested in is 'clothing,' and their emotional state is 'fatigue.' Their current location is 'Chuo Ward, Kyoto City.' Based on this information, please suggest cafes and shops where they can relax.") into the generative AI model and generates an optimal list of suggestions.

[0691] Step 5:

[0692] The server generates coupons for the suggested tourist and commercial facilities and provides them to users via smartphones or web applications.

[0693] Input: Optimized suggestion list

[0694] Output: Coupon code

[0695] Specific operation: The server calls the coupon generation API for each facility on the proposal list, associates the generated coupon code with the user profile, and saves it. The user can then check the coupon information on the application.

[0696] Step 6:

[0697] Users visit the suggested tourist attractions and commercial facilities and check in on their smartphones.

[0698] Input: Visit information, check-in information

[0699] Output: Visit confirmation data

[0700] Specific operation: A user checks in to a facility using the application, and the information is sent to the server. The server updates the visit confirmation data and records it as a visit history.

[0701] Step 7:

[0702] The server distributes rewards to users and commercial establishments based on the verified visit data.

[0703] Input: Visit confirmation data

[0704] Output: Reward (points, coupons for next use, etc.)

[0705] Specific operation: The server analyzes the visit confirmation data and distributes rewards to users and affiliated commercial facilities who meet the conditions. Users are given points and coupons that can be used next time, and commercial facilities are provided with incentives.

[0706] 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.

[0707] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0708] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0709] [Third embodiment]

[0710] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0711] 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.

[0712] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0713] 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.

[0714] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0715] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0716] 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.

[0717] 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.

[0718] 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 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.

[0719] 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.

[0720] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0721] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0722] The present invention relates to a system that proposes local tourist attractions and stores optimized for a specific user and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[0723] The first major component of this system is a means for users to input personal information such as their interests, preferences, planned areas to visit, and length of stay. Users can easily input this information using a smartphone or web app. For example, if a user is interested in "food tours" and "historical buildings," inputting this information makes it possible to extract information relevant to them from other general information.

[0724] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[0725] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, and collected data. The AI ​​model analyzes user input and a vast data set to create personalized recommendations. For example, a user who wants to go on a food tour will be prioritized to see information about affiliated restaurants and cafes.

[0726] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or web app and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0727] When a user actually visits a suggested tourist attraction or store, the visit is confirmed: the user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0728] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0729] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and historical buildings. He then enters that he plans to visit Kyoto. Based on this, the server collects data on Kyoto's food spots and historical buildings, and uses that information to suggest the best tourist spots and stores for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[0730] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests and preferences, and by confirming visits and distributing rewards.

[0731] The processing flow will be explained below.

[0732] Step 1:

[0733] The user enters interest and preference information.

[0734] User: Accesses a smartphone or web app and registers or logs in.

[0735] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[0736] Terminal: Sends the entered information to the server.

[0737] Step 2:

[0738] Store basic information and information about your interests and preferences.

[0739] Server: Creates a user profile based on the received user basic information and interest / preference information.

[0740] Server: Stores the profile in a database.

[0741] Step 3:

[0742] Collect and organize data on local tourist attractions and commercial facilities.

[0743] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[0744] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[0745] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[0746] Step 4:

[0747] Generate suggestions based on user requests.

[0748] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[0749] Terminal: Sends the entered conditions to the server.

[0750] Server: Uses generative AI models to match user profiles with current requests and generate the best list of tourist attractions and stores.

[0751] Server: Sends the generated list to the user's device.

[0752] Step 5:

[0753] View the suggested results.

[0754] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[0755] Step 6:

[0756] Generate a coupon.

[0757] Server: Generates coupon codes for each suggested tourist attraction and store.

[0758] Server: Sends the coupon code to the user's device.

[0759] Step 7:

[0760] View coupons.

[0761] On your device: Display the coupon code in the user's app.

[0762] User: Use coupons at suggested tourist spots and stores.

[0763] Step 8:

[0764] Confirm your visit.

[0765] User: Visits the suggested tourist spot or store and checks in using the app.

[0766] Terminal: Sends check-in information to the server.

[0767] Server: Checks the user's location and check-in data, and records the visit.

[0768] Step 9:

[0769] Distribute rewards.

[0770] Server: After confirming the visit, the user is given points or a coupon to use next time.

[0771] Server: Provide incentives to proposed tourist destinations and stores.

[0772] Step 10:

[0773] Notify compensation information.

[0774] Terminal: Notifies the user of the points or coupons awarded.

[0775] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[0776] Example 1

[0777] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0778] Personalized travel plans are important for modern travelers, but existing systems do not adequately suggest optimal tourist destinations and stores based on users' individual interests and preferences. Furthermore, there is a lack of collaboration with stores and tourist destinations, and systems for providing coupons, confirming visits, and distributing rewards do not function efficiently, resulting in low satisfaction for both users and stores. Another issue is the lack of multilingual support, which limits the global use of tourist information.

[0779] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0780] In this invention, the server includes: means for receiving information about interests and preferences input by a user; means for collecting and organizing data on local tourist attractions and commercial facilities; means for suggesting tourist attractions and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; means for the user to confirm a visit; means for distributing rewards to the user and the facilities based on the confirmed visit; means for inputting the user's input information and the data as prompts into a generative AI model to create personalized recommendations; and means for tagging the collected data in multiple languages. This enables optimal recommendations of tourist attractions and stores based on the user's individual interests and preferences, and efficient confirmation of visits to the suggested tourist attractions and stores and distribution of rewards. Furthermore, multilingual support enables the use of global tourist information.

[0781] "User" refers to an individual who uses this system to receive suggestions of tourist spots and stores based on their interests and preferences.

[0782] "Information about interests and preferences" refers to information entered by the user about personal interests and preferences such as food tours and historical buildings, as well as information about areas to be visited and length of stay.

[0783] "Region" refers to the particular geographic area that a user plans to visit.

[0784] "Tourist destination" refers to a tourist spot or sightseeing spot that users intend to visit.

[0785] "Commercial facilities" refer to facilities that carry out commercial activities such as stores and restaurants.

[0786] "Data" refers to all information handled by the system, including information about tourist destinations and commercial facilities, as well as users' personal information.

[0787] "Generative AI models" refer to AI algorithms or models that generate recommendations for tourist destinations and stores based on user input and collected data.

[0788] A "prompt" refers to a string of characters containing commands or instructions that are input into a generative AI model.

[0789] "Coupon" refers to a code or ticket containing discounts or benefits that can be used by the user at suggested tourist spots or commercial facilities.

[0790] "Visit confirmation" refers to the means or actions to confirm that a user has actually visited a suggested tourist spot or store.

[0791] "Rewards" refers to points, coupons, incentives, etc. provided to users and affiliated facilities based on confirmation of visits.

[0792] "Multilingual tag" refers to identification information that is assigned to collected data to support multiple languages.

[0793] This invention is a system that suggests tourist spots and commercial facilities for users when traveling based on their interests and preferences, provides coupons, and distributes rewards after confirming their visit. This system is realized using a server, terminals, and a generative AI model.

[0794] Entering user information

[0795] Users input information such as their interests and preferences, areas they plan to visit, and the length of their stay via their smartphone or web app. This information is sent from the device to the server and stored in a database. For example, if a user is interested in "eating around" and "historical buildings," they can select these and input that they plan to visit Kyoto.

[0796] Data collection and organization

[0797] The server collects tourist attraction and store data from the internet, public databases, and partner companies. This includes tourist attraction locations, opening hours, photos, user reviews, and more. Python scripts and the Pandas library are used to consolidate and clean the data, tag it in multiple languages, and store it in a MySQL database. For example, the server retrieves and organizes tourist attraction data from a local tourist association's API.

[0798] Suggestions for users

[0799] The server inputs a prompt to a generative AI model, such as GPT-4, based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Area visited: Kyoto, Length of stay: 3 days." The generative AI model generates personalized suggestions based on this prompt, and the server sends the results in JSON format to the user's device.

[0800] Generate and offer coupons

[0801] The server connects with the suggested tourist attractions and stores to generate a unique coupon code, which is then sent to the user's device as a push notification. For example, the user can receive a coupon for a "free coffee" and use it at the store.

[0802] Confirmation of visit

[0803] When a user visits a suggested tourist spot or store, they tap the "Check-in" button in the app. The smartphone acquires the user's location information and sends it to the server. The server records the received check-in information in a database.

[0804] Reward Distribution

[0805] The server checks the user's visit history and calculates rewards based on a point system. For example, if a user visits five tourist spots, they will receive a discount coupon that can be used next time. In addition, partner stores will automatically receive incentives based on the number of visitors.

[0806] This system allows users to enjoy personalized travel plans, use coupons during their trip, and earn rewards after their visit, while businesses benefit from increased visitor numbers and improved customer satisfaction.

[0807] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0808] Step 1:

[0809] Users access the site from their smartphones or web apps and enter information such as their interests and preferences, the areas they plan to visit, and the length of their stay. Specifically, they enter the categories of interest (e.g., "food tours" or "historical buildings"), the areas they plan to visit (e.g., "Kyoto"), and the length of their stay (e.g., "3 days"). The device compiles this information and sends it to the server. The input data includes the interest categories, areas to visit, and length of stay, and when it is sent to the server, it becomes the base data for the next step.

[0810] Step 2:

[0811] The server collects data on tourist attractions and commercial facilities from the Internet, public databases, and partner companies. Specifically, it obtains information such as tourist attraction locations, opening hours, photos, and user reviews from the tourist association's API and partner company databases. The collected data includes basic information about tourist attractions and user reviews. The server uses Python scripts and the Pandas library to consolidate and clean the data, add multilingual tags, and store it in a MySQL database. The input data is the collected raw data, and the output data is the organized and consolidated tourist attraction data.

