System

Abstract

A system is provided.SOLUTION: A system comprising: input means for inputting a user's psychological state or mood; analysis means for collecting data of the input psychological state or mood and analyzing the data by a machine learning algorithm; selection means for selecting an optimal travel destination based on an analysis result; generation means for generating a specific travel plan based on the selected travel destination; and feedback means for collecting feedback from the user and using the feedback to improve accuracy of the analysis means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many modern people feel stressed and tired from their busy daily lives, making it difficult to decide on an appropriate vacation destination. This prevents them from taking a meaningful vacation that effectively refreshes them, and gathering information requires a lot of time and effort. Furthermore, vacation planning can seem cumbersome and troublesome, leading many to postpone the vacation altogether. The present invention aims to solve these problems by providing a system that allows modern people to easily plan and realize a more relaxing and refreshing vacation. [Means for solving the problem]

[0005] The present invention solves the problems by a system including an input means for inputting a user's psychological state and mood, an analysis means for collecting the input psychological state and mood data and analyzing it using a machine learning algorithm, a selection means for selecting an optimal travel destination based on the analysis results, a generation means for generating a specific travel plan based on the selected travel destination, a display means for displaying the generated travel plan on a user terminal, and a feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This system allows users to find an optimal vacation destination without exerting any effort themselves, and to take a meaningful vacation that effectively relieves stress and fatigue.

[0006] "Input means" refers to a device or interface for inputting the user's psychological state or mood into the system.

[0007] "Analysis means" refers to a device or program that collects input psychological state and mood data and analyzes it using a machine learning algorithm.

[0008] The "selection means" refers to a device or program that selects the most suitable travel destination for the user based on the analysis results.

[0009] The "creation means" refers to a device or program for creating a specific travel plan based on the selected travel destinations.

[0010] The "display means" refers to a device or interface for displaying the generated travel plan on a terminal so that the user can check it.

[0011] "Feedback means" refers to devices or programs that collect feedback from users and use it to improve the accuracy of the analysis means.

[0012] An "interactive means" is a device or interface that presents questions to a user in an interactive format and allows the user to input answers.

[0013] The "search means" is a device or program that searches a database for travel destination candidates that have a high refreshing effect based on the user's psychological state and mood. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that uses AI to suggest optimal vacation destinations for busy modern people to refresh their minds and bodies. The system includes the following means.

[0036] 1. Entering user information

[0037] The server presents interactive questions to the user via the terminal to collect information about the user's psychological state and mood. For example, it might ask, "What is your stress level these days?"

[0038] The terminal displays these questions on the screen and receives answers entered by the user.

[0039] The user inputs information about their psychological state and mood and sends it to the server via their terminal.

[0040] 2. Data collection and analysis

[0041] The server collects and temporarily stores the received data.

[0042] The server uses machine learning algorithms to analyze the collected data and understand the user's detailed psychological state and mood.

[0043] 3. Travel destination recommendations

[0044] Based on the analysis results, the server selects the travel destination that best suits the user's requirements, including places rich in nature and places known for their refreshing effects.

[0045] The server uses generative AI technology to generate a specific travel plan based on the selected travel destination, including detailed information on tourist attractions, accommodations, and dining options.

[0046] 4. Displaying the results

[0047] The server transmits the generated travel destination and travel plan to the user terminal.

[0048] The terminal displays the received travel destination and plan on the screen so that the user can check it.

[0049] 5. Gathering User Feedback

[0050] The server displays a question on the terminal to prompt the user to input feedback.

[0051] The user inputs their opinions and suggestions for improvement regarding the proposed travel plan.

[0052] The terminal receives the user's feedback and transmits it to the server.

[0053] The server stores the collected feedback to help improve future suggestions.

[0054] Specific examples

[0055] As an example, consider a case where a user is experiencing moderate stress and prefers to enjoy nature.

[0056] 1. Example Questions and Answers

[0057] Server: "What's your stress level these days?"

[0058] User: "Medium"

[0059] Server: "What activities do you like?"

[0060] User: "Enjoying nature"

[0061] 2. Data collection and analysis

[0062] The server collects the user's responses and analyzes them using a machine learning algorithm. Based on the analysis results, it is determined that the user can expect a refreshing effect in a place rich in nature.

[0063] 3. Travel destination recommendations

[0064] The server selects "Furano in Hokkaido" as the travel destination based on the conditions.

