system

Abstract

Provide a system. The solution includes: a means for receiving travel experience stories written by travelers and storing them in a database; A method for analyzing saved travel stories using natural language processing to extract key topics and keywords; A means of recommending relevant content to other travelers based on the extracted topics and keywords; and A means to track traveler content viewing, liking, and sharing actions; a means for calculating rewards and crediting reward points to an account based on actions taken by a traveler; A system that recommends products and services from travel agencies and related businesses, calculates commissions when a purchase is made, and includes a means of returning the commission as a reward.

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] Travel stories and blog posts written by individual travelers often have a limited scope of influence, with few opportunities for them to be shared widely. Furthermore, there is a lack of mechanisms for them to have concrete value or impact on the travel industry as a whole. As a result, there are few opportunities for travel stories to stimulate travel demand among other people and contribute to the revitalization of the industry as a whole. Furthermore, there is no mechanism for rewarding contributors themselves, so there is a lack of incentive for content providers. A solution to these problems is needed. [Means for solving the problem]

[0005] The present invention provides a means for receiving travel stories written by travelers and storing them in a database, a means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, and a means for recommending content related to other travelers based on the extracted topics and keywords. The system also includes a means for tracking travelers' actions of viewing, liking, and sharing content, a means for calculating rewards based on the travelers' actions and adding reward points to their accounts, and a means for recommending products and services from travel agencies and related businesses, calculating commissions when a purchase is made, and returning the commissions. This system allows travelers' travel stories to spark the interest of other travelers and contribute to the revitalization of the entire travel industry. Furthermore, by providing appropriate rewards to content contributors, the system encourages the provision of high-quality content.

[0006] A "traveler" is someone who shares their travel experiences through travelogues and blog posts.

[0007] A "travel journal" refers to a document in which a traveler records detailed experiences and impressions about their travels.

[0008] "Database" refers to an electronic information system for systematically storing and managing received travel experiences and related data.

[0009] "Natural language processing" refers to the technology of using a computer to analyze human language and extract keywords and main topics.

[0010] "Keywords" are words or short phrases that identify important topics or themes in your travel story.

[0011] "Recommendation" refers to the act of presenting relevant information or content to a specific traveler.

[0012] "Tracking" refers to the process of following and recording traveler behavior and actions.

[0013] "Reward" refers to the compensation given to a traveler for the value of the content provided by the traveler.

[0014] "Points" refers to a unit of reward within the system that has economic value and is credited to a traveler's account.

[0015] "Travel agency" means a business that provides travel-related products and services.

[0016] "Affiliated Businesses" refers to businesses or organizations other than travel agencies that offer travel-related products or services.

[0017] "Fees" refers to fees incurred for transactions when goods or services are purchased through the System.

[0018] "Demand information" refers to data collected from travelers and the market about demand for travel products.

[0019] The "system" refers to an electronic mechanism that comprehensively manages everything from receiving travel experiences to providing rewards. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is implemented as a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows.

[0042] 1. Users enter and upload their travel experiences

[0043] Users create travel stories and blog posts and log into the system from their terminals.

[0044] The terminal inserts the created content into an upload form.

[0045] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0046] The server stores the received content data in a database.

[0047] The server passes the stored data to a natural language processing (NLP) engine for analysis.

[0048] 2. Content analysis and keyword extraction

[0049] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0050] The server stores the extracted keyword information in a relational database.

[0051] The server categorizes the content based on the stored keywords.

[0052] 3. Targeting by recommendation engines

[0053] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[0054] The server generates a list for recommending related content to the user.

[0055] The next time the user logs in, the server will display content from the recommendation list.

[0056] 4. User Views and Feedback

[0057] The user will see the recommended experiences the next time they log in.

[0058] The server tracks users' browsing history and click behavior.

[0059] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[0060] 5. Compensation and Remuneration Management

[0061] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[0062] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0063] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[0064] 6. Product purchases and commissions

[0065] If the user purchases a recommended product or service, the server tracks the purchase.

[0066] The server calculates the commission and rewards at the time of the purchase.

[0067] The server returns a part of the fee to the user who posted the experience as points.

[0068] 7. Industry data analysis and feedback

[0069] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[0070] The server provides the analysis results to travel agencies and related businesses.

[0071] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0072] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0073] Specific examples

[0074] Example 1: User A posts a travel story

[0075] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on the keyword information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[0076] Example 2: User B is planning a trip

[0077] When User B is searching for his next travel destination, the server recommends a "Thailand Travel Experience." User B reads the experience and decides to travel to Thailand. He also books an elephant riding tour based on the information in the experience. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the experience, as points.

[0078] Example 3: Revitalizing the entire industry

[0079] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[0080] In this way, each process works together, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] A user writes a travel experience or blog post and logs into the system from a terminal.

[0084] Step 2:

[0085] The terminal inserts the created content into an upload form.

[0086] Step 3:

[0087] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0088] Step 4:

[0089] The server stores the received content data in a database.

[0090] Step 5:

[0091] The server passes the stored data to a natural language processing (NLP) engine.

[0092] Step 6:

[0093] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0094] Step 7:

[0095] The server stores the extracted keyword information in a relational database.

[0096] Step 8:

[0097] The server categorizes the content based on the stored keywords.

[0098] Step 9:

[0099] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[0100] Step 10:

[0101] The server generates a list for recommending related content to the user.

[0102] Step 11:

[0103] The next time the user logs in, the server will display content from the recommendation list.

[0104] Step 12:

[0105] The user browses the recommended experiences.

[0106] Step 13:

[0107] The server tracks users' browsing history and click behavior.

[0108] Step 14:

[0109] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[0110] Step 15:

[0111] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[0112] Step 16:

[0113] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0114] Step 17:

[0115] The server recommends products and services from travel agencies and related businesses to the user.

[0116] Step 18:

[0117] If the user purchases a recommended product or service, the server tracks the purchase.

[0118] Step 19:

[0119] The server calculates the commission and rewards at the time of the purchase.

[0120] Step 20:

[0121] The server returns a part of the fee to the user who posted the experience as points.

[0122] Step 21:

[0123] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[0124] Step 22:

[0125] The server provides the analysis results to travel agencies and related businesses.

[0126] Step 23:

[0127] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0128] Step 24:

[0129] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0130] Example 1

[0131] 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."

[0132] There is a need to effectively manage and analyze travel stories written by travelers and use that information to make accurate recommendations to other travelers. However, existing systems lack the accuracy of keyword extraction and content recommendation for travel stories, and are unable to fully utilize user behavior data. Furthermore, they do not efficiently calculate rewards based on user actions or provide demand information to travel agencies, resulting in a lack of systems that can contribute to revitalizing the entire travel industry.

[0133] 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.

[0134] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract major topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on the travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses and calculating commissions and returning them as rewards when a purchase is made, means for parsing content using a natural language processing engine and extracting major topics and subtopics, means for categorizing data based on this topic information, means for recording users' browsing history and click behavior to recommend new travel stories, and means for tracking product and service purchase information and calculating commissions. This enables effective management of travel stories, accurate recommendations, utilization of user behavior data, and revitalization of the entire travel industry.

[0135] A "travel journal" is a document or report written by a traveler about their personal travel experiences.

[0136] A "database" is an information system for efficiently storing and managing data.

[0137] "Natural language processing" is a technology that uses computers to understand, generate, and process human language.

[0138] "Topic" refers to the subject or theme of a travel story.

[0139] "Keywords" refer to key information or important words in a travel story.

[0140] "Content recommendation" refers to presenting appropriate information and articles based on a user's interests and history.

[0141] An "action" refers to a specific operation or reaction performed by a user, such as "liking" or "sharing."

[0142] "Tracking" means following and recording a user's actions and operations.

[0143] "Reward points" refer to incentives given to users based on their contributions and activities.

[0144] A "travel agency" is a business that plans, provides, and sells travel products to customers.

[0145] "Affiliated Businesses" means businesses or organizations that offer travel-related goods or services.

[0146] A "natural language processing engine" refers to software or algorithms for performing natural language processing.

[0147] "Parsing" is the process of analyzing text data and extracting its structure and meaning.

[0148] A "subtopic" is a secondary theme or topic related to the main topic.

[0149] "Categorization" means classifying and organizing data according to specific criteria.

[0150] "Viewing history" refers to a record of what content a user has viewed.

[0151] "Click behavior" refers to information about links and buttons that users click on on a web page.

[0152] "Fees" means fees paid for transactions or services.

[0153] A "reward" is a reward or incentive given for a particular action or result.

[0154] This invention is a system that collects, analyzes, and recommends travel stories written by travelers. A specific embodiment of the system will be described in detail.

[0155] System Overview

[0156] This system is primarily composed of three main components: user input, data analysis and recommendations by the server, and data display by the device. The system's main hardware includes the device used by the user (PC, smartphone, tablet) and the server. The software used includes natural language processing engines (e.g., "spaCy" and "BERT") and machine learning algorithms (collaborative filtering, content-based filtering).

[0157] User Input and Data Storing

[0158] Users can create travel stories on their devices (PCs or smartphones) and post them by logging in to the system. When the user clicks the "Post" button, the device sends the content data to the server via an HTTP POST request. The server receives the sent content data and stores it in a database. At this time, it checks for prohibited words and formats, and only saves the data if there are no problems.

[0159] Content analysis and keyword extraction

[0160] The server passes the stored content data to a natural language processing (NLP) engine. Specifically, it uses NLP engines such as "spaCy" and "BERT." The server's NLP engine analyzes the content and extracts key topics and keywords. For example, "Thailand travel," "elephant riding experience," and "local cuisine" are extracted. The extracted keyword information is stored in a relational database, and the content is categorized based on this.

[0161] Recommendations and targeting

[0162] The server identifies users who have topics of interest from the user database. For example, it uses algorithms (collaborative filtering, content-based filtering, etc.) to identify users who are interested in "beach trips." The server adds relevant travel stories to the recommendation list for that user, which will be displayed the next time they log in.

[0163] User behavior tracking and feedback

[0164] The next time the user logs in, the server will display the recommended travel stories. When the user views a story, the server tracks the user's browsing history and click behavior, and records the behavioral data. In addition, if the user "likes" or "shares" the story, the original poster is notified of this information.

[0165] Reward calculation and point allocation

[0166] The server calculates points and rewards based on the number of views, likes, and shares of the experience. The calculation algorithm is executed based on a specific rule (e.g., 1 point for every 100 views). The calculation result is reflected in the account of the user who posted the experience. At the same time, products and services from travel agencies and related businesses are recommended to the user.

[0167] Product purchase and commission calculation

[0168] If a user purchases a recommended product or service, the server tracks the purchase information. Specifically, it monitors the user's purchasing behavior using affiliate links and tracking pixels. When a purchase is made, the server calculates a commission and returns a portion of it to the user who posted the experience as points.

[0169] Data analysis and feedback

[0170] The server aggregates all data (browsing history, purchase history, user feedback) and analyzes it using machine learning algorithms (e.g., k-means clustering, linear regression, deep learning). The results are provided to travel agencies and related businesses, providing insights such as "travel to this region has recently become popular."

[0171] Specific examples

[0172] Example 1: User A posts a travel story

[0173] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on these keywords, the server recommends the account to users who are interested in traveling to Thailand.

[0174] Prompt Sentence Examples

[0175] For example, by providing specific prompts such as, "Please enter an account of your trip to Thailand. For example, please write in detail about your elephant riding experience, local cuisine, and tourist spots," users are guided to create a detailed and comprehensive account of their experience.

[0176] In this way, the system can effectively manage and analyze users' travel experiences and recommend them to other users as valuable information, thereby promoting interaction between travelers and contributing to the revitalization of the entire travel industry.

[0177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0178] System program processing flow

[0179] Step 1:

[0180] Input: The user creates a travel story on the device and clicks the post button.

[0181] Specific behavior:

[0182] Users create travel stories on their devices (PCs, smartphones).

[0183] The user clicks the "Submit" button.

[0184] The terminal sends the content data to the server using an HTTP POST request.

[0185] Output: Content data is sent to the server.

[0186] Step 2:

[0187] Input: Content data sent from the device.

[0188] Specific behavior:

[0189] The server receives the transmitted content data.

[0190] The data received by the server is checked for invalid words and format.

[0191] The server stores the data that passes the check in the database.

[0192] Output: Content data stored in the database.

[0193] Step 3:

[0194] Input: Content data stored in the database.

[0195] Specific behavior:

[0196] The server passes the stored content data to a natural language processing (NLP) engine, specifically, "spaCy" or "BERT" as the NLP engine.

[0197] The server's NLP engine analyzes the content and extracts key topics and keywords.

[0198] For example, keywords such as "travel to Thailand," "elephant riding experience," and "local cuisine" are extracted.

[0199] Output: Extracted keyword information.

[0200] Step 4:

[0201] Input: Extracted keyword information.

[0202] Specific behavior:

[0203] The server stores the extracted keyword information in a relational database.

[0204] The server categorizes the content based on keywords.

[0205] Output: Categorized content data.

[0206] Step 5:

[0207] Input: Categorized content data.

[0208] Specific behavior:

[0209] The server identifies users with topics of interest from an existing user database.

[0210] The algorithms used are collaborative filtering and content-based filtering.

[0211] Output: A list of related users.

[0212] Step 6:

[0213] Input: Associated user lists and categorized content data.

[0214] Specific behavior:

[0215] The server generates a list for recommending content relevant to the user.

[0216] The server displays the content from the recommendation list the next time the user logs in.

[0217] Output: Recommended content list.

[0218] Step 7:

[0219] Input: User login information and recommended content list.

[0220] Specific behavior:

[0221] The next time the user logs in, the server displays the recommended travel experiences.

[0222] Output: A display of recommended travel experiences.

[0223] Step 8:

[0224] Input: User views and acts on recommended experiences.

[0225] Specific behavior:

[0226] The server records and tracks users' browsing history and click behavior.

[0227] When a user "likes" or "shares" a story, the server records that information and notifies the original poster.

[0228] Output: Recorded user behavior data.

[0229] Step 9:

[0230] Input: User behavior data and the number of views, likes, and shares for each experience.

[0231] Specific behavior:

[0232] The server calculates points and rewards.

[0233] The calculation is based on certain rules (e.g. 1 point for every 100 views).

[0234] The server will then assign points to the account of the user who posted the experience based on the calculation results.

[0235] Output: Points awarded to the user account.

[0236] Step 10:

[0237] Input: Purchase data of recommended products and services by the user.

[0238] Specific behavior:

[0239] If the user purchases a recommended product or service, the server tracks the purchase information.

[0240] Use affiliate links and tracking pixels to monitor user purchasing behavior.

[0241] When a purchase is made, the server calculates the commission fee and returns a portion of it to the user who posted the experience as points.

[0242] Output: Redeemed points.

[0243] Step 11:

[0244] Input: All user data (browsing history, purchase history, feedback data).

[0245] Specific behavior:

[0246] The server aggregates all the data and analyzes it using machine learning algorithms.

[0247] Specifically, it uses algorithms such as k-means clustering, linear regression, and deep learning.

[0248] Output: Parsed data and generated insights.

[0249] Step 12:

[0250] Input: Parsed data and insights.

[0251] Specific behavior:

[0252] The server provides the analysis results to travel agencies and related businesses.

[0253] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0254] Output: Providing data to travel agencies and planning new tour packages and campaigns.

[0255] In this way, by clarifying the specific processing content and input / output at each step, the flow of the entire system becomes easier to understand.

[0256] (Application example 1)

[0257] 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."

[0258] Conventional travel story systems had the ability to collect travel stories written by travelers, analyze that data, and make recommendations, but they had difficulty providing appropriate advertisements tailored to the travelers' interests. Furthermore, they were unable to automatically generate and properly target advertisements based on travelers' behavior. This meant that travel story content could not be effectively utilized, making it difficult for travel agencies and advertisers to develop effective advertising strategies.

[0259] 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.

[0260] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating and returning commissions when a purchase is made, and means for automatically generating and displaying advertisements related to the user's specific interests and concerns based on the analysis results using natural language processing. This makes it possible to provide travelers with appropriate content and advertisements tailored to their interests and concerns.

[0261] A "travel journal" is a document or digital content that allows travelers to record their travel experiences and impressions and share them with others.

[0262] A "database" is an information system for efficiently storing, searching, and managing information.

[0263] "Natural language processing" refers to the technology of using computers to analyze, understand, and generate human language.

[0264] "Major topics and keywords" refer to the topics or themes that are considered particularly important in a document, as well as important words related to them.

[0265] "Content" means any information, text, images, video, audio, or other matter primarily in digital format.

[0266] "Recommendation" is the act of suggesting relevant information, products, services, etc. based on specific conditions or data.

[0267] "Tracking" means recording user behavior and actions and collecting and analyzing that data.

[0268] A "reward" is a reward or incentive given for a particular action or result.

[0269] "Advertising" is a promotional activity to introduce and promote products and services to consumers.

[0270] "Fees" are fees required to provide a particular service.

[0271] "Account" means the authentication information and associated settings and data required for a User to access and use a system or service.

[0272] "Travel agents and related businesses" refers to businesses and organizations that offer travel-related products and services.

[0273] The present invention is embodied as a system for collecting, analyzing, and recommending travel stories written by travelers. Specifically, the system includes the following means.

[0274] First, the server receives travel stories written by users and stores them in a database. Users enter their travel experiences in text format and submit them to the server through a dedicated form. The server stores the received text data in the database.

[0275] The server then analyzes the stored travel stories using a natural language processing (NLP) engine to extract key topics and keywords. The NLP engine uses generative AI models such as Hugging Face's Transformer. The analysis results are recorded in a database for further processing.

[0276] The server then recommends content related to other travelers based on the analysis results. For example, travel stories and services related to keywords like "Bali" and "surfing experience" are recommended. This allows users to easily find content that matches their interests.

[0277] Additionally, the server tracks user actions such as viewing, liking, and sharing content, allowing it to understand which content users are interested in and calculate rewards accordingly, which are credited to users' accounts as points that can be redeemed for travel-related products and services.

[0278] The server also recommends products and services from travel agencies and related businesses, and tracks the purchase information when the user purchases them. If a purchase is made, the server calculates a commission and returns a portion of it to the user as a reward, allowing the user to earn additional profits from their actions.

[0279] Furthermore, the server automatically generates and displays advertisements related to the user's specific interests based on the analysis results using natural language processing. These advertisements are customized based on the user's past behavioral data, which can lead to greater effectiveness.

[0280] For example, if User A posts a travel experience story about Bali, keywords such as "Bali," "surfing experience," and "local cuisine" are extracted. The next time User A logs in, advertisements for popular hotels and surfing lessons in Bali are recommended to User A.

[0281] Examples of prompts for generative AI models include:

[0282] "A user posted a travel story about their experience in Bali. Please extract keywords such as 'Bali,' 'surfing experience,' and 'local cuisine.'"

[0283] Something like this.

[0284] As described above, the present invention is a system that effectively utilizes traveler's experiences and appropriately recommends related content and advertisements, thereby improving user convenience.

[0285] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0286] Step 1:

[0287] The server receives travel stories written by users. Users enter their travel experiences in text format and post them by pasting the text data into a dedicated form. The server receives the posted text data and stores it in a database.

[0288] Input: Travel experiences posted by users (text data)

[0289] Output: Travel stories stored in a database

[0290] Step 2:

[0291] The server passes the saved travel stories to a natural language processing (NLP) engine for analysis. The NLP engine uses a generative AI model such as Hugging Face's Transformer. The server extracts key topics and keywords from the text data analyzed by the NLP engine.

[0292] Input: Travel stories stored in the database

[0293] Output: Extracted main topics and keywords

[0294] Step 3:

[0295] The server then recommends related content to other travelers based on the extracted topics and keywords. The server also takes into account the user's past behavioral data. Specifically, the server generates a recommendation list and saves it in association with the user's account.

[0296] Input: Extracted topics and keywords, and user behavior data

[0297] Output: Generated recommendation list

[0298] Step 4:

[0299] The server tracks users' actions when they view recommended content, specifically recording actions such as viewing, liking, and sharing, and stores this data in a database.