[0812] Step 3:

[0813] The server inputs a prompt to the generative AI model based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Areas visited: Kyoto, Length of stay: 3 days." The generative AI model (e.g., GPT-4) generates personalized suggestions based on this prompt. The input data is the user's profile information and collected tourist destination data, and the output data is a personalized list of tourist destinations and commercial facilities. The server sends the generated suggestions in JSON format to the user's device.

[0814] Step 4:

[0815] The server connects with the suggested tourist attractions and commercial facilities and generates a unique coupon code. Specifically, it uses a Python library to generate the coupon code, creating a code such as "COFFEEFREE_12345." The generated coupon code is sent to the user's device as a push notification. The input data is information about the suggested tourist attractions and commercial facilities, and the output data is the generated coupon code. The user can check the coupon code within the app, which is then displayed on the screen.

[0816] Step 5:

[0817] When a user visits a suggested tourist spot or commercial facility, they tap the "Check-in" button in the app. Specifically, the user's smartphone acquires location information, and once the visit is confirmed, the information is sent to the server. The input data is the user's current location information, and the output data is a check-in record. The server records this in a database.

[0818] Step 6:

[0819] The server checks the user's visit history and calculates rewards. For example, if a user visits multiple tourist attractions, points and coupons that can be used next time are awarded according to the number of visits. Specifically, if a user visits five tourist attractions, a discount coupon that can be used next time is generated and sent. Incentives are automatically awarded to affiliated facilities according to the number of visitors. The input data is check-in records, and the output data is points and coupons awarded to users, as well as incentives for affiliated facilities.

[0820] (Application example 1)

[0821] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0822] Conventional systems for recommending tourist spots and commercial facilities can recommend tourist spots and stores based on a user's interests and preferences, but they require the user to actually visit the locations, which requires time and effort and costs. It is also difficult to confirm visits and distribute rewards in real time. Furthermore, optimizing traffic flow and improving the efficiency of reward systems are also issues. To solve these problems, a system is needed that allows users to virtually experience sightseeing and commercial facility visits and receive rewards without actually visiting the locations.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0824] In this invention, the server includes means for receiving interest and preference information input by a user, means for collecting and organizing data on local tourist spots and commercial facilities, and means for suggesting tourist spots and stores based on the user's interest and preference information and the data. This allows a user to visit tourist spots and stores in a virtual space using a virtual reality device and receive rewards based on their visits.

[0825] The system also includes a means for generating coupons for suggested tourist attractions and stores, a means for users to confirm their virtual visits, and a means for distributing rewards to users and stores based on the confirmed visits. It also includes a means for tagging user-entered information and collected data about tourist attractions and commercial facilities in multiple languages, and a means for optimizing recommendations for tourist attractions and stores using a generative AI model based on the user's interests, preferences, and collected data. This improves the efficiency and satisfaction of users' sightseeing and shopping experiences, while simultaneously benefiting stores.

[0826] The "means for receiving information on interests and preferences input by the user" is an interface through which the user inputs and receives information on his or her interests, preferences, and places to visit.

[0827] "Means for collecting and organizing data on local tourist destinations and commercial facilities" refers to a system that automatically collects and organizes information on tourist destinations and commercial facilities from the Internet, public databases, etc.

[0828] "Means for suggesting tourist spots and stores based on the user's interests and preferences and the data" refers to an algorithm or engine that selects the most suitable tourist spots and stores from collected data based on the interests and preferences entered by the user and suggests them to the user.

[0829] The "means for generating coupons for suggested tourist spots and stores" is a system for generating coupons including discounts and special offers for tourist spots and stores that the user plans to visit and providing them to the user.

[0830] "Means for users to confirm their visit" refers to a method for users to confirm that they have actually visited the suggested tourist spots or stores, such as using a check-in function.

[0831] The "means for distributing rewards to users and stores based on confirmed visits" is a system for distributing points or coupons that can be used next time to users and stores based on when a user completes a visit.

[0832] "Means for visiting tourist spots and stores in a virtual space using a virtual reality device" refers to technology and devices that allow users to visit and experience tourist spots and stores in a virtual space using a virtual reality device.

[0833] A "means for distributing rewards to users based on their visits in a virtual space" is a system or method for distributing rewards to users based on their performance when they complete a visit in a virtual space.

[0834] The present invention relates to a system that suggests tourist spots and shops based on a user's preferences and interests, enables the user to visit those places in a virtual space using a virtual reality device, and further confirms the visit and distributes rewards.

[0835] System program implementation

[0836] First, the server receives information about interests and preferences entered by the user. Users enter information about their interests, preferences, and areas they plan to visit through their smartphones or web apps. This information is stored in a cloud database (e.g., Firebase).

[0837] The server then collects and organizes data on local tourist attractions and commercial facilities from the Internet and public databases. The specific software used here is a database management system (e.g., MySQL, PostgreSQL). The collected data includes tourist attraction locations, opening hours, photos, user reviews, etc.

[0838] The server then suggests tourist spots and shops based on the user's preferences and collected data. It uses a generative AI model (e.g., GPT-4) to analyze the user's preferences and related data and generate optimal suggestions. It uses prompts such as:

[0839] User preferences: Foodie, Historical buildings

[0840] Planned visit area: Kyoto

[0841] Please suggest recommended tourist spots and stores.

[0842] Based on the proposed results, the server enables sightseeing in a virtual space using a virtual reality device. Specifically, it generates a virtual tour using a VR head-mounted display (e.g., Oculus Rift S). It uses a VR library (e.g., vrpy) to build a virtual space, allowing users to visit tourist spots and stores within it.

[0843] The server also generates coupons that users can use for the suggested tourist spots and stores, such as a "free cup of coffee" coupon.

[0844] When a user completes a visit within the virtual space, the server verifies the visit using a checkpoint function that records that the user has arrived at a specific point within the virtual reality space.

[0845] Once the visit is confirmed, the server distributes rewards to the user and the store. The user receives points or coupons that can be used next time, and the store receives incentives.

[0846] Specific examples

[0847] For example, if a user is interested in "food tours" and "historical buildings," the system will suggest the most suitable tourist spots and stores in a virtual space based on this. A user planning to visit Kyoto can use a VR device to virtually visit Kyoto's tourist spots and enjoy checking information. In this case, the AI ​​model will suggest the most suitable tourist spots and stores by using the following prompt sentence:

[0848] User preferences: Foodie, Historical buildings

[0849] Planned visit area: Kyoto

[0850] Please suggest recommended tourist spots and stores.

[0851] This invention allows users to enjoy a virtual sightseeing experience before actually visiting, enabling efficient planning. Furthermore, coupons and rewards offered during the on-site visit provide benefits to both users and stores.

[0852] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0853] Step 1:

[0854] The user uses a device to input information about their interests, preferences, and planned visit locations. The input data is stored in a cloud database (e.g., Firebase). The inputs are the user's interests and preferences (e.g., food tours, historical buildings) and the planned visit location (e.g., Kyoto). The output is stored in Firebase.

[0855] Step 2:

[0856] The server collects data about local tourist attractions and businesses from the internet and public databases. The collected data includes location, opening hours, photos, user reviews, etc. This data is stored in a database management system (e.g., MySQL, PostgreSQL). As input, the URLs or API endpoints of the tourist attractions and businesses are used. As output, the collected data is stored in the database.

[0857] Step 3:

[0858] The server uses a generative AI model (e.g., GPT-4) to create suggestions for tourist attractions and stores based on the user's input information and collected data. Specifically, the server sends the following prompt to GPT-4 and receives the results. The inputs used are the user's interests and preferences, as well as information about places they plan to visit. The output is the tourist attraction and store suggestions generated by the AI ​​model.

[0859] User preferences: Foodie, Historical buildings

[0860] Planned visit area: Kyoto

[0861] Please suggest recommended tourist spots and stores.

[0862] Step 4:

[0863] Based on the generated suggestions, the server generates a sightseeing experience in a virtual space using a virtual reality device (e.g., Oculus Rift S). Specifically, it uses a VR library (e.g., vrpy) to build a virtual space in which users can visit tourist attractions and shops. The generated information on the suggested tourist attractions and shops is used as input. The output is a virtual space that can be visited by users.

[0864] Step 5:

[0865] The server generates coupons for tourist attractions and stores in the virtual space. The generated coupons are sent to the user's device and can be used by the user. The input is information about the suggested tourist attractions and stores. The output is the generated coupons that are provided to the user.

[0866] Step 6:

[0867] When a user completes a visit in the virtual space, the device sends the visit information to the server, which verifies the visit information and stores it in a database. The input includes the user's visit information, and the output is the verified visit information.

[0868] Step 7:

[0869] The server distributes reward points and coupons to users based on the confirmed visits. In addition, incentives are also provided to suggested stores. The input is the confirmed visit information. The output is the distribution of rewards to users and stores.

[0870] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0871] The present invention relates to a system that combines interest and preference information input by a user with an emotion engine that recognizes emotions in real time to make more personalized suggestions about tourist spots and commercial facilities, and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[0872] The first major component of this system is a means for users to input personal information such as their interests and preferences, areas they plan to visit, and the length of their stay. Users can easily input this information using a smartphone or web application. For example, if a user is interested in "eating out" and "historical buildings," inputting this information will enable the system to provide optimal sightseeing suggestions to the user.

[0873] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[0874] Another feature of the present invention is the introduction of an emotion engine that recognizes the user's emotional state in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state (e.g., joy, surprise, sadness, stress, etc.). This emotion information, along with the user's interests and preferences, is used to suggest tourist spots and commercial facilities.

[0875] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, emotional state, and collected data. The AI ​​model analyzes all user inputs and real-time collected emotional information, and creates personalized recommendations based on that information. For example, if the emotion engine detects that the user is "stressed," it may suggest a relaxing cafe or a quiet park.

[0876] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0877] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0878] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0879] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and visiting historical buildings. He then enters that he plans to visit Kyoto. The emotion engine then analyzes Yamada's emotional state and determines that he is currently seeking relaxation. Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses this information to suggest tourist spots and stores that are ideal for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[0880] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests, preferences, and emotional information, and by confirming visits and distributing rewards.