[0065] The server generates travel plans such as "viewing flower fields, visiting hot springs, and enjoying local cuisine."

[0066] 4. Displaying the results

[0067] The server sends the generated travel plan to the terminal, and the terminal suggests to the user "viewing the flower fields and visiting hot springs in Furano, Hokkaido."

[0068] 5. Gathering Feedback

[0069] Server: "Please give us your feedback on this plan."

[0070] User: "I wish there was more time to admire the flower fields."

[0071] The server stores the user's feedback and uses it to improve the accuracy of the next suggestion.

[0072] As described above, the present invention is a system that can maximize the user's refreshing effect by providing optimal travel destinations and detailed travel plans based on the user's psychological state and mood.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[0076] Step 2:

[0077] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[0078] Step 3:

[0079] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[0080] Step 4:

[0081] The terminal transmits the answer entered by the user to the server.

[0082] Step 5:

[0083] The server receives the response data sent from the terminal and temporarily stores it.

[0084] Step 6:

[0085] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[0086] Step 7:

[0087] The server analyzes the data using machine learning algorithms and applies models to understand the user's detailed psychological state and mood.

[0088] Step 8:

[0089] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, searching the database for potential travel destinations that match the criteria.

[0090] Step 9:

[0091] The server generates a specific travel plan based on the selected travel destinations, such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine."

[0092] Step 10:

[0093] The server transmits the generated travel plan to the user terminal.

[0094] Step 11:

[0095] The terminal visually displays the received travel destination and plan to the user, for example, "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[0096] Step 12:

[0097] The server generates a question prompting the user to input feedback and transmits it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[0098] Step 13:

[0099] The terminal displays a question on the screen prompting feedback input.

[0100] Step 14:

[0101] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0102] Step 15:

[0103] The terminal receives the user's feedback and transmits it to the server.

[0104] Step 16:

[0105] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0106] Example 1

[0107] 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."

[0108] Modern people often feel stressed in their busy daily lives and need to refresh their minds and bodies. However, finding a vacation destination that suits them is not easy. In addition, selecting a travel destination and creating a travel plan takes time and effort, so there is a need for a system that can suggest travel destinations that are more efficient, appropriate, and have a high refreshing effect.

[0109] 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.

[0110] In this invention, the server includes: an input means for inputting the user's psychological state and mood; a collection means for collecting the input psychological state and mood data and temporarily storing it in a database; an analysis means for analyzing the collected data using a machine learning algorithm to understand the user's detailed psychological state and mood; a selection means using a recommender system to select optimal travel destinations based on the analysis results; a generation means using a generative AI model to generate a specific travel plan based on the selected travel destinations; a display means for sending the generated travel plan to a user terminal and displaying it; a collection means for collecting feedback from users and storing the feedback data in a database; and a feedback analysis means for analyzing the collected feedback and using it to improve the accuracy of the analysis means. This enables busy modern people to efficiently and accurately find the optimal travel destination for themselves, maximizing the benefits of refreshing their mind and body.

[0111] "Input means" refers to a means by which a user inputs data relating to his or her own psychological state or mood.

[0112] "Collection means" refers to the means for receiving input data and feedback and temporarily storing them in a database.

[0113] A "database" is an information storage device for temporarily storing collected data and feedback.

[0114] The "analysis means" is a means for analyzing collected data using machine learning algorithms to understand the user's detailed psychological state and mood.

[0115] A "machine learning algorithm" is a mathematical technique for analyzing collected data and finding patterns and relationships.

[0116] A "recommender system" is a system that recommends suitable travel destinations to users based on analysis results.

[0117] The "selection means" is a means for selecting the optimal travel destination based on the analysis results using a recommender system.

[0118] A "generative AI model" is an artificial intelligence model that generates specific travel plans based on selected travel destinations.

[0119] "Generation means" refers to a means for creating a specific travel plan based on the selected travel destinations using a generative AI model.

[0120] The "display means" is a means for transmitting the generated travel plan to the user terminal and displaying it.

[0121] "Feedback" refers to opinions and information on improvements to travel plans provided by users.

[0122] The "feedback analysis means" is a means for analyzing collected feedback and using it to improve the accuracy of the analysis means.

[0123] The system of this invention proposes optimal travel destinations based on the user's psychological state and mood, creates specific travel plans, and provides them to the user. The various hardware and software required for this purpose are described below.

