System

Abstract

A system is provided.SOLUTION: A system including means for collecting videos and images of social media posted by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model for analyzing the attractiveness of a region from the preprocessed videos and images, means for generating sightseeing content based on the analysis result, means for analyzing customer data and planning a marketing strategy based on nationality and interests, and means for performing customized promotion based on the generated sightseeing content and marketing strategy.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] In order for local tourism businesses to effectively attract tourists, they need to accurately grasp the appeal of the region and promote it in a way that suits a diverse customer base, but current tourism marketing methods do not adequately address this. Furthermore, because tourists have diverse interests and preferences, it is difficult to conduct detailed marketing that responds to these, which is hindering an increase in the number of tourists and the revitalization of the local economy. [Means for solving the problem]

[0005] This invention is a system that includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, and means for implementing customized promotions based on the generated tourism content and marketing strategy.

[0006] This system makes it possible to efficiently discover the attractions of a region from data posted by tourists and consistently create tourism content and marketing tailored to the characteristics of each customer, thereby increasing the number of tourists and revitalizing the local economy.

[0007] "Tourists" are people who visit a particular area or facility for the purpose of sightseeing.

[0008] "Social media" refers to online platforms that enable users to share and interact with information and media content.

[0009] "Video and images" refers to media formats that record visual information, including moving images (video) and still images (photographs).

[0010] "Means of collection" refers to the methods and techniques used to obtain videos and images from social media.

[0011] "Preprocessing" refers to the process of converting and organizing collected data to make it easier to analyze.

[0012] An "artificial intelligence model" is a machine learning algorithm that is trained to perform a specific task based on large amounts of data.

[0013] "Analysis" is the process of examining data or information in detail to understand its content and structure and extract meaningful information.

[0014] "Tourism content" refers to tourism-related services and products such as programs, tours, events, and experiences offered to tourists.

[0015] "Customer Data" refers to information collected about tourists, such as their attributes, interests, and behavioral patterns, for marketing and service provision purposes.

[0016] A "marketing strategy" is a plan or method for effectively targeting a specific customer base with a product or service.

[0017] "Promotion" refers to advertising and campaign activities aimed at spreading awareness of specific products and services to customers and encouraging their purchase or use. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on the nationality and interests of customers. Specific embodiments and program processing for implementing this system are described below.

[0040] 1. Collecting social media data

[0041] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0042] 2. Data Preprocessing

[0043] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[0044] 3. Analysis of regional attractiveness

[0045] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[0046] 4. Extracting the charm

[0047] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[0048] 5. Tourism content proposals

[0049] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[0050] 6. Analysis of Customer Data

[0051] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from tourist surveys or existing databases.

[0052] 7. Targeted Marketing

[0053] The server analyzes customer data and creates the optimal marketing strategy for each customer segment. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0054] 8. Marketing Execution

[0055] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure effectiveness.

[0056] Specific examples

[0057] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[0058] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[0059] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[0060] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0061] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[0062] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[0063] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0067] Step 2:

[0068] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[0069] Step 3:

[0070] The server removes unnecessary metadata and organizes necessary metadata such as timestamps, location information, and hashtags.

[0071] Step 4:

[0072] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it can identify "Mount Fuji" or "Kiyomizu-dera Temple" in a video.

[0073] Step 5:

[0074] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[0075] Step 6:

[0076] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[0077] Step 7:

[0078] The server automatically generates new sightseeing courses and events based on the list of attractive points, such as a "Mt. Fuji sunrise tour" or "Kyoto traditional cultural experience."

[0079] Step 8:

[0080] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[0081] Step 9:

[0082] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[0083] Step 10:

[0084] The server creates marketing strategies for each target group based on customer data, such as "promoting Mount Fuji climbing tours to American tourists and Kyoto cultural experiences to Japanese tourists."

[0085] Step 11:

[0086] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[0087] Step 12:

[0088] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[0089] Example 1

[0090] 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."

[0091] The tourism industry faces challenges in developing individual marketing strategies based on tourists' preferences and attributes and effectively attracting them. Furthermore, there is a lack of a system for utilizing the vast amount of video and image data posted by tourists on social media to extract regional appeal from it and generate tourism content. This makes it difficult to implement efficient and effective tourism promotion.

[0092] 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.

[0093] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze regional characteristics from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on attributes and preferences, means for implementing customized promotions based on the generated tourism content and marketing strategy, and means for monitoring the effectiveness of the customized promotions. This makes it possible to efficiently extract regional attractions using social media data, formulate effective marketing strategies tailored to tourist preferences, and implement tourism promotions.

[0094] A "tourist" is someone who travels to visit a tourist destination.

[0095] "Social media" refers to online services that enable users to share content through posts and comments.

[0096] "Video" refers to a media format that creates the illusion of movement by playing a sequence of still images at a constant speed.

[0097] "Image" refers to static visual data that graphically displays visual information.

[0098] "Collection" refers to the act of gathering data based on specific criteria or filters.

[0099] "Preprocessing" refers to the process of organizing and transforming data to make it easier to analyze and apply models to.

[0100] "Regional characteristics" refers to the characteristics of natural landscapes, famous places, activities, etc. that are unique to a particular region.

[0101] An "artificial intelligence model" refers to an algorithm that learns from large amounts of data, identifies patterns, and makes inferences.

[0102] "Analysis" refers to the process of examining data in detail to derive its characteristics and trends.

[0103] "Tourism content" refers to the content of tours, experiences, events, etc. offered to tourists.

[0104] "Customer Data" refers to information regarding the attributes and behavior of service users.

[0105] "Attributes" refer to personal characteristics of customers, such as nationality, age, and gender.

[0106] "Preferences" refer to a customer's preferred activities or interests.

[0107] A "marketing strategy" is a plan for promoting a product or service based on specific goals.

[0108] "Customized promotion" refers to advertising activities tailored to specific customer groups.

[0109] "Monitoring" refers to the process of measuring and overseeing the effectiveness of a particular activity or promotion.

[0110] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on customer attributes and preferences. Specific embodiments and program processing for implementing this system are described below.

[0111] Social media data collection

[0112] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images based on specific hashtags (e.g., "JapanTravel") and location information. When collecting, authentication is performed using an API key, and post data that matches the conditions is obtained.

[0113] Data Preprocessing

[0114] The server converts the collected video and image data into a format that is easy to analyze (e.g., JPEG or MP4). During this process, it uses the Python Pillow library to standardize the image format, remove unnecessary metadata, and organize important metadata such as timestamps, location information, and hashtags. For example, it extracts and organizes metadata from JSON-formatted data.

[0115] Analysis of regional characteristics

[0116] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from collected and preprocessed video and image data. As a specific example, it uses the YOLOv3 model to identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[0117] Tourism content generation

[0118] The server generates tourism content based on the analysis results. This involves creating a list of popular tourist spots and activities and planning new tourist courses and events. The planned content is stored in a database and made available for reference by tourism operators. For example, a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience" might be generated.

[0119] Customer data analysis

[0120] The device inputs or retrieves tourist profile data (e.g., nationality, age, gender, interests, etc.) The collected data is stored in a MySQL database and analyzed using machine learning libraries such as Scikit-learn.

[0121] Marketing strategy planning

[0122] The server analyzes customer data and develops the optimal marketing strategy for each customer segment. For example, it suggests a "Mount Fuji climbing tour" for American tourists and a "Kyoto night illumination tour" for Japanese tourists.

[0123] Run customized promotions

[0124] The server implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the resulting data on the management screen, allowing the effectiveness of marketing to be measured.

[0125] Prompt Sentence Examples

[0126] "Identify tourist attractions related to the JapanTravel hashtag on Instagram."

[0127] "Please create the best marketing strategy for Kyoto's tourist destinations."

[0128] As described above, this system provides tourism operators with concrete tools to effectively attract tourists and revitalize local economies.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information. At this time, it authenticates using an API key and retrieves posted data that matches the conditions in JSON format. The input is the API request, and the output is the JSON data of the collected videos and images.

[0132] Step 2:

[0133] The video and image data collected by the server is converted into a format that is easy to analyze (for example, JPEG or MP4). The Python Pillow library is used to standardize the image format and remove unnecessary metadata. Important metadata such as timestamps, location information, and hashtags are extracted and organized. The input is JSON-formatted data, and the output is organized metadata and video and image data in a unified format.

[0134] Step 3:

[0135] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from preprocessed video and image data. Specifically, it uses the YOLOv3 model to analyze images and videos and identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple." The input is preprocessed video and image data, and the output is a list of recognized features (scenery, landmarks, and activities).

[0136] Step 4:

[0137] The server evaluates tourist interests based on the analysis results and creates a list of popular tourist attractions and activities. The analysis data is stored in an SQL database and the evaluation results are extracted using SQL queries. The input is a list of analyzed features, and the output is a list of popular tourist attractions and activities.

[0138] Step 5:

[0139] The server plans new sightseeing itineraries and events based on the generated list. This includes creating new sightseeing packages that include specific events at tourist destinations. For example, it proposes a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience." The input is a list of popular spots and activities, and the output is a new sightseeing package proposal.

[0140] Step 6:

[0141] The terminal inputs or acquires tourist profile data (nationality, age, interests, etc.). The input data is collected through a questionnaire form or an existing database. The input is tourist profile data, and the output is a collection of acquired customer data.

[0142] Step 7:

[0143] The server analyzes customer data and uses machine learning libraries such as Scikit-learn to create optimal marketing strategies for each customer segment. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. The input is customer data, and the output is a marketing strategy for each segment.

[0144] Step 8:

[0145] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the collected data on the management screen and measures the effectiveness of the marketing. The input is the marketing plan, and the output is the promotion effectiveness indicators.

[0146] (Application example 1)

[0147] 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."

[0148] In recent years, the number of tourists visiting tourist destinations has increased, making it important to effectively communicate the attractions of tourist destinations. However, a lack of customized information and promotions based on tourists' interests makes it difficult to maximize the attractions of tourist destinations. There is also a need for a system that can efficiently analyze the large amount of collected social media data and generate tourism content.

[0149] 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.

[0150] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for including an application installed on a smartphone, smart glasses, or head-mounted display that provides tourist destination information based on the user's interests, and means for identifying tourist attractions based on data collected from social media and suggesting related information, thereby enabling the provision of efficient and personalized tourism information and promotions to a diverse range of tourists.

[0151] "Tourists" refer to people who visit a particular tourist destination to experience the attractions of that area.

[0152] "Social media" refers to online platforms that enable users to share information and interact via the internet.

[0153] "Video and images" refers to media formats for transmitting visual information, and refers to files containing visually recorded information.

[0154] "Means of collection" refers to methods or technologies for collecting data on a device such as a server based on specific conditions.

[0155] "Preprocessing means" refers to the initial processing performed to convert data into a format that is easier to analyze.

[0156] An "artificial intelligence model" refers to a program that uses technologies such as machine learning and deep learning to analyze data and extract patterns and features.

[0157] "Regional attractions" refer to the points and characteristics that make a particular region interesting and valuable to tourists.

[0158] "Analytical means" refers to techniques and methods for processing data to reveal specific information or patterns.

[0159] "Tourism content" refers to data provided to tourists, including information on tourist destinations, experience plans, events, etc.

[0160] "Means of generation" refers to methods and technologies for creating new data, information, or content based on specific conditions.

[0161] "Customer data" refers to information such as tourists' profile information, interests, and behavioral history.

[0162] A "marketing strategy" refers to a plan or method for effectively promoting a product to a specific target audience.

[0163] "Customized promotion" refers to advertising and marketing techniques that are specifically tailored to an individual customer's interests.

[0164] A "smartphone" refers to a mobile information terminal that has communication functions and can connect to the Internet and use applications.

[0165] "Smart glasses" refers to a wearable device that adds information processing and communication functions to conventional glasses.

[0166] A "head-mounted display" refers to a display device that is worn on the user's head and displays information directly into the user's field of vision.

[0167] "Application" refers to a software program designed to accomplish a specific purpose.

[0168] "Tourist destination information" refers to detailed information and guide content about specific tourist destinations.

[0169] "Means of identification" refers to techniques or methods for recognizing and extracting specific features or information from data.

[0170] This invention is a system that collects videos and images posted on social media by tourists, analyzes them to discover the attractions of a region, and enables the provision of customized information and marketing based on the user's nationality and interests.

[0171] Hardware and software used

[0172] Hardware:

[0173] Servers with high-performance CPU / GPUs, smartphones, smart glasses, or head-mounted displays.

[0174] software:

[0175] Python, Flask (web framework), Google Cloud Vision API, OpenCV (image analysis library).

[0176] System Operation

[0177] The server uses social media APIs to collect videos and images posted by tourists based on specific hashtags and location information, such as posts related to "JapanTravel" or specific regions.

[0178] The collected video and image data is preprocessed by the server and converted into a format that is easy to analyze, and unnecessary metadata is removed and important metadata such as timestamps, location information, and hashtags are organized.

[0179] The preprocessed data is then fed into an artificial intelligence model to analyze the area's attractiveness. Image analysis techniques, such as Google Cloud Vision API and OpenCV, are used to identify landmarks and tourist attractions within the images. From these identified attractions and activities, the model then creates a list of particularly popular places and activities.

[0180] The server then plans new sightseeing courses and events based on the listed attractions. It references user profile data (such as nationality, age, and interests) to propose optimal sightseeing plans for each user. Applications installed on smartphones, smart glasses, and head-mounted displays display real-time information about tourist spots based on the user's interests, improving the sightseeing experience.

[0181] Specific examples

[0182] As a specific example of its use, the system collects Instagram images posted by tourists using the hashtag "KyotoTravel." The server analyzes the images and identifies "Kiyomizu-dera Temple" and "Kinkaku-ji Temple." The server then suggests a "Kinkaku-ji and Kiyomizu-dera Temple tour" to American tourists. These suggestions are provided via the user's smartphone, smart glasses, or head-mounted display.

[0183] Prompt Sentence Examples

[0184] Social media post collection example:

[0185] Region: Kyoto

[0186] Hashtag: KyotoTravel

[0187] Analysis target: Images and videos

[0188] Analysis goal: Identifying tourist attractions (e.g., Kiyomizu-dera Temple, Kinkaku-ji Temple)

[0189] Tourist attraction identification and suggestion example:

[0190] Customer Profile: American Tourists

[0191] Proposal: Tour of Kinkakuji Temple and Kiyomizudera Temple

[0192] By using this system, tourism businesses can provide efficient and personalized tourism information and promotions to a wide range of tourists.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] The server uses social media APIs to collect videos and images posted by tourists. During collection, data is collected based on specific hashtags (e.g., "JapanTravel") and location information. The input to this collection process is hashtags and location information, and the output is the corresponding video and image files.

[0196] Step 2:

[0197] The server preprocesses the collected video and image data. Specifically, it converts the video and images into a format that is easy to analyze and removes unnecessary metadata. The input to the preprocessing is the collected video and image data, and the output is the preprocessed video and image data.

[0198] Step 3:

[0199] The server inputs the preprocessed data into an artificial intelligence model to analyze the area's attractions, using the Google Cloud Vision API and OpenCV to identify landmarks in the images. The input for the analysis is the preprocessed video and image data, and the output is data on identified landmarks and tourist attractions.

[0200] Step 4:

[0201] The server generates tourism content based on the analysis results. Based on the identified tourist attractions and activities, it lists popular places and activities and plans sightseeing courses and events. The input for generation is the analysis result data, and the output is newly generated tourism content and proposed plans.

[0202] Step 5:

[0203] The server analyzes the user's profile data (nationality, age, interests, etc.) and customizes the optimal sightseeing plan for each target. The input of this process is the user's profile data, and the output is a customized sightseeing plan.

[0204] Step 6:

[0205] The terminal (smartphone, smart glasses, head-mounted display) provides the generated tourism content and customized tourism plans to the user. The input of this provision process is the customized tourism plan, and the output is the tourism information displayed to the user.

[0206] Step 7:

[0207] The server collects user feedback and behavioral data to improve the tour plan in real time. The input of this feedback collection process is user behavioral data, and the output is a further optimized tour plan.

[0208] Through these steps, tourism businesses can provide efficient and personalized tourism information and promotions to individual tourists.

[0209] 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.

[0210] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and implements marketing strategies based on the customer's nationality and interests. It incorporates an emotion engine to recognize user emotions and customizes tourism content and marketing strategies based on these. Specific embodiments and program processing for implementing this system are described below.

[0211] 1. Collecting social media data

[0212] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0213] 2. Data Preprocessing

[0214] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[0215] 3. Analysis of regional attractiveness

[0216] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[0217] 4. Extracting the charm

[0218] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[0219] 5. Tourism content proposals

[0220] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[0221] 6. Analysis of Customer Data

[0222] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[0223] 7. Applying the Emotion Engine

[0224] The server analyzes the user's posts and profile information and uses an emotion engine to recognize the user's current emotional state and long-term emotional patterns. For example, it analyzes emotions based on images of the user looking "happy."

[0225] 8. Targeted Marketing

[0226] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0227] 9. Customized Content Generation

[0228] The server customizes sightseeing content based on the user's emotional state identified by the emotion engine. For example, it suggests a "hot spring tour" to a user who wants to relax, and a "hiking tour" to a user who wants to be active.

[0229] 10. Marketing Execution

[0230] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[0231] Specific examples

[0232] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[0233] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[0234] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[0235] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0236] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[0237] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[0238] 7. The server applies an emotion engine to recognize emotions such as "happiness" or "excitement" from the user's posts.

[0239] 8. The server promotes the "Hot Spring Relaxation Plan" to users seeking relaxation and the "Outdoor Adventure Tour" to users seeking an active experience.

[0240] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0244] Step 2:

[0245] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[0246] Step 3:

[0247] The server removes unnecessary metadata and compiles necessary metadata such as timestamps, location information, and hashtags.

[0248] Step 4:

[0249] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[0250] Step 5:

[0251] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[0252] Step 6:

[0253] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[0254] Step 7:

[0255] The server generates new sightseeing courses and events based on the listed attractions, such as a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience."