[0300] Input: User action data for views, likes, and shares

[0301] Output: Action data stored in the database

[0302] Step 5:

[0303] The server calculates reward points based on the user's action data and grants them to the user's account. Specifically, the server adds up the points set for each action and adds the total to the user's account.

[0304] Input: User action data

[0305] Output: Calculated reward points and their allocation

[0306] Step 6:

[0307] The server recommends products and services from travel agencies and related businesses, and calculates the commission when the user purchases them. The purchase information is linked to the user's account, and a portion of the commission is returned to the user as a reward.

[0308] Input: User purchase information and product fee information

[0309] Output: Calculated commission and reward points redeemed

[0310] Step 7:

[0311] The server generates advertisements based on the analyzed travel experience data and related to the user's interests. The advertisements are then displayed in the user's account. The content of the advertisements is determined based on keywords analyzed by the NLP engine and the user's behavioral data.

[0312] Input: Parsed keywords and user behavior data

[0313] Output: The generated ad and its display

[0314] Step 8:

[0315] The server aggregates all data and provides analysis results to travel agencies and related businesses, allowing them to plan travel products and services that are in high demand.

[0316] Input: User behavior data, purchase data, advertising data

[0317] Output: Feedback of analysis results

[0318] 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.

[0319] This invention combines an emotion engine with a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows:

[0320] 1. Users enter and upload their travel experiences

[0321] A user writes a travel experience or blog post and logs into the system from a terminal.

[0322] The terminal inserts the created content into an upload form.

[0323] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0324] The server stores the received content data in a database.

[0325] The server passes the stored data to a natural language processing (NLP) engine and then to an emotion engine.

[0326] 2. Content analysis, keyword extraction, and emotion recognition

[0327] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0328] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[0329] The server stores the extracted keywords and emotion information in a relational database.

[0330] 3. Targeting by recommendation engines

[0331] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[0332] The server generates a list of relevant content recommendations for the user, taking into account emotional information.

[0333] The next time the user logs in, the server will display content from the recommendation list.

[0334] 4. User browsing and emotional feedback

[0335] The user will see the recommended experiences the next time they log in.

[0336] The server tracks users' browsing history and click behavior.

[0337] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the action. For example, if a user expresses emotions such as "empathy" or "movement" by reading a story, this is also stored in the database.

[0338] 5. Compensation and Remuneration Management

[0339] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[0340] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0341] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[0342] 6. Product purchases and commissions

[0343] If the user purchases a recommended product or service, the server tracks the purchase.

[0344] The server calculates the commission and rewards at the time of the purchase.

[0345] The server returns a part of the fee to the user who posted the experience as points.

[0346] 7. Industry data analysis and feedback

[0347] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[0348] The server provides the analysis results to travel agencies and related businesses.

[0349] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0350] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0351] Specific examples

[0352] Example 1: User A posts a travel story

[0353] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving" using an emotion engine. Based on the keyword information and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand and who also show similar emotions.

[0354] Example 2: User B is planning a trip

[0355] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." User B reads the book and decides to travel to Thailand. He also books an elephant riding tour based on the information in the book. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the book, as points. In addition, if User B expresses emotion or empathy through emotional feedback, this is also recorded in the database.

[0356] Example 3: Revitalizing the entire industry

[0357] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry. Furthermore, data based on emotional feedback can be used to provide products that increase user satisfaction.

[0358] In this way, each process is closely linked, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[0359] The processing flow will be explained below.

[0360] Step 1:

[0361] A user writes a travel experience or blog post and logs into the system from a terminal.

[0362] Step 2:

[0363] The terminal inserts the created content into an upload form.

[0364] Step 3:

[0365] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0366] Step 4:

[0367] The server stores the received content data in a database.

[0368] Step 5:

[0369] The server passes the stored data to a natural language processing (NLP) engine and an emotion engine.

[0370] Step 6:

[0371] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0372] Step 7:

[0373] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[0374] Step 8:

[0375] The server stores the extracted keywords and emotion information in a relational database.

[0376] Step 9:

[0377] The server categorizes the content based on the stored keywords and sentiment information.

[0378] Step 10:

[0379] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[0380] Step 11:

[0381] The server generates a list of relevant content recommendations for the user, taking the extracted emotion information into consideration.

[0382] Step 12:

[0383] The next time the user logs in, the server will display content from the recommendation list.

[0384] Step 13:

[0385] The user browses the recommended experiences.

[0386] Step 14:

[0387] The server tracks users' browsing history and click behavior.

[0388] Step 15:

[0389] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the user's actions. For example, emotions such as "empathy" and "emotion" are recognized.

[0390] Step 16:

[0391] The server also stores the emotional feedback information in a database.

[0392] Step 17:

[0393] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[0394] Step 18:

[0395] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0396] Step 19:

[0397] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[0398] Step 20:

[0399] If the user purchases a recommended product or service, the server tracks the purchase.

[0400] Step 21:

[0401] The server calculates the commission and rewards at the time of the purchase.

[0402] Step 22:

[0403] The server returns a part of the fee to the user who posted the experience as points.

[0404] Step 23:

[0405] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[0406] Step 24:

[0407] The server provides the analysis results to travel agencies and related businesses.

[0408] Step 25:

[0409] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0410] Step 26:

[0411] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0412] Example 2

[0413] 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."

[0414] Conventional systems have difficulty effectively analyzing travel stories written by travelers and providing useful information to other travelers and travel agencies. Furthermore, rewards and product recommendations based on travelers' experiences are insufficient, resulting in a lack of systems that contribute to revitalizing the travel industry as a whole. Furthermore, there is a lack of recommendation and reward systems that utilize users' emotional feedback, so an improvement in user experience is needed.

[0415] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving travel stories written by travelers and saving them in a database, means for analyzing the saved travel stories using a natural language processing engine to extract main topics and keywords, means for analyzing the saved travel stories using a sentiment analysis engine to extract emotional information, means for identifying relevant target travelers and recommending content based on the extracted keywords and emotional information, means for tracking travelers' actions such as viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating commissions and returning them as rewards when a purchase is made, and means for aggregating and analyzing all data and providing demand information to travel agencies. This allows for efficient analysis and recommendation of travel stories, improving reward systems and product recommendations for travelers, and revitalizing the entire travel industry.

[0416] A "travel journal" is a record of a traveler's travel experiences, expressed in writing and images.

[0417] A "database" is a system for efficiently storing, managing, and searching information and data.

[0418] A "natural language processing engine" is a technology that analyzes text data written in natural language and understands and extracts meaning and structure.

[0419] An "emotion analysis engine" is a technology that analyzes and identifies emotional elements (e.g., joy, sadness, surprise, etc.) contained in text data.

[0420] A "keyword" is a word or phrase that has a particularly significant meaning in text data.

[0421] A "topic" is a major theme or subject in text data.

[0422] "Traveller" means an individual who travels or provides information about travel.

[0423] "Content" refers to information or entertainment content created or consumed by users, including text, images, and video.

[0424] "Target travelers" are travelers with specific requirements or interests.

[0425] "Action" refers to a specific action a user takes on the system (e.g., view, like, share, etc.).

[0426] "Reward" refers to valuable consideration given to a user for their actions or contributions.

[0427] "Points" refer to numerical units given to users as rewards.

[0428] "Travel agency" refers to a company or organization that plans, provides, and sells travel-related products.

[0429] "Affiliated Businesses" refers to businesses and suppliers that provide travel-related goods and services.

[0430] "Fees" means the fees payable for the provision of Services.

[0431] "Demand information" refers to data about consumer demand and market trends.

[0432] A "tour package" refers to a comprehensive travel plan that includes travel destinations, accommodation, transportation, etc.

[0433] "Analysis results" refers to the analysis results of data obtained through the processes of natural language processing and sentiment analysis.

[0434] This invention is implemented by combining a natural language processing (NLP) engine and a sentiment analysis engine in a system that collects, analyzes, and recommends travel stories written by travelers. Specifically, the system operates in the following way.

[0435] Hardware and software used

[0436] The hardware used is the user's device (PC, smartphone, tablet, etc.) and a cloud server. The software used is a natural language processing engine (e.g., SpaCy or BERT) and a sentiment analysis engine (e.g., Affectiva or IBM Watson (registered trademark) Tone Analyzer). A database (e.g., MySQL (registered trademark) or PostgreSQL) is also required to store each piece of data.

[0437] System Operation

[0438] 1. Users enter and upload their travel experiences

[0439] The user creates a travel experience report and logs into the system from a terminal.

[0440] The terminal inserts the created content into an upload form, and when the user clicks the "Submit" button, the data is sent to the server.

[0441] The server receives the HTTP request and stores the content in a database in JSON format.

[0442] The server passes the stored data to an NLP engine and a sentiment analysis engine.

[0443] 2. Content analysis, keyword extraction, and emotion recognition

[0444] The server's NLP engine processes the content using natural language processing to extract key topics and keywords, such as "travel to Thailand" and "elephant riding experience."

[0445] The server's emotion analysis engine analyzes the content's positive and negative emotions and identifies emotions such as "fun" and "moving."

[0446] The server stores the extracted keywords and emotion information in a relational database.

[0447] 3. Targeting by recommendation engines

[0448] The server queries the user database to find users who are interested in specific keywords or sentiments, for example, by running an SQL query to identify users interested in "traveling to Thailand."

[0449] The server generates a recommendation list for the relevant user and displays the relevant content on the user's dashboard.

[0450] 4. User browsing and emotional feedback

[0451] Users can view the recommended experiences and "like" or "share" them as needed.

[0452] The server tracks and stores user browsing behavior using cookies and session data.

[0453] The server collects information on users clicking "like" or "share" and uses an emotion analysis engine to analyze the emotions associated with the user's actions.

[0454] 5. Compensation and Remuneration Management

[0455] The server calculates reward points based on the number of views, likes, and shares of the experience. The formula and algorithm used to calculate the points is the built-in point management system.

[0456] Based on the calculation result, the server updates the points field in the user table in the database to add points to the user's account.

[0457] The server recommends product information from travel agencies and related businesses to the user, and sends email notifications and displays dashboards.

[0458] 6. Product purchases and commissions

[0459] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process.

[0460] The server tracks purchase information in cooperation with the EC system and records purchase history.

[0461] The server calculates the commission and reward when the purchase is completed, and returns the purchase information and reward to the user who posted the experience as points.

[0462] 7. Industry data analysis and feedback

[0463] The server aggregates and analyzes all log data (browsing history, purchase history, etc.) using a big data analysis tool (e.g., Apache (registered trademark) Hadoop or Spark).

[0464] The server provides the analysis results to travel agencies and related businesses in the form of dashboards and CSV reports.

[0465] Based on this data, travel agencies can plan tours and campaigns that are in high demand and develop effective marketing strategies.

[0466] Specific examples

[0467] Example 1: User A posts a travel story

[0468] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving." Based on these keywords and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[0469] Example 2: User B is planning a trip

[0470] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." After reading the book, User B decides to travel to Thailand and books an elephant riding tour. The server detects that User B has booked the tour and returns a portion of the profits to User A as points. User B's emotional feedback is also recorded in the database.

[0471] Example 3: Revitalizing the entire industry

[0472] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[0473] Prompt Sentence Examples

[0474] "Please submit a travel story. In your story, please write in detail about the places you went, what you did, and how you felt. For example, 'I had fun riding an elephant in Thailand,' or 'I enjoyed the local cuisine.'"

[0475] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0476] Step 1:

[0477] A user creates a travel story and logs into the system from their terminal. They enter their login information (user ID, password) and click the "Post" button. The input is the login information and the story text, which the terminal sends to the server in JSON format. The server stores the received data in a database. Specifically, the server receives an HTTP request and executes an INSERT statement in the MySQL database.

[0478] Step 2:

[0479] The server passes the stored travel story data to a natural language processing engine to extract key topics and keywords. The input is the text data of the travel story, and the NLP engine (e.g., SpaCy) analyzes the text to identify keywords. Specifically, it sends an API request to the NLP engine and stores the keywords returned in response in a database.

[0480] Step 3:

[0481] The server passes the saved travel story data to a sentiment analysis engine to extract emotional information. The input is the text data of the travel story, and the sentiment analysis engine (e.g., IBM Watson Tone Analyzer) analyzes the text to identify the emotion. Specifically, it sends an API request to the sentiment analysis engine, and stores the emotional information returned as a response in a database.

[0482] Step 4:

[0483] The server identifies relevant target travelers based on the extracted keywords and emotion information and recommends content. The input is the saved keywords and emotion information, and the server queries the user database to identify relevant travelers. Specifically, it executes an SQL query to extract the target user ID and generates a recommendation list based on it.

[0484] Step 5:

[0485] The next time the user logs in, the server will display content from the recommendation list on the user's dashboard. The input is the generated recommendation list, and the server updates the user's dashboard. Specifically, when the user logs in, the server uses an HTML template engine to display the recommended content.

[0486] Step 6:

[0487] The user views the recommended content and "likes" or "shares" it as needed. The server tracks these actions and stores them using cookies or session data. The input is the user action, which the server stores in a database. Specifically, JavaScript (registered trademark) is used to capture the user's click event and send it to the server via an AJAX request.

[0488] Step 7:

[0489] The server analyzes the emotional information based on the user's actions and calculates the reward. The input is the user's action data, and the emotion is identified using the emotion analysis engine, and the reward is calculated using the point management system. Specifically, the analysis results are saved in the database and the point table is updated.

[0490] Step 8:

[0491] Based on the calculation results, the server adds points to the user's account and recommends product information from travel agencies and related businesses. The input is the calculated reward points and related product information, which the server notifies the user. Specific operations include email notification and dashboard updates.

[0492] Step 9:

[0493] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process. The server tracks the purchase information and records the purchase history. The input is the purchase information, which the server saves in the database. Specifically, it calls the purchase API to record the purchase data and calculates reward points.

[0494] Step 10:

[0495] The server aggregates and analyzes all data (browsing history, purchase history, etc.) and provides demand information to travel agencies. The input is accumulated log data, which is aggregated and analyzed using big data analysis tools. Specifically, the analysis is performed using Hadoop or Spark, and the results are visualized in CSV reports and dashboards.

[0496] In this way, each processing step works together, and a system is realized in which travel stories posted by users provide value to other travelers and contribute to the revitalization of the entire travel industry.

[0497] (Application example 2)

[0498] 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."

[0499] Conventional travel and restaurant review systems have been unable to take into account the interests and emotions of users, making it difficult to provide users with appropriate and specific recommendations. Furthermore, due to the lack of reward systems based on user feedback and measures to promote the use of food delivery services, it has been difficult to improve user satisfaction and stimulate usage.

[0500] 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.

[0501] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards and granting reward points to accounts based on the travelers' actions, means for analyzing posts by related users and calculating similarity, means for recommending content related to other users based on the extracted similarity, means for recommending restaurants and dishes for food delivery, means for providing discount coupons based on user ratings, means for promoting the use of food delivery, and means for recommending products and services on the food delivery platform and calculating fees and returning them as rewards when a purchase is made. This enables more appropriate and specific content, restaurants, and dishes to be recommended to users, thereby improving user satisfaction and revitalizing usage.

[0502] A "traveler" is an individual who travels or who has traveled and therefore writes about their travel experiences.

[0503] A "travel journal" is a written record of a traveler's experiences and impressions during and after a trip.

[0504] A "database" is a system that systematically collects, stores, and manages multiple data.

[0505] "Natural language processing" is a technology that allows computers to understand, generate, and manipulate human language (natural language).

[0506] A "main topic" is a topic or theme that is centrally addressed in the text being analyzed.

[0507] "Keywords" are words or phrases that are particularly important in a piece of text.

[0508] "Content" means information such as text and images created by users and posted to the system.

[0509] "Tracking" refers to technologies and methods that record and track user behavior.

[0510] "Reward points" are points of a specific value that are awarded for a user's actions.

[0511] "Similarity" refers to the degree to which two or more objects are similar to each other.

[0512] "Food delivery" refers to a service where food and drinks can be ordered and delivered.

[0513] "User Ratings" means feedback or ratings posted by users regarding content or services.

[0514] A "discount coupon" is a coupon that provides the right to purchase a product or service at a lower price than usual.

[0515] "Fees" are fees incurred in connection with transactions or the provision of services.

[0516] The system for implementing this invention comprises a server, a terminal, and a user. The specific operation and configuration of the system will be described below.

[0517] System Configuration

[0518] The system includes the following hardware and software:

[0519] Hardware: Servers, user devices (smartphones, tablets, PCs)

[0520] Software: Natural language processing engine, sentiment analysis engine, database management system, recommendation engine

[0521] Program Operation

[0522] 1. Receiving and storing travel stories

[0523] Users create travel stories from their own devices and post them to the system. The devices send the created content to the database, and the server stores the received data. This operation accumulates new travel stories in the system's database.

[0524] 2. Natural Language Processing and Sentiment Analysis

[0525] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy) to extract key topics and keywords, then analyzes the sentiment contained in the content using a sentiment analysis engine (e.g., TextBlob). The analysis results are stored in a database.

[0526] 3. Content Recommendations

[0527] The server recommends relevant content to other travelers based on the extracted topics, keywords, and sentiment information. The recommendation engine uses similarity calculations (e.g., cosine similarity) to select the most relevant content.

[0528] 4. Tracking User Actions

[0529] The server tracks user actions such as viewing, liking, and sharing content, which allows the server to store user interests and behavioral history in a database to improve recommendation accuracy.

[0530] 5. Reward points awarded

[0531] Based on the user's actions, the server calculates reward points and credits them to the user's account. This reward system encourages users to participate.

[0532] 6. Food delivery recommendations

[0533] The system also handles content related to food delivery, analyzes users' dining experiences (e.g., restaurant reviews), and recommends the best restaurants and dishes to other users. It can also offer discount coupons based on the results of sentiment analysis.

[0534] Specific examples

[0535] Example 1: User A posts a travel story stating, "The Thai food was excellent and the atmosphere was great." The server receives this story, extracts keywords such as "Thai food" and "atmosphere" using a natural language processing engine, and determines positive sentiment using a sentiment analysis engine. Based on this, the server generates a list of recommended food delivery services relevant to User A.

[0536] Example 2: User B posts a review saying, "I had a great dining experience at a new sushi restaurant." The server similarly analyzes this review and extracts keywords such as "sushi" and "great dining experience" and sentiments. As a result, the server recommends this sushi restaurant to other users who have similar travel experiences.

[0537] Example prompt sentence:

[0538] The Thai food was amazing and the ambiance was delightful.

[0539] I had a spectacular dining experience at the new sushi place.

[0540] The pizza was okay, but the pasta was incredible.

[0541] This will enable us to recommend appropriate content, restaurants, and dishes to users, thereby improving user satisfaction and stimulating usage.

[0542] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0543] Step 1:

[0544] Users create travel stories from their own devices and post them to the system. The user inputs the text data of the travel story. The device sends it to the database, where it is received and stored by the server. This allows new travel stories to be stored in the system's database.

[0545] Step 2:

[0546] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy). It receives the travel story text data as input and extracts key topics and keywords. This data processing identifies important keywords and topics and produces a keyword list as output.

[0547] Step 3:

[0548] The server passes the keyword list obtained from the natural language processing engine to a sentiment analysis engine (e.g., TextBlob). The input is text data, which undergoes sentiment analysis to quantify the user's sentiment. This data calculation produces an emotional score, such as positive, negative, or neutral, as the output.

[0549] Step 4:

[0550] The server stores the keyword list and sentiment scores in a database. The input is the analysis results, which are then associated with user data and stored. This allows information related to travel experiences to be managed in an organized manner within the database.

[0551] Step 5:

[0552] The server recommends relevant content to other users based on the extracted keywords and emotion information. The recommendation engine takes the keywords and emotion scores as input and performs a similarity calculation (e.g., cosine similarity). This data calculation identifies the most relevant content and recommends it to the user.