[0124] First, the server generates a question to accept user input and sends it to the device. This question generation is often done using a web server or backend API server running on the server. This API server uses JavaScript or Python. Examples of questions that can be generated include specific questions such as "What is your stress level these days?" and "What activities do you like to do?"

[0125] The device displays the questions received from the server using HTML and JavaScript. The user answers the questions and sends the answers to the server. For example, the user might enter answers such as "My stress level is moderate" or "I enjoy nature."

[0126] The server then temporarily stores the received user responses in a database (e.g., MySQL), and then uses a Python machine learning library (e.g., scikit-learn) to analyze the collected data and understand the user's detailed psychological state.

[0127] A recommender system is used to select the optimal travel destination based on the analysis results. Collaborative filtering algorithms are often used in recommender systems. Then, a generative AI model (e.g., GPT-4) is used to generate a specific travel plan based on the selected travel destination. The generative AI is input with a prompt sentence such as the following:

[0128] "If the user is experiencing moderate stress and enjoys nature, suggest the best travel plan."

[0129] The generated travel plan is sent from the server to the device and displayed on the device using HTML and JavaScript. The travel plan includes detailed information such as tourist spots, accommodations, and places to eat. For example, a plan called "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" may be generated.

[0130] Furthermore, the server collects feedback from users and stores it in a database. This feedback is used to improve the accuracy of the machine learning algorithm. An example of feedback could be a specific request such as "I would like more time to admire the flower fields."

[0131] In this way, by combining servers, terminals, generative AI models, and machine learning algorithms, the system can suggest optimal travel destinations and detailed travel plans to users, and improve the accuracy of the system by incorporating feedback.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] The server generates questions about the user's psychological state and mood and sends them to the device. The input is a question template pre-configured on the server, and the output is structured JSON-formatted question data. In operation, the server generates an API request and sends it to the device.

[0135] Step 2:

[0136] The terminal receives question data sent from the server and displays it on the screen using HTML and JavaScript. The input is JSON format data sent from the server, and the output is a question form displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[0137] Step 3:

[0138] The user enters answers to questions displayed on the terminal. The input is the user's answer, and the output is the answer data in JSON format that is sent by the terminal to the server. In operation, the user enters data into the form and presses the submit button.

[0139] Step 4:

[0140] The server receives the user's response data and temporarily stores it in a database. The input is the JSON formatted response data sent from the device, and the output is a record stored in the database. The server then validates the data and executes an INSERT query.

[0141] Step 5:

[0142] The server analyzes the stored data using a machine learning algorithm to evaluate the user's psychological state and mood. The input is the response data stored in the database, and the output is the user's psychological state data as the analysis result. In operation, the server runs a Python script and analyzes the data using a machine learning model.

[0143] Step 6:

[0144] Based on the analysis results, the server uses a recommender system to select the optimal travel destination. The input is the user's psychological state data as the analysis result, and the output is the selected travel destination data. In operation, the recommender algorithm compares the user data with the travel destination data to generate the optimal proposal.

[0145] Step 7:

[0146] The server uses a generative AI model to generate a specific travel plan based on the selected travel destination. The input is the selected travel destination data and a prompt, and the output is specific travel plan data. The prompt uses the following: "If the user is experiencing moderate stress and prefers to enjoy nature, please suggest the optimal travel plan."

[0147] Step 8:

[0148] The server sends the generated travel plan data to the device. The input is the travel plan data obtained from the generative AI model, and the output is the JSON-formatted travel plan data sent to the device. In operation, the server generates an API request and sends it to the device.

[0149] Step 9:

[0150] The device displays the received travel plan data on the screen using HTML and JavaScript. The input is the JSON-formatted travel plan data sent from the server, and the output is the travel plan displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[0151] Step 10:

[0152] The server sends feedback questions to the terminal and receives feedback from the user. The input is the feedback question as a prompt sentence, and the output is feedback data from the user. The question includes, "Please give us your feedback on this plan."

[0153] Step 11:

[0154] The user enters answers to feedback questions and sends them to the server. The input is the user's feedback, and the output is the JSON-formatted feedback data sent by the device to the server. In operation, the user enters data into the form and presses the submit button.

[0155] Step 12:

[0156] The server receives user feedback and stores it in a database. The input is the JSON-formatted feedback data sent from the device, and the output is a record stored in the database. The server validates the data and executes an INSERT query.