[0881] The processing flow will be explained below.

[0882] Step 1:

[0883] The user enters interest and preference information.

[0884] User: Accesses a smartphone or web app and registers or logs in.

[0885] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[0886] Terminal: Sends the entered information to the server.

[0887] Step 2:

[0888] Store basic information and information about your interests and preferences.

[0889] Server: Creates a user profile based on the received user basic information and interest / preference information.

[0890] Server: Stores the profile in a database.

[0891] Step 3:

[0892] Collect and organize data on local tourist attractions and commercial facilities.

[0893] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[0894] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[0895] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[0896] Step 4:

[0897] An emotion engine is used to collect user emotion information.

[0898] Device: Uses the smartphone's camera and microphone to record the user's facial expressions and voice in real time.

[0899] On the device: An emotion engine is used to analyze the user's current emotional state (e.g., joy, surprise, sadness, stress, etc.).

[0900] Terminal: Sends the analysis results to the server.

[0901] Step 5:

[0902] Generate suggestions based on user requests.

[0903] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[0904] Terminal: Sends the entered conditions to the server.

[0905] Server: Matches user profile, current needs, and sentiment information and uses generative AI models to generate the best list of tourist attractions and stores.

[0906] Server: Sends the generated list to the user's device.

[0907] Step 6:

[0908] View the suggested results.

[0909] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[0910] Step 7:

[0911] Generate a coupon.

[0912] Server: Generates coupon codes for each suggested tourist attraction and store.

[0913] Server: Sends the coupon code to the user's device.

[0914] Step 8:

[0915] View coupons.

[0916] On your device: Display the coupon code in the user's app.

[0917] User: Use coupons at suggested tourist spots and stores.

[0918] Step 9:

[0919] Confirm your visit.

[0920] User: Visits the suggested tourist spot or store and checks in using the app.

[0921] Terminal: Sends check-in information to the server.

[0922] Server: Checks the user's location and check-in data, and records the visit.

[0923] Step 10:

[0924] Distribute rewards.

[0925] Server: After confirming the visit, the user is given points or a coupon to use next time.

[0926] Server: Provide incentives to proposed tourist destinations and stores.

[0927] Step 11:

[0928] Notify compensation information.

[0929] Terminal: Notifies the user of the points or coupons awarded.

[0930] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[0931] Example 2

[0932] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0933] In the modern tourism industry, there is a demand for personalized recommendations of tourist spots and shops that take into account a user's individual interests and emotional state. However, existing systems struggle to analyze a user's emotional information in real time and provide optimal recommendations to the user. Furthermore, there are issues with the fairness and efficiency of distributing rewards for suggested tourist spots and shops. This can lead to low satisfaction for both users and shops.

[0934] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving interest and preference information input by the user, a means for collecting and organizing data on local tourist attractions and commercial facilities, a means for collecting and analyzing emotional information in real time, a means for generating coupons for suggested tourist attractions and stores, a means for the user to confirm a visit, and a means for distributing rewards to the user and the facility based on the confirmed visit. This makes it possible to suggest optimal tourist attractions and stores based on the user's individual interest and preference information and real-time emotional information. Furthermore, the satisfaction of both the user and the store can be increased by confirming the visit and distributing rewards.

[0935] "User" refers to an individual who uses this system.

[0936] "Interest and preference information" is information that indicates a user's particular tastes and interests, including information related to sightseeing and leisure.

[0937] "Data" refers to a comprehensive range of information about tourist attractions and commercial facilities, including location, opening hours, photos, user reviews, etc.

[0938] "Emotion information" is information that indicates the user's emotional state, collected in real time from the user's facial expressions and tone of voice, and includes joy, surprise, sadness, stress, and the like.

[0939] "Coupon" means a code or ticket offering discounts or special offers that can be used at the proposed tourist attractions or stores.

[0940] "Visit confirmation" refers to the process of the system confirming that the user has actually visited the suggested tourist attractions and stores.

[0941] "Rewards" refers to incentives, points, and other benefits distributed to users and stores based on confirmed visits.

[0942] "Server" refers to the computer system that performs centralized data processing and proposal generation for this System.

[0943] A "multilingual tag" refers to an identifier that uniformly organizes information provided in various languages, allowing users to access the information across language barriers.

[0944] "Generative AI model" refers to an artificial intelligence modeling technology that suggests optimal tourist spots and stores based on a user's interests, preferences, emotional information, and collected data.

[0945] MODE FOR CARRYING OUT THE INVENTION

[0946] This invention is a system that combines information on the user's interests and preferences with an emotion engine that recognizes emotions in real time to suggest more personalized tourist spots and commercial facilities, and distributes rewards to both the user and the stores.

[0947] This system can generate suggestions based on personal information entered by users using their smartphones or web applications, such as their interests and preferences, areas they plan to visit, and length of stay. Next, data on local tourist attractions and commercial facilities is automatically collected, integrated, and organized by the server from the Internet, public databases of local governments, and partner companies. This creates a database containing information such as the locations of tourist attractions and stores, their opening hours, photos, and user reviews. Furthermore, this data is tagged in multiple languages, making it possible to provide information across language barriers.

[0948] Another feature of the present invention is that the device has an emotion engine that uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state. This allows emotional information to be collected in real time and used to suggest tourist spots and commercial facilities, along with the user's interests and preferences.

[0949] The server then uses a generative AI model to generate optimal recommendations for tourist spots and commercial facilities based on the user's input and collected emotional information. The AI ​​model analyzes all of the user's input and the emotional information collected in real time, and creates personalized recommendations based on that. For example, if the emotional engine detects that the user's emotional state is "stressed," it may suggest a relaxing cafe or a quiet park.

[0950] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[0951] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[0952] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[0953] Specific examples

[0954] Consider a case where a traveler uses this system. The traveler accesses the app and registers that they are interested in eating out and visiting historical buildings. They then enter Kyoto as the area they plan to visit and set the length of their stay. The emotion engine analyzes the traveler's emotional state and determines that they are currently seeking "relaxation." Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses that information to suggest tourist spots and stores that are ideal for the traveler. For example, it suggests quiet and relaxing cafes. Coupons that can be used at the suggested cafes are issued, and the traveler uses them to visit various locations. Once the visit is confirmed, rewards are distributed to the traveler and the store.

[0955] Prompt Sentence Examples

[0956] "I'm interested in food tours and historical architecture, and I'm planning to visit Kyoto. Can you suggest some relaxing tourist spots?"

[0957] This system proposes optimal tourist spots and commercial facilities based on the user's interests, preferences, and real-time emotional information, and distributes rewards for those visits, thereby benefiting both the user and the store.

[0958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0959] System program processing flow

[0960] Step 1:

[0961] The user collects input information. Using a smartphone or web application, the user inputs personal information such as interests, preferences, areas to visit, and length of stay. This input information is sent to the server via the device. For example, a user may input information such as "interested in food tours" and "historical buildings," "plan to visit Kyoto," and "stay for three days."

[0962] Input: Interests and preferences, areas to visit, length of stay, etc.

[0963] Output: User information data sent to the server

[0964] Step 2:

[0965] This service collects and organizes data on tourist destinations and commercial facilities. The server automatically collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies. This collected data is integrated and centralized, with information such as location, opening hours, photos, and user reviews. In addition, multilingual tags are added, making it possible to provide information across language barriers.

[0966] Input: Internet, public databases of local governments, data from partner companies

[0967] Output: Integrated and organized tourist destination and commercial facility data

[0968] Step 3:

[0969] The device analyzes the user's emotional state in real time. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice, and the emotion engine evaluates the user's current emotional state. This emotional information is sent to the server in real time. For example, if the device detects that the user's facial expression is relaxed, it will determine that the user is "relaxed."

[0970] Input: User's facial expressions and tone of voice data

[0971] Output: Real-time evaluated emotion information

[0972] Step 4:

[0973] The server generates recommendations. Using a generative AI model, the server generates recommendations for optimal tourist spots and commercial facilities based on the user's input and emotional information. The recommendations are personalized by analyzing the user's interests, preferences, emotional state, and collected data. For example, if the user is looking to relax, the server will suggest "quiet cafes" or "peaceful parks."

[0974] Input: User information data, emotion information, organized tourist destination and commercial facility data

[0975] Output: Proposals for the best tourist spots and commercial facilities for the user

[0976] Step 5:

[0977] Generate and provide coupons. The server generates coupon codes for each suggested tourist spot and store, and creates coupons to provide to users. Users can check the coupons on their smartphones or web applications and use them locally. For example, a coupon for "free coffee" may be generated.

[0978] Input: Information on tourist spots and commercial facilities suggested to the user

[0979] Output: Generated coupon code

[0980] Step 6:

[0981] Confirm the user's visit. The user actually visits the suggested tourist spots and stores and checks in using their smartphone. This check-in information is sent to the server, and the user's visit is confirmed.

[0982] Input: User visit confirmation information (check-in)

[0983] Output: Visit history data recorded on the server

[0984] Step 7:

[0985] Rewards are distributed. The server checks the user's visit history and based on that, gives the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits a specific tourist spot, a discount coupon that can be used on the next trip can be distributed based on that history.

[0986] Input: User's visit history data

[0987] Output: Rewards (points or coupons) distributed to users and stores

[0988] The above is the specific processing flow of the program for this system.

[0989] (Application example 2)

[0990] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] Currently, there is a demand for recommendations for tourist destinations and commercial facilities that match the interests and preferences of diverse users. However, there is no system yet that can also recognize a user's emotional state in real time and make optimal recommendations based on the results. As a result, there is a lack of more personalized recommendations, and improving the user experience is an issue. In addition, there is no sufficient system for users to check in and receive rewards when they actually visit, and there are also issues with linking with the commercial facilities to which the recommendations are made. As a result, a situation has arisen in which neither users nor commercial facilities are able to reap sufficient benefits.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0993] In this invention, the server includes means for receiving interest and preference information input by the user, means for collecting and organizing data on local tourist facilities and commercial facilities, means for suggesting tourist facilities and commercial facilities based on the user's interest and preference information and data, means for recognizing the user's emotional state in real time, means for generating coupons for the suggested tourist facilities and commercial facilities, means for the user to confirm their visit, and means for distributing rewards to the user and commercial facilities based on the confirmed visit. This makes it possible to suggest optimal tourist facilities and commercial facilities that take into account the user's interest and preference information as well as their emotional state, thereby improving the user experience. Furthermore, the visit confirmation and reward distribution can increase benefits for both the user and the commercial facilities.