[0553] Step 6:

[0554] The server tracks user actions such as viewing, liking, and sharing content. The input is the log data of user actions, which is then stored in a database. This process records user interests and behavioral history.

[0555] Step 7:

[0556] The server calculates reward points based on the user's actions and adds them to the user's account. The input is the evaluation data of the user's actions, and points are calculated based on that and reflected in the user's account. This process strengthens the user's incentive.

[0557] Step 8:

[0558] The server analyzes food delivery content and recommends related restaurants and dishes. The input is travel experiences related to food delivery, and the server identifies the user's preferences through natural language processing and sentiment analysis. This data processing generates a list of food delivery recommendations that are optimal for the user.

[0559] Step 9:

[0560] The server provides discount coupons based on user ratings. The input is user rating data, and appropriate discount coupons are calculated based on the rating content and provided to the user. This process promotes the use of food delivery services.

[0561] Step 10:

[0562] The server recommends products and services on the food delivery platform to users, and when a purchase is made, calculates a commission and returns it to the user as a reward. The input is product purchase data, and the commission is calculated based on that and returned to the user. This stimulates economic activity within the system.

[0563] 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.

[0564] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0565] 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.

[0566] [Second embodiment]

[0567] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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."

[0579] This invention is implemented as a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows.

[0580] 1. Users enter and upload their travel experiences

[0581] Users create travel stories and blog posts and log into the system from their terminals.

[0582] The terminal inserts the created content into an upload form.

[0583] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0584] The server stores the received content data in a database.

[0585] The server passes the stored data to a natural language processing (NLP) engine for analysis.

[0586] 2. Content analysis and keyword extraction

[0587] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0588] The server stores the extracted keyword information in a relational database.

[0589] The server categorizes the content based on the stored keywords.

[0590] 3. Targeting by recommendation engines

[0591] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[0592] The server generates a list for recommending related content to the user.

[0593] The next time the user logs in, the server will display content from the recommendation list.

[0594] 4. User Views and Feedback

[0595] The user will see the recommended experiences the next time they log in.

[0596] The server tracks users' browsing history and click behavior.

[0597] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[0598] 5. Compensation and Remuneration Management

[0599] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[0600] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0601] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[0602] 6. Product purchases and commissions

[0603] If the user purchases a recommended product or service, the server tracks the purchase.

[0604] The server calculates the commission and rewards at the time of the purchase.

[0605] The server returns a part of the fee to the user who posted the experience as points.

[0606] 7. Industry data analysis and feedback

[0607] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[0608] The server provides the analysis results to travel agencies and related businesses.

[0609] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0610] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0611] Specific examples

[0612] Example 1: User A posts a travel story

[0613] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on the keyword information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[0614] Example 2: User B is planning a trip

[0615] When User B is searching for his next travel destination, the server recommends a "Thailand Travel Experience." User B reads the experience and decides to travel to Thailand. He also books an elephant riding tour based on the information in the experience. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the experience, as points.

[0616] Example 3: Revitalizing the entire industry

[0617] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[0618] In this way, each process works together, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] A user writes a travel experience or blog post and logs into the system from a terminal.

[0622] Step 2:

[0623] The terminal inserts the created content into an upload form.

[0624] Step 3:

[0625] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0626] Step 4:

[0627] The server stores the received content data in a database.

[0628] Step 5:

[0629] The server passes the stored data to a natural language processing (NLP) engine.

[0630] Step 6:

[0631] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0632] Step 7:

[0633] The server stores the extracted keyword information in a relational database.

[0634] Step 8:

[0635] The server categorizes the content based on the stored keywords.

[0636] Step 9:

[0637] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[0638] Step 10:

[0639] The server generates a list for recommending related content to the user.

[0640] Step 11:

[0641] The next time the user logs in, the server will display content from the recommendation list.

[0642] Step 12:

[0643] The user browses the recommended experiences.

[0644] Step 13:

[0645] The server tracks users' browsing history and click behavior.

[0646] Step 14:

[0647] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[0648] Step 15:

[0649] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[0650] Step 16:

[0651] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0652] Step 17:

[0653] The server recommends products and services from travel agencies and related businesses to the user.

[0654] Step 18:

[0655] If the user purchases a recommended product or service, the server tracks the purchase.

[0656] Step 19:

[0657] The server calculates the commission and rewards at the time of the purchase.

[0658] Step 20:

[0659] The server returns a part of the fee to the user who posted the experience as points.

[0660] Step 21:

[0661] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[0662] Step 22:

[0663] The server provides the analysis results to travel agencies and related businesses.

[0664] Step 23:

[0665] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0666] Step 24:

[0667] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0668] Example 1

[0669] 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."

[0670] There is a need to effectively manage and analyze travel stories written by travelers and use that information to make accurate recommendations to other travelers. However, existing systems lack the accuracy of keyword extraction and content recommendation for travel stories, and are unable to fully utilize user behavior data. Furthermore, they do not efficiently calculate rewards based on user actions or provide demand information to travel agencies, resulting in a lack of systems that can contribute to revitalizing the entire travel industry.

[0671] 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.

[0672] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract major topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on the travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses and calculating commissions and returning them as rewards when a purchase is made, means for parsing content using a natural language processing engine and extracting major topics and subtopics, means for categorizing data based on this topic information, means for recording users' browsing history and click behavior to recommend new travel stories, and means for tracking product and service purchase information and calculating commissions. This enables effective management of travel stories, accurate recommendations, utilization of user behavior data, and revitalization of the entire travel industry.

[0673] A "travel journal" is a document or report written by a traveler about their personal travel experiences.

[0674] A "database" is an information system for efficiently storing and managing data.

[0675] "Natural language processing" is a technology that uses computers to understand, generate, and process human language.

[0676] "Topic" refers to the subject or theme of a travel story.

[0677] "Keywords" refer to key information or important words in a travel story.

[0678] "Content recommendation" refers to presenting appropriate information and articles based on a user's interests and history.

[0679] An "action" refers to a specific operation or reaction performed by a user, such as "liking" or "sharing."

[0680] "Tracking" means following and recording a user's actions and operations.

[0681] "Reward points" refer to incentives given to users based on their contributions and activities.

[0682] A "travel agency" is a business that plans, provides, and sells travel products to customers.

[0683] "Affiliated Businesses" means businesses or organizations that offer travel-related goods or services.

[0684] A "natural language processing engine" refers to software or algorithms for performing natural language processing.

[0685] "Parsing" is the process of analyzing text data and extracting its structure and meaning.

[0686] A "subtopic" is a secondary theme or topic related to the main topic.

[0687] "Categorization" means classifying and organizing data according to specific criteria.

[0688] "Viewing history" refers to a record of what content a user has viewed.

[0689] "Click behavior" refers to information about links and buttons that users click on on a web page.

[0690] "Fees" means fees paid for transactions or services.

[0691] A "reward" is a reward or incentive given for a particular action or result.

[0692] This invention is a system that collects, analyzes, and recommends travel stories written by travelers. A specific embodiment of the system will be described in detail.

[0693] System Overview

[0694] This system is primarily composed of three main components: user input, data analysis and recommendations by the server, and data display by the device. The system's main hardware includes the device used by the user (PC, smartphone, tablet) and the server. The software used includes natural language processing engines (e.g., "spaCy" and "BERT") and machine learning algorithms (collaborative filtering, content-based filtering).

[0695] User Input and Data Storing

[0696] Users can create travel stories on their devices (PCs or smartphones) and post them by logging in to the system. When the user clicks the "Post" button, the device sends the content data to the server via an HTTP POST request. The server receives the sent content data and stores it in a database. At this time, it checks for prohibited words and formats, and only saves the data if there are no problems.

[0697] Content analysis and keyword extraction

[0698] The server passes the stored content data to a natural language processing (NLP) engine. Specifically, it uses NLP engines such as "spaCy" and "BERT." The server's NLP engine analyzes the content and extracts key topics and keywords. For example, "Thailand travel," "elephant riding experience," and "local cuisine" are extracted. The extracted keyword information is stored in a relational database, and the content is categorized based on this.

[0699] Recommendations and targeting

[0700] The server identifies users who have topics of interest from the user database. For example, it uses algorithms (collaborative filtering, content-based filtering, etc.) to identify users who are interested in "beach trips." The server adds relevant travel stories to the recommendation list for that user, which will be displayed the next time they log in.

[0701] User behavior tracking and feedback

[0702] The next time the user logs in, the server will display the recommended travel stories. When the user views a story, the server tracks the user's browsing history and click behavior, and records the behavioral data. In addition, if the user "likes" or "shares" the story, the original poster is notified of this information.

[0703] Reward calculation and point allocation

[0704] The server calculates points and rewards based on the number of views, likes, and shares of the experience. The calculation algorithm is executed based on a specific rule (e.g., 1 point for every 100 views). The calculation result is reflected in the account of the user who posted the experience. At the same time, products and services from travel agencies and related businesses are recommended to the user.

[0705] Product purchase and commission calculation

[0706] If a user purchases a recommended product or service, the server tracks the purchase information. Specifically, it monitors the user's purchasing behavior using affiliate links and tracking pixels. When a purchase is made, the server calculates a commission and returns a portion of it to the user who posted the experience as points.

[0707] Data analysis and feedback

[0708] The server aggregates all data (browsing history, purchase history, user feedback) and analyzes it using machine learning algorithms (e.g., k-means clustering, linear regression, deep learning). The results are provided to travel agencies and related businesses, providing insights such as "travel to this region has recently become popular."

[0709] Specific examples

[0710] Example 1: User A posts a travel story

[0711] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on these keywords, the server recommends the account to users who are interested in traveling to Thailand.

[0712] Prompt Sentence Examples

[0713] For example, by providing specific prompts such as, "Please enter an account of your trip to Thailand. For example, please write in detail about your elephant riding experience, local cuisine, and tourist spots," users are guided to create a detailed and comprehensive account of their experience.

[0714] In this way, the system can effectively manage and analyze users' travel experiences and recommend them to other users as valuable information, thereby promoting interaction between travelers and contributing to the revitalization of the entire travel industry.

[0715] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0716] System program processing flow

[0717] Step 1:

[0718] Input: The user creates a travel story on the device and clicks the post button.

[0719] Specific behavior:

[0720] Users create travel stories on their devices (PCs, smartphones).

[0721] The user clicks the "Submit" button.

[0722] The terminal sends the content data to the server using an HTTP POST request.

[0723] Output: Content data is sent to the server.

[0724] Step 2:

[0725] Input: Content data sent from the device.

[0726] Specific behavior:

[0727] The server receives the transmitted content data.

[0728] The data received by the server is checked for invalid words and format.

[0729] The server stores the data that passes the check in the database.

[0730] Output: Content data stored in the database.

[0731] Step 3:

[0732] Input: Content data stored in the database.

[0733] Specific behavior:

[0734] The server passes the stored content data to a natural language processing (NLP) engine, specifically, "spaCy" or "BERT" as the NLP engine.

[0735] The server's NLP engine analyzes the content and extracts key topics and keywords.

[0736] For example, keywords such as "travel to Thailand," "elephant riding experience," and "local cuisine" are extracted.

[0737] Output: Extracted keyword information.

[0738] Step 4:

[0739] Input: Extracted keyword information.

[0740] Specific behavior:

[0741] The server stores the extracted keyword information in a relational database.

[0742] The server categorizes the content based on keywords.

[0743] Output: Categorized content data.

[0744] Step 5:

[0745] Input: Categorized content data.

[0746] Specific behavior:

[0747] The server identifies users with topics of interest from an existing user database.

[0748] The algorithms used are collaborative filtering and content-based filtering.

[0749] Output: A list of related users.

[0750] Step 6:

[0751] Input: Associated user lists and categorized content data.

[0752] Specific behavior:

[0753] The server generates a list for recommending content relevant to the user.

[0754] The server displays the content from the recommendation list the next time the user logs in.

[0755] Output: Recommended content list.

[0756] Step 7:

[0757] Input: User login information and recommended content list.

[0758] Specific behavior:

[0759] The next time the user logs in, the server displays the recommended travel experiences.

[0760] Output: A display of recommended travel experiences.

[0761] Step 8:

[0762] Input: User views and acts on recommended experiences.

[0763] Specific behavior:

[0764] The server records and tracks users' browsing history and click behavior.

[0765] When a user "likes" or "shares" a story, the server records that information and notifies the original poster.

[0766] Output: Recorded user behavior data.

[0767] Step 9:

[0768] Input: User behavior data and the number of views, likes, and shares for each experience.

[0769] Specific behavior:

[0770] The server calculates points and rewards.

[0771] The calculation is based on certain rules (e.g. 1 point for every 100 views).

[0772] The server will then assign points to the account of the user who posted the experience based on the calculation results.

[0773] Output: Points awarded to the user account.

[0774] Step 10:

[0775] Input: Purchase data of recommended products and services by the user.

[0776] Specific behavior:

[0777] If the user purchases a recommended product or service, the server tracks the purchase information.

[0778] Use affiliate links and tracking pixels to monitor user purchasing behavior.

[0779] When a purchase is made, the server calculates the commission fee and returns a portion of it to the user who posted the experience as points.

[0780] Output: Redeemed points.

[0781] Step 11:

[0782] Input: All user data (browsing history, purchase history, feedback data).

[0783] Specific behavior:

[0784] The server aggregates all the data and analyzes it using machine learning algorithms.

[0785] Specifically, it uses algorithms such as k-means clustering, linear regression, and deep learning.

[0786] Output: Parsed data and generated insights.

[0787] Step 12:

[0788] Input: Parsed data and insights.

[0789] Specific behavior:

[0790] The server provides the analysis results to travel agencies and related businesses.

[0791] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0792] Output: Providing data to travel agencies and planning new tour packages and campaigns.

[0793] In this way, by clarifying the specific processing content and input / output at each step, the flow of the entire system becomes easier to understand.

[0794] (Application example 1)

[0795] 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."

[0796] Conventional travel story systems had the ability to collect travel stories written by travelers, analyze that data, and make recommendations, but they had difficulty providing appropriate advertisements tailored to the travelers' interests. Furthermore, they were unable to automatically generate and properly target advertisements based on travelers' behavior. This meant that travel story content could not be effectively utilized, making it difficult for travel agencies and advertisers to develop effective advertising strategies.

[0797] 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.

[0798] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating and returning commissions when a purchase is made, and means for automatically generating and displaying advertisements related to the user's specific interests and concerns based on the analysis results using natural language processing. This makes it possible to provide travelers with appropriate content and advertisements tailored to their interests and concerns.

[0799] A "travel journal" is a document or digital content that allows travelers to record their travel experiences and impressions and share them with others.

[0800] A "database" is an information system for efficiently storing, searching, and managing information.

[0801] "Natural language processing" refers to the technology of using computers to analyze, understand, and generate human language.

[0802] "Major topics and keywords" refer to the topics or themes that are considered particularly important in a document, as well as important words related to them.

[0803] "Content" means any information, text, images, video, audio, or other matter primarily in digital format.

[0804] "Recommendation" is the act of suggesting relevant information, products, services, etc. based on specific conditions or data.

[0805] "Tracking" means recording user behavior and actions and collecting and analyzing that data.

[0806] A "reward" is a reward or incentive given for a particular action or result.

[0807] "Advertising" is a promotional activity to introduce and promote products and services to consumers.

[0808] "Fees" are fees required to provide a particular service.

[0809] "Account" means the authentication information and associated settings and data required for a User to access and use a system or service.

[0810] "Travel agents and related businesses" refers to businesses and organizations that offer travel-related products and services.

[0811] The present invention is embodied as a system for collecting, analyzing, and recommending travel stories written by travelers. Specifically, the system includes the following means.

[0812] First, the server receives travel stories written by users and stores them in a database. Users enter their travel experiences in text format and submit them to the server through a dedicated form. The server stores the received text data in the database.

[0813] The server then analyzes the stored travel stories using a natural language processing (NLP) engine to extract key topics and keywords. The NLP engine uses generative AI models such as Hugging Face's Transformer. The analysis results are recorded in a database for further processing.

[0814] The server then recommends content related to other travelers based on the analysis results. For example, travel stories and services related to keywords like "Bali" and "surfing experience" are recommended. This allows users to easily find content that matches their interests.

[0815] Additionally, the server tracks user actions such as viewing, liking, and sharing content, allowing it to understand which content users are interested in and calculate rewards accordingly, which are credited to users' accounts as points that can be redeemed for travel-related products and services.

[0816] The server also recommends products and services from travel agencies and related businesses, and tracks the purchase information when the user purchases them. If a purchase is made, the server calculates a commission and returns a portion of it to the user as a reward, allowing the user to earn additional profits from their actions.

[0817] Furthermore, the server automatically generates and displays advertisements related to the user's specific interests based on the analysis results using natural language processing. These advertisements are customized based on the user's past behavioral data, which can lead to greater effectiveness.

[0818] For example, if User A posts a travel experience story about Bali, keywords such as "Bali," "surfing experience," and "local cuisine" are extracted. The next time User A logs in, advertisements for popular hotels and surfing lessons in Bali are recommended to User A.

[0819] Examples of prompts for generative AI models include:

[0820] "A user posted a travel story about their experience in Bali. Please extract keywords such as 'Bali,' 'surfing experience,' and 'local cuisine.'"

[0821] Something like this.

[0822] As described above, the present invention is a system that effectively utilizes traveler's experiences and appropriately recommends related content and advertisements, thereby improving user convenience.

[0823] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0824] Step 1:

[0825] The server receives travel stories written by users. Users enter their travel experiences in text format and post them by pasting the text data into a dedicated form. The server receives the posted text data and stores it in a database.

[0826] Input: Travel experiences posted by users (text data)

[0827] Output: Travel stories stored in a database

[0828] Step 2:

[0829] The server passes the saved travel stories to a natural language processing (NLP) engine for analysis. The NLP engine uses a generative AI model such as Hugging Face's Transformer. The server extracts key topics and keywords from the text data analyzed by the NLP engine.

[0830] Input: Travel stories stored in the database

[0831] Output: Extracted main topics and keywords

[0832] Step 3:

[0833] The server then recommends related content to other travelers based on the extracted topics and keywords. The server also takes into account the user's past behavioral data. Specifically, the server generates a recommendation list and saves it in association with the user's account.

[0834] Input: Extracted topics and keywords, and user behavior data

[0835] Output: Generated recommendation list

[0836] Step 4:

[0837] The server tracks users' actions when they view recommended content, specifically recording actions such as viewing, liking, and sharing, and stores this data in a database.

[0838] Input: User action data for views, likes, and shares

[0839] Output: Action data stored in the database

[0840] Step 5:

[0841] The server calculates reward points based on the user's action data and grants them to the user's account. Specifically, the server adds up the points set for each action and adds the total to the user's account.

[0842] Input: User action data

[0843] Output: Calculated reward points and their allocation

[0844] Step 6:

[0845] The server recommends products and services from travel agencies and related businesses, and calculates the commission when the user purchases them. The purchase information is linked to the user's account, and a portion of the commission is returned to the user as a reward.

[0846] Input: User purchase information and product fee information

[0847] Output: Calculated commission and reward points redeemed

[0848] Step 7:

[0849] The server generates advertisements based on the analyzed travel experience data and related to the user's interests. The advertisements are then displayed in the user's account. The content of the advertisements is determined based on keywords analyzed by the NLP engine and the user's behavioral data.

[0850] Input: Parsed keywords and user behavior data

[0851] Output: The generated ad and its display

[0852] Step 8:

[0853] The server aggregates all data and provides analysis results to travel agencies and related businesses, allowing them to plan travel products and services that are in high demand.

[0854] Input: User behavior data, purchase data, advertising data

[0855] Output: Feedback of analysis results

[0856] 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.

[0857] This invention combines an emotion engine with a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows:

[0858] 1. Users enter and upload their travel experiences

[0859] A user writes a travel experience or blog post and logs into the system from a terminal.

[0860] The terminal inserts the created content into an upload form.

[0861] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0862] The server stores the received content data in a database.

[0863] The server passes the stored data to a natural language processing (NLP) engine and then to an emotion engine.