[0157] Step 13:

[0158] The server analyzes the feedback data to help improve the accuracy of the machine learning algorithm. The input is the feedback data stored in the database, and the output is an improved machine learning model. In operation, a new training dataset is created based on the feedback data, and the model is retrained.

[0159] (Application example 1)

[0160] 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."

[0161] In modern society, many people are seeking effective ways to reduce stress in their daily lives and refresh their minds and bodies. However, it is not easy to find a refreshing activity or plan that best suits each user's psychological state and mood. Furthermore, no system exists that efficiently and effectively suggests these refreshing activities or collects feedback. The present invention aims to solve these problems and provide a system that provides users with the optimal refreshing plan.

[0162] 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.

[0163] In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input psychological state and mood data and analyzing it using a machine learning algorithm, selection means for selecting an optimal refreshment plan based on the analysis results, generation means for generating a specific refreshment activity plan based on the selected plan, display means for displaying the generated refreshment activity plan on the user terminal, feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means, generation means for generating a detailed plan for refreshment activities by utilizing a generative AI model, and dialogue means for presenting questions to the user in an interactive format and allowing the user to input answers. This makes it possible to provide a refreshment plan optimal for the user's psychological state and mood and to collect feedback that is useful for improving the accuracy of suggestions.

[0164] Key Word Definitions

[0165] "Input means" refers to a technical element that provides an interface for inputting the user's psychological state and mood.

[0166] The "analysis means" is a technical element that collects input psychological state and mood data and analyzes it using machine learning algorithms.

[0167] The "selection means" is a technical element that selects the optimal refresh plan based on the analysis results.

[0168] The "generation means" is a technical element that generates a specific refreshment activity plan based on the selected plan.

[0169] The "display means" is a technical element that displays the generated refreshment activity plan on the user terminal.

[0170] "Feedback means" refers to a technical element that collects feedback from users and uses it to improve the accuracy of the analysis means.

[0171] "Generative AI Model" means an artificial intelligence model used to generate a detailed plan for refreshment activities.

[0172] "Dialogue means" refers to a technical element that presents questions to the user in an interactive format and provides an interface for the user to input answers.

[0173] This invention relates to an application that uses AI to propose optimal refreshment plans for modern people to refresh their minds and bodies. This system includes elements of a server, a terminal, and a user, and is implemented using the following means:

[0174] 1. Enter your user information:

[0175] The user inputs information about their psychological state and mood using a device such as a smartphone. The device provides an input means for transmitting this information to the server. Specifically, the device uses an interactive means in which questions are presented to the user in an interactive format and the user inputs answers.

[0176] 2. Data collection and analysis:

[0177] The server collects and temporarily stores the input data, and then uses an analytical tool to analyze the input data using machine learning algorithms. This analysis allows for a detailed understanding of the user's psychological state and mood.

[0178] 3. Select a refresh plan:

[0179] Based on the analysis results, the server uses a selection means to select an optimal refreshment plan, which includes a search means to search a database for potential activities with high refreshment effects.

[0180] 4. Generate a concrete refreshment activity plan:

[0181] The server uses a generating means for generating a specific refreshment activity plan based on the selected plan, where a generative AI model is utilized to generate a detailed plan for the refreshment activity, including specific information such as usage time, price, and location.

[0182] 5. View the generated plan:

[0183] The server transmits the generated refresh action plan to the terminal, which provides display means for displaying it to the user.

[0184] 6. Gathering Feedback:

[0185] The server provides a feedback mechanism for users to enter feedback after use, which is collected and stored to help improve future suggestions.

[0186] Examples:

[0187] If a user inputs "My recent stress level is medium" and "My favorite activity is relaxation," the server analyzes this information and selects "Spa & Massage" as the optimal refreshing activity. The server then generates a list of specific spa and massage shops and reservation information, and provides it to the user.

[0188] Example prompts for generative AI models:

[0189] User Answer:

[0190] Stress level: Medium

[0191] Favorite activity: Relaxation

[0192] prompt:

[0193] Based on the user's psychological state, please generate an optimal relaxation plan. Specifically, please include a list of spa and massage parlors available in brick-and-mortar locations, along with details such as opening hours and prices.

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] The server presents interactive questions to the user through the terminal and collects information about their psychological state and mood. At this time, the terminal receives the answers entered by the user and sends them to the server. An interactive means is used for input, and a question such as "What is your recent stress level?" is displayed. If the user answers "medium," the data is sent to the server.