[0256] Step 8:

[0257] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from a questionnaire or an existing database.

[0258] Step 9:

[0259] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[0260] Step 10:

[0261] The server uses an emotion engine to recognize emotions from user posts, for example, determining "happiness" or "surprise" from the post content and facial expressions.

[0262] Step 11:

[0263] The server customizes sightseeing content based on the user's current emotional state, suggesting, for example, a hot spring tour for a user who wants to relax, or a hiking tour for a user who wants to stay active.

[0264] Step 12:

[0265] The server analyzes the long-term emotional patterns identified by the emotion engine and creates marketing strategies based on them, such as "market relaxation plans to users who have posted frequently about wanting to relax over the past year."

[0266] Step 13:

[0267] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[0268] Step 14:

[0269] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[0270] Example 2

[0271] 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."

[0272] In recent years, the spread of social media has led to an increase in postings of information about tourist destinations and events. However, there is a lack of systems that can effectively collect and analyze this information and link it to appropriate tourism content and marketing strategies. Furthermore, there is no system that can grasp tourists' emotional state and propose customized promotions that respond to that. There is a need to solve these issues.

[0273] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0274] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying a machine learning model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for applying a sentiment analysis engine to analyze customer sentiment data, and means for implementing customized promotions based on the generated tourism content and marketing strategy. This makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that meet the needs and sentiments of tourists.

[0275] "Social media" is a platform that allows users to share content and communicate with each other over the Internet.

[0276] "Means for collecting videos and images" refers to the function for obtaining video and image data from social media platforms.

[0277] "Preprocessing" refers to the process of converting collected video and image data into a format suitable for analysis and removing unnecessary metadata.

[0278] A "machine learning model" is an algorithm that learns specific patterns and characteristics from data and makes predictions and classifications on newly acquired data.

[0279] "Tourism content" refers to the collection of information and services related to tourist destinations and activities that are provided to tourists.

[0280] "Customer Data" means information about a tourist, including personal profile information such as nationality, age, and interests, as well as historical behavioral data.

[0281] A "marketing strategy" is a set of plans and methods for promoting and selling products and services in the most optimal way to a particular customer segment.

[0282] An "emotion analysis engine" is an algorithm or software that recognizes and analyzes a user's emotional state from text or images.

[0283] "Customized promotion" refers to specific advertising and promotional activities that are applied based on the needs, interests, and emotional state of individual customers.

[0284] "System" refers to a set of machines, software, databases, etc., whose components work together to achieve a specific purpose.

[0285] This invention is a system that collects and analyzes videos and images posted by tourists on social media, discovers the attractions of a region based on the collected data, and plans and implements marketing strategies tailored to the nationality, interests, and emotions of customers. This system uses the following hardware and software:

[0286] Hardware and software used

[0287] The server is a computer with a powerful processor and sufficient memory, such as an Intel Xeon processor. It also uses APIs to collect data from social media. For example, libraries such as "Tweepy" (for Twitter) and "Instagram API" (for Instagram) are used.

[0288] Python's "OpenCV" and "Pandas" are used for data preprocessing, which enables format conversion of image and video data and organization of metadata.

[0289] For data analysis, machine learning libraries such as "TensorFlow" and "PyTorch" are used to extract the characteristics of tourist spots from the collected videos and images. Suitable machine learning models include "YOLO (You Only Look Once)" and "ResNet," which are widely used in image recognition.

[0290] To input and acquire customer data, data is collected from the user's device (smartphone or PC) via a questionnaire form or an existing database. This data is then sent to the server.

[0291] The emotion engine uses an emotion analysis API (for example, Microsoft Azure Emotion Analysis API), which allows it to analyze user emotions from posted content and profile data.

[0292] Specific examples

[0293] Specifically, the system collects images and videos posted by tourists using the "JapanTravel" hashtag from social media. The data collected by the server using "Tweepy" and the "Instagram API" is preprocessed using "OpenCV" and "Pandas." For example, unnecessary metadata is removed and only the necessary information is saved.

[0294] The preprocessed data is then analyzed using TensorFlow and PyTorch to identify tourist attractions and activities within the images. For example, Mount Fuji and Sensoji Temple are identified. Based on the analysis results, popular activities such as climbing Mount Fuji and wearing a kimono in Kyoto are listed.

[0295] Furthermore, user profile data (nationality, age, interests, etc.) is collected via questionnaire forms and databases. For example, tourist survey data is sent to a server and stored.

[0296] The emotion analysis engine uses the Microsoft Azure Emotion Analysis API to recognize emotions such as "happiness" or "excitement" from user posts. For example, it analyzes emotions from photos of smiling faces and comments such as "That was fun!"

[0297] Finally, the server generates tourism content and marketing strategies based on the analysis data. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. These marketing plans are implemented through social media ads, email campaigns, and dedicated app notifications.

[0298] Prompt Sentence Examples

[0299] Example prompt for collecting the "JapanTravel" tag:

[0300] "Use the Twitter API to collect tweets with the hashtag 'JapanTravel' from the past week."

[0301] Example prompts for image analysis:

[0302] "Please identify landmarks such as 'Mount Fuji' and 'Kiyomizu-dera Temple' from the collected images."

[0303] This system makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that respond to tourists' needs and emotions.

[0304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0305] Step 1:

[0306] Social media data collection

[0307] The server uses social media APIs to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information.

[0308] Input: Collection conditions (hashtags and location information), API key.

[0309] Processing: The server sends a query to the API to retrieve posts that match the criteria. For example, it uses the Tweepy library to search for posts from the past week with a given hashtag.

[0310] Output: Collected video and image data and associated metadata.

[0311] Step 2:

[0312] Data Preprocessing

[0313] The server converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata.

[0314] Input: Collected video and image data and associated metadata.

[0315] Processing: The server uses OpenCV to unify the image formats and Pandas to format metadata such as timestamps and GPS data. For example, it converts all images to JPEG format and saves the metadata in a CSV file.

[0316] Output: Preprocessed video and image data and formatted metadata.

[0317] Step 3:

[0318] Analysis of regional attractiveness

[0319] The server applies machine learning models to analyze the appeal of a region from preprocessed video and image data.

[0320] Input: Preprocessed video and image data and formatted metadata.

[0321] Processing: The server uses TensorFlow to analyze the data with trained models to identify landmarks and activities, for example, using the YOLO model to detect "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[0322] Output: Parsed data (list of landmarks and activities).

[0323] Step 4:

[0324] Extracting Charm

[0325] The server evaluates the popularity of the site based on the analysis results and lists the places and activities that are particularly popular with tourists.

[0326] Input: Parsed data (list of landmarks and activities).

[0327] Processing: The server aggregates the analysis results and ranks them by popularity, for example, placing landmarks that appear frequently at the top of the list.

[0328] Output: A ranked list of popular places and activities.

[0329] Step 5:

[0330] Tourism content proposals

[0331] The server will plan new tourist courses and events based on popular attractions.

[0332] Input: A ranked list of popular places and activities.

[0333] Processing: The server automatically generates a new sightseeing plan and saves it in the database. For example, it plans a "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0334] Output: Details of the proposed itinerary and event (schedule, prices, contact details, etc.).

[0335] Step 6:

[0336] Customer data analysis

[0337] The terminal inputs or retrieves tourist profile data (nationality, age, interests, etc.).

[0338] Input: Tourist survey data or existing database.

[0339] Processing: The device collects data via an input form or API and sends it to a server. For example, data entered into a questionnaire form is sent to a database in real time.

[0340] Output: Collected profile data.

[0341] Step 7:

[0342] Applying the Emotion Engine

[0343] The server analyzes users' posts and profiles and uses an emotion analysis API to recognize their current emotional state and long-term emotional patterns.

[0344] Input: User posts and profile data.

[0345] Processing: The server sends the data to an emotion analysis API to obtain the emotion status. For example, it uses the Microsoft Azure emotion analysis API to analyze "happy" or "excited."

[0346] Output: Parsed emotion status.

[0347] Step 8:

[0348] Targeted Marketing

[0349] The server creates optimal marketing strategies for each customer segment based on customer data and sentiment data.

[0350] Input: Customer data and sentiment status.

[0351] Processing: The server generates a marketing plan for each segment and generates data for social media advertising settings. For example, it proposes a "Mount Fuji climbing tour" for Americans and a "Kyoto night illumination tour" for Japanese people.

[0352] Output: Marketing plan by segment.

[0353] Step 9:

[0354] Customized content generation

[0355] The server customizes the sightseeing content based on the emotional state of the user identified by the emotion engine.

[0356] Input: Emotional status and user profile data.

[0357] Processing: The server generates optimal content based on the user's emotional state and stores it in a database. For example, it suggests a "hot spring relaxation plan" to a user who wants to relax, and an "outdoor adventure tour" to a user who wants to be active.

[0358] Output: Customized tourism content.

[0359] Step 10:

[0360] Marketing Execution

[0361] The server then implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[0362] Input: Generated marketing plan.

[0363] Processing: The server uses social media advertising APIs and email sending tools to send each advertisement and notification in bulk. The device collects advertisement interaction data and sends feedback to the server.

[0364] Output: Data measuring the effectiveness of the advertising campaigns carried out.

[0365] (Application example 2)

[0366] 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."

[0367] In the tourism and related industries, it is important to effectively discover the attractions of a region based on tourists' social media posts and provide them as tourism content. However, it is difficult to effectively analyze collected data, generate customized content based on customers' nationalities, interests, and even emotions, and develop optimal marketing strategies. In particular, there is a lack of marketing methods specifically tailored to the food culture of tourist destinations, and there is a need to propose meal plans tailored to each individual tourist.

[0368] 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.

[0369] In this invention, the server includes means for collecting social media videos and images posted by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for using an emotion engine that recognizes emotions from content posted by tourists and customizing the tourism content based on emotion data, means for collecting social media posts based on specific hashtags and location information using a social media API, and means for adjusting the marketing strategy based on the identified emotions, thereby enabling the provision of customized tour and meal plans that are optimal for each tourist, thereby realizing efficient marketing and promotion in the tourism industry.

[0370] "Tourist" refers to a person who visits for travel or sightseeing.

[0371] "Social media" refers to online platforms for sharing information and engaging in two-way communication over the Internet.

[0372] "Video" refers to a media format that visually conveys movement by displaying a sequence of images.

[0373] "Image" refers to still visual data, including photographs and illustrations.

[0374] "Means of collection" refers to the methods and techniques used to collect the desired data.

[0375] "Preprocessing means" refers to methods and techniques for organizing and transforming data to make it easier to analyze and use.

[0376] An "artificial intelligence model" refers to an algorithm or learning system that analyzes data and performs specific tasks.

[0377] "Regional attractions" refer to the attractive points of a particular region, such as tourist resources, culture, and scenery.

[0378] "Means of analysis" refers to methods and techniques for understanding and interpreting data.

[0379] "Tourism content" refers to the information, experiences, services, etc. provided to tourists.

[0380] "Customer data" refers to information about tourists, including personal profiles and behavioral history.

[0381] "Nationality" refers to the country or region to which an individual belongs.

[0382] "Interests" refers to the interest or concern that an individual has in a particular event or activity.

[0383] "Marketing strategy" refers to the set of commercial activities that includes market analysis, planning, and tactics to achieve specific goals.

[0384] "Customized promotion" refers to advertising and promotional activities that are tailored to the characteristics and preferences of individual customers.

[0385] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from data such as text and images.

[0386] "Emotion" refers to a person's feelings or psychological state.

[0387] "SNS API" refers to the application programming interface provided by a social media platform, which is a means of retrieving and manipulating data from external systems.

[0388] A "hashtag" is a symbol placed before a keyword or phrase used on social media to easily search for and categorize a particular topic.

[0389] "Location information" refers to data that indicates a specific point or location.

[0390] This invention is a system that collects videos and images posted by tourists on social media, analyzes the attractions of a region, and implements a marketing strategy based on customer nationality, interests, and emotional data. This system is implemented using the following means.

[0391] First, the server uses SNS APIs to collect videos and images based on specific hashtags and location information on social media, such as posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0392] The server then converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata, a process that includes cleaning up important metadata like timestamps, location information, and hashtags.

[0393] After the data has been converted, the server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from the video and image data. For example, it can identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[0394] The server then evaluates the popularity of the results and lists the most popular places and activities for tourists, generating tourism content such as "Climbing Mount Fuji" or "Kimono experience in Kyoto."

[0395] The server then analyzes customer data and develops optimal marketing strategies based on the customer's nationality, age, and interests, referencing tourist survey data and existing databases.

[0396] For emotional data analysis, the server analyzes the user's posted content and profile and uses an emotion engine to recognize the user's emotional state. For example, emotions can be analyzed from an image of the user looking "happy" or a sentence expressing "happiness."

[0397] Based on these analysis results, the server will carry out customized promotions, such as suggesting "hot spring tours" to users seeking relaxation and "hiking tours" to users seeking more active experiences.

[0398] Specific hardware and software for this system include:

[0399] Server: Collects and analyzes data (e.g. AWS EC2).

[0400] API: We collect data using the Instagram API.

[0401] Image analysis model: Analyzes image data using TensorFlow and ResNet50.

[0402] Text analysis: Analyze the text of social media posts using TextBlob.

[0403] Specific examples

[0404] For example, suppose posts with the hashtag "TokyoFood" are collected and many images of "sushi" are found among the posts. Based on the analysis results, a "food tour centered on sushi" can be suggested. Furthermore, if emotions such as "satisfaction" and "excitement" are frequently found in user posts, the promotion can be customized based on these emotions.

[0405] Prompt Sentence Examples

[0406] Inexpensive image URL list and post text example

[0407] image_urls = ["https: / / example.com / image1.jpg", "https: / / example.com / image2.jpg"]

[0408] example_text = "This sushi is amazing!"

[0409] API call prompt

[0410] fetch_sns_data("TokyoFood")

[0411] Image Analysis Prompts

[0412] analyze_images(image_urls)

[0413] Sentiment Analysis Prompts

[0414] analyze_sentiment(example_text)

[0415] In this way, tourism operators can provide attractive tourism content tailored to individual needs and develop effective marketing strategies.

[0416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0417] Step 1:

[0418] The server uses the SNS API to collect videos and images based on specific hashtags and location information. As input, the hashtag and location information are specified and an API request is sent. As output, the posted video and image data is obtained. Specifically, the server accesses the Instagram API and collects posts related to the specified hashtag "JapanTravel" and location information (e.g., Kyoto, around Mt. Fuji).

[0419] Step 2:

[0420] The server converts the collected video and images into a format that is easy to analyze, and extracts and organizes important metadata. The input is the collected video and image data and its metadata. The output is data converted into a format suitable for analysis. Specifically, the server extracts important metadata such as timestamps, location information, and hashtags, and removes unnecessary information.

[0421] Step 3:

[0422] The server applies an artificial intelligence model to extract features such as scenery, landmarks, and activities from the format-converted video and image data. The preprocessed video and image data are used as input. The output is a dataset with each feature extracted. Specifically, the server uses TensorFlow and a ResNet50 model to identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[0423] Step 4:

[0424] The server evaluates the popularity of the analysis results and lists the places and activities that are popular with tourists. The feature-extracted dataset is used as input. As output, a list of popular spots and activities is generated. Specifically, the server aggregates the collected data and lists the places and activities that are mentioned in the most posts.

[0425] Step 5:

[0426] The server generates new tourism content based on the listed attractions. It uses a list of popular spots and activities as input. It generates data to suggest new tourism packages and events as output. Specifically, the server creates new tourism plans such as a "Mt. Fuji Sunrise Tour" or "Kyoto Traditional Cultural Experience."

[0427] Step 6:

[0428] The terminal acquires tourist data and analyzes the customer's nationality, age, interests, etc. Tourist profile data is used as input. Profile information appropriate for each tourist is obtained as output. Specifically, the terminal references tourist survey data and existing databases to acquire the necessary data.

[0429] Step 7:

[0430] The server analyzes the user's posts and profile and uses an emotion engine to recognize the user's emotional state. The user's posts and profile data are used as input. The output is the analysis result of the emotional state. Specifically, the server uses an emotion analysis tool such as TextBlob to identify emotions such as "happy" or "fun" from the text and images of the posts.

[0431] Step 8:

[0432] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. It uses the analyzed customer data and emotional data as input. It generates a customized marketing plan as output. Specifically, the server promotes a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0433] Step 9:

[0434] The server customizes sightseeing content based on emotional data. The input is the result of analyzing the emotional state. The output is to generate sightseeing content customized to the user's emotions. Specifically, the server suggests a "hot spring tour" to a user seeking relaxation, and a "hiking tour" to a user seeking to be more active.

[0435] Step 10:

[0436] The server and device implement the generated marketing plan and monitor its effectiveness. The customized marketing plan is used as input. Effectiveness measurement data such as ad views and click rates is obtained as output. Specifically, the server implements promotions using digital methods such as social media ads, email campaigns, and dedicated app notifications, and the device monitors the data in real time.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Second embodiment]

[0441] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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."

[0453] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on the nationality and interests of customers. Specific embodiments and program processing for implementing this system are described below.

[0454] 1. Collecting social media data

[0455] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0456] 2. Data Preprocessing

[0457] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[0458] 3. Analysis of regional attractiveness

[0459] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[0460] 4. Extracting the charm

[0461] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[0462] 5. Tourism content proposals

[0463] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[0464] 6. Analysis of Customer Data

[0465] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from tourist surveys or existing databases.

[0466] 7. Targeted Marketing

[0467] The server analyzes customer data and creates the optimal marketing strategy for each customer segment. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0468] 8. Marketing Execution

[0469] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure effectiveness.

[0470] Specific examples

[0471] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[0472] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[0473] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[0474] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0475] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[0476] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[0477] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[0478] The processing flow will be explained below.

[0479] Step 1:

[0480] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0481] Step 2:

[0482] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[0483] Step 3:

[0484] The server removes unnecessary metadata and organizes necessary metadata such as timestamps, location information, and hashtags.