[0864] 2. Content analysis, keyword extraction, and emotion recognition

[0865] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0866] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[0867] The server stores the extracted keywords and emotion information in a relational database.

[0868] 3. Targeting by recommendation engines

[0869] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[0870] The server generates a list of relevant content recommendations for the user, taking into account emotional information.

[0871] The next time the user logs in, the server will display content from the recommendation list.

[0872] 4. User browsing and emotional feedback

[0873] The user will see the recommended experiences the next time they log in.

[0874] The server tracks users' browsing history and click behavior.

[0875] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the action. For example, if a user expresses emotions such as "empathy" or "movement" by reading a story, this is also stored in the database.

[0876] 5. Compensation and Remuneration Management

[0877] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[0878] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0879] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[0880] 6. Product purchases and commissions

[0881] If the user purchases a recommended product or service, the server tracks the purchase.

[0882] The server calculates the commission and rewards at the time of the purchase.

[0883] The server returns a part of the fee to the user who posted the experience as points.

[0884] 7. Industry data analysis and feedback

[0885] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[0886] The server provides the analysis results to travel agencies and related businesses.

[0887] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0888] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0889] Specific examples

[0890] Example 1: User A posts a travel story

[0891] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving" using an emotion engine. Based on the keyword information and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand and who also show similar emotions.

[0892] Example 2: User B is planning a trip

[0893] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." User B reads the book and decides to travel to Thailand. He also books an elephant riding tour based on the information in the book. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the book, as points. In addition, if User B expresses emotion or empathy through emotional feedback, this is also recorded in the database.

[0894] Example 3: Revitalizing the entire industry

[0895] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry. Furthermore, data based on emotional feedback can be used to provide products that increase user satisfaction.

[0896] In this way, each process is closely linked, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[0897] The processing flow will be explained below.

[0898] Step 1:

[0899] A user writes a travel experience or blog post and logs into the system from a terminal.

[0900] Step 2:

[0901] The terminal inserts the created content into an upload form.

[0902] Step 3:

[0903] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[0904] Step 4:

[0905] The server stores the received content data in a database.

[0906] Step 5:

[0907] The server passes the stored data to a natural language processing (NLP) engine and an emotion engine.

[0908] Step 6:

[0909] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[0910] Step 7:

[0911] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[0912] Step 8:

[0913] The server stores the extracted keywords and emotion information in a relational database.

[0914] Step 9:

[0915] The server categorizes the content based on the stored keywords and sentiment information.

[0916] Step 10:

[0917] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[0918] Step 11:

[0919] The server generates a list of relevant content recommendations for the user, taking the extracted emotion information into consideration.

[0920] Step 12:

[0921] The next time the user logs in, the server will display content from the recommendation list.

[0922] Step 13:

[0923] The user browses the recommended experiences.

[0924] Step 14:

[0925] The server tracks users' browsing history and click behavior.

[0926] Step 15:

[0927] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the user's actions. For example, emotions such as "empathy" and "emotion" are recognized.

[0928] Step 16:

[0929] The server also stores the emotional feedback information in a database.

[0930] Step 17:

[0931] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[0932] Step 18:

[0933] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[0934] Step 19:

[0935] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[0936] Step 20:

[0937] If the user purchases a recommended product or service, the server tracks the purchase.

[0938] Step 21:

[0939] The server calculates the commission and rewards at the time of the purchase.

[0940] Step 22:

[0941] The server returns a part of the fee to the user who posted the experience as points.

[0942] Step 23:

[0943] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[0944] Step 24:

[0945] The server provides the analysis results to travel agencies and related businesses.

[0946] Step 25:

[0947] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[0948] Step 26:

[0949] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[0950] Example 2

[0951] 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."

[0952] Conventional systems have difficulty effectively analyzing travel stories written by travelers and providing useful information to other travelers and travel agencies. Furthermore, rewards and product recommendations based on travelers' experiences are insufficient, resulting in a lack of systems that contribute to revitalizing the travel industry as a whole. Furthermore, there is a lack of recommendation and reward systems that utilize users' emotional feedback, so an improvement in user experience is needed.

[0953] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving travel stories written by travelers and saving them in a database, means for analyzing the saved travel stories using a natural language processing engine to extract main topics and keywords, means for analyzing the saved travel stories using a sentiment analysis engine to extract emotional information, means for identifying relevant target travelers and recommending content based on the extracted keywords and emotional information, means for tracking travelers' actions such as viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating commissions and returning them as rewards when a purchase is made, and means for aggregating and analyzing all data and providing demand information to travel agencies. This allows for efficient analysis and recommendation of travel stories, improving reward systems and product recommendations for travelers, and revitalizing the entire travel industry.

[0954] A "travel journal" is a record of a traveler's travel experiences, expressed in writing and images.

[0955] A "database" is a system for efficiently storing, managing, and searching information and data.

[0956] A "natural language processing engine" is a technology that analyzes text data written in natural language and understands and extracts meaning and structure.

[0957] An "emotion analysis engine" is a technology that analyzes and identifies emotional elements (e.g., joy, sadness, surprise, etc.) contained in text data.

[0958] A "keyword" is a word or phrase that has a particularly significant meaning in text data.

[0959] A "topic" is a major theme or subject in text data.

[0960] "Traveller" means an individual who travels or provides information about travel.

[0961] "Content" refers to information or entertainment content created or consumed by users, including text, images, and video.

[0962] "Target travelers" are travelers with specific requirements or interests.

[0963] "Action" refers to a specific action a user takes on the system (e.g., view, like, share, etc.).

[0964] "Reward" refers to valuable consideration given to a user for their actions or contributions.

[0965] "Points" refer to numerical units given to users as rewards.

[0966] "Travel agency" refers to a company or organization that plans, provides, and sells travel-related products.

[0967] "Affiliated Businesses" refers to businesses and suppliers that provide travel-related goods and services.

[0968] "Fees" means the fees payable for the provision of Services.

[0969] "Demand information" refers to data about consumer demand and market trends.

[0970] A "tour package" refers to a comprehensive travel plan that includes travel destinations, accommodation, transportation, etc.

[0971] "Analysis results" refers to the analysis results of data obtained through the processes of natural language processing and sentiment analysis.

[0972] This invention is implemented by combining a natural language processing (NLP) engine and a sentiment analysis engine in a system that collects, analyzes, and recommends travel stories written by travelers. Specifically, the system operates in the following way.

[0973] Hardware and software used

[0974] The hardware used is the user's device (PC, smartphone, tablet, etc.) and a cloud server. The software used is a natural language processing engine (e.g., SpaCy or BERT) and a sentiment analysis engine (e.g., Affectiva or IBM Watson Tone Analyzer). A database (e.g., MySQL or PostgreSQL) is also required to store each piece of data.

[0975] System Operation

[0976] 1. Users enter and upload their travel experiences

[0977] The user creates a travel experience report and logs into the system from a terminal.

[0978] The terminal inserts the created content into an upload form, and when the user clicks the "Submit" button, the data is sent to the server.

[0979] The server receives the HTTP request and stores the content in a database in JSON format.

[0980] The server passes the stored data to an NLP engine and a sentiment analysis engine.

[0981] 2. Content analysis, keyword extraction, and emotion recognition

[0982] The server's NLP engine processes the content using natural language processing to extract key topics and keywords, such as "travel to Thailand" and "elephant riding experience."

[0983] The server's emotion analysis engine analyzes the content's positive and negative emotions and identifies emotions such as "fun" and "moving."

[0984] The server stores the extracted keywords and emotion information in a relational database.

[0985] 3. Targeting by recommendation engines

[0986] The server queries the user database to find users who are interested in specific keywords or sentiments, for example, by running an SQL query to identify users interested in "traveling to Thailand."

[0987] The server generates a recommendation list for the relevant user and displays the relevant content on the user's dashboard.

[0988] 4. User browsing and emotional feedback

[0989] Users can view the recommended experiences and "like" or "share" them as needed.

[0990] The server tracks and stores user browsing behavior using cookies and session data.

[0991] The server collects information on users clicking "like" or "share" and uses an emotion analysis engine to analyze the emotions associated with the user's actions.

[0992] 5. Compensation and Remuneration Management

[0993] The server calculates reward points based on the number of views, likes, and shares of the experience. The formula and algorithm used to calculate the points is the built-in point management system.

[0994] Based on the calculation result, the server updates the points field in the user table in the database to add points to the user's account.

[0995] The server recommends product information from travel agencies and related businesses to the user, and sends email notifications and displays dashboards.

[0996] 6. Product purchases and commissions

[0997] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process.

[0998] The server tracks purchase information in cooperation with the EC system and records purchase history.

[0999] The server calculates the commission and reward when the purchase is completed, and returns the purchase information and reward to the user who posted the experience as points.

[1000] 7. Industry data analysis and feedback

[1001] The server aggregates and analyzes all log data (browsing history, purchase history, etc.) using big data analysis tools (e.g., Apache Hadoop or Spark).

[1002] The server provides the analysis results to travel agencies and related businesses in the form of dashboards and CSV reports.

[1003] Based on this data, travel agencies can plan tours and campaigns that are in high demand and develop effective marketing strategies.

[1004] Specific examples

[1005] Example 1: User A posts a travel story

[1006] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving." Based on these keywords and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[1007] Example 2: User B is planning a trip

[1008] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." After reading the book, User B decides to travel to Thailand and books an elephant riding tour. The server detects that User B has booked the tour and returns a portion of the profits to User A as points. User B's emotional feedback is also recorded in the database.

[1009] Example 3: Revitalizing the entire industry

[1010] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[1011] Prompt Sentence Examples

[1012] "Please submit a travel story. In your story, please write in detail about the places you went, what you did, and how you felt. For example, 'I had fun riding an elephant in Thailand,' or 'I enjoyed the local cuisine.'"

[1013] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1014] Step 1:

[1015] A user creates a travel story and logs into the system from their terminal. They enter their login information (user ID, password) and click the "Post" button. The input is the login information and the story text, which the terminal sends to the server in JSON format. The server stores the received data in a database. Specifically, the server receives an HTTP request and executes an INSERT statement in the MySQL database.

[1016] Step 2:

[1017] The server passes the stored travel story data to a natural language processing engine to extract key topics and keywords. The input is the text data of the travel story, and the NLP engine (e.g., SpaCy) analyzes the text to identify keywords. Specifically, it sends an API request to the NLP engine and stores the keywords returned in response in a database.

[1018] Step 3:

[1019] The server passes the saved travel story data to a sentiment analysis engine to extract emotional information. The input is the text data of the travel story, and the sentiment analysis engine (e.g., IBM Watson Tone Analyzer) analyzes the text to identify the emotion. Specifically, it sends an API request to the sentiment analysis engine, and stores the emotional information returned as a response in a database.

[1020] Step 4:

[1021] The server identifies relevant target travelers based on the extracted keywords and emotion information and recommends content. The input is the saved keywords and emotion information, and the server queries the user database to identify relevant travelers. Specifically, it executes an SQL query to extract the target user ID and generates a recommendation list based on it.

[1022] Step 5:

[1023] The next time the user logs in, the server will display content from the recommendation list on the user's dashboard. The input is the generated recommendation list, and the server updates the user's dashboard. Specifically, when the user logs in, the server uses an HTML template engine to display the recommended content.

[1024] Step 6:

[1025] The user views the recommended content and "likes" or "shares" it as needed. The server tracks these actions and stores them using cookies or session data. The input is the user action, which the server stores in a database. Specifically, JavaScript is used to capture the user's click event and send it to the server via an AJAX request.

[1026] Step 7:

[1027] The server analyzes the emotional information based on the user's actions and calculates the reward. The input is the user's action data, and the emotion is identified using the emotion analysis engine, and the reward is calculated using the point management system. Specifically, the analysis results are saved in the database and the point table is updated.

[1028] Step 8:

[1029] Based on the calculation results, the server adds points to the user's account and recommends product information from travel agencies and related businesses. The input is the calculated reward points and related product information, which the server notifies the user. Specific operations include email notification and dashboard updates.

[1030] Step 9:

[1031] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process. The server tracks the purchase information and records the purchase history. The input is the purchase information, which the server saves in the database. Specifically, it calls the purchase API to record the purchase data and calculates reward points.

[1032] Step 10:

[1033] The server aggregates and analyzes all data (browsing history, purchase history, etc.) and provides demand information to travel agencies. The input is accumulated log data, which is aggregated and analyzed using big data analysis tools. Specifically, the analysis is performed using Hadoop or Spark, and the results are visualized in CSV reports and dashboards.

[1034] In this way, each processing step works together, and a system is realized in which travel stories posted by users provide value to other travelers and contribute to the revitalization of the entire travel industry.

[1035] (Application example 2)

[1036] 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."

[1037] Conventional travel and restaurant review systems have been unable to take into account the interests and emotions of users, making it difficult to provide users with appropriate and specific recommendations. Furthermore, due to the lack of reward systems based on user feedback and measures to promote the use of food delivery services, it has been difficult to improve user satisfaction and stimulate usage.

[1038] 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.

[1039] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards and granting reward points to accounts based on the travelers' actions, means for analyzing posts by related users and calculating similarity, means for recommending content related to other users based on the extracted similarity, means for recommending restaurants and dishes for food delivery, means for providing discount coupons based on user ratings, means for promoting the use of food delivery, and means for recommending products and services on the food delivery platform and calculating fees and returning them as rewards when a purchase is made. This enables more appropriate and specific content, restaurants, and dishes to be recommended to users, thereby improving user satisfaction and revitalizing usage.

[1040] A "traveler" is an individual who travels or who has traveled and therefore writes about their travel experiences.

[1041] A "travel journal" is a written record of a traveler's experiences and impressions during and after a trip.

[1042] A "database" is a system that systematically collects, stores, and manages multiple data.

[1043] "Natural language processing" is a technology that allows computers to understand, generate, and manipulate human language (natural language).

[1044] A "main topic" is a topic or theme that is centrally addressed in the text being analyzed.

[1045] "Keywords" are words or phrases that are particularly important in a piece of text.

[1046] "Content" means information such as text and images created by users and posted to the system.

[1047] "Tracking" refers to technologies and methods that record and track user behavior.

[1048] "Reward points" are points of a specific value that are awarded for a user's actions.

[1049] "Similarity" refers to the degree to which two or more objects are similar to each other.

[1050] "Food delivery" refers to a service where food and drinks can be ordered and delivered.

[1051] "User Ratings" means feedback or ratings posted by users regarding content or services.

[1052] A "discount coupon" is a coupon that provides the right to purchase a product or service at a lower price than usual.

[1053] "Fees" are fees incurred in connection with transactions or the provision of services.

[1054] The system for implementing this invention comprises a server, a terminal, and a user. The specific operation and configuration of the system will be described below.

[1055] System Configuration

[1056] The system includes the following hardware and software:

[1057] Hardware: Servers, user devices (smartphones, tablets, PCs)

[1058] Software: Natural language processing engine, sentiment analysis engine, database management system, recommendation engine

[1059] Program Operation

[1060] 1. Receiving and storing travel stories

[1061] Users create travel stories from their own devices and post them to the system. The devices send the created content to the database, and the server stores the received data. This operation accumulates new travel stories in the system's database.

[1062] 2. Natural Language Processing and Sentiment Analysis

[1063] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy) to extract key topics and keywords, then analyzes the sentiment contained in the content using a sentiment analysis engine (e.g., TextBlob). The analysis results are stored in a database.

[1064] 3. Content Recommendations

[1065] The server recommends relevant content to other travelers based on the extracted topics, keywords, and sentiment information. The recommendation engine uses similarity calculations (e.g., cosine similarity) to select the most relevant content.

[1066] 4. Tracking User Actions

[1067] The server tracks user actions such as viewing, liking, and sharing content, which allows the server to store user interests and behavioral history in a database to improve recommendation accuracy.

[1068] 5. Reward points awarded

[1069] Based on the user's actions, the server calculates reward points and credits them to the user's account. This reward system encourages users to participate.

[1070] 6. Food delivery recommendations

[1071] The system also handles content related to food delivery, analyzes users' dining experiences (e.g., restaurant reviews), and recommends the best restaurants and dishes to other users. It can also offer discount coupons based on the results of sentiment analysis.

[1072] Specific examples

[1073] Example 1: User A posts a travel story stating, "The Thai food was excellent and the atmosphere was great." The server receives this story, extracts keywords such as "Thai food" and "atmosphere" using a natural language processing engine, and determines positive sentiment using a sentiment analysis engine. Based on this, the server generates a list of recommended food delivery services relevant to User A.

[1074] Example 2: User B posts a review saying, "I had a great dining experience at a new sushi restaurant." The server similarly analyzes this review and extracts keywords such as "sushi" and "great dining experience" and sentiments. As a result, the server recommends this sushi restaurant to other users who have similar travel experiences.

[1075] Example prompt sentence:

[1076] The Thai food was amazing and the ambiance was delightful.

[1077] I had a spectacular dining experience at the new sushi place.

[1078] The pizza was okay, but the pasta was incredible.

[1079] This will enable us to recommend appropriate content, restaurants, and dishes to users, thereby improving user satisfaction and stimulating usage.

[1080] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1081] Step 1:

[1082] Users create travel stories from their own devices and post them to the system. The user inputs the text data of the travel story. The device sends it to the database, where it is received and stored by the server. This allows new travel stories to be stored in the system's database.

[1083] Step 2:

[1084] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy). It receives the travel story text data as input and extracts key topics and keywords. This data processing identifies important keywords and topics and produces a keyword list as output.

[1085] Step 3:

[1086] The server passes the keyword list obtained from the natural language processing engine to a sentiment analysis engine (e.g., TextBlob). The input is text data, which undergoes sentiment analysis to quantify the user's sentiment. This data calculation produces an emotional score, such as positive, negative, or neutral, as the output.

[1087] Step 4:

[1088] The server stores the keyword list and sentiment scores in a database. The input is the analysis results, which are then associated with user data and stored. This allows information related to travel experiences to be managed in an organized manner within the database.

[1089] Step 5:

[1090] The server recommends relevant content to other users based on the extracted keywords and emotion information. The recommendation engine takes the keywords and emotion scores as input and performs a similarity calculation (e.g., cosine similarity). This data calculation identifies the most relevant content and recommends it to the user.

[1091] Step 6:

[1092] The server tracks user actions such as viewing, liking, and sharing content. The input is the log data of user actions, which is then stored in a database. This process records user interests and behavioral history.

[1093] Step 7:

[1094] The server calculates reward points based on the user's actions and adds them to the user's account. The input is the evaluation data of the user's actions, and points are calculated based on that and reflected in the user's account. This process strengthens the user's incentive.

[1095] Step 8:

[1096] The server analyzes food delivery content and recommends related restaurants and dishes. The input is travel experiences related to food delivery, and the server identifies the user's preferences through natural language processing and sentiment analysis. This data processing generates a list of food delivery recommendations that are optimal for the user.

[1097] Step 9:

[1098] The server provides discount coupons based on user ratings. The input is user rating data, and appropriate discount coupons are calculated based on the rating content and provided to the user. This process promotes the use of food delivery services.

[1099] Step 10:

[1100] The server recommends products and services on the food delivery platform to users, and when a purchase is made, calculates a commission and returns it to the user as a reward. The input is product purchase data, and the commission is calculated based on that and returned to the user. This stimulates economic activity within the system.

[1101] 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.

[1102] 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.

[1103] 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.

[1104] [Third embodiment]

[1105] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1106] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1107] 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).

[1108] 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.

[1109] 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.

[1110] 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).

[1111] 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.

[1112] 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.

[1113] 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.

[1114] 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.

[1115] 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.

[1116] 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."

[1117] This invention is implemented as a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows.

[1118] 1. Users enter and upload their travel experiences

[1119] Users create travel stories and blog posts and log into the system from their terminals.

[1120] The terminal inserts the created content into an upload form.

[1121] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1122] The server stores the received content data in a database.

[1123] The server passes the stored data to a natural language processing (NLP) engine for analysis.