[0994] "Interest and Preference Information" is data that describes a user's interests and preferences, such as specific product categories, experiences, or activities.

[0995] "Tourist and commercial facilities" refers to places that offer services and goods to tourists and consumers. This includes tourist attractions, shops, restaurants, cafes, etc.

[0996] An "emotional state" refers to the emotion a user is feeling at a particular moment, such as happiness, surprise, sadness, or stress.

[0997] "Suggestion" refers to the system's act of recommending appropriate tourist facilities or commercial facilities to users.

[0998] "Coupon" refers to a code or voucher that provides a User with discounts or benefits that can be used at suggested tourist or commercial facilities.

[0999] "Visit confirmation" refers to the act of confirming that a user has actually visited a suggested tourist attraction or commercial facility.

[1000] "Rewards" refers to points, coupons that can be used next time, incentives, etc. that are provided to users and commercial facilities based on confirmation of their visit.

[1001] This invention is a system that suggests tourist attractions and commercial facilities based on the user's input of interest and preference information and emotional state recognized in real time, and distributes rewards to both the user and the commercial facilities. The system includes the following means.

[1002] First, smartphones and web applications are used to collect information about users' interests and preferences. Users enter information such as their interests and preferences, areas they plan to visit, and the length of their stay into a specific application. For example, if a user enters that they are interested in "cafes" and "relaxation," subsequent suggestions will be made based on this information.

[1003] Next, data on local tourist and commercial facilities is collected and organized. The server uses a cloud API to automatically collect data from the internet, public databases of local governments, and partner companies. This integrates a variety of information, such as the locations, opening hours, photos, and user reviews of tourist and commercial facilities. The collected data is also tagged in multiple languages, making it possible to provide information across language barriers.

[1004] Furthermore, it uses an emotion engine that recognizes the user's emotional state in real time. It uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice to assess their current emotional state. This emotional information is then used together with the user's interests and preferences.

[1005] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on the user's profile, current needs, and emotional information. For example, if the emotion engine detects that the user is feeling "fatigue," it will suggest cafes and stores with a relaxing effect.

[1006] For this proposed facility, the server generates a coupon code containing discounts and special offers and provides it to the user. The user can check this on their smartphone or web application and use it locally. An example of the coupon content is "free coffee at the cafe."

[1007] When a user actually visits a suggested tourist attraction or commercial facility, they can easily check in on their smartphone. This information is sent to the server, and a record of the visit is kept. Finally, based on the confirmed visit, the server distributes rewards to the user and the commercial facility. For example, if a user visits five suggested destinations and checks in at all, they will receive a discount coupon for their next trip.

[1008] As a concrete example, consider a scenario where a user uses the app to register their interest in "clothes" and "cafes," and then puts on smart glasses and goes shopping. If the emotion engine recognizes that the user's current emotion is "fatigue," the suggested store will be a cafe with a relaxation effect. When the user visits the suggested cafe and checks in, the result is recorded and the user will receive a coupon for their next visit.

[1009] An example of a prompt is as follows:

[1010] "The product category the user is interested in is 'clothing' and their emotional state is 'fatigue'. Their current location is 'Chuo-ku, Kyoto City'. Based on this information, please suggest cafes and shops where they can relax."

[1011] This system will enable users to receive more personalized suggestions, improving the user experience, and will also benefit both users and commercial establishments by encouraging them to visit the site and distributing rewards.

[1012] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1013] Step 1:

[1014] Users enter their interests, preferences, areas they plan to visit, and length of stay into a smartphone or web application.

[1015] Input: Interests and preferences, planned areas to visit, length of stay

[1016] Output: User profile information

[1017] Specific operation: The user launches the application and enters the product categories, tourist spots, and length of stay they are interested in into the text boxes. The input information is sent to the server and saved as individual profile information.

[1018] Step 2:

[1019] The server collects data on local tourist and commercial facilities from the Internet, public databases of local governments, and affiliated companies.

[1020] Input: Area to visit

[1021] Output: Facility database

[1022] Specific operation: The server makes an API request based on the area to be visited and collects information on tourist facilities and commercial facilities in the area. The collected data is integrated, organized, and stored in a database.

[1023] Step 3:

[1024] The server uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice, and uses an emotion engine to evaluate their current emotional state.

[1025] Input: Camera video, audio data

[1026] Output: Emotion evaluation result

[1027] How it works: The device's camera and microphone capture the user's facial expressions and voice in real time, and the data is sent to the emotion engine, which analyzes it and evaluates the user's emotional state.

[1028] Step 4:

[1029] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on user profile information, emotion evaluation results, and a facility database.

[1030] Input: User profile information, emotion evaluation results, facility database

[1031] Output: Optimized suggestion list

[1032] Specific operation: The server inputs a prompt statement (e.g., "The product category the user is interested in is 'clothing,' and their emotional state is 'fatigue.' Their current location is 'Chuo Ward, Kyoto City.' Based on this information, please suggest cafes and shops where they can relax.") into the generative AI model and generates an optimal list of suggestions.

[1033] Step 5:

[1034] The server generates coupons for the suggested tourist and commercial facilities and provides them to users via smartphones or web applications.

[1035] Input: Optimized suggestion list

[1036] Output: Coupon code

[1037] Specific operation: The server calls the coupon generation API for each facility on the proposal list, associates the generated coupon code with the user profile, and saves it. The user can then check the coupon information on the application.

[1038] Step 6:

[1039] Users visit the suggested tourist attractions and commercial facilities and check in on their smartphones.

[1040] Input: Visit information, check-in information

[1041] Output: Visit confirmation data

[1042] Specific operation: A user checks in to a facility using the application, and the information is sent to the server. The server updates the visit confirmation data and records it as a visit history.

[1043] Step 7:

[1044] The server distributes rewards to users and commercial establishments based on the verified visit data.

[1045] Input: Visit confirmation data

[1046] Output: Reward (points, coupons for next use, etc.)

[1047] Specific operation: The server analyzes the visit confirmation data and distributes rewards to users and affiliated commercial facilities who meet the conditions. Users are given points and coupons that can be used next time, and commercial facilities are provided with incentives.

[1048] 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.

[1049] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1050] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1051] [Fourth embodiment]

[1052] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1053] 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.

[1054] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1055] 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.

[1056] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1057] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1058] 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.

[1059] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1060] 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.

[1061] 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 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.

[1062] 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.

[1063] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1064] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1065] The present invention relates to a system that proposes local tourist attractions and stores optimized for a specific user and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[1066] The first major component of this system is a means for users to input personal information such as their interests, preferences, planned areas to visit, and length of stay. Users can easily input this information using a smartphone or web app. For example, if a user is interested in "food tours" and "historical buildings," inputting this information makes it possible to extract information relevant to them from other general information.

[1067] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[1068] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, and collected data. The AI ​​model analyzes user input and a vast data set to create personalized recommendations. For example, a user who wants to go on a food tour will be prioritized to see information about affiliated restaurants and cafes.

[1069] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or web app and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[1070] When a user actually visits a suggested tourist attraction or store, the visit is confirmed: the user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[1071] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[1072] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and historical buildings. He then enters that he plans to visit Kyoto. Based on this, the server collects data on Kyoto's food spots and historical buildings, and uses that information to suggest the best tourist spots and stores for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[1073] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests and preferences, and by confirming visits and distributing rewards.

[1074] The processing flow will be explained below.

[1075] Step 1:

[1076] The user enters interest and preference information.

[1077] User: Accesses a smartphone or web app and registers or logs in.

[1078] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[1079] Terminal: Sends the entered information to the server.

[1080] Step 2:

[1081] Store basic information and information about your interests and preferences.

[1082] Server: Creates a user profile based on the received user basic information and interest / preference information.

[1083] Server: Stores the profile in a database.

[1084] Step 3:

[1085] Collect and organize data on local tourist attractions and commercial facilities.

[1086] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[1087] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[1088] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[1089] Step 4:

[1090] Generate suggestions based on user requests.

[1091] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[1092] Terminal: Sends the entered conditions to the server.

[1093] Server: Uses generative AI models to match user profiles with current requests and generate the best list of tourist attractions and stores.

[1094] Server: Sends the generated list to the user's device.

[1095] Step 5:

[1096] View the suggested results.

[1097] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[1098] Step 6:

[1099] Generate a coupon.

[1100] Server: Generates coupon codes for each suggested tourist attraction and store.

[1101] Server: Sends the coupon code to the user's device.

[1102] Step 7:

[1103] View coupons.

[1104] On your device: Display the coupon code in the user's app.

[1105] User: Use coupons at suggested tourist spots and stores.

[1106] Step 8:

[1107] Confirm your visit.

[1108] User: Visits the suggested tourist spot or store and checks in using the app.

[1109] Terminal: Sends check-in information to the server.

[1110] Server: Checks the user's location and check-in data, and records the visit.

[1111] Step 9:

[1112] Distribute rewards.

[1113] Server: After confirming the visit, the user is given points or a coupon to use next time.

[1114] Server: Provide incentives to proposed tourist destinations and stores.

[1115] Step 10:

[1116] Notify compensation information.

[1117] Terminal: Notifies the user of the points or coupons awarded.