[0197] Step 2:

[0198] The server collects and temporarily stores the received user response data. After sufficient data is collected, it analyzes the data using machine learning algorithms. Using the analytical means, calculations are performed to understand the user's detailed psychological state and mood. For example, if the stress level is "medium" and the preferred activity is "relaxation," the server will use this to evaluate the user's state.

[0199] Step 3:

[0200] The server selects the optimal refreshment plan based on the analysis results. At this time, it uses a selection method to search a database for activities with a high refreshing effect, such as spa or massage. The refreshment activity is selected based on the user's input psychological state and preferences.

[0201] Step 4:

[0202] The server generates a specific relaxation activity plan based on the selected relaxation plan. Using a generation means and a generative AI model, the server generates a detailed plan, including a list of specific spas and massage parlors, usage times, and prices. The plan is customized based on the user's input data.

[0203] Step 5:

[0204] The server sends the generated relaxation activity plan to the terminal, which then displays it to the user. Using the display means, the details of the specific relaxation plan are visually provided to the user. For example, a "list of spas and massages" is displayed, showing opening hours, prices, reservation information, etc.

[0205] Step 6:

[0206] After using the proposed refresh plan, the user inputs feedback through the terminal. The server receives and collects this feedback. The feedback collected from different users by the feedback means is used to improve the accuracy of the analysis means. For example, specific opinions such as "I'm satisfied with the plan" or "Something to improve is ____" are input.

[0207] Step 7:

[0208] The server stores the collected feedback in a database and uses it to improve the accuracy of future refreshment plan suggestions. This will make the suggestions for the next user more relevant and effective. For example, it will update the evaluation criteria for a new refreshment activity based on past feedback.

[0209] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0210] The present invention is a system that uses AI to suggest the best vacation destinations for busy modern people to refresh their minds and bodies. This system can be effectively implemented by including the following means.

[0211] 1. Entering user information

[0212] The server generates interactive questions and sends them to the device. These questions include questions about the user's psychological state and mood. For example, a question might be, "What is your stress level these days?"

[0213] The terminal displays these questions on the screen and allows the user to enter answers.

[0214] The user inputs information about his / her mental state and mood through the terminal and transmits it to the server.

[0215] 2. Use of emotion recognition engine

[0216] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice.

[0217] The server uses an emotion recognition engine to analyze the user's emotions from the collected data, and the analysis results are used to understand the user's detailed emotional state.

[0218] 3. Data collection and analysis

[0219] The server consolidates and temporarily stores the collected psychological state, mood, and emotion data.

[0220] The server preprocesses the combined data and converts it into a format suitable for input into machine learning algorithms.

[0221] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[0222] 4. Travel destination recommendations

[0223] Based on the analysis results, the server selects the travel destination that best suits the user's criteria. The selection process involves searching the database for potential travel destinations that match the criteria.

[0224] The server uses generative AI technology to generate a specific travel plan based on the selected destinations, such as a plan to view flower fields, visit hot springs, and enjoy local cuisine.

[0225] 5. Displaying the results

[0226] The server transmits the generated travel plan to the user terminal.

[0227] The terminal visually displays the received travel destination and plan to the user. For example, it may display "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[0228] 6. Gathering User Feedback

[0229] The server generates a question to prompt the user to input feedback and sends it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[0230] The terminal displays a question on the screen prompting feedback input.

[0231] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0232] The terminal receives the user's feedback and transmits it to the server.

[0233] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0234] Specific examples

[0235] As an example, consider a user who experiences moderate stress and enjoys nature.

[0236] 1. Example of Questions and Emotion Recognition

[0237] Server: "What's your stress level these days?"

[0238] User: "Medium"

[0239] The device uses an emotion recognition engine to analyze the user's facial expressions to determine whether they are smiling or tired, and acquires emotion data indicating "feeling tired."

[0240] 2. Data collection and analysis

[0241] The server collects the user's response data and emotion recognition data and analyzes them using a machine learning algorithm. For example, it analyzes the data based on the user's "moderate stress, likes to enjoy nature" and "feeling tired."

[0242] 3. Travel destination recommendations

[0243] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal plan for refreshing. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[0244] 4. Displaying the results

[0245] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[0246] 5. Gathering Feedback

[0247] Server: "Please give us your feedback on this plan."

[0248] User: "I wish there was more time to admire the flower fields."

[0249] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[0250] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[0251] The processing flow will be explained below.