[1124] 2. Content analysis and keyword extraction

[1125] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1126] The server stores the extracted keyword information in a relational database.

[1127] The server categorizes the content based on the stored keywords.

[1128] 3. Targeting by recommendation engines

[1129] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[1130] The server generates a list for recommending related content to the user.

[1131] The next time the user logs in, the server will display content from the recommendation list.

[1132] 4. User Views and Feedback

[1133] The user will see the recommended experiences the next time they log in.

[1134] The server tracks users' browsing history and click behavior.

[1135] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[1136] 5. Compensation and Remuneration Management

[1137] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[1138] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1139] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[1140] 6. Product purchases and commissions

[1141] If the user purchases a recommended product or service, the server tracks the purchase.

[1142] The server calculates the commission and rewards at the time of the purchase.

[1143] The server returns a part of the fee to the user who posted the experience as points.

[1144] 7. Industry data analysis and feedback

[1145] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[1146] The server provides the analysis results to travel agencies and related businesses.

[1147] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1148] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1149] Specific examples

[1150] Example 1: User A posts a travel story

[1151] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on the keyword information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[1152] Example 2: User B is planning a trip

[1153] When User B is searching for his next travel destination, the server recommends a "Thailand Travel Experience." User B reads the experience and decides to travel to Thailand. He also books an elephant riding tour based on the information in the experience. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the experience, as points.

[1154] Example 3: Revitalizing the entire industry

[1155] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[1156] In this way, each process works together, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[1157] The processing flow will be explained below.

[1158] Step 1:

[1159] A user writes a travel experience or blog post and logs into the system from a terminal.

[1160] Step 2:

[1161] The terminal inserts the created content into an upload form.

[1162] Step 3:

[1163] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1164] Step 4:

[1165] The server stores the received content data in a database.

[1166] Step 5:

[1167] The server passes the stored data to a natural language processing (NLP) engine.

[1168] Step 6:

[1169] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1170] Step 7:

[1171] The server stores the extracted keyword information in a relational database.

[1172] Step 8:

[1173] The server categorizes the content based on the stored keywords.

[1174] Step 9:

[1175] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[1176] Step 10:

[1177] The server generates a list for recommending related content to the user.

[1178] Step 11:

[1179] The next time the user logs in, the server will display content from the recommendation list.

[1180] Step 12:

[1181] The user browses the recommended experiences.

[1182] Step 13:

[1183] The server tracks users' browsing history and click behavior.

[1184] Step 14:

[1185] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[1186] Step 15:

[1187] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[1188] Step 16:

[1189] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1190] Step 17:

[1191] The server recommends products and services from travel agencies and related businesses to the user.

[1192] Step 18:

[1193] If the user purchases a recommended product or service, the server tracks the purchase.

[1194] Step 19:

[1195] The server calculates the commission and rewards at the time of the purchase.

[1196] Step 20:

[1197] The server returns a part of the fee to the user who posted the experience as points.

[1198] Step 21:

[1199] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[1200] Step 22:

[1201] The server provides the analysis results to travel agencies and related businesses.

[1202] Step 23:

[1203] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1204] Step 24:

[1205] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1206] Example 1

[1207] 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."

[1208] There is a need to effectively manage and analyze travel stories written by travelers and use that information to make accurate recommendations to other travelers. However, existing systems lack the accuracy of keyword extraction and content recommendation for travel stories, and are unable to fully utilize user behavior data. Furthermore, they do not efficiently calculate rewards based on user actions or provide demand information to travel agencies, resulting in a lack of systems that can contribute to revitalizing the entire travel industry.

[1209] 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.

[1210] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract major topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on the travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses and calculating commissions and returning them as rewards when a purchase is made, means for parsing content using a natural language processing engine and extracting major topics and subtopics, means for categorizing data based on this topic information, means for recording users' browsing history and click behavior to recommend new travel stories, and means for tracking product and service purchase information and calculating commissions. This enables effective management of travel stories, accurate recommendations, utilization of user behavior data, and revitalization of the entire travel industry.

[1211] A "travel journal" is a document or report written by a traveler about their personal travel experiences.

[1212] A "database" is an information system for efficiently storing and managing data.

[1213] "Natural language processing" is a technology that uses computers to understand, generate, and process human language.

[1214] "Topic" refers to the subject or theme of a travel story.

[1215] "Keywords" refer to key information or important words in a travel story.

[1216] "Content recommendation" refers to presenting appropriate information and articles based on a user's interests and history.

[1217] An "action" refers to a specific operation or reaction performed by a user, such as "liking" or "sharing."

[1218] "Tracking" means following and recording a user's actions and operations.

[1219] "Reward points" refer to incentives given to users based on their contributions and activities.

[1220] A "travel agency" is a business that plans, provides, and sells travel products to customers.

[1221] "Affiliated Businesses" means businesses or organizations that offer travel-related goods or services.

[1222] A "natural language processing engine" refers to software or algorithms for performing natural language processing.

[1223] "Parsing" is the process of analyzing text data and extracting its structure and meaning.

[1224] A "subtopic" is a secondary theme or topic related to the main topic.

[1225] "Categorization" means classifying and organizing data according to specific criteria.

[1226] "Viewing history" refers to a record of what content a user has viewed.

[1227] "Click behavior" refers to information about links and buttons that users click on on a web page.

[1228] "Fees" means fees paid for transactions or services.

[1229] A "reward" is a reward or incentive given for a particular action or result.

[1230] This invention is a system that collects, analyzes, and recommends travel stories written by travelers. A specific embodiment of the system will be described in detail.

[1231] System Overview

[1232] This system is primarily composed of three main components: user input, data analysis and recommendations by the server, and data display by the device. The system's main hardware includes the device used by the user (PC, smartphone, tablet) and the server. The software used includes natural language processing engines (e.g., "spaCy" and "BERT") and machine learning algorithms (collaborative filtering, content-based filtering).

[1233] User Input and Data Storing

[1234] Users can create travel stories on their devices (PCs or smartphones) and post them by logging in to the system. When the user clicks the "Post" button, the device sends the content data to the server via an HTTP POST request. The server receives the sent content data and stores it in a database. At this time, it checks for prohibited words and formats, and only saves the data if there are no problems.

[1235] Content analysis and keyword extraction

[1236] The server passes the stored content data to a natural language processing (NLP) engine. Specifically, it uses NLP engines such as "spaCy" and "BERT." The server's NLP engine analyzes the content and extracts key topics and keywords. For example, "Thailand travel," "elephant riding experience," and "local cuisine" are extracted. The extracted keyword information is stored in a relational database, and the content is categorized based on this.

[1237] Recommendations and targeting

[1238] The server identifies users who have topics of interest from the user database. For example, it uses algorithms (collaborative filtering, content-based filtering, etc.) to identify users who are interested in "beach trips." The server adds relevant travel stories to the recommendation list for that user, which will be displayed the next time they log in.

[1239] User behavior tracking and feedback

[1240] The next time the user logs in, the server will display the recommended travel stories. When the user views a story, the server tracks the user's browsing history and click behavior, and records the behavioral data. In addition, if the user "likes" or "shares" the story, the original poster is notified of this information.

[1241] Reward calculation and point allocation

[1242] The server calculates points and rewards based on the number of views, likes, and shares of the experience. The calculation algorithm is executed based on a specific rule (e.g., 1 point for every 100 views). The calculation result is reflected in the account of the user who posted the experience. At the same time, products and services from travel agencies and related businesses are recommended to the user.

[1243] Product purchase and commission calculation

[1244] If a user purchases a recommended product or service, the server tracks the purchase information. Specifically, it monitors the user's purchasing behavior using affiliate links and tracking pixels. When a purchase is made, the server calculates a commission and returns a portion of it to the user who posted the experience as points.

[1245] Data analysis and feedback

[1246] The server aggregates all data (browsing history, purchase history, user feedback) and analyzes it using machine learning algorithms (e.g., k-means clustering, linear regression, deep learning). The results are provided to travel agencies and related businesses, providing insights such as "travel to this region has recently become popular."

[1247] Specific examples

[1248] Example 1: User A posts a travel story

[1249] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on these keywords, the server recommends the account to users who are interested in traveling to Thailand.

[1250] Prompt Sentence Examples

[1251] For example, by providing specific prompts such as, "Please enter an account of your trip to Thailand. For example, please write in detail about your elephant riding experience, local cuisine, and tourist spots," users are guided to create a detailed and comprehensive account of their experience.

[1252] In this way, the system can effectively manage and analyze users' travel experiences and recommend them to other users as valuable information, thereby promoting interaction between travelers and contributing to the revitalization of the entire travel industry.

[1253] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1254] System program processing flow

[1255] Step 1:

[1256] Input: The user creates a travel story on the device and clicks the post button.

[1257] Specific behavior:

[1258] Users create travel stories on their devices (PCs, smartphones).

[1259] The user clicks the "Submit" button.

[1260] The terminal sends the content data to the server using an HTTP POST request.

[1261] Output: Content data is sent to the server.

[1262] Step 2:

[1263] Input: Content data sent from the device.

[1264] Specific behavior:

[1265] The server receives the transmitted content data.

[1266] The data received by the server is checked for invalid words and format.

[1267] The server stores the data that passes the check in the database.

[1268] Output: Content data stored in the database.

[1269] Step 3:

[1270] Input: Content data stored in the database.

[1271] Specific behavior:

[1272] The server passes the stored content data to a natural language processing (NLP) engine, specifically, "spaCy" or "BERT" as the NLP engine.

[1273] The server's NLP engine analyzes the content and extracts key topics and keywords.

[1274] For example, keywords such as "travel to Thailand," "elephant riding experience," and "local cuisine" are extracted.

[1275] Output: Extracted keyword information.

[1276] Step 4:

[1277] Input: Extracted keyword information.

[1278] Specific behavior:

[1279] The server stores the extracted keyword information in a relational database.

[1280] The server categorizes the content based on keywords.

[1281] Output: Categorized content data.

[1282] Step 5:

[1283] Input: Categorized content data.

[1284] Specific behavior:

[1285] The server identifies users with topics of interest from an existing user database.

[1286] The algorithms used are collaborative filtering and content-based filtering.

[1287] Output: A list of related users.

[1288] Step 6:

[1289] Input: Associated user lists and categorized content data.

[1290] Specific behavior:

[1291] The server generates a list for recommending content relevant to the user.

[1292] The server displays the content from the recommendation list the next time the user logs in.

[1293] Output: Recommended content list.

[1294] Step 7:

[1295] Input: User login information and recommended content list.

[1296] Specific behavior:

[1297] The next time the user logs in, the server displays the recommended travel experiences.

[1298] Output: A display of recommended travel experiences.

[1299] Step 8:

[1300] Input: User views and acts on recommended experiences.

[1301] Specific behavior:

[1302] The server records and tracks users' browsing history and click behavior.

[1303] When a user "likes" or "shares" a story, the server records that information and notifies the original poster.

[1304] Output: Recorded user behavior data.

[1305] Step 9:

[1306] Input: User behavior data and the number of views, likes, and shares for each experience.

[1307] Specific behavior:

[1308] The server calculates points and rewards.

[1309] The calculation is based on certain rules (e.g. 1 point for every 100 views).

[1310] The server will then assign points to the account of the user who posted the experience based on the calculation results.

[1311] Output: Points awarded to the user account.

[1312] Step 10:

[1313] Input: Purchase data of recommended products and services by the user.

[1314] Specific behavior:

[1315] If the user purchases a recommended product or service, the server tracks the purchase information.

[1316] Use affiliate links and tracking pixels to monitor user purchasing behavior.

[1317] When a purchase is made, the server calculates the commission fee and returns a portion of it to the user who posted the experience as points.

[1318] Output: Redeemed points.

[1319] Step 11:

[1320] Input: All user data (browsing history, purchase history, feedback data).

[1321] Specific behavior:

[1322] The server aggregates all the data and analyzes it using machine learning algorithms.

[1323] Specifically, it uses algorithms such as k-means clustering, linear regression, and deep learning.

[1324] Output: Parsed data and generated insights.

[1325] Step 12:

[1326] Input: Parsed data and insights.

[1327] Specific behavior:

[1328] The server provides the analysis results to travel agencies and related businesses.

[1329] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1330] Output: Providing data to travel agencies and planning new tour packages and campaigns.

[1331] In this way, by clarifying the specific processing content and input / output at each step, the flow of the entire system becomes easier to understand.

[1332] (Application example 1)

[1333] 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."

[1334] Conventional travel story systems had the ability to collect travel stories written by travelers, analyze that data, and make recommendations, but they had difficulty providing appropriate advertisements tailored to the travelers' interests. Furthermore, they were unable to automatically generate and properly target advertisements based on travelers' behavior. This meant that travel story content could not be effectively utilized, making it difficult for travel agencies and advertisers to develop effective advertising strategies.

[1335] 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.

[1336] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating and returning commissions when a purchase is made, and means for automatically generating and displaying advertisements related to the user's specific interests and concerns based on the analysis results using natural language processing. This makes it possible to provide travelers with appropriate content and advertisements tailored to their interests and concerns.

[1337] A "travel journal" is a document or digital content that allows travelers to record their travel experiences and impressions and share them with others.

[1338] A "database" is an information system for efficiently storing, searching, and managing information.

[1339] "Natural language processing" refers to the technology of using computers to analyze, understand, and generate human language.

[1340] "Major topics and keywords" refer to the topics or themes that are considered particularly important in a document, as well as important words related to them.

[1341] "Content" means any information, text, images, video, audio, or other matter primarily in digital format.

[1342] "Recommendation" is the act of suggesting relevant information, products, services, etc. based on specific conditions or data.

[1343] "Tracking" means recording user behavior and actions and collecting and analyzing that data.

[1344] A "reward" is a reward or incentive given for a particular action or result.

[1345] "Advertising" is a promotional activity to introduce and promote products and services to consumers.

[1346] "Fees" are fees required to provide a particular service.

[1347] "Account" means the authentication information and associated settings and data required for a User to access and use a system or service.

[1348] "Travel agents and related businesses" refers to businesses and organizations that offer travel-related products and services.

[1349] The present invention is embodied as a system for collecting, analyzing, and recommending travel stories written by travelers. Specifically, the system includes the following means.

[1350] First, the server receives travel stories written by users and stores them in a database. Users enter their travel experiences in text format and submit them to the server through a dedicated form. The server stores the received text data in the database.

[1351] The server then analyzes the stored travel stories using a natural language processing (NLP) engine to extract key topics and keywords. The NLP engine uses generative AI models such as Hugging Face's Transformer. The analysis results are recorded in a database for further processing.

[1352] The server then recommends content related to other travelers based on the analysis results. For example, travel stories and services related to keywords like "Bali" and "surfing experience" are recommended. This allows users to easily find content that matches their interests.

[1353] Additionally, the server tracks user actions such as viewing, liking, and sharing content, allowing it to understand which content users are interested in and calculate rewards accordingly, which are credited to users' accounts as points that can be redeemed for travel-related products and services.

[1354] The server also recommends products and services from travel agencies and related businesses, and tracks the purchase information when the user purchases them. If a purchase is made, the server calculates a commission and returns a portion of it to the user as a reward, allowing the user to earn additional profits from their actions.

[1355] Furthermore, the server automatically generates and displays advertisements related to the user's specific interests based on the analysis results using natural language processing. These advertisements are customized based on the user's past behavioral data, which can lead to greater effectiveness.

[1356] For example, if User A posts a travel experience story about Bali, keywords such as "Bali," "surfing experience," and "local cuisine" are extracted. The next time User A logs in, advertisements for popular hotels and surfing lessons in Bali are recommended to User A.

[1357] Examples of prompts for generative AI models include:

[1358] "A user posted a travel story about their experience in Bali. Please extract keywords such as 'Bali,' 'surfing experience,' and 'local cuisine.'"

[1359] Something like this.

[1360] As described above, the present invention is a system that effectively utilizes traveler's experiences and appropriately recommends related content and advertisements, thereby improving user convenience.

[1361] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1362] Step 1:

[1363] The server receives travel stories written by users. Users enter their travel experiences in text format and post them by pasting the text data into a dedicated form. The server receives the posted text data and stores it in a database.

[1364] Input: Travel experiences posted by users (text data)

[1365] Output: Travel stories stored in a database

[1366] Step 2:

[1367] The server passes the saved travel stories to a natural language processing (NLP) engine for analysis. The NLP engine uses a generative AI model such as Hugging Face's Transformer. The server extracts key topics and keywords from the text data analyzed by the NLP engine.

[1368] Input: Travel stories stored in the database

[1369] Output: Extracted main topics and keywords

[1370] Step 3:

[1371] The server then recommends related content to other travelers based on the extracted topics and keywords. The server also takes into account the user's past behavioral data. Specifically, the server generates a recommendation list and saves it in association with the user's account.

[1372] Input: Extracted topics and keywords, and user behavior data

[1373] Output: Generated recommendation list

[1374] Step 4:

[1375] The server tracks users' actions when they view recommended content, specifically recording actions such as viewing, liking, and sharing, and stores this data in a database.

[1376] Input: User action data for views, likes, and shares

[1377] Output: Action data stored in the database

[1378] Step 5:

[1379] The server calculates reward points based on the user's action data and grants them to the user's account. Specifically, the server adds up the points set for each action and adds the total to the user's account.

[1380] Input: User action data

[1381] Output: Calculated reward points and their allocation

[1382] Step 6:

[1383] The server recommends products and services from travel agencies and related businesses, and calculates the commission when the user purchases them. The purchase information is linked to the user's account, and a portion of the commission is returned to the user as a reward.

[1384] Input: User purchase information and product fee information

[1385] Output: Calculated commission and reward points redeemed

[1386] Step 7:

[1387] The server generates advertisements based on the analyzed travel experience data and related to the user's interests. The advertisements are then displayed in the user's account. The content of the advertisements is determined based on keywords analyzed by the NLP engine and the user's behavioral data.

[1388] Input: Parsed keywords and user behavior data

[1389] Output: The generated ad and its display

[1390] Step 8:

[1391] The server aggregates all data and provides analysis results to travel agencies and related businesses, allowing them to plan travel products and services that are in high demand.

[1392] Input: User behavior data, purchase data, advertising data

[1393] Output: Feedback of analysis results

[1394] 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.

[1395] This invention combines an emotion engine with a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows:

[1396] 1. Users enter and upload their travel experiences

[1397] A user writes a travel experience or blog post and logs into the system from a terminal.

[1398] The terminal inserts the created content into an upload form.

[1399] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1400] The server stores the received content data in a database.

[1401] The server passes the stored data to a natural language processing (NLP) engine and then to an emotion engine.

[1402] 2. Content analysis, keyword extraction, and emotion recognition

[1403] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1404] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[1405] The server stores the extracted keywords and emotion information in a relational database.

[1406] 3. Targeting by recommendation engines

[1407] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[1408] The server generates a list of relevant content recommendations for the user, taking into account emotional information.

[1409] The next time the user logs in, the server will display content from the recommendation list.

[1410] 4. User browsing and emotional feedback

[1411] The user will see the recommended experiences the next time they log in.

[1412] The server tracks users' browsing history and click behavior.

[1413] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the action. For example, if a user expresses emotions such as "empathy" or "movement" by reading a story, this is also stored in the database.

[1414] 5. Compensation and Remuneration Management

[1415] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[1416] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1417] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[1418] 6. Product purchases and commissions

[1419] If the user purchases a recommended product or service, the server tracks the purchase.

[1420] The server calculates the commission and rewards at the time of the purchase.

[1421] The server returns a part of the fee to the user who posted the experience as points.

[1422] 7. Industry data analysis and feedback

[1423] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[1424] The server provides the analysis results to travel agencies and related businesses.

[1425] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1426] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1427] Specific examples

[1428] Example 1: User A posts a travel story

[1429] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving" using an emotion engine. Based on the keyword information and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand and who also show similar emotions.

[1430] Example 2: User B is planning a trip

[1431] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." User B reads the book and decides to travel to Thailand. He also books an elephant riding tour based on the information in the book. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the book, as points. In addition, if User B expresses emotion or empathy through emotional feedback, this is also recorded in the database.