[1118] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[1119] Example 1

[1120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1121] Personalized travel plans are important for modern travelers, but existing systems do not adequately suggest optimal tourist destinations and stores based on users' individual interests and preferences. Furthermore, there is a lack of collaboration with stores and tourist destinations, and systems for providing coupons, confirming visits, and distributing rewards do not function efficiently, resulting in low satisfaction for both users and stores. Another issue is the lack of multilingual support, which limits the global use of tourist information.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1123] In this invention, the server includes: means for receiving information about interests and preferences input by a user; means for collecting and organizing data on local tourist attractions and commercial facilities; means for suggesting tourist attractions and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; means for the user to confirm a visit; means for distributing rewards to the user and the facilities based on the confirmed visit; means for inputting the user's input information and the data as prompts into a generative AI model to create personalized recommendations; and means for tagging the collected data in multiple languages. This enables optimal recommendations of tourist attractions and stores based on the user's individual interests and preferences, and efficient confirmation of visits to the suggested tourist attractions and stores and distribution of rewards. Furthermore, multilingual support enables the use of global tourist information.

[1124] "User" refers to an individual who uses this system to receive suggestions of tourist spots and stores based on their interests and preferences.

[1125] "Information about interests and preferences" refers to information entered by the user about personal interests and preferences such as food tours and historical buildings, as well as information about areas to be visited and length of stay.

[1126] "Region" refers to the particular geographic area that a user plans to visit.

[1127] "Tourist destination" refers to a tourist spot or sightseeing spot that users intend to visit.

[1128] "Commercial facilities" refer to facilities that carry out commercial activities such as stores and restaurants.

[1129] "Data" refers to all information handled by the system, including information about tourist destinations and commercial facilities, as well as users' personal information.

[1130] "Generative AI models" refer to AI algorithms or models that generate recommendations for tourist destinations and stores based on user input and collected data.

[1131] A "prompt" refers to a string of characters containing commands or instructions that are input into a generative AI model.

[1132] "Coupon" refers to a code or ticket containing discounts or benefits that can be used by the user at suggested tourist spots or commercial facilities.

[1133] "Visit confirmation" refers to the means or actions to confirm that a user has actually visited a suggested tourist spot or store.

[1134] "Rewards" refers to points, coupons, incentives, etc. provided to users and affiliated facilities based on confirmation of visits.

[1135] "Multilingual tag" refers to identification information that is assigned to collected data to support multiple languages.

[1136] This invention is a system that suggests tourist spots and commercial facilities for users when traveling based on their interests and preferences, provides coupons, and distributes rewards after confirming their visit. This system is realized using a server, terminals, and a generative AI model.

[1137] Entering user information

[1138] Users input information such as their interests and preferences, areas they plan to visit, and the length of their stay via their smartphone or web app. This information is sent from the device to the server and stored in a database. For example, if a user is interested in "eating around" and "historical buildings," they can select these and input that they plan to visit Kyoto.

[1139] Data collection and organization

[1140] The server collects tourist attraction and store data from the internet, public databases, and partner companies. This includes tourist attraction locations, opening hours, photos, user reviews, and more. Python scripts and the Pandas library are used to consolidate and clean the data, tag it in multiple languages, and store it in a MySQL database. For example, the server retrieves and organizes tourist attraction data from a local tourist association's API.

[1141] Suggestions for users

[1142] The server inputs a prompt to a generative AI model, such as GPT-4, based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Area visited: Kyoto, Length of stay: 3 days." The generative AI model generates personalized suggestions based on this prompt, and the server sends the results in JSON format to the user's device.

[1143] Generate and offer coupons

[1144] The server connects with the suggested tourist attractions and stores to generate a unique coupon code, which is then sent to the user's device as a push notification. For example, the user can receive a coupon for a "free coffee" and use it at the store.

[1145] Confirmation of visit

[1146] When a user visits a suggested tourist spot or store, they tap the "Check-in" button in the app. The smartphone acquires the user's location information and sends it to the server. The server records the received check-in information in a database.

[1147] Reward Distribution

[1148] The server checks the user's visit history and calculates rewards based on a point system. For example, if a user visits five tourist spots, they will receive a discount coupon that can be used next time. In addition, partner stores will automatically receive incentives based on the number of visitors.

[1149] This system allows users to enjoy personalized travel plans, use coupons during their trip, and earn rewards after their visit, while businesses benefit from increased visitor numbers and improved customer satisfaction.

[1150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1151] Step 1:

[1152] Users access the site from their smartphones or web apps and enter information such as their interests and preferences, the areas they plan to visit, and the length of their stay. Specifically, they enter the categories of interest (e.g., "food tours" or "historical buildings"), the areas they plan to visit (e.g., "Kyoto"), and the length of their stay (e.g., "3 days"). The device compiles this information and sends it to the server. The input data includes the interest categories, areas to visit, and length of stay, and when it is sent to the server, it becomes the base data for the next step.

[1153] Step 2:

[1154] The server collects data on tourist attractions and commercial facilities from the Internet, public databases, and partner companies. Specifically, it obtains information such as tourist attraction locations, opening hours, photos, and user reviews from the tourist association's API and partner company databases. The collected data includes basic information about tourist attractions and user reviews. The server uses Python scripts and the Pandas library to consolidate and clean the data, add multilingual tags, and store it in a MySQL database. The input data is the collected raw data, and the output data is the organized and consolidated tourist attraction data.

[1155] Step 3:

[1156] The server inputs a prompt to the generative AI model based on the user's profile information. An example of a specific prompt is "Female in her 40s, Interests: Eating out, Historical buildings, Areas visited: Kyoto, Length of stay: 3 days." The generative AI model (e.g., GPT-4) generates personalized suggestions based on this prompt. The input data is the user's profile information and collected tourist destination data, and the output data is a personalized list of tourist destinations and commercial facilities. The server sends the generated suggestions in JSON format to the user's device.

[1157] Step 4:

[1158] The server connects with the suggested tourist attractions and commercial facilities and generates a unique coupon code. Specifically, it uses a Python library to generate the coupon code, creating a code such as "COFFEEFREE_12345." The generated coupon code is sent to the user's device as a push notification. The input data is information about the suggested tourist attractions and commercial facilities, and the output data is the generated coupon code. The user can check the coupon code within the app, which is then displayed on the screen.

[1159] Step 5:

[1160] When a user visits a suggested tourist spot or commercial facility, they tap the "Check-in" button in the app. Specifically, the user's smartphone acquires location information, and once the visit is confirmed, the information is sent to the server. The input data is the user's current location information, and the output data is a check-in record. The server records this in a database.

[1161] Step 6:

[1162] The server checks the user's visit history and calculates rewards. For example, if a user visits multiple tourist attractions, points and coupons that can be used next time are awarded according to the number of visits. Specifically, if a user visits five tourist attractions, a discount coupon that can be used next time is generated and sent. Incentives are automatically awarded to affiliated facilities according to the number of visitors. The input data is check-in records, and the output data is points and coupons awarded to users, as well as incentives for affiliated facilities.

[1163] (Application example 1)

[1164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1165] Conventional systems for recommending tourist spots and commercial facilities can recommend tourist spots and stores based on a user's interests and preferences, but they require the user to actually visit the locations, which requires time and effort and costs. It is also difficult to confirm visits and distribute rewards in real time. Furthermore, optimizing traffic flow and improving the efficiency of reward systems are also issues. To solve these problems, a system is needed that allows users to virtually experience sightseeing and commercial facility visits and receive rewards without actually visiting the locations.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1167] In this invention, the server includes means for receiving interest and preference information input by a user, means for collecting and organizing data on local tourist spots and commercial facilities, and means for suggesting tourist spots and stores based on the user's interest and preference information and the data. This allows a user to visit tourist spots and stores in a virtual space using a virtual reality device and receive rewards based on their visits.

[1168] The system also includes a means for generating coupons for suggested tourist attractions and stores, a means for users to confirm their virtual visits, and a means for distributing rewards to users and stores based on the confirmed visits. It also includes a means for tagging user-entered information and collected data about tourist attractions and commercial facilities in multiple languages, and a means for optimizing recommendations for tourist attractions and stores using a generative AI model based on the user's interests, preferences, and collected data. This improves the efficiency and satisfaction of users' sightseeing and shopping experiences, while simultaneously benefiting stores.

[1169] The "means for receiving information on interests and preferences input by the user" is an interface through which the user inputs and receives information on his or her interests, preferences, and places to visit.

[1170] "Means for collecting and organizing data on local tourist destinations and commercial facilities" refers to a system that automatically collects and organizes information on tourist destinations and commercial facilities from the Internet, public databases, etc.

[1171] "Means for suggesting tourist spots and stores based on the user's interests and preferences and the data" refers to an algorithm or engine that selects the most suitable tourist spots and stores from collected data based on the interests and preferences entered by the user and suggests them to the user.

[1172] The "means for generating coupons for suggested tourist spots and stores" is a system for generating coupons including discounts and special offers for tourist spots and stores that the user plans to visit and providing them to the user.

[1173] "Means for users to confirm their visit" refers to a method for users to confirm that they have actually visited the suggested tourist spots or stores, such as using a check-in function.

[1174] The "means for distributing rewards to users and stores based on confirmed visits" is a system for distributing points or coupons that can be used next time to users and stores based on when a user completes a visit.

[1175] "Means for visiting tourist spots and stores in a virtual space using a virtual reality device" refers to technology and devices that allow users to visit and experience tourist spots and stores in a virtual space using a virtual reality device.

[1176] A "means for distributing rewards to users based on their visits in a virtual space" is a system or method for distributing rewards to users based on their performance when they complete a visit in a virtual space.

[1177] The present invention relates to a system that suggests tourist spots and shops based on a user's preferences and interests, enables the user to visit those places in a virtual space using a virtual reality device, and further confirms the visit and distributes rewards.

[1178] System program implementation

[1179] First, the server receives information about interests and preferences entered by the user. Users enter information about their interests, preferences, and areas they plan to visit through their smartphones or web apps. This information is stored in a cloud database (e.g., Firebase).

[1180] The server then collects and organizes data on local tourist attractions and commercial facilities from the Internet and public databases. The specific software used here is a database management system (e.g., MySQL, PostgreSQL). The collected data includes tourist attraction locations, opening hours, photos, user reviews, etc.