[0485] Step 4:

[0486] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it can identify "Mount Fuji" or "Kiyomizu-dera Temple" in a video.

[0487] Step 5:

[0488] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[0489] Step 6:

[0490] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[0491] Step 7:

[0492] The server automatically generates new sightseeing courses and events based on the list of attractive points, such as a "Mt. Fuji sunrise tour" or "Kyoto traditional cultural experience."

[0493] Step 8:

[0494] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[0495] Step 9:

[0496] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[0497] Step 10:

[0498] The server creates marketing strategies for each target group based on customer data, such as "promoting Mount Fuji climbing tours to American tourists and Kyoto cultural experiences to Japanese tourists."

[0499] Step 11:

[0500] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[0501] Step 12:

[0502] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[0503] Example 1

[0504] 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."

[0505] The tourism industry faces challenges in developing individual marketing strategies based on tourists' preferences and attributes and effectively attracting them. Furthermore, there is a lack of a system for utilizing the vast amount of video and image data posted by tourists on social media to extract regional appeal from it and generate tourism content. This makes it difficult to implement efficient and effective tourism promotion.

[0506] 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.

[0507] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze regional characteristics from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on attributes and preferences, means for implementing customized promotions based on the generated tourism content and marketing strategy, and means for monitoring the effectiveness of the customized promotions. This makes it possible to efficiently extract regional attractions using social media data, formulate effective marketing strategies tailored to tourist preferences, and implement tourism promotions.

[0508] A "tourist" is someone who travels to visit a tourist destination.

[0509] "Social media" refers to online services that enable users to share content through posts and comments.

[0510] "Video" refers to a media format that creates the illusion of movement by playing a sequence of still images at a constant speed.

[0511] "Image" refers to static visual data that graphically displays visual information.

[0512] "Collection" refers to the act of gathering data based on specific criteria or filters.

[0513] "Preprocessing" refers to the process of organizing and transforming data to make it easier to analyze and apply models to.

[0514] "Regional characteristics" refers to the characteristics of natural landscapes, famous places, activities, etc. that are unique to a particular region.

[0515] An "artificial intelligence model" refers to an algorithm that learns from large amounts of data, identifies patterns, and makes inferences.

[0516] "Analysis" refers to the process of examining data in detail to derive its characteristics and trends.

[0517] "Tourism content" refers to the content of tours, experiences, events, etc. offered to tourists.

[0518] "Customer Data" refers to information regarding the attributes and behavior of service users.

[0519] "Attributes" refer to personal characteristics of customers, such as nationality, age, and gender.

[0520] "Preferences" refer to a customer's preferred activities or interests.

[0521] A "marketing strategy" is a plan for promoting a product or service based on specific goals.

[0522] "Customized promotion" refers to advertising activities tailored to specific customer groups.

[0523] "Monitoring" refers to the process of measuring and overseeing the effectiveness of a particular activity or promotion.

[0524] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on customer attributes and preferences. Specific embodiments and program processing for implementing this system are described below.

[0525] Social media data collection

[0526] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images based on specific hashtags (e.g., "JapanTravel") and location information. When collecting, authentication is performed using an API key, and post data that matches the conditions is obtained.

[0527] Data Preprocessing

[0528] The server converts the collected video and image data into a format that is easy to analyze (e.g., JPEG or MP4). During this process, it uses the Python Pillow library to standardize the image format, remove unnecessary metadata, and organize important metadata such as timestamps, location information, and hashtags. For example, it extracts and organizes metadata from JSON-formatted data.

[0529] Analysis of regional characteristics

[0530] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from collected and preprocessed video and image data. As a specific example, it uses the YOLOv3 model to identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[0531] Tourism content generation

[0532] The server generates tourism content based on the analysis results. This involves creating a list of popular tourist spots and activities and planning new tourist courses and events. The planned content is stored in a database and made available for reference by tourism operators. For example, a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience" might be generated.

[0533] Customer data analysis

[0534] The device inputs or retrieves tourist profile data (e.g., nationality, age, gender, interests, etc.) The collected data is stored in a MySQL database and analyzed using machine learning libraries such as Scikit-learn.

[0535] Marketing strategy planning

[0536] The server analyzes customer data and develops the optimal marketing strategy for each customer segment. For example, it suggests a "Mount Fuji climbing tour" for American tourists and a "Kyoto night illumination tour" for Japanese tourists.

[0537] Run customized promotions

[0538] The server implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the resulting data on the management screen, allowing the effectiveness of marketing to be measured.

[0539] Prompt Sentence Examples

[0540] "Identify tourist attractions related to the JapanTravel hashtag on Instagram."

[0541] "Please create the best marketing strategy for Kyoto's tourist destinations."

[0542] As described above, this system provides tourism operators with concrete tools to effectively attract tourists and revitalize local economies.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information. At this time, it authenticates using an API key and retrieves posted data that matches the conditions in JSON format. The input is the API request, and the output is the JSON data of the collected videos and images.

[0546] Step 2:

[0547] The video and image data collected by the server is converted into a format that is easy to analyze (for example, JPEG or MP4). The Python Pillow library is used to standardize the image format and remove unnecessary metadata. Important metadata such as timestamps, location information, and hashtags are extracted and organized. The input is JSON-formatted data, and the output is organized metadata and video and image data in a unified format.

[0548] Step 3:

[0549] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from preprocessed video and image data. Specifically, it uses the YOLOv3 model to analyze images and videos and identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple." The input is preprocessed video and image data, and the output is a list of recognized features (scenery, landmarks, and activities).

[0550] Step 4:

[0551] The server evaluates tourist interests based on the analysis results and creates a list of popular tourist attractions and activities. The analysis data is stored in an SQL database and the evaluation results are extracted using SQL queries. The input is a list of analyzed features, and the output is a list of popular tourist attractions and activities.

[0552] Step 5:

[0553] The server plans new sightseeing itineraries and events based on the generated list. This includes creating new sightseeing packages that include specific events at tourist destinations. For example, it proposes a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience." The input is a list of popular spots and activities, and the output is a new sightseeing package proposal.

[0554] Step 6:

[0555] The terminal inputs or acquires tourist profile data (nationality, age, interests, etc.). The input data is collected through a questionnaire form or an existing database. The input is tourist profile data, and the output is a collection of acquired customer data.

[0556] Step 7:

[0557] The server analyzes customer data and uses machine learning libraries such as Scikit-learn to create optimal marketing strategies for each customer segment. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. The input is customer data, and the output is a marketing strategy for each segment.

[0558] Step 8:

[0559] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the collected data on the management screen and measures the effectiveness of the marketing. The input is the marketing plan, and the output is the promotion effectiveness indicators.

[0560] (Application example 1)

[0561] 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."

[0562] In recent years, the number of tourists visiting tourist destinations has increased, making it important to effectively communicate the attractions of tourist destinations. However, a lack of customized information and promotions based on tourists' interests makes it difficult to maximize the attractions of tourist destinations. There is also a need for a system that can efficiently analyze the large amount of collected social media data and generate tourism content.

[0563] 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.

[0564] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for including an application installed on a smartphone, smart glasses, or head-mounted display that provides tourist destination information based on the user's interests, and means for identifying tourist attractions based on data collected from social media and suggesting related information, thereby enabling the provision of efficient and personalized tourism information and promotions to a diverse range of tourists.

[0565] "Tourists" refer to people who visit a particular tourist destination to experience the attractions of that area.

[0566] "Social media" refers to online platforms that enable users to share information and interact via the internet.

[0567] "Video and images" refers to media formats for transmitting visual information, and refers to files containing visually recorded information.

[0568] "Means of collection" refers to methods or technologies for collecting data on a device such as a server based on specific conditions.

[0569] "Preprocessing means" refers to the initial processing performed to convert data into a format that is easier to analyze.

[0570] An "artificial intelligence model" refers to a program that uses technologies such as machine learning and deep learning to analyze data and extract patterns and features.

[0571] "Regional attractions" refer to the points and characteristics that make a particular region interesting and valuable to tourists.

[0572] "Analytical means" refers to techniques and methods for processing data to reveal specific information or patterns.

[0573] "Tourism content" refers to data provided to tourists, including information on tourist destinations, experience plans, events, etc.

[0574] "Means of generation" refers to methods and technologies for creating new data, information, or content based on specific conditions.

[0575] "Customer data" refers to information such as tourists' profile information, interests, and behavioral history.

[0576] A "marketing strategy" refers to a plan or method for effectively promoting a product to a specific target audience.

[0577] "Customized promotion" refers to advertising and marketing techniques that are specifically tailored to an individual customer's interests.

[0578] A "smartphone" refers to a mobile information terminal that has communication functions and can connect to the Internet and use applications.

[0579] "Smart glasses" refers to a wearable device that adds information processing and communication functions to conventional glasses.

[0580] A "head-mounted display" refers to a display device that is worn on the user's head and displays information directly into the user's field of vision.

[0581] "Application" refers to a software program designed to accomplish a specific purpose.

[0582] "Tourist destination information" refers to detailed information and guide content about specific tourist destinations.

[0583] "Means of identification" refers to techniques or methods for recognizing and extracting specific features or information from data.

[0584] This invention is a system that collects videos and images posted on social media by tourists, analyzes them to discover the attractions of a region, and enables the provision of customized information and marketing based on the user's nationality and interests.

[0585] Hardware and software used

[0586] Hardware:

[0587] Servers with high-performance CPU / GPUs, smartphones, smart glasses, or head-mounted displays.

[0588] software:

[0589] Python, Flask (web framework), Google Cloud Vision API, OpenCV (image analysis library).

[0590] System Operation

[0591] The server uses social media APIs to collect videos and images posted by tourists based on specific hashtags and location information, such as posts related to "JapanTravel" or specific regions.

[0592] The collected video and image data is preprocessed by the server and converted into a format that is easy to analyze, and unnecessary metadata is removed and important metadata such as timestamps, location information, and hashtags are organized.

[0593] The preprocessed data is then fed into an artificial intelligence model to analyze the area's attractiveness. Image analysis techniques, such as Google Cloud Vision API and OpenCV, are used to identify landmarks and tourist attractions within the images. From these identified attractions and activities, the model then creates a list of particularly popular places and activities.

[0594] The server then plans new sightseeing courses and events based on the listed attractions. It references user profile data (such as nationality, age, and interests) to propose optimal sightseeing plans for each user. Applications installed on smartphones, smart glasses, and head-mounted displays display real-time information about tourist spots based on the user's interests, improving the sightseeing experience.

[0595] Specific examples

[0596] As a specific example of its use, the system collects Instagram images posted by tourists using the hashtag "KyotoTravel." The server analyzes the images and identifies "Kiyomizu-dera Temple" and "Kinkaku-ji Temple." The server then suggests a "Kinkaku-ji and Kiyomizu-dera Temple tour" to American tourists. These suggestions are provided via the user's smartphone, smart glasses, or head-mounted display.

[0597] Prompt Sentence Examples

[0598] Social media post collection example:

[0599] Region: Kyoto

[0600] Hashtag: KyotoTravel

[0601] Analysis target: Images and videos

[0602] Analysis goal: Identifying tourist attractions (e.g., Kiyomizu-dera Temple, Kinkaku-ji Temple)

[0603] Tourist attraction identification and suggestion example:

[0604] Customer Profile: American Tourists

[0605] Proposal: Tour of Kinkakuji Temple and Kiyomizudera Temple

[0606] By using this system, tourism businesses can provide efficient and personalized tourism information and promotions to a wide range of tourists.

[0607] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0608] Step 1:

[0609] The server uses social media APIs to collect videos and images posted by tourists. During collection, data is collected based on specific hashtags (e.g., "JapanTravel") and location information. The input to this collection process is hashtags and location information, and the output is the corresponding video and image files.

[0610] Step 2:

[0611] The server preprocesses the collected video and image data. Specifically, it converts the video and images into a format that is easy to analyze and removes unnecessary metadata. The input to the preprocessing is the collected video and image data, and the output is the preprocessed video and image data.

[0612] Step 3:

[0613] The server inputs the preprocessed data into an artificial intelligence model to analyze the area's attractions, using the Google Cloud Vision API and OpenCV to identify landmarks in the images. The input for the analysis is the preprocessed video and image data, and the output is data on identified landmarks and tourist attractions.

[0614] Step 4:

[0615] The server generates tourism content based on the analysis results. Based on the identified tourist attractions and activities, it lists popular places and activities and plans sightseeing courses and events. The input for generation is the analysis result data, and the output is newly generated tourism content and proposed plans.

[0616] Step 5:

[0617] The server analyzes the user's profile data (nationality, age, interests, etc.) and customizes the optimal sightseeing plan for each target. The input of this process is the user's profile data, and the output is a customized sightseeing plan.

[0618] Step 6:

[0619] The terminal (smartphone, smart glasses, head-mounted display) provides the generated tourism content and customized tourism plans to the user. The input of this provision process is the customized tourism plan, and the output is the tourism information displayed to the user.

[0620] Step 7:

[0621] The server collects user feedback and behavioral data to improve the tour plan in real time. The input of this feedback collection process is user behavioral data, and the output is a further optimized tour plan.

[0622] Through these steps, tourism businesses can provide efficient and personalized tourism information and promotions to individual tourists.

[0623] 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.

[0624] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and implements marketing strategies based on the customer's nationality and interests. It incorporates an emotion engine to recognize user emotions and customizes tourism content and marketing strategies based on these. Specific embodiments and program processing for implementing this system are described below.

[0625] 1. Collecting social media data

[0626] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0627] 2. Data Preprocessing

[0628] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[0629] 3. Analysis of regional attractiveness

[0630] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[0631] 4. Extracting the charm

[0632] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[0633] 5. Tourism content proposals

[0634] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[0635] 6. Analysis of Customer Data

[0636] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[0637] 7. Applying the Emotion Engine

[0638] The server analyzes the user's posts and profile information and uses an emotion engine to recognize the user's current emotional state and long-term emotional patterns. For example, it analyzes emotions based on images of the user looking "happy."

[0639] 8. Targeted Marketing

[0640] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0641] 9. Customized Content Generation

[0642] The server customizes sightseeing content based on the user's emotional state identified by the emotion engine. For example, it suggests a "hot spring tour" to a user who wants to relax, and a "hiking tour" to a user who wants to be active.

[0643] 10. Marketing Execution

[0644] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[0645] Specific examples

[0646] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[0647] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[0648] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[0649] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0650] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[0651] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[0652] 7. The server applies an emotion engine to recognize emotions such as "happiness" or "excitement" from the user's posts.

[0653] 8. The server promotes the "Hot Spring Relaxation Plan" to users seeking relaxation and the "Outdoor Adventure Tour" to users seeking an active experience.

[0654] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[0655] The processing flow will be explained below.

[0656] Step 1:

[0657] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0658] Step 2:

[0659] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[0660] Step 3:

[0661] The server removes unnecessary metadata and compiles necessary metadata such as timestamps, location information, and hashtags.

[0662] Step 4:

[0663] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[0664] Step 5:

[0665] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[0666] Step 6:

[0667] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[0668] Step 7:

[0669] The server generates new sightseeing courses and events based on the listed attractions, such as a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience."

[0670] Step 8:

[0671] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from a questionnaire or an existing database.

[0672] Step 9:

[0673] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[0674] Step 10:

[0675] The server uses an emotion engine to recognize emotions from user posts, for example, determining "happiness" or "surprise" from the post content and facial expressions.

[0676] Step 11:

[0677] The server customizes sightseeing content based on the user's current emotional state, suggesting, for example, a hot spring tour for a user who wants to relax, or a hiking tour for a user who wants to stay active.

[0678] Step 12:

[0679] The server analyzes the long-term emotional patterns identified by the emotion engine and creates marketing strategies based on them, such as "market relaxation plans to users who have posted frequently about wanting to relax over the past year."

[0680] Step 13:

[0681] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[0682] Step 14:

[0683] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[0684] Example 2

[0685] 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."

[0686] In recent years, the spread of social media has led to an increase in postings of information about tourist destinations and events. However, there is a lack of systems that can effectively collect and analyze this information and link it to appropriate tourism content and marketing strategies. Furthermore, there is no system that can grasp tourists' emotional state and propose customized promotions that respond to that. There is a need to solve these issues.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0688] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying a machine learning model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for applying a sentiment analysis engine to analyze customer sentiment data, and means for implementing customized promotions based on the generated tourism content and marketing strategy. This makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that meet the needs and sentiments of tourists.

[0689] "Social media" is a platform that allows users to share content and communicate with each other over the Internet.

[0690] "Means for collecting videos and images" refers to the function for obtaining video and image data from social media platforms.

[0691] "Preprocessing" refers to the process of converting collected video and image data into a format suitable for analysis and removing unnecessary metadata.

[0692] A "machine learning model" is an algorithm that learns specific patterns and characteristics from data and makes predictions and classifications on newly acquired data.

[0693] "Tourism content" refers to the collection of information and services related to tourist destinations and activities that are provided to tourists.

[0694] "Customer Data" means information about a tourist, including personal profile information such as nationality, age, and interests, as well as historical behavioral data.

[0695] A "marketing strategy" is a set of plans and methods for promoting and selling products and services in the most optimal way to a particular customer segment.

[0696] An "emotion analysis engine" is an algorithm or software that recognizes and analyzes a user's emotional state from text or images.

[0697] "Customized promotion" refers to specific advertising and promotional activities that are applied based on the needs, interests, and emotional state of individual customers.

[0698] "System" refers to a set of machines, software, databases, etc., whose components work together to achieve a specific purpose.

[0699] This invention is a system that collects and analyzes videos and images posted by tourists on social media, discovers the attractions of a region based on the collected data, and plans and implements marketing strategies tailored to the nationality, interests, and emotions of customers. This system uses the following hardware and software:

[0700] Hardware and software used

[0701] The server is a computer with a powerful processor and sufficient memory, such as an Intel Xeon processor. It also uses APIs to collect data from social media. For example, libraries such as "Tweepy" (for Twitter) and "Instagram API" (for Instagram) are used.