[1432] Example 3: Revitalizing the entire industry

[1433] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry. Furthermore, data based on emotional feedback can be used to provide products that increase user satisfaction.

[1434] In this way, each process is closely linked, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[1435] The processing flow will be explained below.

[1436] Step 1:

[1437] A user writes a travel experience or blog post and logs into the system from a terminal.

[1438] Step 2:

[1439] The terminal inserts the created content into an upload form.

[1440] Step 3:

[1441] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1442] Step 4:

[1443] The server stores the received content data in a database.

[1444] Step 5:

[1445] The server passes the stored data to a natural language processing (NLP) engine and an emotion engine.

[1446] Step 6:

[1447] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1448] Step 7:

[1449] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[1450] Step 8:

[1451] The server stores the extracted keywords and emotion information in a relational database.

[1452] Step 9:

[1453] The server categorizes the content based on the stored keywords and sentiment information.

[1454] Step 10:

[1455] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[1456] Step 11:

[1457] The server generates a list of relevant content recommendations for the user, taking the extracted emotion information into consideration.

[1458] Step 12:

[1459] The next time the user logs in, the server will display content from the recommendation list.

[1460] Step 13:

[1461] The user browses the recommended experiences.

[1462] Step 14:

[1463] The server tracks users' browsing history and click behavior.

[1464] Step 15:

[1465] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the user's actions. For example, emotions such as "empathy" and "emotion" are recognized.

[1466] Step 16:

[1467] The server also stores the emotional feedback information in a database.

[1468] Step 17:

[1469] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[1470] Step 18:

[1471] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1472] Step 19:

[1473] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[1474] Step 20:

[1475] If the user purchases a recommended product or service, the server tracks the purchase.

[1476] Step 21:

[1477] The server calculates the commission and rewards at the time of the purchase.

[1478] Step 22:

[1479] The server returns a part of the fee to the user who posted the experience as points.

[1480] Step 23:

[1481] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[1482] Step 24:

[1483] The server provides the analysis results to travel agencies and related businesses.

[1484] Step 25:

[1485] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1486] Step 26:

[1487] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1488] Example 2

[1489] 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."

[1490] Conventional systems have difficulty effectively analyzing travel stories written by travelers and providing useful information to other travelers and travel agencies. Furthermore, rewards and product recommendations based on travelers' experiences are insufficient, resulting in a lack of systems that contribute to revitalizing the travel industry as a whole. Furthermore, there is a lack of recommendation and reward systems that utilize users' emotional feedback, so an improvement in user experience is needed.

[1491] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving travel stories written by travelers and saving them in a database, means for analyzing the saved travel stories using a natural language processing engine to extract main topics and keywords, means for analyzing the saved travel stories using a sentiment analysis engine to extract emotional information, means for identifying relevant target travelers and recommending content based on the extracted keywords and emotional information, means for tracking travelers' actions such as viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating commissions and returning them as rewards when a purchase is made, and means for aggregating and analyzing all data and providing demand information to travel agencies. This allows for efficient analysis and recommendation of travel stories, improving reward systems and product recommendations for travelers, and revitalizing the entire travel industry.

[1492] A "travel journal" is a record of a traveler's travel experiences, expressed in writing and images.

[1493] A "database" is a system for efficiently storing, managing, and searching information and data.

[1494] A "natural language processing engine" is a technology that analyzes text data written in natural language and understands and extracts meaning and structure.

[1495] An "emotion analysis engine" is a technology that analyzes and identifies emotional elements (e.g., joy, sadness, surprise, etc.) contained in text data.

[1496] A "keyword" is a word or phrase that has a particularly significant meaning in text data.

[1497] A "topic" is a major theme or subject in text data.

[1498] "Traveller" means an individual who travels or provides information about travel.

[1499] "Content" refers to information or entertainment content created or consumed by users, including text, images, and video.

[1500] "Target travelers" are travelers with specific requirements or interests.

[1501] "Action" refers to a specific action a user takes on the system (e.g., view, like, share, etc.).

[1502] "Reward" refers to valuable consideration given to a user for their actions or contributions.

[1503] "Points" refer to numerical units given to users as rewards.

[1504] "Travel agency" refers to a company or organization that plans, provides, and sells travel-related products.

[1505] "Affiliated Businesses" refers to businesses and suppliers that provide travel-related goods and services.

[1506] "Fees" means the fees payable for the provision of Services.

[1507] "Demand information" refers to data about consumer demand and market trends.

[1508] A "tour package" refers to a comprehensive travel plan that includes travel destinations, accommodation, transportation, etc.

[1509] "Analysis results" refers to the analysis results of data obtained through the processes of natural language processing and sentiment analysis.

[1510] This invention is implemented by combining a natural language processing (NLP) engine and a sentiment analysis engine in a system that collects, analyzes, and recommends travel stories written by travelers. Specifically, the system operates in the following way.

[1511] Hardware and software used

[1512] The hardware used is the user's device (PC, smartphone, tablet, etc.) and a cloud server. The software used is a natural language processing engine (e.g., SpaCy or BERT) and a sentiment analysis engine (e.g., Affectiva or IBM Watson Tone Analyzer). A database (e.g., MySQL or PostgreSQL) is also required to store each piece of data.

[1513] System Operation

[1514] 1. Users enter and upload their travel experiences

[1515] The user creates a travel experience report and logs into the system from a terminal.

[1516] The terminal inserts the created content into an upload form, and when the user clicks the "Submit" button, the data is sent to the server.

[1517] The server receives the HTTP request and stores the content in a database in JSON format.

[1518] The server passes the stored data to an NLP engine and a sentiment analysis engine.

[1519] 2. Content analysis, keyword extraction, and emotion recognition

[1520] The server's NLP engine processes the content using natural language processing to extract key topics and keywords, such as "travel to Thailand" and "elephant riding experience."

[1521] The server's emotion analysis engine analyzes the content's positive and negative emotions and identifies emotions such as "fun" and "moving."

[1522] The server stores the extracted keywords and emotion information in a relational database.

[1523] 3. Targeting by recommendation engines

[1524] The server queries the user database to find users who are interested in specific keywords or sentiments, for example, by running an SQL query to identify users interested in "traveling to Thailand."

[1525] The server generates a recommendation list for the relevant user and displays the relevant content on the user's dashboard.

[1526] 4. User browsing and emotional feedback

[1527] Users can view the recommended experiences and "like" or "share" them as needed.

[1528] The server tracks and stores user browsing behavior using cookies and session data.

[1529] The server collects information on users clicking "like" or "share" and uses an emotion analysis engine to analyze the emotions associated with the user's actions.

[1530] 5. Compensation and Remuneration Management

[1531] The server calculates reward points based on the number of views, likes, and shares of the experience. The formula and algorithm used to calculate the points is the built-in point management system.

[1532] Based on the calculation result, the server updates the points field in the user table in the database to add points to the user's account.

[1533] The server recommends product information from travel agencies and related businesses to the user, and sends email notifications and displays dashboards.

[1534] 6. Product purchases and commissions

[1535] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process.

[1536] The server tracks purchase information in cooperation with the EC system and records purchase history.

[1537] The server calculates the commission and reward when the purchase is completed, and returns the purchase information and reward to the user who posted the experience as points.

[1538] 7. Industry data analysis and feedback

[1539] The server aggregates and analyzes all log data (browsing history, purchase history, etc.) using big data analysis tools (e.g., Apache Hadoop or Spark).

[1540] The server provides the analysis results to travel agencies and related businesses in the form of dashboards and CSV reports.

[1541] Based on this data, travel agencies can plan tours and campaigns that are in high demand and develop effective marketing strategies.

[1542] Specific examples

[1543] Example 1: User A posts a travel story

[1544] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving." Based on these keywords and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[1545] Example 2: User B is planning a trip

[1546] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." After reading the book, User B decides to travel to Thailand and books an elephant riding tour. The server detects that User B has booked the tour and returns a portion of the profits to User A as points. User B's emotional feedback is also recorded in the database.

[1547] Example 3: Revitalizing the entire industry

[1548] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[1549] Prompt Sentence Examples

[1550] "Please submit a travel story. In your story, please write in detail about the places you went, what you did, and how you felt. For example, 'I had fun riding an elephant in Thailand,' or 'I enjoyed the local cuisine.'"

[1551] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1552] Step 1:

[1553] A user creates a travel story and logs into the system from their terminal. They enter their login information (user ID, password) and click the "Post" button. The input is the login information and the story text, which the terminal sends to the server in JSON format. The server stores the received data in a database. Specifically, the server receives an HTTP request and executes an INSERT statement in the MySQL database.

[1554] Step 2:

[1555] The server passes the stored travel story data to a natural language processing engine to extract key topics and keywords. The input is the text data of the travel story, and the NLP engine (e.g., SpaCy) analyzes the text to identify keywords. Specifically, it sends an API request to the NLP engine and stores the keywords returned in response in a database.

[1556] Step 3:

[1557] The server passes the saved travel story data to a sentiment analysis engine to extract emotional information. The input is the text data of the travel story, and the sentiment analysis engine (e.g., IBM Watson Tone Analyzer) analyzes the text to identify the emotion. Specifically, it sends an API request to the sentiment analysis engine, and stores the emotional information returned as a response in a database.

[1558] Step 4:

[1559] The server identifies relevant target travelers based on the extracted keywords and emotion information and recommends content. The input is the saved keywords and emotion information, and the server queries the user database to identify relevant travelers. Specifically, it executes an SQL query to extract the target user ID and generates a recommendation list based on it.

[1560] Step 5:

[1561] The next time the user logs in, the server will display content from the recommendation list on the user's dashboard. The input is the generated recommendation list, and the server updates the user's dashboard. Specifically, when the user logs in, the server uses an HTML template engine to display the recommended content.

[1562] Step 6:

[1563] The user views the recommended content and "likes" or "shares" it as needed. The server tracks these actions and stores them using cookies or session data. The input is the user action, which the server stores in a database. Specifically, JavaScript is used to capture the user's click event and send it to the server via an AJAX request.

[1564] Step 7:

[1565] The server analyzes the emotional information based on the user's actions and calculates the reward. The input is the user's action data, and the emotion is identified using the emotion analysis engine, and the reward is calculated using the point management system. Specifically, the analysis results are saved in the database and the point table is updated.

[1566] Step 8:

[1567] Based on the calculation results, the server adds points to the user's account and recommends product information from travel agencies and related businesses. The input is the calculated reward points and related product information, which the server notifies the user. Specific operations include email notification and dashboard updates.

[1568] Step 9:

[1569] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process. The server tracks the purchase information and records the purchase history. The input is the purchase information, which the server saves in the database. Specifically, it calls the purchase API to record the purchase data and calculates reward points.

[1570] Step 10:

[1571] The server aggregates and analyzes all data (browsing history, purchase history, etc.) and provides demand information to travel agencies. The input is accumulated log data, which is aggregated and analyzed using big data analysis tools. Specifically, the analysis is performed using Hadoop or Spark, and the results are visualized in CSV reports and dashboards.

[1572] In this way, each processing step works together, and a system is realized in which travel stories posted by users provide value to other travelers and contribute to the revitalization of the entire travel industry.

[1573] (Application example 2)

[1574] 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."

[1575] Conventional travel and restaurant review systems have been unable to take into account the interests and emotions of users, making it difficult to provide users with appropriate and specific recommendations. Furthermore, due to the lack of reward systems based on user feedback and measures to promote the use of food delivery services, it has been difficult to improve user satisfaction and stimulate usage.

[1576] 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.

[1577] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards and granting reward points to accounts based on the travelers' actions, means for analyzing posts by related users and calculating similarity, means for recommending content related to other users based on the extracted similarity, means for recommending restaurants and dishes for food delivery, means for providing discount coupons based on user ratings, means for promoting the use of food delivery, and means for recommending products and services on the food delivery platform and calculating fees and returning them as rewards when a purchase is made. This enables more appropriate and specific content, restaurants, and dishes to be recommended to users, thereby improving user satisfaction and revitalizing usage.

[1578] A "traveler" is an individual who travels or who has traveled and therefore writes about their travel experiences.

[1579] A "travel journal" is a written record of a traveler's experiences and impressions during and after a trip.

[1580] A "database" is a system that systematically collects, stores, and manages multiple data.

[1581] "Natural language processing" is a technology that allows computers to understand, generate, and manipulate human language (natural language).

[1582] A "main topic" is a topic or theme that is centrally addressed in the text being analyzed.

[1583] "Keywords" are words or phrases that are particularly important in a piece of text.

[1584] "Content" means information such as text and images created by users and posted to the system.

[1585] "Tracking" refers to technologies and methods that record and track user behavior.

[1586] "Reward points" are points of a specific value that are awarded for a user's actions.

[1587] "Similarity" refers to the degree to which two or more objects are similar to each other.

[1588] "Food delivery" refers to a service where food and drinks can be ordered and delivered.

[1589] "User Ratings" means feedback or ratings posted by users regarding content or services.

[1590] A "discount coupon" is a coupon that provides the right to purchase a product or service at a lower price than usual.

[1591] "Fees" are fees incurred in connection with transactions or the provision of services.

[1592] The system for implementing this invention comprises a server, a terminal, and a user. The specific operation and configuration of the system will be described below.

[1593] System Configuration

[1594] The system includes the following hardware and software:

[1595] Hardware: Servers, user devices (smartphones, tablets, PCs)

[1596] Software: Natural language processing engine, sentiment analysis engine, database management system, recommendation engine

[1597] Program Operation

[1598] 1. Receiving and storing travel stories

[1599] Users create travel stories from their own devices and post them to the system. The devices send the created content to the database, and the server stores the received data. This operation accumulates new travel stories in the system's database.

[1600] 2. Natural Language Processing and Sentiment Analysis

[1601] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy) to extract key topics and keywords, then analyzes the sentiment contained in the content using a sentiment analysis engine (e.g., TextBlob). The analysis results are stored in a database.

[1602] 3. Content Recommendations

[1603] The server recommends relevant content to other travelers based on the extracted topics, keywords, and sentiment information. The recommendation engine uses similarity calculations (e.g., cosine similarity) to select the most relevant content.

[1604] 4. Tracking User Actions

[1605] The server tracks user actions such as viewing, liking, and sharing content, which allows the server to store user interests and behavioral history in a database to improve recommendation accuracy.

[1606] 5. Reward points awarded

[1607] Based on the user's actions, the server calculates reward points and credits them to the user's account. This reward system encourages users to participate.

[1608] 6. Food delivery recommendations

[1609] The system also handles content related to food delivery, analyzes users' dining experiences (e.g., restaurant reviews), and recommends the best restaurants and dishes to other users. It can also offer discount coupons based on the results of sentiment analysis.

[1610] Specific examples

[1611] Example 1: User A posts a travel story stating, "The Thai food was excellent and the atmosphere was great." The server receives this story, extracts keywords such as "Thai food" and "atmosphere" using a natural language processing engine, and determines positive sentiment using a sentiment analysis engine. Based on this, the server generates a list of recommended food delivery services relevant to User A.

[1612] Example 2: User B posts a review saying, "I had a great dining experience at a new sushi restaurant." The server similarly analyzes this review and extracts keywords such as "sushi" and "great dining experience" and sentiments. As a result, the server recommends this sushi restaurant to other users who have similar travel experiences.

[1613] Example prompt sentence:

[1614] The Thai food was amazing and the ambiance was delightful.

[1615] I had a spectacular dining experience at the new sushi place.

[1616] The pizza was okay, but the pasta was incredible.

[1617] This will enable us to recommend appropriate content, restaurants, and dishes to users, thereby improving user satisfaction and stimulating usage.

[1618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1619] Step 1:

[1620] Users create travel stories from their own devices and post them to the system. The user inputs the text data of the travel story. The device sends it to the database, where it is received and stored by the server. This allows new travel stories to be stored in the system's database.

[1621] Step 2:

[1622] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy). It receives the travel story text data as input and extracts key topics and keywords. This data processing identifies important keywords and topics and produces a keyword list as output.

[1623] Step 3:

[1624] The server passes the keyword list obtained from the natural language processing engine to a sentiment analysis engine (e.g., TextBlob). The input is text data, which undergoes sentiment analysis to quantify the user's sentiment. This data calculation produces an emotional score, such as positive, negative, or neutral, as the output.

[1625] Step 4:

[1626] The server stores the keyword list and sentiment scores in a database. The input is the analysis results, which are then associated with user data and stored. This allows information related to travel experiences to be managed in an organized manner within the database.

[1627] Step 5:

[1628] The server recommends relevant content to other users based on the extracted keywords and emotion information. The recommendation engine takes the keywords and emotion scores as input and performs a similarity calculation (e.g., cosine similarity). This data calculation identifies the most relevant content and recommends it to the user.

[1629] Step 6:

[1630] The server tracks user actions such as viewing, liking, and sharing content. The input is the log data of user actions, which is then stored in a database. This process records user interests and behavioral history.

[1631] Step 7:

[1632] The server calculates reward points based on the user's actions and adds them to the user's account. The input is the evaluation data of the user's actions, and points are calculated based on that and reflected in the user's account. This process strengthens the user's incentive.

[1633] Step 8:

[1634] The server analyzes food delivery content and recommends related restaurants and dishes. The input is travel experiences related to food delivery, and the server identifies the user's preferences through natural language processing and sentiment analysis. This data processing generates a list of food delivery recommendations that are optimal for the user.

[1635] Step 9:

[1636] The server provides discount coupons based on user ratings. The input is user rating data, and appropriate discount coupons are calculated based on the rating content and provided to the user. This process promotes the use of food delivery services.

[1637] Step 10:

[1638] The server recommends products and services on the food delivery platform to users, and when a purchase is made, calculates a commission and returns it to the user as a reward. The input is product purchase data, and the commission is calculated based on that and returned to the user. This stimulates economic activity within the system.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] [Fourth embodiment]

[1643] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1644] 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.

[1645] 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).

[1646] 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.

[1647] 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.

[1648] 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).

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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."

[1656] This invention is implemented as a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows.

[1657] 1. Users enter and upload their travel experiences

[1658] Users create travel stories and blog posts and log into the system from their terminals.

[1659] The terminal inserts the created content into an upload form.

[1660] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1661] The server stores the received content data in a database.

[1662] The server passes the stored data to a natural language processing (NLP) engine for analysis.

[1663] 2. Content analysis and keyword extraction

[1664] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1665] The server stores the extracted keyword information in a relational database.

[1666] The server categorizes the content based on the stored keywords.

[1667] 3. Targeting by recommendation engines

[1668] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[1669] The server generates a list for recommending related content to the user.

[1670] The next time the user logs in, the server will display content from the recommendation list.

[1671] 4. User Views and Feedback

[1672] The user will see the recommended experiences the next time they log in.

[1673] The server tracks users' browsing history and click behavior.

[1674] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[1675] 5. Compensation and Remuneration Management

[1676] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[1677] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1678] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[1679] 6. Product purchases and commissions

[1680] If the user purchases a recommended product or service, the server tracks the purchase.

[1681] The server calculates the commission and rewards at the time of the purchase.

[1682] The server returns a part of the fee to the user who posted the experience as points.

[1683] 7. Industry data analysis and feedback

[1684] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[1685] The server provides the analysis results to travel agencies and related businesses.

[1686] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1687] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1688] Specific examples

[1689] Example 1: User A posts a travel story

[1690] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on the keyword information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[1691] Example 2: User B is planning a trip

[1692] When User B is searching for his next travel destination, the server recommends a "Thailand Travel Experience." User B reads the experience and decides to travel to Thailand. He also books an elephant riding tour based on the information in the experience. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the experience, as points.

[1693] Example 3: Revitalizing the entire industry

[1694] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[1695] In this way, each process works together, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[1696] The processing flow will be explained below.

[1697] Step 1:

[1698] A user writes a travel experience or blog post and logs into the system from a terminal.

[1699] Step 2:

[1700] The terminal inserts the created content into an upload form.