[1181] The server then suggests tourist spots and shops based on the user's preferences and collected data. It uses a generative AI model (e.g., GPT-4) to analyze the user's preferences and related data and generate optimal suggestions. It uses prompts such as:

[1182] User preferences: Foodie, Historical buildings

[1183] Planned visit area: Kyoto

[1184] Please suggest recommended tourist spots and stores.

[1185] Based on the proposed results, the server enables sightseeing in a virtual space using a virtual reality device. Specifically, it generates a virtual tour using a VR head-mounted display (e.g., Oculus Rift S). It uses a VR library (e.g., vrpy) to build a virtual space, allowing users to visit tourist spots and stores within it.

[1186] The server also generates coupons that users can use for the suggested tourist spots and stores, such as a "free cup of coffee" coupon.

[1187] When a user completes a visit within the virtual space, the server verifies the visit using a checkpoint function that records that the user has arrived at a specific point within the virtual reality space.

[1188] Once the visit is confirmed, the server distributes rewards to the user and the store. The user receives points or coupons that can be used next time, and the store receives incentives.

[1189] Specific examples

[1190] For example, if a user is interested in "food tours" and "historical buildings," the system will suggest the most suitable tourist spots and stores in a virtual space based on this. A user planning to visit Kyoto can use a VR device to virtually visit Kyoto's tourist spots and enjoy checking information. In this case, the AI ​​model will suggest the most suitable tourist spots and stores by using the following prompt sentence:

[1191] User preferences: Foodie, Historical buildings

[1192] Planned visit area: Kyoto

[1193] Please suggest recommended tourist spots and stores.

[1194] This invention allows users to enjoy a virtual sightseeing experience before actually visiting, enabling efficient planning. Furthermore, coupons and rewards offered during the on-site visit provide benefits to both users and stores.

[1195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1196] Step 1:

[1197] The user uses a device to input information about their interests, preferences, and planned visit locations. The input data is stored in a cloud database (e.g., Firebase). The inputs are the user's interests and preferences (e.g., food tours, historical buildings) and the planned visit location (e.g., Kyoto). The output is stored in Firebase.

[1198] Step 2:

[1199] The server collects data about local tourist attractions and businesses from the internet and public databases. The collected data includes location, opening hours, photos, user reviews, etc. This data is stored in a database management system (e.g., MySQL, PostgreSQL). As input, the URLs or API endpoints of the tourist attractions and businesses are used. As output, the collected data is stored in the database.

[1200] Step 3:

[1201] The server uses a generative AI model (e.g., GPT-4) to create suggestions for tourist attractions and stores based on the user's input information and collected data. Specifically, the server sends the following prompt to GPT-4 and receives the results. The inputs used are the user's interests and preferences, as well as information about places they plan to visit. The output is the tourist attraction and store suggestions generated by the AI ​​model.

[1202] User preferences: Foodie, Historical buildings

[1203] Planned visit area: Kyoto

[1204] Please suggest recommended tourist spots and stores.

[1205] Step 4:

[1206] Based on the generated suggestions, the server generates a sightseeing experience in a virtual space using a virtual reality device (e.g., Oculus Rift S). Specifically, it uses a VR library (e.g., vrpy) to build a virtual space in which users can visit tourist attractions and shops. The generated information on the suggested tourist attractions and shops is used as input. The output is a virtual space that can be visited by users.

[1207] Step 5:

[1208] The server generates coupons for tourist attractions and stores in the virtual space. The generated coupons are sent to the user's device and can be used by the user. The input is information about the suggested tourist attractions and stores. The output is the generated coupons that are provided to the user.

[1209] Step 6:

[1210] When a user completes a visit in the virtual space, the device sends the visit information to the server, which verifies the visit information and stores it in a database. The input includes the user's visit information, and the output is the verified visit information.

[1211] Step 7:

[1212] The server distributes reward points and coupons to users based on the confirmed visits. In addition, incentives are also provided to suggested stores. The input is the confirmed visit information. The output is the distribution of rewards to users and stores.

[1213] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1214] The present invention relates to a system that combines interest and preference information input by a user with an emotion engine that recognizes emotions in real time to make more personalized suggestions about tourist spots and commercial facilities, and distributes rewards to both the user and the stores. Below, specific embodiments of the present invention and the processing of the program are explained in natural language.

[1215] The first major component of this system is a means for users to input personal information such as their interests and preferences, areas they plan to visit, and the length of their stay. Users can easily input this information using a smartphone or web application. For example, if a user is interested in "eating out" and "historical buildings," inputting this information will enable the system to provide optimal sightseeing suggestions to the user.

[1216] Next, data on local tourist attractions and commercial facilities is collected and organized. The server automatically collects data from the Internet, public databases of local governments, and partner companies, and then integrates and organizes it. Collected data includes tourist spot locations, opening hours, photos, user reviews, and more. Furthermore, by adding multilingual tags to this data, it becomes possible to provide information across language barriers.

[1217] Another feature of the present invention is the introduction of an emotion engine that recognizes the user's emotional state in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state (e.g., joy, surprise, sadness, stress, etc.). This emotion information, along with the user's interests and preferences, is used to suggest tourist spots and commercial facilities.

[1218] The server then uses a generative AI model to generate recommendations based on the user's interests, preferences, emotional state, and collected data. The AI ​​model analyzes all user inputs and real-time collected emotional information, and creates personalized recommendations based on that information. For example, if the emotion engine detects that the user is "stressed," it may suggest a relaxing cafe or a quiet park.

[1219] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[1220] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[1221] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[1222] As a concrete example, consider the case where a traveler named Yamada uses this system. Yamada accesses the app and registers that he is interested in eating out and visiting historical buildings. He then enters that he plans to visit Kyoto. The emotion engine then analyzes Yamada's emotional state and determines that he is currently seeking relaxation. Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses this information to suggest tourist spots and stores that are ideal for Yamada. Coupons are issued to the suggested stores, which Yamada uses to visit the various locations. Once the visit is confirmed, rewards are distributed to Yamada and the stores.

[1223] As described above, the present invention is a system that brings benefits to both users and stores by suggesting optimal tourist spots and stores based on the user's individual interests, preferences, and emotional information, and by confirming visits and distributing rewards.

[1224] The processing flow will be explained below.

[1225] Step 1:

[1226] The user enters interest and preference information.

[1227] User: Accesses a smartphone or web app and registers or logs in.

[1228] User: Enter their interests and preferences (food, history, nature, etc.), the area they want to visit, and the length of their stay.

[1229] Terminal: Sends the entered information to the server.

[1230] Step 2:

[1231] Store basic information and information about your interests and preferences.

[1232] Server: Creates a user profile based on the received user basic information and interest / preference information.

[1233] Server: Stores the profile in a database.

[1234] Step 3:

[1235] Collect and organize data on local tourist attractions and commercial facilities.

[1236] Server: Collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies.

[1237] Server: Consolidates collected data and organizes it into categories (food, history, nature, etc.).

[1238] Server: Stores detailed information (location, opening hours, photos, reviews) of each tourist attraction and store in a database and adds multilingual tags.

[1239] Step 4:

[1240] An emotion engine is used to collect user emotion information.

[1241] Device: Uses the smartphone's camera and microphone to record the user's facial expressions and voice in real time.

[1242] On the device: An emotion engine is used to analyze the user's current emotional state (e.g., joy, surprise, sadness, stress, etc.).

[1243] Terminal: Sends the analysis results to the server.

[1244] Step 5:

[1245] Generate suggestions based on user requests.

[1246] Users: Enter their specific interests or requirements (food, historical sites, quick trips, etc.) into the app.

[1247] Terminal: Sends the entered conditions to the server.

[1248] Server: Matches user profile, current needs, and sentiment information and uses generative AI models to generate the best list of tourist attractions and stores.

[1249] Server: Sends the generated list to the user's device.

[1250] Step 6:

[1251] View the suggested results.

[1252] Terminal: Suggested tourist spots and shops are displayed to the user in list format.

[1253] Step 7:

[1254] Generate a coupon.

[1255] Server: Generates coupon codes for each suggested tourist attraction and store.

[1256] Server: Sends the coupon code to the user's device.

[1257] Step 8:

[1258] View coupons.

[1259] On your device: Display the coupon code in the user's app.

[1260] User: Use coupons at suggested tourist spots and stores.

[1261] Step 9:

[1262] Confirm your visit.

[1263] User: Visits the suggested tourist spot or store and checks in using the app.

[1264] Terminal: Sends check-in information to the server.

[1265] Server: Checks the user's location and check-in data, and records the visit.

[1266] Step 10:

[1267] Distribute rewards.

[1268] Server: After confirming the visit, the user is given points or a coupon to use next time.

[1269] Server: Provide incentives to proposed tourist destinations and stores.

[1270] Step 11:

[1271] Notify compensation information.

[1272] Terminal: Notifies the user of the points or coupons awarded.

[1273] Server: Notifies the proposed tourist spots and stores of the payment of rewards.

[1274] Example 2

[1275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] In the modern tourism industry, there is a demand for personalized recommendations of tourist spots and shops that take into account a user's individual interests and emotional state. However, existing systems struggle to analyze a user's emotional information in real time and provide optimal recommendations to the user. Furthermore, there are issues with the fairness and efficiency of distributing rewards for suggested tourist spots and shops. This can lead to low satisfaction for both users and shops.

[1277] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving interest and preference information input by the user, a means for collecting and organizing data on local tourist attractions and commercial facilities, a means for collecting and analyzing emotional information in real time, a means for generating coupons for suggested tourist attractions and stores, a means for the user to confirm a visit, and a means for distributing rewards to the user and the facility based on the confirmed visit. This makes it possible to suggest optimal tourist attractions and stores based on the user's individual interest and preference information and real-time emotional information. Furthermore, the satisfaction of both the user and the store can be increased by confirming the visit and distributing rewards.