[0702] Python's "OpenCV" and "Pandas" are used for data preprocessing, which enables format conversion of image and video data and organization of metadata.

[0703] For data analysis, machine learning libraries such as "TensorFlow" and "PyTorch" are used to extract the characteristics of tourist spots from the collected videos and images. Suitable machine learning models include "YOLO (You Only Look Once)" and "ResNet," which are widely used in image recognition.

[0704] To input and acquire customer data, data is collected from the user's device (smartphone or PC) via a questionnaire form or an existing database. This data is then sent to the server.

[0705] The emotion engine uses an emotion analysis API (for example, Microsoft Azure Emotion Analysis API), which allows it to analyze user emotions from posted content and profile data.

[0706] Specific examples

[0707] Specifically, the system collects images and videos posted by tourists using the "JapanTravel" hashtag from social media. The data collected by the server using "Tweepy" and the "Instagram API" is preprocessed using "OpenCV" and "Pandas." For example, unnecessary metadata is removed and only the necessary information is saved.

[0708] The preprocessed data is then analyzed using TensorFlow and PyTorch to identify tourist attractions and activities within the images. For example, Mount Fuji and Sensoji Temple are identified. Based on the analysis results, popular activities such as climbing Mount Fuji and wearing a kimono in Kyoto are listed.

[0709] Furthermore, user profile data (nationality, age, interests, etc.) is collected via questionnaire forms and databases. For example, tourist survey data is sent to a server and stored.

[0710] The emotion analysis engine uses the Microsoft Azure Emotion Analysis API to recognize emotions such as "happiness" or "excitement" from user posts. For example, it analyzes emotions from photos of smiling faces and comments such as "That was fun!"

[0711] Finally, the server generates tourism content and marketing strategies based on the analysis data. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. These marketing plans are implemented through social media ads, email campaigns, and dedicated app notifications.

[0712] Prompt Sentence Examples

[0713] Example prompt for collecting the "JapanTravel" tag:

[0714] "Use the Twitter API to collect tweets with the hashtag 'JapanTravel' from the past week."

[0715] Example prompts for image analysis:

[0716] "Please identify landmarks such as 'Mount Fuji' and 'Kiyomizu-dera Temple' from the collected images."

[0717] This system makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that respond to tourists' needs and emotions.

[0718] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0719] Step 1:

[0720] Social media data collection

[0721] The server uses social media APIs to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information.

[0722] Input: Collection conditions (hashtags and location information), API key.

[0723] Processing: The server sends a query to the API to retrieve posts that match the criteria. For example, it uses the Tweepy library to search for posts from the past week with a given hashtag.

[0724] Output: Collected video and image data and associated metadata.

[0725] Step 2:

[0726] Data Preprocessing

[0727] The server converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata.

[0728] Input: Collected video and image data and associated metadata.

[0729] Processing: The server uses OpenCV to unify the image formats and Pandas to format metadata such as timestamps and GPS data. For example, it converts all images to JPEG format and saves the metadata in a CSV file.

[0730] Output: Preprocessed video and image data and formatted metadata.

[0731] Step 3:

[0732] Analysis of regional attractiveness

[0733] The server applies machine learning models to analyze the appeal of a region from preprocessed video and image data.

[0734] Input: Preprocessed video and image data and formatted metadata.

[0735] Processing: The server uses TensorFlow to analyze the data with trained models to identify landmarks and activities, for example, using the YOLO model to detect "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[0736] Output: Parsed data (list of landmarks and activities).

[0737] Step 4:

[0738] Extracting Charm

[0739] The server evaluates the popularity of the site based on the analysis results and lists the places and activities that are particularly popular with tourists.

[0740] Input: Parsed data (list of landmarks and activities).

[0741] Processing: The server aggregates the analysis results and ranks them by popularity, for example, placing landmarks that appear frequently at the top of the list.

[0742] Output: A ranked list of popular places and activities.

[0743] Step 5:

[0744] Tourism content proposals

[0745] The server will plan new tourist courses and events based on popular attractions.

[0746] Input: A ranked list of popular places and activities.

[0747] Processing: The server automatically generates a new sightseeing plan and saves it in the database. For example, it plans a "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0748] Output: Details of the proposed itinerary and event (schedule, prices, contact details, etc.).

[0749] Step 6:

[0750] Customer data analysis

[0751] The terminal inputs or retrieves tourist profile data (nationality, age, interests, etc.).

[0752] Input: Tourist survey data or existing database.

[0753] Processing: The device collects data via an input form or API and sends it to a server. For example, data entered into a questionnaire form is sent to a database in real time.

[0754] Output: Collected profile data.

[0755] Step 7:

[0756] Applying the Emotion Engine

[0757] The server analyzes users' posts and profiles and uses an emotion analysis API to recognize their current emotional state and long-term emotional patterns.

[0758] Input: User posts and profile data.

[0759] Processing: The server sends the data to an emotion analysis API to obtain the emotion status. For example, it uses the Microsoft Azure emotion analysis API to analyze "happy" or "excited."

[0760] Output: Parsed emotion status.

[0761] Step 8:

[0762] Targeted Marketing

[0763] The server creates optimal marketing strategies for each customer segment based on customer data and sentiment data.

[0764] Input: Customer data and sentiment status.

[0765] Processing: The server generates a marketing plan for each segment and generates data for social media advertising settings. For example, it proposes a "Mount Fuji climbing tour" for Americans and a "Kyoto night illumination tour" for Japanese people.

[0766] Output: Marketing plan by segment.

[0767] Step 9:

[0768] Customized content generation

[0769] The server customizes the sightseeing content based on the emotional state of the user identified by the emotion engine.

[0770] Input: Emotional status and user profile data.

[0771] Processing: The server generates optimal content based on the user's emotional state and stores it in a database. For example, it suggests a "hot spring relaxation plan" to a user who wants to relax, and an "outdoor adventure tour" to a user who wants to be active.

[0772] Output: Customized tourism content.

[0773] Step 10:

[0774] Marketing Execution

[0775] The server then implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[0776] Input: Generated marketing plan.

[0777] Processing: The server uses social media advertising APIs and email sending tools to send each advertisement and notification in bulk. The device collects advertisement interaction data and sends feedback to the server.

[0778] Output: Data measuring the effectiveness of the advertising campaigns carried out.

[0779] (Application example 2)

[0780] 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."

[0781] In the tourism and related industries, it is important to effectively discover the attractions of a region based on tourists' social media posts and provide them as tourism content. However, it is difficult to effectively analyze collected data, generate customized content based on customers' nationalities, interests, and even emotions, and develop optimal marketing strategies. In particular, there is a lack of marketing methods specifically tailored to the food culture of tourist destinations, and there is a need to propose meal plans tailored to each individual tourist.

[0782] 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.

[0783] In this invention, the server includes means for collecting social media videos and images posted by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for using an emotion engine that recognizes emotions from content posted by tourists and customizing the tourism content based on emotion data, means for collecting social media posts based on specific hashtags and location information using a social media API, and means for adjusting the marketing strategy based on the identified emotions, thereby enabling the provision of customized tour and meal plans that are optimal for each tourist, thereby realizing efficient marketing and promotion in the tourism industry.

[0784] "Tourist" refers to a person who visits for travel or sightseeing.

[0785] "Social media" refers to online platforms for sharing information and engaging in two-way communication over the Internet.

[0786] "Video" refers to a media format that visually conveys movement by displaying a sequence of images.

[0787] "Image" refers to still visual data, including photographs and illustrations.

[0788] "Means of collection" refers to the methods and techniques used to collect the desired data.

[0789] "Preprocessing means" refers to methods and techniques for organizing and transforming data to make it easier to analyze and use.

[0790] An "artificial intelligence model" refers to an algorithm or learning system that analyzes data and performs specific tasks.

[0791] "Regional attractions" refer to the attractive points of a particular region, such as tourist resources, culture, and scenery.

[0792] "Means of analysis" refers to methods and techniques for understanding and interpreting data.

[0793] "Tourism content" refers to the information, experiences, services, etc. provided to tourists.

[0794] "Customer data" refers to information about tourists, including personal profiles and behavioral history.

[0795] "Nationality" refers to the country or region to which an individual belongs.

[0796] "Interests" refers to the interest or concern that an individual has in a particular event or activity.

[0797] "Marketing strategy" refers to the set of commercial activities that includes market analysis, planning, and tactics to achieve specific goals.

[0798] "Customized promotion" refers to advertising and promotional activities that are tailored to the characteristics and preferences of individual customers.

[0799] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from data such as text and images.

[0800] "Emotion" refers to a person's feelings or psychological state.

[0801] "SNS API" refers to the application programming interface provided by a social media platform, which is a means of retrieving and manipulating data from external systems.

[0802] A "hashtag" is a symbol placed before a keyword or phrase used on social media to easily search for and categorize a particular topic.

[0803] "Location information" refers to data that indicates a specific point or location.

[0804] This invention is a system that collects videos and images posted by tourists on social media, analyzes the attractions of a region, and implements a marketing strategy based on customer nationality, interests, and emotional data. This system is implemented using the following means.

[0805] First, the server uses SNS APIs to collect videos and images based on specific hashtags and location information on social media, such as posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0806] The server then converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata, a process that includes cleaning up important metadata like timestamps, location information, and hashtags.

[0807] After the data has been converted, the server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from the video and image data. For example, it can identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[0808] The server then evaluates the popularity of the results and lists the most popular places and activities for tourists, generating tourism content such as "Climbing Mount Fuji" or "Kimono experience in Kyoto."

[0809] The server then analyzes customer data and develops optimal marketing strategies based on the customer's nationality, age, and interests, referencing tourist survey data and existing databases.

[0810] For emotional data analysis, the server analyzes the user's posted content and profile and uses an emotion engine to recognize the user's emotional state. For example, emotions can be analyzed from an image of the user looking "happy" or a sentence expressing "happiness."

[0811] Based on these analysis results, the server will carry out customized promotions, such as suggesting "hot spring tours" to users seeking relaxation and "hiking tours" to users seeking more active experiences.

[0812] Specific hardware and software for this system include:

[0813] Server: Collects and analyzes data (e.g. AWS EC2).

[0814] API: We collect data using the Instagram API.

[0815] Image analysis model: Analyzes image data using TensorFlow and ResNet50.

[0816] Text analysis: Analyze the text of social media posts using TextBlob.

[0817] Specific examples

[0818] For example, suppose posts with the hashtag "TokyoFood" are collected and many images of "sushi" are found among the posts. Based on the analysis results, a "food tour centered on sushi" can be suggested. Furthermore, if emotions such as "satisfaction" and "excitement" are frequently found in user posts, the promotion can be customized based on these emotions.

[0819] Prompt Sentence Examples

[0820] Inexpensive image URL list and post text example

[0821] image_urls = ["https: / / example.com / image1.jpg", "https: / / example.com / image2.jpg"]

[0822] example_text = "This sushi is amazing!"

[0823] API call prompt

[0824] fetch_sns_data("TokyoFood")

[0825] Image Analysis Prompts

[0826] analyze_images(image_urls)

[0827] Sentiment Analysis Prompts

[0828] analyze_sentiment(example_text)

[0829] In this way, tourism operators can provide attractive tourism content tailored to individual needs and develop effective marketing strategies.

[0830] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0831] Step 1:

[0832] The server uses the SNS API to collect videos and images based on specific hashtags and location information. As input, the hashtag and location information are specified and an API request is sent. As output, the posted video and image data is obtained. Specifically, the server accesses the Instagram API and collects posts related to the specified hashtag "JapanTravel" and location information (e.g., Kyoto, around Mt. Fuji).

[0833] Step 2:

[0834] The server converts the collected video and images into a format that is easy to analyze, and extracts and organizes important metadata. The input is the collected video and image data and its metadata. The output is data converted into a format suitable for analysis. Specifically, the server extracts important metadata such as timestamps, location information, and hashtags, and removes unnecessary information.

[0835] Step 3:

[0836] The server applies an artificial intelligence model to extract features such as scenery, landmarks, and activities from the format-converted video and image data. The preprocessed video and image data are used as input. The output is a dataset with each feature extracted. Specifically, the server uses TensorFlow and a ResNet50 model to identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[0837] Step 4:

[0838] The server evaluates the popularity of the analysis results and lists the places and activities that are popular with tourists. The feature-extracted dataset is used as input. As output, a list of popular spots and activities is generated. Specifically, the server aggregates the collected data and lists the places and activities that are mentioned in the most posts.

[0839] Step 5:

[0840] The server generates new tourism content based on the listed attractions. It uses a list of popular spots and activities as input. It generates data to suggest new tourism packages and events as output. Specifically, the server creates new tourism plans such as a "Mt. Fuji Sunrise Tour" or "Kyoto Traditional Cultural Experience."

[0841] Step 6:

[0842] The terminal acquires tourist data and analyzes the customer's nationality, age, interests, etc. Tourist profile data is used as input. Profile information appropriate for each tourist is obtained as output. Specifically, the terminal references tourist survey data and existing databases to acquire the necessary data.

[0843] Step 7:

[0844] The server analyzes the user's posts and profile and uses an emotion engine to recognize the user's emotional state. The user's posts and profile data are used as input. The output is the analysis result of the emotional state. Specifically, the server uses an emotion analysis tool such as TextBlob to identify emotions such as "happy" or "fun" from the text and images of the posts.

[0845] Step 8:

[0846] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. It uses the analyzed customer data and emotional data as input. It generates a customized marketing plan as output. Specifically, the server promotes a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0847] Step 9:

[0848] The server customizes sightseeing content based on emotional data. The input is the result of analyzing the emotional state. The output is to generate sightseeing content customized to the user's emotions. Specifically, the server suggests a "hot spring tour" to a user seeking relaxation, and a "hiking tour" to a user seeking to be more active.

[0849] Step 10:

[0850] The server and device implement the generated marketing plan and monitor its effectiveness. The customized marketing plan is used as input. Effectiveness measurement data such as ad views and click rates is obtained as output. Specifically, the server implements promotions using digital methods such as social media ads, email campaigns, and dedicated app notifications, and the device monitors the data in real time.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] [Third embodiment]

[0855] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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).

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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."

[0867] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on the nationality and interests of customers. Specific embodiments and program processing for implementing this system are described below.

[0868] 1. Collecting social media data

[0869] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0870] 2. Data Preprocessing

[0871] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[0872] 3. Analysis of regional attractiveness

[0873] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[0874] 4. Extracting the charm

[0875] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[0876] 5. Tourism content proposals

[0877] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[0878] 6. Analysis of Customer Data

[0879] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from tourist surveys or existing databases.

[0880] 7. Targeted Marketing

[0881] The server analyzes customer data and creates the optimal marketing strategy for each customer segment. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[0882] 8. Marketing Execution

[0883] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure effectiveness.

[0884] Specific examples

[0885] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[0886] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[0887] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[0888] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[0889] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[0890] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[0891] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[0895] Step 2:

[0896] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[0897] Step 3:

[0898] The server removes unnecessary metadata and organizes necessary metadata such as timestamps, location information, and hashtags.

[0899] Step 4:

[0900] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it can identify "Mount Fuji" or "Kiyomizu-dera Temple" in a video.

[0901] Step 5:

[0902] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[0903] Step 6:

[0904] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[0905] Step 7:

[0906] The server automatically generates new sightseeing courses and events based on the list of attractive points, such as a "Mt. Fuji sunrise tour" or "Kyoto traditional cultural experience."

[0907] Step 8:

[0908] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[0909] Step 9:

[0910] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[0911] Step 10:

[0912] The server creates marketing strategies for each target group based on customer data, such as "promoting Mount Fuji climbing tours to American tourists and Kyoto cultural experiences to Japanese tourists."

[0913] Step 11:

[0914] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[0915] Step 12:

[0916] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[0917] Example 1

[0918] 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."

[0919] The tourism industry faces challenges in developing individual marketing strategies based on tourists' preferences and attributes and effectively attracting them. Furthermore, there is a lack of a system for utilizing the vast amount of video and image data posted by tourists on social media to extract regional appeal from it and generate tourism content. This makes it difficult to implement efficient and effective tourism promotion.

[0920] 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.

[0921] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze regional characteristics from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on attributes and preferences, means for implementing customized promotions based on the generated tourism content and marketing strategy, and means for monitoring the effectiveness of the customized promotions. This makes it possible to efficiently extract regional attractions using social media data, formulate effective marketing strategies tailored to tourist preferences, and implement tourism promotions.

[0922] A "tourist" is someone who travels to visit a tourist destination.

[0923] "Social media" refers to online services that enable users to share content through posts and comments.

[0924] "Video" refers to a media format that creates the illusion of movement by playing a sequence of still images at a constant speed.

[0925] "Image" refers to static visual data that graphically displays visual information.

[0926] "Collection" refers to the act of gathering data based on specific criteria or filters.

[0927] "Preprocessing" refers to the process of organizing and transforming data to make it easier to analyze and apply models to.

[0928] "Regional characteristics" refers to the characteristics of natural landscapes, famous places, activities, etc. that are unique to a particular region.

[0929] An "artificial intelligence model" refers to an algorithm that learns from large amounts of data, identifies patterns, and makes inferences.

[0930] "Analysis" refers to the process of examining data in detail to derive its characteristics and trends.

[0931] "Tourism content" refers to the content of tours, experiences, events, etc. offered to tourists.

[0932] "Customer Data" refers to information regarding the attributes and behavior of service users.

[0933] "Attributes" refer to personal characteristics of customers, such as nationality, age, and gender.

[0934] "Preferences" refer to a customer's preferred activities or interests.

[0935] A "marketing strategy" is a plan for promoting a product or service based on specific goals.

[0936] "Customized promotion" refers to advertising activities tailored to specific customer groups.

[0937] "Monitoring" refers to the process of measuring and overseeing the effectiveness of a particular activity or promotion.

[0938] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on customer attributes and preferences. Specific embodiments and program processing for implementing this system are described below.

[0939] Social media data collection

[0940] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images based on specific hashtags (e.g., "JapanTravel") and location information. When collecting, authentication is performed using an API key, and post data that matches the conditions is obtained.