[0252] Step 1:

[0253] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[0254] Step 2:

[0255] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[0256] Step 3:

[0257] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[0258] Step 4:

[0259] The terminal transmits the answer entered by the user to the server.

[0260] Step 5:

[0261] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice, for example, to detect smiles and signs of fatigue.

[0262] Step 6:

[0263] The terminal transmits the collected emotion data to the server.

[0264] Step 7:

[0265] The server receives the response data and emotion data and temporarily stores them.

[0266] Step 8:

[0267] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[0268] Step 9:

[0269] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[0270] Step 10:

[0271] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, for example, searching a database for travel destinations that match criteria such as "places rich in nature" or "places with a high level of refreshing effect."

[0272] Step 11:

[0273] The server generates a specific travel plan based on the selected travel destinations. The plan includes tourist spots, accommodations, dining options, etc. For example, a plan called "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" can be created.

[0274] Step 12:

[0275] The server transmits the generated travel plan to the user terminal.

[0276] Step 13:

[0277] The device visually displays the received travel destination and plan to the user. For example, the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" is displayed on the screen.

[0278] Step 14:

[0279] The server generates a question to prompt the user to input feedback and sends it to the terminal. For example, the server generates a question such as "Please give us your feedback on this plan."

[0280] Step 15:

[0281] The terminal displays a question on the screen prompting feedback input.

[0282] Step 16:

[0283] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0284] Step 17:

[0285] The terminal receives the user's feedback and transmits it to the server.

[0286] Step 18:

[0287] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0288] Example 2

[0289] 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."

[0290] In modern society, many people find it difficult to find the perfect vacation destination to refresh their mind and body from their busy daily lives. In particular, conventional systems have not been able to fully grasp a user's psychological and emotional state and suggest appropriate travel destinations and specific travel plans based on that understanding. The present invention aims to provide a system that analyzes a user's psychological and emotional data and suggests optimal vacation destinations and travel plans.

[0291] 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 an input means for inputting the user's psychological state and mood, an analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, a collection means for collecting emotion data on the user's facial expressions and voice using a sensor, an emotion recognition means for analyzing the collected data, a selection means for selecting an optimal travel destination based on the analysis results, a generation means for generating a specific travel plan based on the selected travel destination, a display means for displaying the generated travel plan on the user terminal, and a feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal travel destinations and specific travel plans based on the user's detailed psychological and emotional state.

[0292] "Input means" refers to the means by which a user inputs his or her own psychological state or mood, and includes the terminal interface and forms.

[0293] "Analysis means" refers to devices or software that collect input data and analyze it using machine learning algorithms, etc.

[0294] "Collection means" refers to a mechanism for collecting emotional data such as the user's facial expressions and voice using sensors.

[0295] "Emotion recognition means" refers to the engine or algorithm that analyzes collected emotion data and determines the user's emotional state.

[0296] "Selection Method" refers to the algorithm or process used to select the most suitable travel destination based on the data obtained by the Analysis Method.

[0297] "Generator" means a process or algorithm for generating a specific itinerary based on selected travel destinations.

[0298] The "display means" refers to an interface or display device for visually displaying the generated travel plan on the user terminal.

[0299] "Feedback measures" refers to the processes and systems used to collect feedback from users and use that data to improve the accuracy of analytical measures.

[0300] The present invention is a system that proposes optimal travel destinations and travel plans based on the user's psychological state and mood. Specific embodiments of the system are described below.

[0301] Enter user information

[0302] The server generates a program that allows the user to input information about their psychological state and mood in the form of a question, and sends it to the device. This question might include, for example, "What is your stress level these days?" The device displays this question on the screen so that the user can input it. The user then inputs information about their psychological state and mood through the device and sends it to the server. This is done using Python's Flask framework and a RESTful API.

[0303] Use of emotion recognition engine

[0304] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice. WebRTC technology is used for this collection. The collected data is sent to a server and analyzed using, for example, Microsoft Azure's emotion recognition engine. This allows the user's emotional state to be analyzed.

[0305] Data collection and analysis

[0306] The server collects user input data and emotion recognition data and temporarily stores them in MongoDB. The server preprocesses this data and converts it into a format suitable for input into machine learning algorithms, using the Python Pandas library. The preprocessed data is then analyzed using Scikit-learn, and a model is applied to understand the user's detailed psychological and emotional state.