[1701] Step 3:

[1702] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1703] Step 4:

[1704] The server stores the received content data in a database.

[1705] Step 5:

[1706] The server passes the stored data to a natural language processing (NLP) engine.

[1707] Step 6:

[1708] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1709] Step 7:

[1710] The server stores the extracted keyword information in a relational database.

[1711] Step 8:

[1712] The server categorizes the content based on the stored keywords.

[1713] Step 9:

[1714] The server identifies users who have a topic of interest (e.g., "travel to Thailand") from an existing user database.

[1715] Step 10:

[1716] The server generates a list for recommending related content to the user.

[1717] Step 11:

[1718] The next time the user logs in, the server will display content from the recommendation list.

[1719] Step 12:

[1720] The user browses the recommended experiences.

[1721] Step 13:

[1722] The server tracks users' browsing history and click behavior.

[1723] Step 14:

[1724] When a user "likes" or "shares" a story, the server stores that information and provides feedback to the original poster of the story.

[1725] Step 15:

[1726] The server calculates points and rewards based on the number of views, likes, and shares of the experience.

[1727] Step 16:

[1728] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1729] Step 17:

[1730] The server recommends products and services from travel agencies and related businesses to the user.

[1731] Step 18:

[1732] If the user purchases a recommended product or service, the server tracks the purchase.

[1733] Step 19:

[1734] The server calculates the commission and rewards at the time of the purchase.

[1735] Step 20:

[1736] The server returns a part of the fee to the user who posted the experience as points.

[1737] Step 21:

[1738] The server aggregates and analyzes all data (browsing history, purchase history, user feedback).

[1739] Step 22:

[1740] The server provides the analysis results to travel agencies and related businesses.

[1741] Step 23:

[1742] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1743] Step 24:

[1744] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1745] Example 1

[1746] 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."

[1747] There is a need to effectively manage and analyze travel stories written by travelers and use that information to make accurate recommendations to other travelers. However, existing systems lack the accuracy of keyword extraction and content recommendation for travel stories, and are unable to fully utilize user behavior data. Furthermore, they do not efficiently calculate rewards based on user actions or provide demand information to travel agencies, resulting in a lack of systems that can contribute to revitalizing the entire travel industry.

[1748] 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.

[1749] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract major topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on the travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses and calculating commissions and returning them as rewards when a purchase is made, means for parsing content using a natural language processing engine and extracting major topics and subtopics, means for categorizing data based on this topic information, means for recording users' browsing history and click behavior to recommend new travel stories, and means for tracking product and service purchase information and calculating commissions. This enables effective management of travel stories, accurate recommendations, utilization of user behavior data, and revitalization of the entire travel industry.

[1750] A "travel journal" is a document or report written by a traveler about their personal travel experiences.

[1751] A "database" is an information system for efficiently storing and managing data.

[1752] "Natural language processing" is a technology that uses computers to understand, generate, and process human language.

[1753] "Topic" refers to the subject or theme of a travel story.

[1754] "Keywords" refer to key information or important words in a travel story.

[1755] "Content recommendation" refers to presenting appropriate information and articles based on a user's interests and history.

[1756] An "action" refers to a specific operation or reaction performed by a user, such as "liking" or "sharing."

[1757] "Tracking" means following and recording a user's actions and operations.

[1758] "Reward points" refer to incentives given to users based on their contributions and activities.

[1759] A "travel agency" is a business that plans, provides, and sells travel products to customers.

[1760] "Affiliated Businesses" means businesses or organizations that offer travel-related goods or services.

[1761] A "natural language processing engine" refers to software or algorithms for performing natural language processing.

[1762] "Parsing" is the process of analyzing text data and extracting its structure and meaning.

[1763] A "subtopic" is a secondary theme or topic related to the main topic.

[1764] "Categorization" means classifying and organizing data according to specific criteria.

[1765] "Viewing history" refers to a record of what content a user has viewed.

[1766] "Click behavior" refers to information about links and buttons that users click on on a web page.

[1767] "Fees" means fees paid for transactions or services.

[1768] A "reward" is a reward or incentive given for a particular action or result.

[1769] This invention is a system that collects, analyzes, and recommends travel stories written by travelers. A specific embodiment of the system will be described in detail.

[1770] System Overview

[1771] This system is primarily composed of three main components: user input, data analysis and recommendations by the server, and data display by the device. The system's main hardware includes the device used by the user (PC, smartphone, tablet) and the server. The software used includes natural language processing engines (e.g., "spaCy" and "BERT") and machine learning algorithms (collaborative filtering, content-based filtering).

[1772] User Input and Data Storing

[1773] Users can create travel stories on their devices (PCs or smartphones) and post them by logging in to the system. When the user clicks the "Post" button, the device sends the content data to the server via an HTTP POST request. The server receives the sent content data and stores it in a database. At this time, it checks for prohibited words and formats, and only saves the data if there are no problems.

[1774] Content analysis and keyword extraction

[1775] The server passes the stored content data to a natural language processing (NLP) engine. Specifically, it uses NLP engines such as "spaCy" and "BERT." The server's NLP engine analyzes the content and extracts key topics and keywords. For example, "Thailand travel," "elephant riding experience," and "local cuisine" are extracted. The extracted keyword information is stored in a relational database, and the content is categorized based on this.

[1776] Recommendations and targeting

[1777] The server identifies users who have topics of interest from the user database. For example, it uses algorithms (collaborative filtering, content-based filtering, etc.) to identify users who are interested in "beach trips." The server adds relevant travel stories to the recommendation list for that user, which will be displayed the next time they log in.

[1778] User behavior tracking and feedback

[1779] The next time the user logs in, the server will display the recommended travel stories. When the user views a story, the server tracks the user's browsing history and click behavior, and records the behavioral data. In addition, if the user "likes" or "shares" the story, the original poster is notified of this information.

[1780] Reward calculation and point allocation

[1781] The server calculates points and rewards based on the number of views, likes, and shares of the experience. The calculation algorithm is executed based on a specific rule (e.g., 1 point for every 100 views). The calculation result is reflected in the account of the user who posted the experience. At the same time, products and services from travel agencies and related businesses are recommended to the user.

[1782] Product purchase and commission calculation

[1783] If a user purchases a recommended product or service, the server tracks the purchase information. Specifically, it monitors the user's purchasing behavior using affiliate links and tracking pixels. When a purchase is made, the server calculates a commission and returns a portion of it to the user who posted the experience as points.

[1784] Data analysis and feedback

[1785] The server aggregates all data (browsing history, purchase history, user feedback) and analyzes it using machine learning algorithms (e.g., k-means clustering, linear regression, deep learning). The results are provided to travel agencies and related businesses, providing insights such as "travel to this region has recently become popular."

[1786] Specific examples

[1787] Example 1: User A posts a travel story

[1788] User A writes an account of his / her trip to Thailand and posts it to the system from his / her terminal. The server analyzes the posted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine." Based on these keywords, the server recommends the account to users who are interested in traveling to Thailand.

[1789] Prompt Sentence Examples

[1790] For example, by providing specific prompts such as, "Please enter an account of your trip to Thailand. For example, please write in detail about your elephant riding experience, local cuisine, and tourist spots," users are guided to create a detailed and comprehensive account of their experience.

[1791] In this way, the system can effectively manage and analyze users' travel experiences and recommend them to other users as valuable information, thereby promoting interaction between travelers and contributing to the revitalization of the entire travel industry.

[1792] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1793] System program processing flow

[1794] Step 1:

[1795] Input: The user creates a travel story on the device and clicks the post button.

[1796] Specific behavior:

[1797] Users create travel stories on their devices (PCs, smartphones).

[1798] The user clicks the "Submit" button.

[1799] The terminal sends the content data to the server using an HTTP POST request.

[1800] Output: Content data is sent to the server.

[1801] Step 2:

[1802] Input: Content data sent from the device.

[1803] Specific behavior:

[1804] The server receives the transmitted content data.

[1805] The data received by the server is checked for invalid words and format.

[1806] The server stores the data that passes the check in the database.

[1807] Output: Content data stored in the database.

[1808] Step 3:

[1809] Input: Content data stored in the database.

[1810] Specific behavior:

[1811] The server passes the stored content data to a natural language processing (NLP) engine, specifically, "spaCy" or "BERT" as the NLP engine.

[1812] The server's NLP engine analyzes the content and extracts key topics and keywords.

[1813] For example, keywords such as "travel to Thailand," "elephant riding experience," and "local cuisine" are extracted.

[1814] Output: Extracted keyword information.

[1815] Step 4:

[1816] Input: Extracted keyword information.

[1817] Specific behavior:

[1818] The server stores the extracted keyword information in a relational database.

[1819] The server categorizes the content based on keywords.

[1820] Output: Categorized content data.

[1821] Step 5:

[1822] Input: Categorized content data.

[1823] Specific behavior:

[1824] The server identifies users with topics of interest from an existing user database.

[1825] The algorithms used are collaborative filtering and content-based filtering.

[1826] Output: A list of related users.

[1827] Step 6:

[1828] Input: Associated user lists and categorized content data.

[1829] Specific behavior:

[1830] The server generates a list for recommending content relevant to the user.

[1831] The server displays the content from the recommendation list the next time the user logs in.

[1832] Output: Recommended content list.

[1833] Step 7:

[1834] Input: User login information and recommended content list.

[1835] Specific behavior:

[1836] The next time the user logs in, the server displays the recommended travel experiences.

[1837] Output: A display of recommended travel experiences.

[1838] Step 8:

[1839] Input: User views and acts on recommended experiences.

[1840] Specific behavior:

[1841] The server records and tracks users' browsing history and click behavior.

[1842] When a user "likes" or "shares" a story, the server records that information and notifies the original poster.

[1843] Output: Recorded user behavior data.

[1844] Step 9:

[1845] Input: User behavior data and the number of views, likes, and shares for each experience.

[1846] Specific behavior:

[1847] The server calculates points and rewards.

[1848] The calculation is based on certain rules (e.g. 1 point for every 100 views).

[1849] The server will then assign points to the account of the user who posted the experience based on the calculation results.

[1850] Output: Points awarded to the user account.

[1851] Step 10:

[1852] Input: Purchase data of recommended products and services by the user.

[1853] Specific behavior:

[1854] If the user purchases a recommended product or service, the server tracks the purchase information.

[1855] Use affiliate links and tracking pixels to monitor user purchasing behavior.

[1856] When a purchase is made, the server calculates the commission fee and returns a portion of it to the user who posted the experience as points.

[1857] Output: Redeemed points.

[1858] Step 11:

[1859] Input: All user data (browsing history, purchase history, feedback data).

[1860] Specific behavior:

[1861] The server aggregates all the data and analyzes it using machine learning algorithms.

[1862] Specifically, it uses algorithms such as k-means clustering, linear regression, and deep learning.

[1863] Output: Parsed data and generated insights.

[1864] Step 12:

[1865] Input: Parsed data and insights.

[1866] Specific behavior:

[1867] The server provides the analysis results to travel agencies and related businesses.

[1868] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1869] Output: Providing data to travel agencies and planning new tour packages and campaigns.

[1870] In this way, by clarifying the specific processing content and input / output at each step, the flow of the entire system becomes easier to understand.

[1871] (Application example 1)

[1872] 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."

[1873] Conventional travel story systems had the ability to collect travel stories written by travelers, analyze that data, and make recommendations, but they had difficulty providing appropriate advertisements tailored to the travelers' interests. Furthermore, they were unable to automatically generate and properly target advertisements based on travelers' behavior. This meant that travel story content could not be effectively utilized, making it difficult for travel agencies and advertisers to develop effective advertising strategies.

[1874] 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.

[1875] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating and returning commissions when a purchase is made, and means for automatically generating and displaying advertisements related to the user's specific interests and concerns based on the analysis results using natural language processing. This makes it possible to provide travelers with appropriate content and advertisements tailored to their interests and concerns.

[1876] A "travel journal" is a document or digital content that allows travelers to record their travel experiences and impressions and share them with others.

[1877] A "database" is an information system for efficiently storing, searching, and managing information.

[1878] "Natural language processing" refers to the technology of using computers to analyze, understand, and generate human language.

[1879] "Major topics and keywords" refer to the topics or themes that are considered particularly important in a document, as well as important words related to them.

[1880] "Content" means any information, text, images, video, audio, or other matter primarily in digital format.

[1881] "Recommendation" is the act of suggesting relevant information, products, services, etc. based on specific conditions or data.

[1882] "Tracking" means recording user behavior and actions and collecting and analyzing that data.

[1883] A "reward" is a reward or incentive given for a particular action or result.

[1884] "Advertising" is a promotional activity to introduce and promote products and services to consumers.

[1885] "Fees" are fees required to provide a particular service.

[1886] "Account" means the authentication information and associated settings and data required for a User to access and use a system or service.

[1887] "Travel agents and related businesses" refers to businesses and organizations that offer travel-related products and services.

[1888] The present invention is embodied as a system for collecting, analyzing, and recommending travel stories written by travelers. Specifically, the system includes the following means.

[1889] First, the server receives travel stories written by users and stores them in a database. Users enter their travel experiences in text format and submit them to the server through a dedicated form. The server stores the received text data in the database.

[1890] The server then analyzes the stored travel stories using a natural language processing (NLP) engine to extract key topics and keywords. The NLP engine uses generative AI models such as Hugging Face's Transformer. The analysis results are recorded in a database for further processing.

[1891] The server then recommends content related to other travelers based on the analysis results. For example, travel stories and services related to keywords like "Bali" and "surfing experience" are recommended. This allows users to easily find content that matches their interests.

[1892] Additionally, the server tracks user actions such as viewing, liking, and sharing content, allowing it to understand which content users are interested in and calculate rewards accordingly, which are credited to users' accounts as points that can be redeemed for travel-related products and services.

[1893] The server also recommends products and services from travel agencies and related businesses, and tracks the purchase information when the user purchases them. If a purchase is made, the server calculates a commission and returns a portion of it to the user as a reward, allowing the user to earn additional profits from their actions.

[1894] Furthermore, the server automatically generates and displays advertisements related to the user's specific interests based on the analysis results using natural language processing. These advertisements are customized based on the user's past behavioral data, which can lead to greater effectiveness.

[1895] For example, if User A posts a travel experience story about Bali, keywords such as "Bali," "surfing experience," and "local cuisine" are extracted. The next time User A logs in, advertisements for popular hotels and surfing lessons in Bali are recommended to User A.

[1896] Examples of prompts for generative AI models include:

[1897] "A user posted a travel story about their experience in Bali. Please extract keywords such as 'Bali,' 'surfing experience,' and 'local cuisine.'"

[1898] Something like this.

[1899] As described above, the present invention is a system that effectively utilizes traveler's experiences and appropriately recommends related content and advertisements, thereby improving user convenience.

[1900] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1901] Step 1:

[1902] The server receives travel stories written by users. Users enter their travel experiences in text format and post them by pasting the text data into a dedicated form. The server receives the posted text data and stores it in a database.

[1903] Input: Travel experiences posted by users (text data)

[1904] Output: Travel stories stored in a database

[1905] Step 2:

[1906] The server passes the saved travel stories to a natural language processing (NLP) engine for analysis. The NLP engine uses a generative AI model such as Hugging Face's Transformer. The server extracts key topics and keywords from the text data analyzed by the NLP engine.

[1907] Input: Travel stories stored in the database

[1908] Output: Extracted main topics and keywords

[1909] Step 3:

[1910] The server then recommends related content to other travelers based on the extracted topics and keywords. The server also takes into account the user's past behavioral data. Specifically, the server generates a recommendation list and saves it in association with the user's account.

[1911] Input: Extracted topics and keywords, and user behavior data

[1912] Output: Generated recommendation list

[1913] Step 4:

[1914] The server tracks users' actions when they view recommended content, specifically recording actions such as viewing, liking, and sharing, and stores this data in a database.

[1915] Input: User action data for views, likes, and shares

[1916] Output: Action data stored in the database

[1917] Step 5:

[1918] The server calculates reward points based on the user's action data and grants them to the user's account. Specifically, the server adds up the points set for each action and adds the total to the user's account.

[1919] Input: User action data

[1920] Output: Calculated reward points and their allocation

[1921] Step 6:

[1922] The server recommends products and services from travel agencies and related businesses, and calculates the commission when the user purchases them. The purchase information is linked to the user's account, and a portion of the commission is returned to the user as a reward.

[1923] Input: User purchase information and product fee information

[1924] Output: Calculated commission and reward points redeemed

[1925] Step 7:

[1926] The server generates advertisements based on the analyzed travel experience data and related to the user's interests. The advertisements are then displayed in the user's account. The content of the advertisements is determined based on keywords analyzed by the NLP engine and the user's behavioral data.

[1927] Input: Parsed keywords and user behavior data

[1928] Output: The generated ad and its display

[1929] Step 8:

[1930] The server aggregates all data and provides analysis results to travel agencies and related businesses, allowing them to plan travel products and services that are in high demand.

[1931] Input: User behavior data, purchase data, advertising data

[1932] Output: Feedback of analysis results

[1933] 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.

[1934] This invention combines an emotion engine with a system that collects travel stories written by travelers, analyzes them, and recommends them. Specifically, the program operates as follows:

[1935] 1. Users enter and upload their travel experiences

[1936] A user writes a travel experience or blog post and logs into the system from a terminal.

[1937] The terminal inserts the created content into an upload form.

[1938] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1939] The server stores the received content data in a database.

[1940] The server passes the stored data to a natural language processing (NLP) engine and then to an emotion engine.

[1941] 2. Content analysis, keyword extraction, and emotion recognition

[1942] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1943] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[1944] The server stores the extracted keywords and emotion information in a relational database.

[1945] 3. Targeting by recommendation engines

[1946] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[1947] The server generates a list of relevant content recommendations for the user, taking into account emotional information.

[1948] The next time the user logs in, the server will display content from the recommendation list.

[1949] 4. User browsing and emotional feedback

[1950] The user will see the recommended experiences the next time they log in.

[1951] The server tracks users' browsing history and click behavior.

[1952] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the action. For example, if a user expresses emotions such as "empathy" or "movement" by reading a story, this is also stored in the database.

[1953] 5. Compensation and Remuneration Management

[1954] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[1955] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[1956] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[1957] 6. Product purchases and commissions

[1958] If the user purchases a recommended product or service, the server tracks the purchase.

[1959] The server calculates the commission and rewards at the time of the purchase.

[1960] The server returns a part of the fee to the user who posted the experience as points.

[1961] 7. Industry data analysis and feedback

[1962] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[1963] The server provides the analysis results to travel agencies and related businesses.

[1964] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[1965] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[1966] Specific examples

[1967] Example 1: User A posts a travel story

[1968] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving" using an emotion engine. Based on the keyword information and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand and who also show similar emotions.

[1969] Example 2: User B is planning a trip

[1970] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." User B reads the book and decides to travel to Thailand. He also books an elephant riding tour based on the information in the book. The server detects that User B has booked the elephant riding tour and returns a portion of the profits to User A, who posted the book, as points. In addition, if User B expresses emotion or empathy through emotional feedback, this is also recorded in the database.

[1971] Example 3: Revitalizing the entire industry

[1972] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry. Furthermore, data based on emotional feedback can be used to provide products that increase user satisfaction.

[1973] In this way, each process is closely linked, and a system is implemented in which travel stories posted by users provide value to other users and contribute to the revitalization of the entire travel industry.

[1974] The processing flow will be explained below.

[1975] Step 1:

[1976] A user writes a travel experience or blog post and logs into the system from a terminal.

[1977] Step 2:

[1978] The terminal inserts the created content into an upload form.

[1979] Step 3:

[1980] When the user clicks the "Post" button, the terminal transmits the content data to the server.

[1981] Step 4:

[1982] The server stores the received content data in a database.

[1983] Step 5:

[1984] The server passes the stored data to a natural language processing (NLP) engine and an emotion engine.

[1985] Step 6:

[1986] The server's NLP engine analyzes the uploaded content and extracts key topics and keywords, such as "Thailand travel," "elephant riding experience," and "local cuisine."