[0941] Data Preprocessing

[0942] The server converts the collected video and image data into a format that is easy to analyze (e.g., JPEG or MP4). During this process, it uses the Python Pillow library to standardize the image format, remove unnecessary metadata, and organize important metadata such as timestamps, location information, and hashtags. For example, it extracts and organizes metadata from JSON-formatted data.

[0943] Analysis of regional characteristics

[0944] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from collected and preprocessed video and image data. As a specific example, it uses the YOLOv3 model to identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[0945] Tourism content generation

[0946] The server generates tourism content based on the analysis results. This involves creating a list of popular tourist spots and activities and planning new tourist courses and events. The planned content is stored in a database and made available for reference by tourism operators. For example, a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience" might be generated.

[0947] Customer data analysis

[0948] The device inputs or retrieves tourist profile data (e.g., nationality, age, gender, interests, etc.) The collected data is stored in a MySQL database and analyzed using machine learning libraries such as Scikit-learn.

[0949] Marketing strategy planning

[0950] The server analyzes customer data and develops the optimal marketing strategy for each customer segment. For example, it suggests a "Mount Fuji climbing tour" for American tourists and a "Kyoto night illumination tour" for Japanese tourists.

[0951] Run customized promotions

[0952] The server implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the resulting data on the management screen, allowing the effectiveness of marketing to be measured.

[0953] Prompt Sentence Examples

[0954] "Identify tourist attractions related to the JapanTravel hashtag on Instagram."

[0955] "Please create the best marketing strategy for Kyoto's tourist destinations."

[0956] As described above, this system provides tourism operators with concrete tools to effectively attract tourists and revitalize local economies.

[0957] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0958] Step 1:

[0959] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information. At this time, it authenticates using an API key and retrieves posted data that matches the conditions in JSON format. The input is the API request, and the output is the JSON data of the collected videos and images.

[0960] Step 2:

[0961] The video and image data collected by the server is converted into a format that is easy to analyze (for example, JPEG or MP4). The Python Pillow library is used to standardize the image format and remove unnecessary metadata. Important metadata such as timestamps, location information, and hashtags are extracted and organized. The input is JSON-formatted data, and the output is organized metadata and video and image data in a unified format.

[0962] Step 3:

[0963] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from preprocessed video and image data. Specifically, it uses the YOLOv3 model to analyze images and videos and identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple." The input is preprocessed video and image data, and the output is a list of recognized features (scenery, landmarks, and activities).

[0964] Step 4:

[0965] The server evaluates tourist interests based on the analysis results and creates a list of popular tourist attractions and activities. The analysis data is stored in an SQL database and the evaluation results are extracted using SQL queries. The input is a list of analyzed features, and the output is a list of popular tourist attractions and activities.

[0966] Step 5:

[0967] The server plans new sightseeing itineraries and events based on the generated list. This includes creating new sightseeing packages that include specific events at tourist destinations. For example, it proposes a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience." The input is a list of popular spots and activities, and the output is a new sightseeing package proposal.

[0968] Step 6:

[0969] The terminal inputs or acquires tourist profile data (nationality, age, interests, etc.). The input data is collected through a questionnaire form or an existing database. The input is tourist profile data, and the output is a collection of acquired customer data.

[0970] Step 7:

[0971] The server analyzes customer data and uses machine learning libraries such as Scikit-learn to create optimal marketing strategies for each customer segment. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. The input is customer data, and the output is a marketing strategy for each segment.

[0972] Step 8:

[0973] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the collected data on the management screen and measures the effectiveness of the marketing. The input is the marketing plan, and the output is the promotion effectiveness indicators.

[0974] (Application example 1)

[0975] 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."

[0976] In recent years, the number of tourists visiting tourist destinations has increased, making it important to effectively communicate the attractions of tourist destinations. However, a lack of customized information and promotions based on tourists' interests makes it difficult to maximize the attractions of tourist destinations. There is also a need for a system that can efficiently analyze the large amount of collected social media data and generate tourism content.

[0977] 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.

[0978] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for including an application installed on a smartphone, smart glasses, or head-mounted display that provides tourist destination information based on the user's interests, and means for identifying tourist attractions based on data collected from social media and suggesting related information, thereby enabling the provision of efficient and personalized tourism information and promotions to a diverse range of tourists.

[0979] "Tourists" refer to people who visit a particular tourist destination to experience the attractions of that area.

[0980] "Social media" refers to online platforms that enable users to share information and interact via the internet.

[0981] "Video and images" refers to media formats for transmitting visual information, and refers to files containing visually recorded information.

[0982] "Means of collection" refers to methods or technologies for collecting data on a device such as a server based on specific conditions.

[0983] "Preprocessing means" refers to the initial processing performed to convert data into a format that is easier to analyze.

[0984] An "artificial intelligence model" refers to a program that uses technologies such as machine learning and deep learning to analyze data and extract patterns and features.

[0985] "Regional attractions" refer to the points and characteristics that make a particular region interesting and valuable to tourists.

[0986] "Analytical means" refers to techniques and methods for processing data to reveal specific information or patterns.

[0987] "Tourism content" refers to data provided to tourists, including information on tourist destinations, experience plans, events, etc.

[0988] "Means of generation" refers to methods and technologies for creating new data, information, or content based on specific conditions.

[0989] "Customer data" refers to information such as tourists' profile information, interests, and behavioral history.

[0990] A "marketing strategy" refers to a plan or method for effectively promoting a product to a specific target audience.

[0991] "Customized promotion" refers to advertising and marketing techniques that are specifically tailored to an individual customer's interests.

[0992] A "smartphone" refers to a mobile information terminal that has communication functions and can connect to the Internet and use applications.

[0993] "Smart glasses" refers to a wearable device that adds information processing and communication functions to conventional glasses.

[0994] A "head-mounted display" refers to a display device that is worn on the user's head and displays information directly into the user's field of vision.

[0995] "Application" refers to a software program designed to accomplish a specific purpose.

[0996] "Tourist destination information" refers to detailed information and guide content about specific tourist destinations.

[0997] "Means of identification" refers to techniques or methods for recognizing and extracting specific features or information from data.

[0998] This invention is a system that collects videos and images posted on social media by tourists, analyzes them to discover the attractions of a region, and enables the provision of customized information and marketing based on the user's nationality and interests.

[0999] Hardware and software used

[1000] Hardware:

[1001] Servers with high-performance CPU / GPUs, smartphones, smart glasses, or head-mounted displays.

[1002] software:

[1003] Python, Flask (web framework), Google Cloud Vision API, OpenCV (image analysis library).

[1004] System Operation

[1005] The server uses social media APIs to collect videos and images posted by tourists based on specific hashtags and location information, such as posts related to "JapanTravel" or specific regions.

[1006] The collected video and image data is preprocessed by the server and converted into a format that is easy to analyze, and unnecessary metadata is removed and important metadata such as timestamps, location information, and hashtags are organized.

[1007] The preprocessed data is then fed into an artificial intelligence model to analyze the area's attractiveness. Image analysis techniques, such as Google Cloud Vision API and OpenCV, are used to identify landmarks and tourist attractions within the images. From these identified attractions and activities, the model then creates a list of particularly popular places and activities.

[1008] The server then plans new sightseeing courses and events based on the listed attractions. It references user profile data (such as nationality, age, and interests) to propose optimal sightseeing plans for each user. Applications installed on smartphones, smart glasses, and head-mounted displays display real-time information about tourist spots based on the user's interests, improving the sightseeing experience.

[1009] Specific examples

[1010] As a specific example of its use, the system collects Instagram images posted by tourists using the hashtag "KyotoTravel." The server analyzes the images and identifies "Kiyomizu-dera Temple" and "Kinkaku-ji Temple." The server then suggests a "Kinkaku-ji and Kiyomizu-dera Temple tour" to American tourists. These suggestions are provided via the user's smartphone, smart glasses, or head-mounted display.

[1011] Prompt Sentence Examples

[1012] Social media post collection example:

[1013] Region: Kyoto

[1014] Hashtag: KyotoTravel

[1015] Analysis target: Images and videos

[1016] Analysis goal: Identifying tourist attractions (e.g., Kiyomizu-dera Temple, Kinkaku-ji Temple)

[1017] Tourist attraction identification and suggestion example:

[1018] Customer Profile: American Tourists

[1019] Proposal: Tour of Kinkakuji Temple and Kiyomizudera Temple

[1020] By using this system, tourism businesses can provide efficient and personalized tourism information and promotions to a wide range of tourists.

[1021] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1022] Step 1:

[1023] The server uses social media APIs to collect videos and images posted by tourists. During collection, data is collected based on specific hashtags (e.g., "JapanTravel") and location information. The input to this collection process is hashtags and location information, and the output is the corresponding video and image files.

[1024] Step 2:

[1025] The server preprocesses the collected video and image data. Specifically, it converts the video and images into a format that is easy to analyze and removes unnecessary metadata. The input to the preprocessing is the collected video and image data, and the output is the preprocessed video and image data.

[1026] Step 3:

[1027] The server inputs the preprocessed data into an artificial intelligence model to analyze the area's attractions, using the Google Cloud Vision API and OpenCV to identify landmarks in the images. The input for the analysis is the preprocessed video and image data, and the output is data on identified landmarks and tourist attractions.

[1028] Step 4:

[1029] The server generates tourism content based on the analysis results. Based on the identified tourist attractions and activities, it lists popular places and activities and plans sightseeing courses and events. The input for generation is the analysis result data, and the output is newly generated tourism content and proposed plans.

[1030] Step 5:

[1031] The server analyzes the user's profile data (nationality, age, interests, etc.) and customizes the optimal sightseeing plan for each target. The input of this process is the user's profile data, and the output is a customized sightseeing plan.

[1032] Step 6:

[1033] The terminal (smartphone, smart glasses, head-mounted display) provides the generated tourism content and customized tourism plans to the user. The input of this provision process is the customized tourism plan, and the output is the tourism information displayed to the user.

[1034] Step 7:

[1035] The server collects user feedback and behavioral data to improve the tour plan in real time. The input of this feedback collection process is user behavioral data, and the output is a further optimized tour plan.

[1036] Through these steps, tourism businesses can provide efficient and personalized tourism information and promotions to individual tourists.

[1037] 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.

[1038] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and implements marketing strategies based on the customer's nationality and interests. It incorporates an emotion engine to recognize user emotions and customizes tourism content and marketing strategies based on these. Specific embodiments and program processing for implementing this system are described below.

[1039] 1. Collecting social media data

[1040] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1041] 2. Data Preprocessing

[1042] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[1043] 3. Analysis of regional attractiveness

[1044] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[1045] 4. Extracting the charm

[1046] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[1047] 5. Tourism content proposals

[1048] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[1049] 6. Analysis of Customer Data

[1050] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[1051] 7. Applying the Emotion Engine

[1052] The server analyzes the user's posts and profile information and uses an emotion engine to recognize the user's current emotional state and long-term emotional patterns. For example, it analyzes emotions based on images of the user looking "happy."

[1053] 8. Targeted Marketing

[1054] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[1055] 9. Customized Content Generation

[1056] The server customizes sightseeing content based on the user's emotional state identified by the emotion engine. For example, it suggests a "hot spring tour" to a user who wants to relax, and a "hiking tour" to a user who wants to be active.

[1057] 10. Marketing Execution

[1058] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[1059] Specific examples

[1060] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[1061] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[1062] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[1063] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[1064] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[1065] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[1066] 7. The server applies an emotion engine to recognize emotions such as "happiness" or "excitement" from the user's posts.

[1067] 8. The server promotes the "Hot Spring Relaxation Plan" to users seeking relaxation and the "Outdoor Adventure Tour" to users seeking an active experience.

[1068] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[1069] The processing flow will be explained below.

[1070] Step 1:

[1071] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1072] Step 2:

[1073] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[1074] Step 3:

[1075] The server removes unnecessary metadata and compiles necessary metadata such as timestamps, location information, and hashtags.

[1076] Step 4:

[1077] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[1078] Step 5:

[1079] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[1080] Step 6:

[1081] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[1082] Step 7:

[1083] The server generates new sightseeing courses and events based on the listed attractions, such as a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience."

[1084] Step 8:

[1085] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from a questionnaire or an existing database.

[1086] Step 9:

[1087] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[1088] Step 10:

[1089] The server uses an emotion engine to recognize emotions from user posts, for example, determining "happiness" or "surprise" from the post content and facial expressions.

[1090] Step 11:

[1091] The server customizes sightseeing content based on the user's current emotional state, suggesting, for example, a hot spring tour for a user who wants to relax, or a hiking tour for a user who wants to stay active.

[1092] Step 12:

[1093] The server analyzes the long-term emotional patterns identified by the emotion engine and creates marketing strategies based on them, such as "market relaxation plans to users who have posted frequently about wanting to relax over the past year."

[1094] Step 13:

[1095] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[1096] Step 14:

[1097] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[1098] Example 2

[1099] 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."

[1100] In recent years, the spread of social media has led to an increase in postings of information about tourist destinations and events. However, there is a lack of systems that can effectively collect and analyze this information and link it to appropriate tourism content and marketing strategies. Furthermore, there is no system that can grasp tourists' emotional state and propose customized promotions that respond to that. There is a need to solve these issues.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1102] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying a machine learning model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for applying a sentiment analysis engine to analyze customer sentiment data, and means for implementing customized promotions based on the generated tourism content and marketing strategy. This makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that meet the needs and sentiments of tourists.

[1103] "Social media" is a platform that allows users to share content and communicate with each other over the Internet.

[1104] "Means for collecting videos and images" refers to the function for obtaining video and image data from social media platforms.

[1105] "Preprocessing" refers to the process of converting collected video and image data into a format suitable for analysis and removing unnecessary metadata.

[1106] A "machine learning model" is an algorithm that learns specific patterns and characteristics from data and makes predictions and classifications on newly acquired data.

[1107] "Tourism content" refers to the collection of information and services related to tourist destinations and activities that are provided to tourists.

[1108] "Customer Data" means information about a tourist, including personal profile information such as nationality, age, and interests, as well as historical behavioral data.

[1109] A "marketing strategy" is a set of plans and methods for promoting and selling products and services in the most optimal way to a particular customer segment.

[1110] An "emotion analysis engine" is an algorithm or software that recognizes and analyzes a user's emotional state from text or images.

[1111] "Customized promotion" refers to specific advertising and promotional activities that are applied based on the needs, interests, and emotional state of individual customers.

[1112] "System" refers to a set of machines, software, databases, etc., whose components work together to achieve a specific purpose.

[1113] This invention is a system that collects and analyzes videos and images posted by tourists on social media, discovers the attractions of a region based on the collected data, and plans and implements marketing strategies tailored to the nationality, interests, and emotions of customers. This system uses the following hardware and software:

[1114] Hardware and software used

[1115] The server is a computer with a powerful processor and sufficient memory, such as an Intel Xeon processor. It also uses APIs to collect data from social media. For example, libraries such as "Tweepy" (for Twitter) and "Instagram API" (for Instagram) are used.

[1116] Python's "OpenCV" and "Pandas" are used for data preprocessing, which enables format conversion of image and video data and organization of metadata.

[1117] For data analysis, machine learning libraries such as "TensorFlow" and "PyTorch" are used to extract the characteristics of tourist spots from the collected videos and images. Suitable machine learning models include "YOLO (You Only Look Once)" and "ResNet," which are widely used in image recognition.

[1118] To input and acquire customer data, data is collected from the user's device (smartphone or PC) via a questionnaire form or an existing database. This data is then sent to the server.

[1119] The emotion engine uses an emotion analysis API (for example, Microsoft Azure Emotion Analysis API), which allows it to analyze user emotions from posted content and profile data.

[1120] Specific examples

[1121] Specifically, the system collects images and videos posted by tourists using the "JapanTravel" hashtag from social media. The data collected by the server using "Tweepy" and the "Instagram API" is preprocessed using "OpenCV" and "Pandas." For example, unnecessary metadata is removed and only the necessary information is saved.

[1122] The preprocessed data is then analyzed using TensorFlow and PyTorch to identify tourist attractions and activities within the images. For example, Mount Fuji and Sensoji Temple are identified. Based on the analysis results, popular activities such as climbing Mount Fuji and wearing a kimono in Kyoto are listed.

[1123] Furthermore, user profile data (nationality, age, interests, etc.) is collected via questionnaire forms and databases. For example, tourist survey data is sent to a server and stored.

[1124] The emotion analysis engine uses the Microsoft Azure Emotion Analysis API to recognize emotions such as "happiness" or "excitement" from user posts. For example, it analyzes emotions from photos of smiling faces and comments such as "That was fun!"

[1125] Finally, the server generates tourism content and marketing strategies based on the analysis data. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. These marketing plans are implemented through social media ads, email campaigns, and dedicated app notifications.

[1126] Prompt Sentence Examples

[1127] Example prompt for collecting the "JapanTravel" tag:

[1128] "Use the Twitter API to collect tweets with the hashtag 'JapanTravel' from the past week."

[1129] Example prompts for image analysis:

[1130] "Please identify landmarks such as 'Mount Fuji' and 'Kiyomizu-dera Temple' from the collected images."

[1131] This system makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that respond to tourists' needs and emotions.

[1132] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] Social media data collection

[1135] The server uses social media APIs to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information.

[1136] Input: Collection conditions (hashtags and location information), API key.

[1137] Processing: The server sends a query to the API to retrieve posts that match the criteria. For example, it uses the Tweepy library to search for posts from the past week with a given hashtag.

[1138] Output: Collected video and image data and associated metadata.

[1139] Step 2:

[1140] Data Preprocessing

[1141] The server converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata.

[1142] Input: Collected video and image data and associated metadata.

[1143] Processing: The server uses OpenCV to unify the image formats and Pandas to format metadata such as timestamps and GPS data. For example, it converts all images to JPEG format and saves the metadata in a CSV file.

[1144] Output: Preprocessed video and image data and formatted metadata.