[1278] "User" refers to an individual who uses this system.

[1279] "Interest and preference information" is information that indicates a user's particular tastes and interests, including information related to sightseeing and leisure.

[1280] "Data" refers to a comprehensive range of information about tourist attractions and commercial facilities, including location, opening hours, photos, user reviews, etc.

[1281] "Emotion information" is information that indicates the user's emotional state, collected in real time from the user's facial expressions and tone of voice, and includes joy, surprise, sadness, stress, and the like.

[1282] "Coupon" means a code or ticket offering discounts or special offers that can be used at the proposed tourist attractions or stores.

[1283] "Visit confirmation" refers to the process of the system confirming that the user has actually visited the suggested tourist attractions and stores.

[1284] "Rewards" refers to incentives, points, and other benefits distributed to users and stores based on confirmed visits.

[1285] "Server" refers to the computer system that performs centralized data processing and proposal generation for this System.

[1286] A "multilingual tag" refers to an identifier that uniformly organizes information provided in various languages, allowing users to access the information across language barriers.

[1287] "Generative AI model" refers to an artificial intelligence modeling technology that suggests optimal tourist spots and stores based on a user's interests, preferences, emotional information, and collected data.

[1288] MODE FOR CARRYING OUT THE INVENTION

[1289] This invention is a system that combines information on the user's interests and preferences with an emotion engine that recognizes emotions in real time to suggest more personalized tourist spots and commercial facilities, and distributes rewards to both the user and the stores.

[1290] This system can generate suggestions based on personal information entered by users using their smartphones or web applications, such as their interests and preferences, areas they plan to visit, and length of stay. Next, data on local tourist attractions and commercial facilities is automatically collected, integrated, and organized by the server from the Internet, public databases of local governments, and partner companies. This creates a database containing information such as the locations of tourist attractions and stores, their opening hours, photos, and user reviews. Furthermore, this data is tagged in multiple languages, making it possible to provide information across language barriers.

[1291] Another feature of the present invention is that the device has an emotion engine that uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to evaluate their current emotional state. This allows emotional information to be collected in real time and used to suggest tourist spots and commercial facilities, along with the user's interests and preferences.

[1292] The server then uses a generative AI model to generate optimal recommendations for tourist spots and commercial facilities based on the user's input and collected emotional information. The AI ​​model analyzes all of the user's input and the emotional information collected in real time, and creates personalized recommendations based on that. For example, if the emotional engine detects that the user's emotional state is "stressed," it may suggest a relaxing cafe or a quiet park.

[1293] In the next step, coupons are generated for the suggested tourist spots and stores and provided to the user. The server works with each suggested destination to generate coupon codes that include discounts and special offers. The user can check these on their smartphone or a web application and use them locally. For example, a suggested cafe may issue a coupon for a "free coffee."

[1294] When a user actually visits a suggested tourist attraction or shop, the visit is confirmed and recorded. The user checks in on their smartphone and the information is sent to the server, which ensures an accurate record of the visit and serves as the basis for reward distribution in the next step.

[1295] Finally, rewards are distributed to the user and the store. The server checks the user's visit history and, based on that, awards the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits five suggested locations and completes check-in at all of them, they will be given a discount coupon that can be used on their next trip.

[1296] Specific examples

[1297] Consider a case where a traveler uses this system. The traveler accesses the app and registers that they are interested in eating out and visiting historical buildings. They then enter Kyoto as the area they plan to visit and set the length of their stay. The emotion engine analyzes the traveler's emotional state and determines that they are currently seeking "relaxation." Based on this, the server collects data on Kyoto's relaxing eating spots and historical buildings, and uses that information to suggest tourist spots and stores that are ideal for the traveler. For example, it suggests quiet and relaxing cafes. Coupons that can be used at the suggested cafes are issued, and the traveler uses them to visit various locations. Once the visit is confirmed, rewards are distributed to the traveler and the store.

[1298] Prompt Sentence Examples

[1299] "I'm interested in food tours and historical architecture, and I'm planning to visit Kyoto. Can you suggest some relaxing tourist spots?"

[1300] This system proposes optimal tourist spots and commercial facilities based on the user's interests, preferences, and real-time emotional information, and distributes rewards for those visits, thereby benefiting both the user and the store.

[1301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1302] System program processing flow

[1303] Step 1:

[1304] The user collects input information. Using a smartphone or web application, the user inputs personal information such as interests, preferences, areas to visit, and length of stay. This input information is sent to the server via the device. For example, a user may input information such as "interested in food tours" and "historical buildings," "plan to visit Kyoto," and "stay for three days."

[1305] Input: Interests and preferences, areas to visit, length of stay, etc.

[1306] Output: User information data sent to the server

[1307] Step 2:

[1308] This service collects and organizes data on tourist destinations and commercial facilities. The server automatically collects data on tourist destinations and commercial facilities from the Internet, public databases of local governments, and partner companies. This collected data is integrated and centralized, with information such as location, opening hours, photos, and user reviews. In addition, multilingual tags are added, making it possible to provide information across language barriers.

[1309] Input: Internet, public databases of local governments, data from partner companies

[1310] Output: Integrated and organized tourist destination and commercial facility data

[1311] Step 3:

[1312] The device analyzes the user's emotional state in real time. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice, and the emotion engine evaluates the user's current emotional state. This emotional information is sent to the server in real time. For example, if the device detects that the user's facial expression is relaxed, it will determine that the user is "relaxed."

[1313] Input: User's facial expressions and tone of voice data

[1314] Output: Real-time evaluated emotion information

[1315] Step 4:

[1316] The server generates recommendations. Using a generative AI model, the server generates recommendations for optimal tourist spots and commercial facilities based on the user's input and emotional information. The recommendations are personalized by analyzing the user's interests, preferences, emotional state, and collected data. For example, if the user is looking to relax, the server will suggest "quiet cafes" or "peaceful parks."

[1317] Input: User information data, emotion information, organized tourist destination and commercial facility data

[1318] Output: Proposals for the best tourist spots and commercial facilities for the user

[1319] Step 5:

[1320] Generate and provide coupons. The server generates coupon codes for each suggested tourist spot and store, and creates coupons to provide to users. Users can check the coupons on their smartphones or web applications and use them locally. For example, a coupon for "free coffee" may be generated.

[1321] Input: Information on tourist spots and commercial facilities suggested to the user

[1322] Output: Generated coupon code

[1323] Step 6:

[1324] Confirm the user's visit. The user actually visits the suggested tourist spots and stores and checks in using their smartphone. This check-in information is sent to the server, and the user's visit is confirmed.

[1325] Input: User visit confirmation information (check-in)

[1326] Output: Visit history data recorded on the server

[1327] Step 7:

[1328] Rewards are distributed. The server checks the user's visit history and based on that, gives the user points or coupons that can be used next time. Incentives are also provided to affiliated stores. For example, if a user visits a specific tourist spot, a discount coupon that can be used on the next trip can be distributed based on that history.

[1329] Input: User's visit history data

[1330] Output: Rewards (points or coupons) distributed to users and stores

[1331] The above is the specific processing flow of the program for this system.

[1332] (Application example 2)

[1333] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1334] Currently, there is a demand for recommendations for tourist destinations and commercial facilities that match the interests and preferences of diverse users. However, there is no system yet that can also recognize a user's emotional state in real time and make optimal recommendations based on the results. As a result, there is a lack of more personalized recommendations, and improving the user experience is an issue. In addition, there is no sufficient system for users to check in and receive rewards when they actually visit, and there are also issues with linking with the commercial facilities to which the recommendations are made. As a result, a situation has arisen in which neither users nor commercial facilities are able to reap sufficient benefits.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1336] In this invention, the server includes means for receiving interest and preference information input by the user, means for collecting and organizing data on local tourist facilities and commercial facilities, means for suggesting tourist facilities and commercial facilities based on the user's interest and preference information and data, means for recognizing the user's emotional state in real time, means for generating coupons for the suggested tourist facilities and commercial facilities, means for the user to confirm their visit, and means for distributing rewards to the user and commercial facilities based on the confirmed visit. This makes it possible to suggest optimal tourist facilities and commercial facilities that take into account the user's interest and preference information as well as their emotional state, thereby improving the user experience. Furthermore, the visit confirmation and reward distribution can increase benefits for both the user and the commercial facilities.

[1337] "Interest and Preference Information" is data that describes a user's interests and preferences, such as specific product categories, experiences, or activities.

[1338] "Tourist and commercial facilities" refers to places that offer services and goods to tourists and consumers. This includes tourist attractions, shops, restaurants, cafes, etc.

[1339] An "emotional state" refers to the emotion a user is feeling at a particular moment, such as happiness, surprise, sadness, or stress.

[1340] "Suggestion" refers to the system's act of recommending appropriate tourist facilities or commercial facilities to users.

[1341] "Coupon" refers to a code or voucher that provides a User with discounts or benefits that can be used at suggested tourist or commercial facilities.

[1342] "Visit confirmation" refers to the act of confirming that a user has actually visited a suggested tourist attraction or commercial facility.

[1343] "Rewards" refers to points, coupons that can be used next time, incentives, etc. that are provided to users and commercial facilities based on confirmation of their visit.

[1344] This invention is a system that suggests tourist attractions and commercial facilities based on the user's input of interest and preference information and emotional state recognized in real time, and distributes rewards to both the user and the commercial facilities. The system includes the following means.

[1345] First, smartphones and web applications are used to collect information about users' interests and preferences. Users enter information such as their interests and preferences, areas they plan to visit, and the length of their stay into a specific application. For example, if a user enters that they are interested in "cafes" and "relaxation," subsequent suggestions will be made based on this information.

[1346] Next, data on local tourist and commercial facilities is collected and organized. The server uses a cloud API to automatically collect data from the internet, public databases of local governments, and partner companies. This integrates a variety of information, such as the locations, opening hours, photos, and user reviews of tourist and commercial facilities. The collected data is also tagged in multiple languages, making it possible to provide information across language barriers.