[1987] Step 7:

[1988] The server's emotion engine recognizes the user's emotions in the analyzed content, extracting emotions such as "fun," "excitement," and "movement."

[1989] Step 8:

[1990] The server stores the extracted keywords and emotion information in a relational database.

[1991] Step 9:

[1992] The server categorizes the content based on the stored keywords and sentiment information.

[1993] Step 10:

[1994] The server identifies users from an existing user database who have a topic of interest (e.g., "travel to Thailand") and who exhibit a particular emotion.

[1995] Step 11:

[1996] The server generates a list of relevant content recommendations for the user, taking the extracted emotion information into consideration.

[1997] Step 12:

[1998] The next time the user logs in, the server will display content from the recommendation list.

[1999] Step 13:

[2000] The user browses the recommended experiences.

[2001] Step 14:

[2002] The server tracks users' browsing history and click behavior.

[2003] Step 15:

[2004] When a user "likes" or "shares" a story, the server stores that information and uses an emotion engine to analyze the emotions associated with the user's actions. For example, emotions such as "empathy" and "emotion" are recognized.

[2005] Step 16:

[2006] The server also stores the emotional feedback information in a database.

[2007] Step 17:

[2008] The server calculates points and rewards based on the number of views, likes, shares, and emotional feedback of the experience.

[2009] Step 18:

[2010] Based on the calculation results, the server transfers points to the account of the user who posted the experience.

[2011] Step 19:

[2012] The server simultaneously recommends products and services from travel agencies and related businesses to the user.

[2013] Step 20:

[2014] If the user purchases a recommended product or service, the server tracks the purchase.

[2015] Step 21:

[2016] The server calculates the commission and rewards at the time of the purchase.

[2017] Step 22:

[2018] The server returns a part of the fee to the user who posted the experience as points.

[2019] Step 23:

[2020] The server aggregates and analyzes all data (browsing history, purchase history, user feedback, emotional feedback).

[2021] Step 24:

[2022] The server provides the analysis results to travel agencies and related businesses.

[2023] Step 25:

[2024] Travel agencies use the data provided to plan in-demand tour packages and campaigns.

[2025] Step 26:

[2026] The server recommends new product and service information to the user, and the flow from browsing to purchasing is repeated again.

[2027] Example 2

[2028] 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."

[2029] Conventional systems have difficulty effectively analyzing travel stories written by travelers and providing useful information to other travelers and travel agencies. Furthermore, rewards and product recommendations based on travelers' experiences are insufficient, resulting in a lack of systems that contribute to revitalizing the travel industry as a whole. Furthermore, there is a lack of recommendation and reward systems that utilize users' emotional feedback, so an improvement in user experience is needed.

[2030] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving travel stories written by travelers and saving them in a database, means for analyzing the saved travel stories using a natural language processing engine to extract main topics and keywords, means for analyzing the saved travel stories using a sentiment analysis engine to extract emotional information, means for identifying relevant target travelers and recommending content based on the extracted keywords and emotional information, means for tracking travelers' actions such as viewing, liking, and sharing content, means for calculating rewards based on travelers' actions and adding reward points to their accounts, means for recommending products and services from travel agencies and related businesses, and calculating commissions and returning them as rewards when a purchase is made, and means for aggregating and analyzing all data and providing demand information to travel agencies. This allows for efficient analysis and recommendation of travel stories, improving reward systems and product recommendations for travelers, and revitalizing the entire travel industry.

[2031] A "travel journal" is a record of a traveler's travel experiences, expressed in writing and images.

[2032] A "database" is a system for efficiently storing, managing, and searching information and data.

[2033] A "natural language processing engine" is a technology that analyzes text data written in natural language and understands and extracts meaning and structure.

[2034] An "emotion analysis engine" is a technology that analyzes and identifies emotional elements (e.g., joy, sadness, surprise, etc.) contained in text data.

[2035] A "keyword" is a word or phrase that has a particularly significant meaning in text data.

[2036] A "topic" is a major theme or subject in text data.

[2037] "Traveller" means an individual who travels or provides information about travel.

[2038] "Content" refers to information or entertainment content created or consumed by users, including text, images, and video.

[2039] "Target travelers" are travelers with specific requirements or interests.

[2040] "Action" refers to a specific action a user takes on the system (e.g., view, like, share, etc.).

[2041] "Reward" refers to valuable consideration given to a user for their actions or contributions.

[2042] "Points" refer to numerical units given to users as rewards.

[2043] "Travel agency" refers to a company or organization that plans, provides, and sells travel-related products.

[2044] "Affiliated Businesses" refers to businesses and suppliers that provide travel-related goods and services.

[2045] "Fees" means the fees payable for the provision of Services.

[2046] "Demand information" refers to data about consumer demand and market trends.

[2047] A "tour package" refers to a comprehensive travel plan that includes travel destinations, accommodation, transportation, etc.

[2048] "Analysis results" refers to the analysis results of data obtained through the processes of natural language processing and sentiment analysis.

[2049] This invention is implemented by combining a natural language processing (NLP) engine and a sentiment analysis engine in a system that collects, analyzes, and recommends travel stories written by travelers. Specifically, the system operates in the following way.

[2050] Hardware and software used

[2051] The hardware used is the user's device (PC, smartphone, tablet, etc.) and a cloud server. The software used is a natural language processing engine (e.g., SpaCy or BERT) and a sentiment analysis engine (e.g., Affectiva or IBM Watson Tone Analyzer). A database (e.g., MySQL or PostgreSQL) is also required to store each piece of data.

[2052] System Operation

[2053] 1. Users enter and upload their travel experiences

[2054] The user creates a travel experience report and logs into the system from a terminal.

[2055] The terminal inserts the created content into an upload form, and when the user clicks the "Submit" button, the data is sent to the server.

[2056] The server receives the HTTP request and stores the content in a database in JSON format.

[2057] The server passes the stored data to an NLP engine and a sentiment analysis engine.

[2058] 2. Content analysis, keyword extraction, and emotion recognition

[2059] The server's NLP engine processes the content using natural language processing to extract key topics and keywords, such as "travel to Thailand" and "elephant riding experience."

[2060] The server's emotion analysis engine analyzes the content's positive and negative emotions and identifies emotions such as "fun" and "moving."

[2061] The server stores the extracted keywords and emotion information in a relational database.

[2062] 3. Targeting by recommendation engines

[2063] The server queries the user database to find users who are interested in specific keywords or sentiments, for example, by running an SQL query to identify users interested in "traveling to Thailand."

[2064] The server generates a recommendation list for the relevant user and displays the relevant content on the user's dashboard.

[2065] 4. User browsing and emotional feedback

[2066] Users can view the recommended experiences and "like" or "share" them as needed.

[2067] The server tracks and stores user browsing behavior using cookies and session data.

[2068] The server collects information on users clicking "like" or "share" and uses an emotion analysis engine to analyze the emotions associated with the user's actions.

[2069] 5. Compensation and Remuneration Management

[2070] The server calculates reward points based on the number of views, likes, and shares of the experience. The formula and algorithm used to calculate the points is the built-in point management system.

[2071] Based on the calculation result, the server updates the points field in the user table in the database to add points to the user's account.

[2072] The server recommends product information from travel agencies and related businesses to the user, and sends email notifications and displays dashboards.

[2073] 6. Product purchases and commissions

[2074] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process.

[2075] The server tracks purchase information in cooperation with the EC system and records purchase history.

[2076] The server calculates the commission and reward when the purchase is completed, and returns the purchase information and reward to the user who posted the experience as points.

[2077] 7. Industry data analysis and feedback

[2078] The server aggregates and analyzes all log data (browsing history, purchase history, etc.) using big data analysis tools (e.g., Apache Hadoop or Spark).

[2079] The server provides the analysis results to travel agencies and related businesses in the form of dashboards and CSV reports.

[2080] Based on this data, travel agencies can plan tours and campaigns that are in high demand and develop effective marketing strategies.

[2081] Specific examples

[2082] Example 1: User A posts a travel story

[2083] User A writes an account of his / her trip to Thailand and submits it to the system from his / her terminal. The server analyzes the submitted content and extracts keywords such as "Thailand trip," "elephant riding experience," and "local cuisine," as well as emotions such as "fun" and "moving." Based on these keywords and emotion information, the server recommends the account to users B and C who are interested in traveling to Thailand.

[2084] Example 2: User B is planning a trip

[2085] When User B is searching for his next travel destination, the server recommends a "Thailand travel experience book." After reading the book, User B decides to travel to Thailand and books an elephant riding tour. The server detects that User B has booked the tour and returns a portion of the profits to User A as points. User B's emotional feedback is also recorded in the database.

[2086] Example 3: Revitalizing the entire industry

[2087] The server centrally analyzes popular travel destinations and services and provides the data to travel agencies and related businesses. Travel agencies use this data to plan in-demand tour packages and campaigns and promote sales. If users become interested in the proposed travel products and purchase them, it will lead to the revitalization of the entire travel industry.

[2088] Prompt Sentence Examples

[2089] "Please submit a travel story. In your story, please write in detail about the places you went, what you did, and how you felt. For example, 'I had fun riding an elephant in Thailand,' or 'I enjoyed the local cuisine.'"

[2090] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2091] Step 1:

[2092] A user creates a travel story and logs into the system from their terminal. They enter their login information (user ID, password) and click the "Post" button. The input is the login information and the story text, which the terminal sends to the server in JSON format. The server stores the received data in a database. Specifically, the server receives an HTTP request and executes an INSERT statement in the MySQL database.

[2093] Step 2:

[2094] The server passes the stored travel story data to a natural language processing engine to extract key topics and keywords. The input is the text data of the travel story, and the NLP engine (e.g., SpaCy) analyzes the text to identify keywords. Specifically, it sends an API request to the NLP engine and stores the keywords returned in response in a database.

[2095] Step 3:

[2096] The server passes the saved travel story data to a sentiment analysis engine to extract emotional information. The input is the text data of the travel story, and the sentiment analysis engine (e.g., IBM Watson Tone Analyzer) analyzes the text to identify the emotion. Specifically, it sends an API request to the sentiment analysis engine, and stores the emotional information returned as a response in a database.

[2097] Step 4:

[2098] The server identifies relevant target travelers based on the extracted keywords and emotion information and recommends content. The input is the saved keywords and emotion information, and the server queries the user database to identify relevant travelers. Specifically, it executes an SQL query to extract the target user ID and generates a recommendation list based on it.

[2099] Step 5:

[2100] The next time the user logs in, the server will display content from the recommendation list on the user's dashboard. The input is the generated recommendation list, and the server updates the user's dashboard. Specifically, when the user logs in, the server uses an HTML template engine to display the recommended content.

[2101] Step 6:

[2102] The user views the recommended content and "likes" or "shares" it as needed. The server tracks these actions and stores them using cookies or session data. The input is the user action, which the server stores in a database. Specifically, JavaScript is used to capture the user's click event and send it to the server via an AJAX request.

[2103] Step 7:

[2104] The server analyzes the emotional information based on the user's actions and calculates the reward. The input is the user's action data, and the emotion is identified using the emotion analysis engine, and the reward is calculated using the point management system. Specifically, the analysis results are saved in the database and the point table is updated.

[2105] Step 8:

[2106] Based on the calculation results, the server adds points to the user's account and recommends product information from travel agencies and related businesses. The input is the calculated reward points and related product information, which the server notifies the user. Specific operations include email notification and dashboard updates.

[2107] Step 9:

[2108] When a user wants to purchase a recommended product, they access the purchase form and complete the purchase process. The server tracks the purchase information and records the purchase history. The input is the purchase information, which the server saves in the database. Specifically, it calls the purchase API to record the purchase data and calculates reward points.

[2109] Step 10:

[2110] The server aggregates and analyzes all data (browsing history, purchase history, etc.) and provides demand information to travel agencies. The input is accumulated log data, which is aggregated and analyzed using big data analysis tools. Specifically, the analysis is performed using Hadoop or Spark, and the results are visualized in CSV reports and dashboards.

[2111] In this way, each processing step works together, and a system is realized in which travel stories posted by users provide value to other travelers and contribute to the revitalization of the entire travel industry.

[2112] (Application example 2)

[2113] 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."

[2114] Conventional travel and restaurant review systems have been unable to take into account the interests and emotions of users, making it difficult to provide users with appropriate and specific recommendations. Furthermore, due to the lack of reward systems based on user feedback and measures to promote the use of food delivery services, it has been difficult to improve user satisfaction and stimulate usage.

[2115] 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.

[2116] In this invention, the server includes means for receiving travel stories written by travelers and storing them in a database, means for analyzing the stored travel stories using natural language processing to extract key topics and keywords, means for recommending content related to other travelers based on the extracted topics and keywords, means for tracking travelers' actions of viewing, liking, and sharing content, means for calculating rewards and granting reward points to accounts based on the travelers' actions, means for analyzing posts by related users and calculating similarity, means for recommending content related to other users based on the extracted similarity, means for recommending restaurants and dishes for food delivery, means for providing discount coupons based on user ratings, means for promoting the use of food delivery, and means for recommending products and services on the food delivery platform and calculating fees and returning them as rewards when a purchase is made. This enables more appropriate and specific content, restaurants, and dishes to be recommended to users, thereby improving user satisfaction and revitalizing usage.

[2117] A "traveler" is an individual who travels or who has traveled and therefore writes about their travel experiences.

[2118] A "travel journal" is a written record of a traveler's experiences and impressions during and after a trip.

[2119] A "database" is a system that systematically collects, stores, and manages multiple data.

[2120] "Natural language processing" is a technology that allows computers to understand, generate, and manipulate human language (natural language).

[2121] A "main topic" is a topic or theme that is centrally addressed in the text being analyzed.

[2122] "Keywords" are words or phrases that are particularly important in a piece of text.

[2123] "Content" means information such as text and images created by users and posted to the system.

[2124] "Tracking" refers to technologies and methods that record and track user behavior.

[2125] "Reward points" are points of a specific value that are awarded for a user's actions.

[2126] "Similarity" refers to the degree to which two or more objects are similar to each other.

[2127] "Food delivery" refers to a service where food and drinks can be ordered and delivered.

[2128] "User Ratings" means feedback or ratings posted by users regarding content or services.

[2129] A "discount coupon" is a coupon that provides the right to purchase a product or service at a lower price than usual.

[2130] "Fees" are fees incurred in connection with transactions or the provision of services.

[2131] The system for implementing this invention comprises a server, a terminal, and a user. The specific operation and configuration of the system will be described below.

[2132] System Configuration

[2133] The system includes the following hardware and software:

[2134] Hardware: Servers, user devices (smartphones, tablets, PCs)

[2135] Software: Natural language processing engine, sentiment analysis engine, database management system, recommendation engine

[2136] Program Operation

[2137] 1. Receiving and storing travel stories

[2138] Users create travel stories from their own devices and post them to the system. The devices send the created content to the database, and the server stores the received data. This operation accumulates new travel stories in the system's database.

[2139] 2. Natural Language Processing and Sentiment Analysis

[2140] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy) to extract key topics and keywords, then analyzes the sentiment contained in the content using a sentiment analysis engine (e.g., TextBlob). The analysis results are stored in a database.

[2141] 3. Content Recommendations

[2142] The server recommends relevant content to other travelers based on the extracted topics, keywords, and sentiment information. The recommendation engine uses similarity calculations (e.g., cosine similarity) to select the most relevant content.

[2143] 4. Tracking User Actions

[2144] The server tracks user actions such as viewing, liking, and sharing content, which allows the server to store user interests and behavioral history in a database to improve recommendation accuracy.

[2145] 5. Reward points awarded

[2146] Based on the user's actions, the server calculates reward points and credits them to the user's account. This reward system encourages users to participate.

[2147] 6. Food delivery recommendations

[2148] The system also handles content related to food delivery, analyzes users' dining experiences (e.g., restaurant reviews), and recommends the best restaurants and dishes to other users. It can also offer discount coupons based on the results of sentiment analysis.

[2149] Specific examples

[2150] Example 1: User A posts a travel story stating, "The Thai food was excellent and the atmosphere was great." The server receives this story, extracts keywords such as "Thai food" and "atmosphere" using a natural language processing engine, and determines positive sentiment using a sentiment analysis engine. Based on this, the server generates a list of recommended food delivery services relevant to User A.

[2151] Example 2: User B posts a review saying, "I had a great dining experience at a new sushi restaurant." The server similarly analyzes this review and extracts keywords such as "sushi" and "great dining experience" and sentiments. As a result, the server recommends this sushi restaurant to other users who have similar travel experiences.

[2152] Example prompt sentence:

[2153] The Thai food was amazing and the ambiance was delightful.

[2154] I had a spectacular dining experience at the new sushi place.

[2155] The pizza was okay, but the pasta was incredible.

[2156] This will enable us to recommend appropriate content, restaurants, and dishes to users, thereby improving user satisfaction and stimulating usage.

[2157] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2158] Step 1:

[2159] Users create travel stories from their own devices and post them to the system. The user inputs the text data of the travel story. The device sends it to the database, where it is received and stored by the server. This allows new travel stories to be stored in the system's database.

[2160] Step 2:

[2161] The server passes the stored travel stories to a natural language processing engine (e.g., Spicy). It receives the travel story text data as input and extracts key topics and keywords. This data processing identifies important keywords and topics and produces a keyword list as output.

[2162] Step 3:

[2163] The server passes the keyword list obtained from the natural language processing engine to a sentiment analysis engine (e.g., TextBlob). The input is text data, which undergoes sentiment analysis to quantify the user's sentiment. This data calculation produces an emotional score, such as positive, negative, or neutral, as the output.

[2164] Step 4:

[2165] The server stores the keyword list and sentiment scores in a database. The input is the analysis results, which are then associated with user data and stored. This allows information related to travel experiences to be managed in an organized manner within the database.

[2166] Step 5:

[2167] The server recommends relevant content to other users based on the extracted keywords and emotion information. The recommendation engine takes the keywords and emotion scores as input and performs a similarity calculation (e.g., cosine similarity). This data calculation identifies the most relevant content and recommends it to the user.

[2168] Step 6:

[2169] The server tracks user actions such as viewing, liking, and sharing content. The input is the log data of user actions, which is then stored in a database. This process records user interests and behavioral history.

[2170] Step 7:

[2171] The server calculates reward points based on the user's actions and adds them to the user's account. The input is the evaluation data of the user's actions, and points are calculated based on that and reflected in the user's account. This process strengthens the user's incentive.

[2172] Step 8:

[2173] The server analyzes food delivery content and recommends related restaurants and dishes. The input is travel experiences related to food delivery, and the server identifies the user's preferences through natural language processing and sentiment analysis. This data processing generates a list of food delivery recommendations that are optimal for the user.

[2174] Step 9:

[2175] The server provides discount coupons based on user ratings. The input is user rating data, and appropriate discount coupons are calculated based on the rating content and provided to the user. This process promotes the use of food delivery services.

[2176] Step 10:

[2177] The server recommends products and services on the food delivery platform to users, and when a purchase is made, calculates a commission and returns it to the user as a reward. The input is product purchase data, and the commission is calculated based on that and returned to the user. This stimulates economic activity within the system.

[2178] 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.

[2179] 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.

[2180] 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.

[2181] 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 identif...

Claims

1. A means for receiving travel experience stories written by travelers and storing them in a database; A method for analyzing saved travel stories using natural language processing to extract key topics and keywords; A means of recommending relevant content to other travelers based on the extracted topics and keywords; and A means to track traveler content viewing, liking, and sharing actions; a means for calculating rewards and crediting reward points to an account based on actions taken by a traveler; A system that recommends products and services from travel agencies and related businesses, calculates commissions when a purchase is made, and includes a means of returning the commission as a reward.

2. 2. The system according to claim 1, further comprising means for providing information on travel products that are in high demand to travel agents based on the analysis of the stored data.

3. 2. The system of claim 1, further comprising means for travel agents to plan new tour packages and services based on the provided demand information and make recommendations within the system.

Images