[1145] Step 3:

[1146] Analysis of regional attractiveness

[1147] The server applies machine learning models to analyze the appeal of a region from preprocessed video and image data.

[1148] Input: Preprocessed video and image data and formatted metadata.

[1149] Processing: The server uses TensorFlow to analyze the data with trained models to identify landmarks and activities, for example, using the YOLO model to detect "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[1150] Output: Parsed data (list of landmarks and activities).

[1151] Step 4:

[1152] Extracting Charm

[1153] The server evaluates the popularity of the site based on the analysis results and lists the places and activities that are particularly popular with tourists.

[1154] Input: Parsed data (list of landmarks and activities).

[1155] Processing: The server aggregates the analysis results and ranks them by popularity, for example, placing landmarks that appear frequently at the top of the list.

[1156] Output: A ranked list of popular places and activities.

[1157] Step 5:

[1158] Tourism content proposals

[1159] The server will plan new tourist courses and events based on popular attractions.

[1160] Input: A ranked list of popular places and activities.

[1161] Processing: The server automatically generates a new sightseeing plan and saves it in the database. For example, it plans a "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[1162] Output: Details of the proposed itinerary and event (schedule, prices, contact details, etc.).

[1163] Step 6:

[1164] Customer data analysis

[1165] The terminal inputs or retrieves tourist profile data (nationality, age, interests, etc.).

[1166] Input: Tourist survey data or existing database.

[1167] Processing: The device collects data via an input form or API and sends it to a server. For example, data entered into a questionnaire form is sent to a database in real time.

[1168] Output: Collected profile data.

[1169] Step 7:

[1170] Applying the Emotion Engine

[1171] The server analyzes users' posts and profiles and uses an emotion analysis API to recognize their current emotional state and long-term emotional patterns.

[1172] Input: User posts and profile data.

[1173] Processing: The server sends the data to an emotion analysis API to obtain the emotion status. For example, it uses the Microsoft Azure emotion analysis API to analyze "happy" or "excited."

[1174] Output: Parsed emotion status.

[1175] Step 8:

[1176] Targeted Marketing

[1177] The server creates optimal marketing strategies for each customer segment based on customer data and sentiment data.

[1178] Input: Customer data and sentiment status.

[1179] Processing: The server generates a marketing plan for each segment and generates data for social media advertising settings. For example, it proposes a "Mount Fuji climbing tour" for Americans and a "Kyoto night illumination tour" for Japanese people.

[1180] Output: Marketing plan by segment.

[1181] Step 9:

[1182] Customized content generation

[1183] The server customizes the sightseeing content based on the emotional state of the user identified by the emotion engine.

[1184] Input: Emotional status and user profile data.

[1185] Processing: The server generates optimal content based on the user's emotional state and stores it in a database. For example, it suggests a "hot spring relaxation plan" to a user who wants to relax, and an "outdoor adventure tour" to a user who wants to be active.

[1186] Output: Customized tourism content.

[1187] Step 10:

[1188] Marketing Execution

[1189] The server then implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[1190] Input: Generated marketing plan.

[1191] Processing: The server uses social media advertising APIs and email sending tools to send each advertisement and notification in bulk. The device collects advertisement interaction data and sends feedback to the server.

[1192] Output: Data measuring the effectiveness of the advertising campaigns carried out.

[1193] (Application example 2)

[1194] 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."

[1195] In the tourism and related industries, it is important to effectively discover the attractions of a region based on tourists' social media posts and provide them as tourism content. However, it is difficult to effectively analyze collected data, generate customized content based on customers' nationalities, interests, and even emotions, and develop optimal marketing strategies. In particular, there is a lack of marketing methods specifically tailored to the food culture of tourist destinations, and there is a need to propose meal plans tailored to each individual tourist.

[1196] 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.

[1197] In this invention, the server includes means for collecting social media videos and images posted by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for using an emotion engine that recognizes emotions from content posted by tourists and customizing the tourism content based on emotion data, means for collecting social media posts based on specific hashtags and location information using a social media API, and means for adjusting the marketing strategy based on the identified emotions, thereby enabling the provision of customized tour and meal plans that are optimal for each tourist, thereby realizing efficient marketing and promotion in the tourism industry.

[1198] "Tourist" refers to a person who visits for travel or sightseeing.

[1199] "Social media" refers to online platforms for sharing information and engaging in two-way communication over the Internet.

[1200] "Video" refers to a media format that visually conveys movement by displaying a sequence of images.

[1201] "Image" refers to still visual data, including photographs and illustrations.

[1202] "Means of collection" refers to the methods and techniques used to collect the desired data.

[1203] "Preprocessing means" refers to methods and techniques for organizing and transforming data to make it easier to analyze and use.

[1204] An "artificial intelligence model" refers to an algorithm or learning system that analyzes data and performs specific tasks.

[1205] "Regional attractions" refer to the attractive points of a particular region, such as tourist resources, culture, and scenery.

[1206] "Means of analysis" refers to methods and techniques for understanding and interpreting data.

[1207] "Tourism content" refers to the information, experiences, services, etc. provided to tourists.

[1208] "Customer data" refers to information about tourists, including personal profiles and behavioral history.

[1209] "Nationality" refers to the country or region to which an individual belongs.

[1210] "Interests" refers to the interest or concern that an individual has in a particular event or activity.

[1211] "Marketing strategy" refers to the set of commercial activities that includes market analysis, planning, and tactics to achieve specific goals.

[1212] "Customized promotion" refers to advertising and promotional activities that are tailored to the characteristics and preferences of individual customers.

[1213] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from data such as text and images.

[1214] "Emotion" refers to a person's feelings or psychological state.

[1215] "SNS API" refers to the application programming interface provided by a social media platform, which is a means of retrieving and manipulating data from external systems.

[1216] A "hashtag" is a symbol placed before a keyword or phrase used on social media to easily search for and categorize a particular topic.

[1217] "Location information" refers to data that indicates a specific point or location.

[1218] This invention is a system that collects videos and images posted by tourists on social media, analyzes the attractions of a region, and implements a marketing strategy based on customer nationality, interests, and emotional data. This system is implemented using the following means.

[1219] First, the server uses SNS APIs to collect videos and images based on specific hashtags and location information on social media, such as posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1220] The server then converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata, a process that includes cleaning up important metadata like timestamps, location information, and hashtags.

[1221] After the data has been converted, the server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from the video and image data. For example, it can identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[1222] The server then evaluates the popularity of the results and lists the most popular places and activities for tourists, generating tourism content such as "Climbing Mount Fuji" or "Kimono experience in Kyoto."

[1223] The server then analyzes customer data and develops optimal marketing strategies based on the customer's nationality, age, and interests, referencing tourist survey data and existing databases.

[1224] For emotional data analysis, the server analyzes the user's posted content and profile and uses an emotion engine to recognize the user's emotional state. For example, emotions can be analyzed from an image of the user looking "happy" or a sentence expressing "happiness."

[1225] Based on these analysis results, the server will carry out customized promotions, such as suggesting "hot spring tours" to users seeking relaxation and "hiking tours" to users seeking more active experiences.

[1226] Specific hardware and software for this system include:

[1227] Server: Collects and analyzes data (e.g. AWS EC2).

[1228] API: We collect data using the Instagram API.

[1229] Image analysis model: Analyzes image data using TensorFlow and ResNet50.

[1230] Text analysis: Analyze the text of social media posts using TextBlob.

[1231] Specific examples

[1232] For example, suppose posts with the hashtag "TokyoFood" are collected and many images of "sushi" are found among the posts. Based on the analysis results, a "food tour centered on sushi" can be suggested. Furthermore, if emotions such as "satisfaction" and "excitement" are frequently found in user posts, the promotion can be customized based on these emotions.

[1233] Prompt Sentence Examples

[1234] Inexpensive image URL list and post text example

[1235] image_urls = ["https: / / example.com / image1.jpg", "https: / / example.com / image2.jpg"]

[1236] example_text = "This sushi is amazing!"

[1237] API call prompt

[1238] fetch_sns_data("TokyoFood")

[1239] Image Analysis Prompts

[1240] analyze_images(image_urls)

[1241] Sentiment Analysis Prompts

[1242] analyze_sentiment(example_text)

[1243] In this way, tourism operators can provide attractive tourism content tailored to individual needs and develop effective marketing strategies.

[1244] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1245] Step 1:

[1246] The server uses the SNS API to collect videos and images based on specific hashtags and location information. As input, the hashtag and location information are specified and an API request is sent. As output, the posted video and image data is obtained. Specifically, the server accesses the Instagram API and collects posts related to the specified hashtag "JapanTravel" and location information (e.g., Kyoto, around Mt. Fuji).

[1247] Step 2:

[1248] The server converts the collected video and images into a format that is easy to analyze, and extracts and organizes important metadata. The input is the collected video and image data and its metadata. The output is data converted into a format suitable for analysis. Specifically, the server extracts important metadata such as timestamps, location information, and hashtags, and removes unnecessary information.

[1249] Step 3:

[1250] The server applies an artificial intelligence model to extract features such as scenery, landmarks, and activities from the format-converted video and image data. The preprocessed video and image data are used as input. The output is a dataset with each feature extracted. Specifically, the server uses TensorFlow and a ResNet50 model to identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[1251] Step 4:

[1252] The server evaluates the popularity of the analysis results and lists the places and activities that are popular with tourists. The feature-extracted dataset is used as input. As output, a list of popular spots and activities is generated. Specifically, the server aggregates the collected data and lists the places and activities that are mentioned in the most posts.

[1253] Step 5:

[1254] The server generates new tourism content based on the listed attractions. It uses a list of popular spots and activities as input. It generates data to suggest new tourism packages and events as output. Specifically, the server creates new tourism plans such as a "Mt. Fuji Sunrise Tour" or "Kyoto Traditional Cultural Experience."

[1255] Step 6:

[1256] The terminal acquires tourist data and analyzes the customer's nationality, age, interests, etc. Tourist profile data is used as input. Profile information appropriate for each tourist is obtained as output. Specifically, the terminal references tourist survey data and existing databases to acquire the necessary data.

[1257] Step 7:

[1258] The server analyzes the user's posts and profile and uses an emotion engine to recognize the user's emotional state. The user's posts and profile data are used as input. The output is the analysis result of the emotional state. Specifically, the server uses an emotion analysis tool such as TextBlob to identify emotions such as "happy" or "fun" from the text and images of the posts.

[1259] Step 8:

[1260] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. It uses the analyzed customer data and emotional data as input. It generates a customized marketing plan as output. Specifically, the server promotes a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[1261] Step 9:

[1262] The server customizes sightseeing content based on emotional data. The input is the result of analyzing the emotional state. The output is to generate sightseeing content customized to the user's emotions. Specifically, the server suggests a "hot spring tour" to a user seeking relaxation, and a "hiking tour" to a user seeking to be more active.

[1263] Step 10:

[1264] The server and device implement the generated marketing plan and monitor its effectiveness. The customized marketing plan is used as input. Effectiveness measurement data such as ad views and click rates is obtained as output. Specifically, the server implements promotions using digital methods such as social media ads, email campaigns, and dedicated app notifications, and the device monitors the data in real time.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] [Fourth embodiment]

[1269] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1270] 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.

[1271] 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).

[1272] 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.

[1273] 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.

[1274] 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).

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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.

[1280] 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.

[1281] 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."

[1282] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on the nationality and interests of customers. Specific embodiments and program processing for implementing this system are described below.

[1283] 1. Collecting social media data

[1284] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1285] 2. Data Preprocessing

[1286] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[1287] 3. Analysis of regional attractiveness

[1288] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[1289] 4. Extracting the charm

[1290] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[1291] 5. Tourism content proposals

[1292] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[1293] 6. Analysis of Customer Data

[1294] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from tourist surveys or existing databases.

[1295] 7. Targeted Marketing

[1296] The server analyzes customer data and creates the optimal marketing strategy for each customer segment. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[1297] 8. Marketing Execution

[1298] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure effectiveness.

[1299] Specific examples

[1300] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[1301] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[1302] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[1303] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[1304] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[1305] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[1306] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[1307] The processing flow will be explained below.

[1308] Step 1:

[1309] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1310] Step 2:

[1311] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[1312] Step 3:

[1313] The server removes unnecessary metadata and organizes necessary metadata such as timestamps, location information, and hashtags.

[1314] Step 4:

[1315] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it can identify "Mount Fuji" or "Kiyomizu-dera Temple" in a video.

[1316] Step 5:

[1317] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[1318] Step 6:

[1319] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[1320] Step 7:

[1321] The server automatically generates new sightseeing courses and events based on the list of attractive points, such as a "Mt. Fuji sunrise tour" or "Kyoto traditional cultural experience."

[1322] Step 8:

[1323] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[1324] Step 9:

[1325] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[1326] Step 10:

[1327] The server creates marketing strategies for each target group based on customer data, such as "promoting Mount Fuji climbing tours to American tourists and Kyoto cultural experiences to Japanese tourists."

[1328] Step 11:

[1329] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[1330] Step 12:

[1331] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[1332] Example 1

[1333] 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."

[1334] The tourism industry faces challenges in developing individual marketing strategies based on tourists' preferences and attributes and effectively attracting them. Furthermore, there is a lack of a system for utilizing the vast amount of video and image data posted by tourists on social media to extract regional appeal from it and generate tourism content. This makes it difficult to implement efficient and effective tourism promotion.

[1335] 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.

[1336] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze regional characteristics from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on attributes and preferences, means for implementing customized promotions based on the generated tourism content and marketing strategy, and means for monitoring the effectiveness of the customized promotions. This makes it possible to efficiently extract regional attractions using social media data, formulate effective marketing strategies tailored to tourist preferences, and implement tourism promotions.

[1337] A "tourist" is someone who travels to visit a tourist destination.

[1338] "Social media" refers to online services that enable users to share content through posts and comments.

[1339] "Video" refers to a media format that creates the illusion of movement by playing a sequence of still images at a constant speed.

[1340] "Image" refers to static visual data that graphically displays visual information.

[1341] "Collection" refers to the act of gathering data based on specific criteria or filters.

[1342] "Preprocessing" refers to the process of organizing and transforming data to make it easier to analyze and apply models to.

[1343] "Regional characteristics" refers to the characteristics of natural landscapes, famous places, activities, etc. that are unique to a particular region.

[1344] An "artificial intelligence model" refers to an algorithm that learns from large amounts of data, identifies patterns, and makes inferences.

[1345] "Analysis" refers to the process of examining data in detail to derive its characteristics and trends.

[1346] "Tourism content" refers to the content of tours, experiences, events, etc. offered to tourists.

[1347] "Customer Data" refers to information regarding the attributes and behavior of service users.

[1348] "Attributes" refer to personal characteristics of customers, such as nationality, age, and gender.

[1349] "Preferences" refer to a customer's preferred activities or interests.

[1350] A "marketing strategy" is a plan for promoting a product or service based on specific goals.

[1351] "Customized promotion" refers to advertising activities tailored to specific customer groups.

[1352] "Monitoring" refers to the process of measuring and overseeing the effectiveness of a particular activity or promotion.

[1353] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and then implements a marketing strategy based on customer attributes and preferences. Specific embodiments and program processing for implementing this system are described below.

[1354] Social media data collection

[1355] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images based on specific hashtags (e.g., "JapanTravel") and location information. When collecting, authentication is performed using an API key, and post data that matches the conditions is obtained.

[1356] Data Preprocessing

[1357] The server converts the collected video and image data into a format that is easy to analyze (e.g., JPEG or MP4). During this process, it uses the Python Pillow library to standardize the image format, remove unnecessary metadata, and organize important metadata such as timestamps, location information, and hashtags. For example, it extracts and organizes metadata from JSON-formatted data.

[1358] Analysis of regional characteristics

[1359] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from collected and preprocessed video and image data. As a specific example, it uses the YOLOv3 model to identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[1360] Tourism content generation

[1361] The server generates tourism content based on the analysis results. This involves creating a list of popular tourist spots and activities and planning new tourist courses and events. The planned content is stored in a database and made available for reference by tourism operators. For example, a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience" might be generated.

[1362] Customer data analysis

[1363] The device inputs or retrieves tourist profile data (e.g., nationality, age, gender, interests, etc.) The collected data is stored in a MySQL database and analyzed using machine learning libraries such as Scikit-learn.

[1364] Marketing strategy planning

[1365] The server analyzes customer data and develops the optimal marketing strategy for each customer segment. For example, it suggests a "Mount Fuji climbing tour" for American tourists and a "Kyoto night illumination tour" for Japanese tourists.

[1366] Run customized promotions

[1367] The server implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the resulting data on the management screen, allowing the effectiveness of marketing to be measured.

[1368] Prompt Sentence Examples

[1369] "Identify tourist attractions related to the JapanTravel hashtag on Instagram."

[1370] "Please create the best marketing strategy for Kyoto's tourist destinations."

[1371] As described above, this system provides tourism operators with concrete tools to effectively attract tourists and revitalize local economies.

[1372] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1373] Step 1:

[1374] The server uses social media APIs (e.g., Instagram API or Twitter API) to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information. At this time, it authenticates using an API key and retrieves posted data that matches the conditions in JSON format. The input is the API request, and the output is the JSON data of the collected videos and images.

[1375] Step 2:

[1376] The video and image data collected by the server is converted into a format that is easy to analyze (for example, JPEG or MP4). The Python Pillow library is used to standardize the image format and remove unnecessary metadata. Important metadata such as timestamps, location information, and hashtags are extracted and organized. The input is JSON-formatted data, and the output is organized metadata and video and image data in a unified format.

[1377] Step 3:

[1378] The server uses generative AI models such as TensorFlow and PyTorch to analyze features such as scenery, landmarks, and activities from preprocessed video and image data. Specifically, it uses the YOLOv3 model to analyze images and videos and identify landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple." The input is preprocessed video and image data, and the output is a list of recognized features (scenery, landmarks, and activities).