[1347] Furthermore, it uses an emotion engine that recognizes the user's emotional state in real time. It uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice to assess their current emotional state. This emotional information is then used together with the user's interests and preferences.

[1348] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on the user's profile, current needs, and emotional information. For example, if the emotion engine detects that the user is feeling "fatigue," it will suggest cafes and stores with a relaxing effect.

[1349] For this proposed facility, the server generates a coupon code containing discounts and special offers and provides it to the user. The user can check this on their smartphone or web application and use it locally. An example of the coupon content is "free coffee at the cafe."

[1350] When a user actually visits a suggested tourist attraction or commercial facility, they can easily check in on their smartphone. This information is sent to the server, and a record of the visit is kept. Finally, based on the confirmed visit, the server distributes rewards to the user and the commercial facility. For example, if a user visits five suggested destinations and checks in at all, they will receive a discount coupon for their next trip.

[1351] As a concrete example, consider a scenario where a user uses the app to register their interest in "clothes" and "cafes," and then puts on smart glasses and goes shopping. If the emotion engine recognizes that the user's current emotion is "fatigue," the suggested store will be a cafe with a relaxation effect. When the user visits the suggested cafe and checks in, the result is recorded and the user will receive a coupon for their next visit.

[1352] An example of a prompt is as follows:

[1353] "The product category the user is interested in is 'clothing' and their emotional state is 'fatigue'. Their current location is 'Chuo-ku, Kyoto City'. Based on this information, please suggest cafes and shops where they can relax."

[1354] This system will enable users to receive more personalized suggestions, improving the user experience, and will also benefit both users and commercial establishments by encouraging them to visit the site and distributing rewards.

[1355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1356] Step 1:

[1357] Users enter their interests, preferences, areas they plan to visit, and length of stay into a smartphone or web application.

[1358] Input: Interests and preferences, planned areas to visit, length of stay

[1359] Output: User profile information

[1360] Specific operation: The user launches the application and enters the product categories, tourist spots, and length of stay they are interested in into the text boxes. The input information is sent to the server and saved as individual profile information.

[1361] Step 2:

[1362] The server collects data on local tourist and commercial facilities from the Internet, public databases of local governments, and affiliated companies.

[1363] Input: Area to visit

[1364] Output: Facility database

[1365] Specific operation: The server makes an API request based on the area to be visited and collects information on tourist facilities and commercial facilities in the area. The collected data is integrated, organized, and stored in a database.

[1366] Step 3:

[1367] The server uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and tone of voice, and uses an emotion engine to evaluate their current emotional state.

[1368] Input: Camera video, audio data

[1369] Output: Emotion evaluation result

[1370] How it works: The device's camera and microphone capture the user's facial expressions and voice in real time, and the data is sent to the emotion engine, which analyzes it and evaluates the user's emotional state.

[1371] Step 4:

[1372] The server uses a generative AI model to suggest optimal tourist and commercial facilities based on user profile information, emotion evaluation results, and a facility database.

[1373] Input: User profile information, emotion evaluation results, facility database

[1374] Output: Optimized suggestion list

[1375] Specific operation: The server inputs a prompt statement (e.g., "The product category the user is interested in is 'clothing,' and their emotional state is 'fatigue.' Their current location is 'Chuo Ward, Kyoto City.' Based on this information, please suggest cafes and shops where they can relax.") into the generative AI model and generates an optimal list of suggestions.

[1376] Step 5:

[1377] The server generates coupons for the suggested tourist and commercial facilities and provides them to users via smartphones or web applications.

[1378] Input: Optimized suggestion list

[1379] Output: Coupon code

[1380] Specific operation: The server calls the coupon generation API for each facility on the proposal list, associates the generated coupon code with the user profile, and saves it. The user can then check the coupon information on the application.

[1381] Step 6:

[1382] Users visit the suggested tourist attractions and commercial facilities and check in on their smartphones.

[1383] Input: Visit information, check-in information

[1384] Output: Visit confirmation data

[1385] Specific operation: A user checks in to a facility using the application, and the information is sent to the server. The server updates the visit confirmation data and records it as a visit history.

[1386] Step 7:

[1387] The server distributes rewards to users and commercial establishments based on the verified visit data.

[1388] Input: Visit confirmation data

[1389] Output: Reward (points, coupons for next use, etc.)

[1390] Specific operation: The server analyzes the visit confirmation data and distributes rewards to users and affiliated commercial facilities who meet the conditions. Users are given points and coupons that can be used next time, and commercial facilities are provided with incentives.

[1391] 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.

[1392] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1393] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1394] 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.

[1395] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1396] 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.

[1397] 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).

[1398] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1399] 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."

[1400] 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.

[1401] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1402] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1407] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] The following is further disclosed regarding the above embodiment.

[1413] (Claim 1)

[1414] means for receiving user-entered interest and preference information;

[1415] A means of collecting and organizing data on local tourist destinations and commercial facilities,

[1416] A means for suggesting tourist spots and stores based on the user's interests and preferences and the data;

[1417] means for generating coupons for the suggested tourist attractions and stores;

[1418] means for the user to confirm the visit;

[1419] The system includes means for distributing rewards to users and stores based on the confirmed visits.

[1420] (Claim 2)

[1421] 10. The system of claim 1, further comprising means for tagging the information entered by the user and the collected data relating to tourist attractions and commercial facilities in multiple languages.

[1422] (Claim 3)

[1423] 10. The system of claim 1, further comprising means for using a generative AI model to provide optimized recommendations for tourist attractions and stores based on the user's interest and preference information and the collected data.

[1424] "Example 1"

[1425] (Claim 1)

[1426] means for receiving user input information about interests and preferences;

[1427] A means of collecting and organizing data on tourist attractions and commercial facilities related to the region;

[1428] A means for suggesting tourist spots and stores based on the user's interest and preference information and the data;

[1429] means for generating coupons for the suggested tourist attractions and stores;

[1430] means for the user to confirm the visit;

[1431] means for distributing rewards to users and facilities based on said verified visits;

[1432] means for inputting the user's input information and the data as prompts into a generative AI model to generate personalized recommendations;

[1433] a means for adding multilingual tags to the collected data;

[1434] A system including:

[1435] (Claim 2)

[1436] The system of claim 1 , further comprising: means for generating a coupon code including a discount or special offer based on the generated offer.

[1437] (Claim 3)

[1438] 10. The system of claim 1, further comprising means for collecting user check-in information and verifying visit history.

[1439] "Application Example 1"

[1440] New Claims

[1441] (Claim 1)

[1442] means for receiving user-entered interest and preference information;

[1443] A means of collecting and organizing data on local tourist destinations and commercial facilities,

[1444] A means for suggesting tourist spots and stores based on the user's interests and preferences and the data;

[1445] means for generating coupons for the suggested tourist attractions and stores;

[1446] means for the user to confirm the visit;

[1447] means for distributing rewards to users and stores based on the confirmed visits;

[1448] A means for allowing a user to visit tourist spots and stores in a virtual space using a virtual reality device;

[1449] means for distributing rewards to users based on visits in said virtual space;

[1450] A system including:

[1451] (Claim 2)

[1452] 10. The system of claim 1, further comprising means for tagging the information entered by the user and the collected data relating to tourist attractions and commercial facilities in multiple languages.

[1453] (Claim 3)

[1454] 10. The system of claim 1, further comprising means for using a generative AI model to provide optimized recommendations for tourist attractions and stores based on the user's interest and preference information and the collected data.

[1455] "Example 2: Combining Emotion Engines"

[1456] (Claim 1)

[1457] means for receiving user-entered interest and preference information;

[1458] A means of collecting and organizing data on local tourist destinations and commercial facilities,

[1459] A means for suggesting tourist spots and stores based on the user's interests and preferences and the data;

[1460] A means of collecting and analyzing emotional information in real time,

[1461] means for generating coupons for the suggested tourist attractions and stores;

[1462] means for the user to confirm the visit;

[1463] means for distributing rewards to users and facilities based on said verified visits;

[1464] A system including:

[1465] (Claim 2)

[1466] 10. The system of claim 1, further comprising means for tagging the information entered by the user and the collected data relating to tourist attractions and commercial facilities in multiple languages.

[1467] (Claim 3)

[1468] The system of claim 1, further comprising means for using a generative AI model to make optimized recommendations for tourist attractions and stores based on the user's interests, preference information, emotional information, and collected data.

[1469] "Application example 2 when combining emotion engines"

[1470] (Claim 1)

[1471] means for receiving user-entered interest and preference information;

[1472] A means of collecting and organizing data on local tourist and commercial facilities,

[1473] A means for suggesting tourist facilities and commercial facilities based on the user's interest and preference information and the data;

[1474] means for recognizing the emotional state of the user in real time;

[1475] means for generating coupons for the proposed tourist and commercial facilities;

[1476] means for the user to confirm the visit;

[1477] The system includes means for distributing rewards to users and commercial establishments based on said verified visits.

[1478] (Claim 2)

[1479] 10. The system of claim 1, further comprising means for tagging user-entered information and collected data about tourist and commercial facilities in multiple languages.

[1480] (Claim 3)

[1481] 10. The system of claim 1, further comprising means for using a generative AI model to provide optimized recommendations for tourist facilities and commercial facilities based on the user's interest and preference information and the collected data. [Explanation of symbols]

[1482] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user-entered interest and preference information; A means of collecting and organizing data on local tourist attractions and commercial facilities, A means for suggesting tourist spots and stores based on the user's interest and preference information and the data; means for generating coupons for the suggested tourist attractions and stores; a means for the user to confirm the visit; The system includes means for distributing rewards to users and stores based on the confirmed visits.

2. The system according to claim 1, further comprising means for tagging the information input by the user and the collected data relating to the tourist spots and commercial facilities in multiple languages.

3. The system of claim 1 further comprising means for using a generative AI model to make optimized recommendations for tourist attractions and stores based on the user's interest and preference information and the collected data.

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