[1379] Step 4:

[1380] The server evaluates tourist interests based on the analysis results and creates a list of popular tourist attractions and activities. The analysis data is stored in an SQL database and the evaluation results are extracted using SQL queries. The input is a list of analyzed features, and the output is a list of popular tourist attractions and activities.

[1381] Step 5:

[1382] The server plans new sightseeing itineraries and events based on the generated list. This includes creating new sightseeing packages that include specific events at tourist destinations. For example, it proposes a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience." The input is a list of popular spots and activities, and the output is a new sightseeing package proposal.

[1383] Step 6:

[1384] The terminal inputs or acquires tourist profile data (nationality, age, interests, etc.). The input data is collected through a questionnaire form or an existing database. The input is tourist profile data, and the output is a collection of acquired customer data.

[1385] Step 7:

[1386] The server analyzes customer data and uses machine learning libraries such as Scikit-learn to create optimal marketing strategies for each customer segment. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. The input is customer data, and the output is a marketing strategy for each segment.

[1387] Step 8:

[1388] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications. For example, an advertising campaign is run using the Facebook Ads API, and data such as ad views and click rates is monitored in real time using Google Analytics. The device displays the collected data on the management screen and measures the effectiveness of the marketing. The input is the marketing plan, and the output is the promotion effectiveness indicators.

[1389] (Application example 1)

[1390] 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."

[1391] In recent years, the number of tourists visiting tourist destinations has increased, making it important to effectively communicate the attractions of tourist destinations. However, a lack of customized information and promotions based on tourists' interests makes it difficult to maximize the attractions of tourist destinations. There is also a need for a system that can efficiently analyze the large amount of collected social media data and generate tourism content.

[1392] 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.

[1393] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for including an application installed on a smartphone, smart glasses, or head-mounted display that provides tourist destination information based on the user's interests, and means for identifying tourist attractions based on data collected from social media and suggesting related information, thereby enabling the provision of efficient and personalized tourism information and promotions to a diverse range of tourists.

[1394] "Tourists" refer to people who visit a particular tourist destination to experience the attractions of that area.

[1395] "Social media" refers to online platforms that enable users to share information and interact via the internet.

[1396] "Video and images" refers to media formats for transmitting visual information, and refers to files containing visually recorded information.

[1397] "Means of collection" refers to methods or technologies for collecting data on a device such as a server based on specific conditions.

[1398] "Preprocessing means" refers to the initial processing performed to convert data into a format that is easier to analyze.

[1399] An "artificial intelligence model" refers to a program that uses technologies such as machine learning and deep learning to analyze data and extract patterns and features.

[1400] "Regional attractions" refer to the points and characteristics that make a particular region interesting and valuable to tourists.

[1401] "Analytical means" refers to techniques and methods for processing data to reveal specific information or patterns.

[1402] "Tourism content" refers to data provided to tourists, including information on tourist destinations, experience plans, events, etc.

[1403] "Means of generation" refers to methods and technologies for creating new data, information, or content based on specific conditions.

[1404] "Customer data" refers to information such as tourists' profile information, interests, and behavioral history.

[1405] A "marketing strategy" refers to a plan or method for effectively promoting a product to a specific target audience.

[1406] "Customized promotion" refers to advertising and marketing techniques that are specifically tailored to an individual customer's interests.

[1407] A "smartphone" refers to a mobile information terminal that has communication functions and can connect to the Internet and use applications.

[1408] "Smart glasses" refers to a wearable device that adds information processing and communication functions to conventional glasses.

[1409] A "head-mounted display" refers to a display device that is worn on the user's head and displays information directly into the user's field of vision.

[1410] "Application" refers to a software program designed to accomplish a specific purpose.

[1411] "Tourist destination information" refers to detailed information and guide content about specific tourist destinations.

[1412] "Means of identification" refers to techniques or methods for recognizing and extracting specific features or information from data.

[1413] This invention is a system that collects videos and images posted on social media by tourists, analyzes them to discover the attractions of a region, and enables the provision of customized information and marketing based on the user's nationality and interests.

[1414] Hardware and software used

[1415] Hardware:

[1416] Servers with high-performance CPU / GPUs, smartphones, smart glasses, or head-mounted displays.

[1417] software:

[1418] Python, Flask (web framework), Google Cloud Vision API, OpenCV (image analysis library).

[1419] System Operation

[1420] The server uses social media APIs to collect videos and images posted by tourists based on specific hashtags and location information, such as posts related to "JapanTravel" or specific regions.

[1421] The collected video and image data is preprocessed by the server and converted into a format that is easy to analyze, and unnecessary metadata is removed and important metadata such as timestamps, location information, and hashtags are organized.

[1422] The preprocessed data is then fed into an artificial intelligence model to analyze the area's attractiveness. Image analysis techniques, such as Google Cloud Vision API and OpenCV, are used to identify landmarks and tourist attractions within the images. From these identified attractions and activities, the model then creates a list of particularly popular places and activities.

[1423] The server then plans new sightseeing courses and events based on the listed attractions. It references user profile data (such as nationality, age, and interests) to propose optimal sightseeing plans for each user. Applications installed on smartphones, smart glasses, and head-mounted displays display real-time information about tourist spots based on the user's interests, improving the sightseeing experience.

[1424] Specific examples

[1425] As a specific example of its use, the system collects Instagram images posted by tourists using the hashtag "KyotoTravel." The server analyzes the images and identifies "Kiyomizu-dera Temple" and "Kinkaku-ji Temple." The server then suggests a "Kinkaku-ji and Kiyomizu-dera Temple tour" to American tourists. These suggestions are provided via the user's smartphone, smart glasses, or head-mounted display.

[1426] Prompt Sentence Examples

[1427] Social media post collection example:

[1428] Region: Kyoto

[1429] Hashtag: KyotoTravel

[1430] Analysis target: Images and videos

[1431] Analysis goal: Identifying tourist attractions (e.g., Kiyomizu-dera Temple, Kinkaku-ji Temple)

[1432] Tourist attraction identification and suggestion example:

[1433] Customer Profile: American Tourists

[1434] Proposal: Tour of Kinkakuji Temple and Kiyomizudera Temple

[1435] By using this system, tourism businesses can provide efficient and personalized tourism information and promotions to a wide range of tourists.

[1436] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1437] Step 1:

[1438] The server uses social media APIs to collect videos and images posted by tourists. During collection, data is collected based on specific hashtags (e.g., "JapanTravel") and location information. The input to this collection process is hashtags and location information, and the output is the corresponding video and image files.

[1439] Step 2:

[1440] The server preprocesses the collected video and image data. Specifically, it converts the video and images into a format that is easy to analyze and removes unnecessary metadata. The input to the preprocessing is the collected video and image data, and the output is the preprocessed video and image data.

[1441] Step 3:

[1442] The server inputs the preprocessed data into an artificial intelligence model to analyze the area's attractions, using the Google Cloud Vision API and OpenCV to identify landmarks in the images. The input for the analysis is the preprocessed video and image data, and the output is data on identified landmarks and tourist attractions.

[1443] Step 4:

[1444] The server generates tourism content based on the analysis results. Based on the identified tourist attractions and activities, it lists popular places and activities and plans sightseeing courses and events. The input for generation is the analysis result data, and the output is newly generated tourism content and proposed plans.

[1445] Step 5:

[1446] The server analyzes the user's profile data (nationality, age, interests, etc.) and customizes the optimal sightseeing plan for each target. The input of this process is the user's profile data, and the output is a customized sightseeing plan.

[1447] Step 6:

[1448] The terminal (smartphone, smart glasses, head-mounted display) provides the generated tourism content and customized tourism plans to the user. The input of this provision process is the customized tourism plan, and the output is the tourism information displayed to the user.

[1449] Step 7:

[1450] The server collects user feedback and behavioral data to improve the tour plan in real time. The input of this feedback collection process is user behavioral data, and the output is a further optimized tour plan.

[1451] Through these steps, tourism businesses can provide efficient and personalized tourism information and promotions to individual tourists.

[1452] 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.

[1453] This invention is a system that collects videos and images posted by tourists on social media, analyzes them to discover the attractions of a region, and implements marketing strategies based on the customer's nationality and interests. It incorporates an emotion engine to recognize user emotions and customizes tourism content and marketing strategies based on these. Specific embodiments and program processing for implementing this system are described below.

[1454] 1. Collecting social media data

[1455] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1456] 2. Data Preprocessing

[1457] The server converts the collected videos and images into an easily parseable format and removes unnecessary metadata, including cleaning up important metadata like timestamps, location information, and hashtags.

[1458] 3. Analysis of regional attractiveness

[1459] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies tourist spots such as "Mount Fuji" and "Kiyomizu-dera Temple" in the images.

[1460] 4. Extracting the charm

[1461] The server evaluates the popularity of the results of the analysis and lists the places and activities that are especially popular with tourists, such as "climbing Mount Fuji" and "wearing a kimono in Kyoto."

[1462] 5. Tourism content proposals

[1463] The server will then plan new sightseeing courses and events based on the list of attractions. Specific examples include a "Mount Fuji Sunrise Tour" and a "Kyoto Traditional Cultural Experience."

[1464] 6. Analysis of Customer Data

[1465] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example from a tourist survey or an existing database.

[1466] 7. Applying the Emotion Engine

[1467] The server analyzes the user's posts and profile information and uses an emotion engine to recognize the user's current emotional state and long-term emotional patterns. For example, it analyzes emotions based on images of the user looking "happy."

[1468] 8. Targeted Marketing

[1469] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. For example, it might promote a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[1470] 9. Customized Content Generation

[1471] The server customizes sightseeing content based on the user's emotional state identified by the emotion engine. For example, it suggests a "hot spring tour" to a user who wants to relax, and a "hiking tour" to a user who wants to be active.

[1472] 10. Marketing Execution

[1473] The marketing plan generated by the server is implemented using digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[1474] Specific examples

[1475] 1. The server collects posts with the hashtag "JapanTravel" from Instagram.

[1476] 2. The server uses image analysis AI to detect important landmarks and activities in each post, for example, identifying "Mount Fuji" or "Sensoji Temple."

[1477] 3. The server analyzes them and determines that climbing Mount Fuji and experiencing Kyoto are popular activities.

[1478] 4. The server plans a new tourist package: "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[1479] 5. The device references a tourist database and analyzes the tourism trends of Americans and Japanese people.

[1480] 6. The server distributes social media ads for Mt. Fuji climbing tours to Americans and Kyoto cultural experiences to Japanese people.

[1481] 7. The server applies an emotion engine to recognize emotions such as "happiness" or "excitement" from the user's posts.

[1482] 8. The server promotes the "Hot Spring Relaxation Plan" to users seeking relaxation and the "Outdoor Adventure Tour" to users seeking an active experience.

[1483] In this way, tourism operators can effectively attract tourists and revitalize the local economy.

[1484] The processing flow will be explained below.

[1485] Step 1:

[1486] The server collects videos and images of tourists based on specific hashtags and location information via social media APIs, for example, collecting posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1487] Step 2:

[1488] The server converts the collected videos and images into a format that is easy to analyze. For example, convert videos into MP4 format and images into JPEG format.

[1489] Step 3:

[1490] The server removes unnecessary metadata and compiles necessary metadata such as timestamps, location information, and hashtags.

[1491] Step 4:

[1492] The server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from video and image data. For example, it identifies landmarks such as "Mount Fuji" and "Kiyomizu-dera Temple."

[1493] Step 5:

[1494] The server calculates the frequency of occurrence of features and evaluates the popularity of each feature, for example, counting how many times "Mount Fuji" appears.

[1495] Step 6:

[1496] The server uses the analysis results to create a list of popular places and activities, such as "climbing Mount Fuji" or "wearing a kimono in Kyoto."

[1497] Step 7:

[1498] The server generates new sightseeing courses and events based on the listed attractions, such as a "Mt. Fuji Sunrise Tour" or a "Kyoto Traditional Cultural Experience."

[1499] Step 8:

[1500] The device inputs or retrieves tourist profile data (nationality, age, interests, etc.), for example by collecting information from a questionnaire or an existing database.

[1501] Step 9:

[1502] The server analyzes customer data and identifies each customer's interests and behavioral patterns. For example, it finds a pattern that "American tourists are interested in nature tourism."

[1503] Step 10:

[1504] The server uses an emotion engine to recognize emotions from user posts, for example, determining "happiness" or "surprise" from the post content and facial expressions.

[1505] Step 11:

[1506] The server customizes sightseeing content based on the user's current emotional state, suggesting, for example, a hot spring tour for a user who wants to relax, or a hiking tour for a user who wants to stay active.

[1507] Step 12:

[1508] The server analyzes the long-term emotional patterns identified by the emotion engine and creates marketing strategies based on them, such as "market relaxation plans to users who have posted frequently about wanting to relax over the past year."

[1509] Step 13:

[1510] The marketing plan generated by the server is implemented using digital methods such as social media advertising, email campaigns, and dedicated app notifications.

[1511] Step 14:

[1512] The device monitors data such as ad views and click rates in real time to measure the effectiveness of promotions. For example, it analyzes "advertisements with high click rates" and "events with increased booking rates."

[1513] Example 2

[1514] 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."

[1515] In recent years, the spread of social media has led to an increase in postings of information about tourist destinations and events. However, there is a lack of systems that can effectively collect and analyze this information and link it to appropriate tourism content and marketing strategies. Furthermore, there is no system that can grasp tourists' emotional state and propose customized promotions that respond to that. There is a need to solve these issues.

[1516] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1517] In this invention, the server includes means for collecting videos and images posted on social media by tourists, means for preprocessing the collected videos and images, means for applying a machine learning model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for applying a sentiment analysis engine to analyze customer sentiment data, and means for implementing customized promotions based on the generated tourism content and marketing strategy. This makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that meet the needs and sentiments of tourists.

[1518] "Social media" is a platform that allows users to share content and communicate with each other over the Internet.

[1519] "Means for collecting videos and images" refers to the function for obtaining video and image data from social media platforms.

[1520] "Preprocessing" refers to the process of converting collected video and image data into a format suitable for analysis and removing unnecessary metadata.

[1521] A "machine learning model" is an algorithm that learns specific patterns and characteristics from data and makes predictions and classifications on newly acquired data.

[1522] "Tourism content" refers to the collection of information and services related to tourist destinations and activities that are provided to tourists.

[1523] "Customer Data" means information about a tourist, including personal profile information such as nationality, age, and interests, as well as historical behavioral data.

[1524] A "marketing strategy" is a set of plans and methods for promoting and selling products and services in the most optimal way to a particular customer segment.

[1525] An "emotion analysis engine" is an algorithm or software that recognizes and analyzes a user's emotional state from text or images.

[1526] "Customized promotion" refers to specific advertising and promotional activities that are applied based on the needs, interests, and emotional state of individual customers.

[1527] "System" refers to a set of machines, software, databases, etc., whose components work together to achieve a specific purpose.

[1528] This invention is a system that collects and analyzes videos and images posted by tourists on social media, discovers the attractions of a region based on the collected data, and plans and implements marketing strategies tailored to the nationality, interests, and emotions of customers. This system uses the following hardware and software:

[1529] Hardware and software used

[1530] The server is a computer with a powerful processor and sufficient memory, such as an Intel Xeon processor. It also uses APIs to collect data from social media. For example, libraries such as "Tweepy" (for Twitter) and "Instagram API" (for Instagram) are used.

[1531] Python's "OpenCV" and "Pandas" are used for data preprocessing, which enables format conversion of image and video data and organization of metadata.

[1532] For data analysis, machine learning libraries such as "TensorFlow" and "PyTorch" are used to extract the characteristics of tourist spots from the collected videos and images. Suitable machine learning models include "YOLO (You Only Look Once)" and "ResNet," which are widely used in image recognition.

[1533] To input and acquire customer data, data is collected from the user's device (smartphone or PC) via a questionnaire form or an existing database. This data is then sent to the server.

[1534] The emotion engine uses an emotion analysis API (for example, Microsoft Azure Emotion Analysis API), which allows it to analyze user emotions from posted content and profile data.

[1535] Specific examples

[1536] Specifically, the system collects images and videos posted by tourists using the "JapanTravel" hashtag from social media. The data collected by the server using "Tweepy" and the "Instagram API" is preprocessed using "OpenCV" and "Pandas." For example, unnecessary metadata is removed and only the necessary information is saved.

[1537] The preprocessed data is then analyzed using TensorFlow and PyTorch to identify tourist attractions and activities within the images. For example, Mount Fuji and Sensoji Temple are identified. Based on the analysis results, popular activities such as climbing Mount Fuji and wearing a kimono in Kyoto are listed.

[1538] Furthermore, user profile data (nationality, age, interests, etc.) is collected via questionnaire forms and databases. For example, tourist survey data is sent to a server and stored.

[1539] The emotion analysis engine uses the Microsoft Azure Emotion Analysis API to recognize emotions such as "happiness" or "excitement" from user posts. For example, it analyzes emotions from photos of smiling faces and comments such as "That was fun!"

[1540] Finally, the server generates tourism content and marketing strategies based on the analysis data. For example, it might suggest a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists. These marketing plans are implemented through social media ads, email campaigns, and dedicated app notifications.

[1541] Prompt Sentence Examples

[1542] Example prompt for collecting the "JapanTravel" tag:

[1543] "Use the Twitter API to collect tweets with the hashtag 'JapanTravel' from the past week."

[1544] Example prompts for image analysis:

[1545] "Please identify landmarks such as 'Mount Fuji' and 'Kiyomizu-dera Temple' from the collected images."

[1546] This system makes it possible to effectively utilize a variety of data and provide optimal tourism content and marketing strategies that respond to tourists' needs and emotions.

[1547] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1548] Step 1:

[1549] Social media data collection

[1550] The server uses social media APIs to collect videos and images of tourists based on specific hashtags (e.g., "JapanTravel") and location information.

[1551] Input: Collection conditions (hashtags and location information), API key.

[1552] Processing: The server sends a query to the API to retrieve posts that match the criteria. For example, it uses the Tweepy library to search for posts from the past week with a given hashtag.

[1553] Output: Collected video and image data and associated metadata.

[1554] Step 2:

[1555] Data Preprocessing

[1556] The server converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata.

[1557] Input: Collected video and image data and associated metadata.

[1558] Processing: The server uses OpenCV to unify the image formats and Pandas to format metadata such as timestamps and GPS data. For example, it converts all images to JPEG format and saves the metadata in a CSV file.

[1559] Output: Preprocessed video and image data and formatted metadata.

[1560] Step 3:

[1561] Analysis of regional attractiveness

[1562] The server applies machine learning models to analyze the appeal of a region from preprocessed video and image data.

[1563] Input: Preprocessed video and image data and formatted metadata.

[1564] Processing: The server uses TensorFlow to analyze the data with trained models to identify landmarks and activities, for example, using the YOLO model to detect "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[1565] Output: Parsed data (list of landmarks and activities).

[1566] Step 4:

[1567] Extracting Charm

[1568] The server evaluates the popularity of the site based on the analysis results and lists the places and activities that are particularly popular with tourists.

[1569] Input: Parsed data (list of landmarks and activities).

[1570] Processing: The server aggregates the analysis results and ranks them by popularity, for example, placing landmarks that appear frequently at the top of the list.

[1571] Output: A ranked list of popular places and activities.

[1572] Step 5:

[1573] Tourism content proposals

[1574] The server will plan new tourist courses and events based on popular attractions.

[1575] Input: A ranked list of popular places and activities.

[1576] Processing: The server automatically generates a new sightseeing plan and saves it in the database. For example, it plans a "Mt. Fuji Sunrise Tour + Hot Spring Relaxation Plan."

[1577] Output: Details of the proposed itinerary and event (schedule, prices, contact details, etc.).

[1578] Step 6:

[1579] Customer data analysis

[1580] The terminal inputs or retrieves tourist profile data (nationality, age, interests, etc.).

[1581] Input: Tourist survey data or existing database.

[1582] Processing: The device collects data via an input form or API and sends it to a server. For example, data entered into a questionnaire form is sent to a database in real time.

[1583] Output: Collected profile data.

[1584] Step 7:

[1585] Applying the Emotion Engine

[1586] The server analyzes users' posts and profiles and uses an emotion analysis API to recognize their current emotional state and long-term emotional patterns.

[1587] Input: User posts and profile data.

[1588] Processing: The server sends the data to an emotion analysis API to obtain the emotion status. For example, it uses the Microsoft Azure emotion analysis API to analyze "happy" or "excited."

[1589] Output: Parsed emotion status.

[1590] Step 8:

[1591] Targeted Marketing

[1592] The server creates optimal marketing strategies for each customer segment based on customer data and sentiment data.

[1593] Input: Customer data and sentiment status.

[1594] Processing: The server generates a marketing plan for each segment and generates data for social media advertising settings. For example, it proposes a "Mount Fuji climbing tour" for Americans and a "Kyoto night illumination tour" for Japanese people.

[1595] Output: Marketing plan by segment.

[1596] Step 9:

[1597] Customized content generation

[1598] The server customizes the sightseeing content based on the emotional state of the user identified by the emotion engine.

[1599] Input: Emotional status and user profile data.

[1600] Processing: The server generates optimal content based on the user's emotional state and stores it in a database. For example, it suggests a "hot spring relaxation plan" to a user who wants to relax, and an "outdoor adventure tour" to a user who wants to be active.

[1601] Output: Customized tourism content.

[1602] Step 10:

[1603] Marketing Execution

[1604] The server then implements the generated marketing plan through digital methods such as social media ads, email campaigns, and dedicated app notifications. The device monitors data such as ad views and click rates in real time to measure the effectiveness of the promotion.

[1605] Input: Generated marketing plan.

[1606] Processing: The server uses social media advertising APIs and email sending tools to send each advertisement and notification in bulk. The device collects advertisement interaction data and sends feedback to the server.

[1607] Output: Data measuring the effectiveness of the advertising campaigns carried out.

[1608] (Application example 2)

[1609] 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."

[1610] In the tourism and related industries, it is important to effectively discover the attractions of a region based on tourists' social media posts and provide them as tourism content. However, it is difficult to effectively analyze collected data, generate customized content based on customers' nationalities, interests, and even emotions, and develop optimal marketing strategies. In particular, there is a lack of marketing methods specifically tailored to the food culture of tourist destinations, and there is a need to propose meal plans tailored to each individual tourist.

[1611] 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.

[1612] In this invention, the server includes means for collecting social media videos and images posted by tourists, means for preprocessing the collected videos and images, means for applying an artificial intelligence model to analyze the attractions of a region from the preprocessed videos and images, means for generating tourism content based on the analysis results, means for analyzing customer data and formulating a marketing strategy based on nationality and interests, means for implementing customized promotions based on the generated tourism content and marketing strategy, means for using an emotion engine that recognizes emotions from content posted by tourists and customizing the tourism content based on emotion data, means for collecting social media posts based on specific hashtags and location information using a social media API, and means for adjusting the marketing strategy based on the identified emotions, thereby enabling the provision of customized tour and meal plans that are optimal for each tourist, thereby realizing efficient marketing and promotion in the tourism industry.

[1613] "Tourist" refers to a person who visits for travel or sightseeing.

[1614] "Social media" refers to online platforms for sharing information and engaging in two-way communication over the Internet.

[1615] "Video" refers to a media format that visually conveys movement by displaying a sequence of images.

[1616] "Image" refers to still visual data, including photographs and illustrations.

[1617] "Means of collection" refers to the methods and techniques used to collect the desired data.

[1618] "Preprocessing means" refers to methods and techniques for organizing and transforming data to make it easier to analyze and use.

[1619] An "artificial intelligence model" refers to an algorithm or learning system that analyzes data and performs specific tasks.

[1620] "Regional attractions" refer to the attractive points of a particular region, such as tourist resources, culture, and scenery.

[1621] "Means of analysis" refers to methods and techniques for understanding and interpreting data.

[1622] "Tourism content" refers to the information, experiences, services, etc. provided to tourists.

[1623] "Customer data" refers to information about tourists, including personal profiles and behavioral history.

[1624] "Nationality" refers to the country or region to which an individual belongs.

[1625] "Interests" refers to the interest or concern that an individual has in a particular event or activity.

[1626] "Marketing strategy" refers to the set of commercial activities that includes market analysis, planning, and tactics to achieve specific goals.

[1627] "Customized promotion" refers to advertising and promotional activities that are tailored to the characteristics and preferences of individual customers.

[1628] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from data such as text and images.

[1629] "Emotion" refers to a person's feelings or psychological state.

[1630] "SNS API" refers to the application programming interface provided by a social media platform, which is a means of retrieving and manipulating data from external systems.

[1631] A "hashtag" is a symbol placed before a keyword or phrase used on social media to easily search for and categorize a particular topic.

[1632] "Location information" refers to data that indicates a specific point or location.

[1633] This invention is a system that collects videos and images posted by tourists on social media, analyzes the attractions of a region, and implements a marketing strategy based on customer nationality, interests, and emotional data. This system is implemented using the following means.

[1634] First, the server uses SNS APIs to collect videos and images based on specific hashtags and location information on social media, such as posts related to "JapanTravel" or specific regions (Kyoto, around Mount Fuji).

[1635] The server then converts the collected videos and images into a format that is easy to analyze and removes unnecessary metadata, a process that includes cleaning up important metadata like timestamps, location information, and hashtags.

[1636] After the data has been converted, the server applies artificial intelligence models to extract features such as scenery, landmarks, and activities from the video and image data. For example, it can identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[1637] The server then evaluates the popularity of the results and lists the most popular places and activities for tourists, generating tourism content such as "Climbing Mount Fuji" or "Kimono experience in Kyoto."

[1638] The server then analyzes customer data and develops optimal marketing strategies based on the customer's nationality, age, and interests, referencing tourist survey data and existing databases.

[1639] For emotional data analysis, the server analyzes the user's posted content and profile and uses an emotion engine to recognize the user's emotional state. For example, emotions can be analyzed from an image of the user looking "happy" or a sentence expressing "happiness."

[1640] Based on these analysis results, the server will carry out customized promotions, such as suggesting "hot spring tours" to users seeking relaxation and "hiking tours" to users seeking more active experiences.

[1641] Specific hardware and software for this system include:

[1642] Server: Collects and analyzes data (e.g. AWS EC2).

[1643] API: We collect data using the Instagram API.

[1644] Image analysis model: Analyzes image data using TensorFlow and ResNet50.

[1645] Text analysis: Analyze the text of social media posts using TextBlob.

[1646] Specific examples

[1647] For example, suppose posts with the hashtag "TokyoFood" are collected and many images of "sushi" are found among the posts. Based on the analysis results, a "food tour centered on sushi" can be suggested. Furthermore, if emotions such as "satisfaction" and "excitement" are frequently found in user posts, the promotion can be customized based on these emotions.

[1648] Prompt Sentence Examples

[1649] Inexpensive image URL list and post text example

[1650] image_urls = ["https: / / example.com / image1.jpg", "https: / / example.com / image2.jpg"]

[1651] example_text = "This sushi is amazing!"

[1652] API call prompt

[1653] fetch_sns_data("TokyoFood")

[1654] Image Analysis Prompts

[1655] analyze_images(image_urls)

[1656] Sentiment Analysis Prompts

[1657] analyze_sentiment(example_text)

[1658] In this way, tourism operators can provide attractive tourism content tailored to individual needs and develop effective marketing strategies.

[1659] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1660] Step 1:

[1661] The server uses the SNS API to collect videos and images based on specific hashtags and location information. As input, the hashtag and location information are specified and an API request is sent. As output, the posted video and image data is obtained. Specifically, the server accesses the Instagram API and collects posts related to the specified hashtag "JapanTravel" and location information (e.g., Kyoto, around Mt. Fuji).

[1662] Step 2:

[1663] The server converts the collected video and images into a format that is easy to analyze, and extracts and organizes important metadata. The input is the collected video and image data and its metadata. The output is data converted into a format suitable for analysis. Specifically, the server extracts important metadata such as timestamps, location information, and hashtags, and removes unnecessary information.

[1664] Step 3:

[1665] The server applies an artificial intelligence model to extract features such as scenery, landmarks, and activities from the format-converted video and image data. The preprocessed video and image data are used as input. The output is a dataset with each feature extracted. Specifically, the server uses TensorFlow and a ResNet50 model to identify tourist attractions such as "Mount Fuji" and "Kiyomizu-dera Temple" in the image.

[1666] Step 4:

[1667] The server evaluates the popularity of the analysis results and lists the places and activities that are popular with tourists. The feature-extracted dataset is used as input. As output, a list of popular spots and activities is generated. Specifically, the server aggregates the collected data and lists the places and activities that are mentioned in the most posts.

[1668] Step 5:

[1669] The server generates new tourism content based on the listed attractions. It uses a list of popular spots and activities as input. It generates data to suggest new tourism packages and events as output. Specifically, the server creates new tourism plans such as a "Mt. Fuji Sunrise Tour" or "Kyoto Traditional Cultural Experience."

[1670] Step 6:

[1671] The terminal acquires tourist data and analyzes the customer's nationality, age, interests, etc. Tourist profile data is used as input. Profile information appropriate for each tourist is obtained as output. Specifically, the terminal references tourist survey data and existing databases to acquire the necessary data.

[1672] Step 7:

[1673] The server analyzes the user's posts and profile and uses an emotion engine to recognize the user's emotional state. The user's posts and profile data are used as input. The output is the analysis result of the emotional state. Specifically, the server uses an emotion analysis tool such as TextBlob to identify emotions such as "happy" or "fun" from the text and images of the posts.

[1674] Step 8:

[1675] The server creates the optimal marketing strategy for each customer segment based on customer data and emotional data. It uses the analyzed customer data and emotional data as input. It generates a customized marketing plan as output. Specifically, the server promotes a "Mount Fuji climbing tour" to American tourists and a "Kyoto night illumination tour" to Japanese tourists.

[1676] Step 9:

[1677] The server customizes sightseeing content based on emotional data. The input is the result of analyzing the emotional state. The output is to generate sightseeing content customized to the user's emotions. Specifically, the server suggests a "hot spring tour" to a user seeking relaxation, and a "hiking tour" to a user seeking to be more active.

[1678] Step 10:

[1679] The server and device implement the generated marketing plan and monitor its effectiveness. The customized marketing plan is used as input. Effectiveness measurement data such as ad views and click rates is obtained as output. Specifically, the server implements promotions using digital methods such as social media ads, email campaigns, and dedicated app notifications, and the device monitors the data in real time.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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).

[1687] 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.

[1688] 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."

[1689] 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.

[1690] 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).

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] The following is further disclosed regarding the above embodiment.

[1702] (Claim 1)

[1703] A means of collecting videos and images posted by tourists on social media;

[1704] A means of pre-processing the collected video and images;

[1705] A means for applying an artificial intelligence model to analyze the attractiveness of a region from preprocessed videos and images;

[1706] A means for generating tourism content based on the analysis results;

[1707] A means of analyzing customer data and developing marketing strategies based on nationality and interests,

[1708] The system includes a means for implementing customized promotions based on the generated tourism content and marketing strategies.

[1709] (Claim 2)

[1710] The system according to claim 1, which converts the format of video and image data collected from social media and organizes metadata.

[1711] (Claim 3)

[1712] The system according to claim 1, wherein an artificial intelligence model is used to extract features of scenery, landmarks, and activities from video and image data, and extracts local attractions based on the extracted features.

[1713] "Example 1"

[1714] (Claim 1)

[1715] A means of collecting videos and images posted by tourists on social media;

[1716] A means of pre-processing the collected video and images;

[1717] means for applying an artificial intelligence model to analyze regional features from the preprocessed video / images;

[1718] A means for generating tourism content based on the analysis results;

[1719] A means of analyzing customer data and formulating marketing strategies based on attributes and preferences,

[1720] A means of implementing customized promotions based on the generated tourism content and marketing strategies;

[1721] A system including a means for monitoring the effectiveness of customized promotions.

[1722] (Claim 2)

[1723] The system according to claim 1, which converts the format of video and image data collected from social media and organizes metadata.

[1724] (Claim 3)

[1725] The system according to claim 1, wherein an artificial intelligence model is used to extract features of natural landscapes, famous places, and activities from video and image data, and regional characteristics are extracted based on the extracted features.

[1726] "Application Example 1"

[1727] (Claim 1)

[1728] A means of collecting videos and images posted by tourists on social media;

[1729] A means of pre-processing the collected video and images;

[1730] A means for applying an artificial intelligence model to analyze the attractiveness of a region from preprocessed videos and images;

[1731] A means for generating tourism content based on the analysis results;

[1732] A means of analyzing customer data and developing marketing strategies based on nationality and interests,

[1733] A means of implementing customized promotions based on the generated tourism content and marketing strategies;

[1734] A means including an application installed on a smartphone, smart glasses, or head-mounted display, which provides tourist destination information based on the user's interests;

[1735] A means of identifying tourist attractions and suggesting relevant information based on data collected from social media

[1736] A system including:

[1737] (Claim 2)

[1738] The system according to claim 1, which converts the format of video and image data collected from social media and organizes metadata.

[1739] (Claim 3)

[1740] The system according to claim 1, wherein an artificial intelligence model is used to extract features of scenery, landmarks, and activities from video and image data, and extracts local attractions based on the extracted features.

[1741] "Example 2: Combining Emotion Engines"

[1742] (Claim 1)

[1743] A means of collecting videos and images posted by tourists on social media;

[1744] A means of pre-processing the collected video and images;

[1745] A means of applying machine learning models to analyze local attractiveness from preprocessed videos and images;

[1746] A means for generating tourism content based on the analysis results;

[1747] A means of analyzing customer data and developing marketing strategies based on nationality and interests,

[1748] means for applying a sentiment analysis engine to analyze customer sentiment data;

[1749] The system includes a means for implementing customized promotions based on the generated tourism content and marketing strategies.

[1750] (Claim 2)

[1751] The system according to claim 1, which converts the format of video and image data collected from social media and organizes metadata.

[1752] (Claim 3)

[1753] The system according to claim 1, wherein a machine learning model is used to extract features of scenery, landmarks, and activities from video and image data, and extracts local attractions based on the extracted features.

[1754] "Application example 2 when combining emotion engines"

[1755] (Claim 1)

[1756] A means of collecting videos and images posted by tourists on social media;

[1757] A means of pre-processing the collected video and images;

[1758] A means for applying an artificial intelligence model to analyze the attractiveness of a region from preprocessed videos and images;

[1759] A means for generating tourism content based on the analysis results;

[1760] A means of analyzing customer data and developing marketing strategies based on nationality and interests,

[1761] A means of implementing customized promotions based on the generated tourism content and marketing strategies;

[1762] a means for customizing tourism content based on emotion data using an emotion engine that recognizes emotions from tourist posts;

[1763] A system that includes a means for using a social media API to aggregate social media posts based on specific hashtags or location information, and a means for tailoring marketing strategies based on identified sentiment.

[1764] (Claim 2)

[1765] The system according to claim 1, which converts the format of video and image data collected from social media and organizes metadata.

[1766] (Claim 3)

[1767] The system according to claim 1, wherein an artificial intelligence model is used to extract features of scenery, landmarks, and activities from video and image data, and extracts local attractions based on the extracted features. [Explanation of symbols]

[1768] 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. A means of collecting videos and images posted by tourists on social media; A means of preprocessing the collected videos and images; A means of applying artificial intelligence models to analyze the attractiveness of a region from preprocessed videos and images; A means for generating tourism content based on the analysis results; A means of analyzing customer data and developing marketing strategies based on nationality and interests, The system includes a means for implementing customized promotions based on the generated tourism content and marketing strategies.

2. 2. The system according to claim 1, wherein the system converts the format of video and image data collected from social media and organizes metadata.

3. The system according to claim 1, wherein an artificial intelligence model is used to extract features of scenery, landmarks, and activities from video and image data, and the attractiveness of a region is extracted based on the extracted features.

Images