[0307] Travel destination recommendations

[0308] Based on the analysis results, the server selects the travel destination that best suits the criteria. SQL queries are used to search for travel destination candidates from the MySQL database. Based on the selected travel destinations, the server uses GPT-4, a generative AI technology, to generate a specific travel plan. For example, a plan such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" may be generated.

[0309] Displaying the results

[0310] The server sends the generated travel plan to the user's device. The data is sent in JSON format. The device then displays the received travel plan on the screen using HTML and CSS to visually display it to the user.

[0311] Collecting User Feedback

[0312] The server generates a question to prompt the user to enter feedback and sends it to the device. For example, a question could be, "Please give us your feedback on this plan." The device displays this question on the screen and allows the user to enter feedback. The user enters their opinions and suggestions for improvement regarding the provided plan, and the device sends this feedback to the server. The server saves the received feedback and registers it in a database to improve the accuracy of future proposals.

[0313] Specific examples

[0314] For example, consider the case where a user experiences moderate stress and enjoys enjoying nature.

[0315] 1. Example of Questions and Emotion Recognition

[0316] Server: "What's your stress level these days?"

[0317] User: "Medium"

[0318] The device uses a webcam to analyze the user's facial expressions, such as smiles and tiredness, using an emotion recognition engine, and obtains emotional data such as "feeling tired."

[0319] 2. Data collection and analysis

[0320] The server collects user response data and emotion recognition data and analyzes them using Scikit-learn. The analysis is based on the data "moderate stress, prefers enjoying nature" and "feeling tired."

[0321] 3. Travel destination recommendations

[0322] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal refreshment plan using GPT-4. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[0323] 4. Displaying the results

[0324] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[0325] 5. Gathering Feedback

[0326] Server: "Please give us your feedback on this plan."

[0327] User: "I wish there was more time to admire the flower fields."

[0328] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[0329] The following prompt is used as an example of a prompt sentence:

[0330] "Please suggest a travel plan that would be ideal for a Hokkaido resident who is moderately stressed, enjoys nature, and feels a bit tired. Please include viewing flower fields and visiting hot springs."

[0331] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] Enter user information

[0335] The server generates questions to inquire about the user's state of mind and mood, such as "What is your stress level these days?"

[0336] The device receives questions from the server and displays them on the screen, specifically by rendering a question form using HTML and JavaScript.

[0337] The user inputs the answer to the question into the terminal, for example, "moderate stress."

[0338] The device sends the user's answers to the server. This data is sent using a RESTful API.

[0339] Input: User's state of mind or mood (e.g., "moderate stress")

[0340] Output: Collected response data

[0341] Step 2:

[0342] Collecting Emotional Data

[0343] The device uses a camera and microphone to collect the user's facial expressions and voice, and this operation uses WebRTC technology.

[0344] The terminal sends the collected data to the server. This data is encoded in BASE64 format, for example, and sent via a POST request.

[0345] Input: User's facial expression data, voice data

[0346] Output: Collected emotion data

[0347] Step 3:

[0348] Emotional Data Analysis

[0349] The server then sends the received emotional data to a Microsoft Azure emotion recognition engine, which analyzes the data and determines the user's emotional state, such as "feeling tired."

[0350] Input: Collected emotion data

[0351] Output: Parsed emotional state (e.g., "Feeling tired")

[0352] Step 4:

[0353] Data collection and preprocessing

[0354] The server combines the user response data with the analyzed emotion data and temporarily stores it in MongoDB.

[0355] The server preprocesses the aggregated data and converts it into a format suitable for machine learning algorithms, using the Python Pandas library for this preprocessing.

[0356] Input: User response data and emotion data

[0357] Output: Preprocessed data format

[0358] Step 5:

[0359] Data analysis

[0360] The server inputs the preprocessed data into a Scikit-learn machine learning model to analyze the user's detailed psychological and emotional state.

[0361] Input: Preprocessed data format

[0362] Output: Analysis result (e.g., "I am moderately stressed and enjoy nature.")

[0363] Step 6:

[0364] Selecting a travel destination

[0365] The server then searches the MySQL database for the best travel destination based on the analysis results, using an SQL query to identify potential travel destinations such as "Furano, Hokkaido."

[0366] Input: Analysis results

[0367] Output: Travel destination candidates (e.g. "Furano, Hokkaido")

[0368] Step 7:

[0369] Generate a travel plan

[0370] The server generates a travel plan using GPT-4 based on the selected travel destinations. It uses the Python OpenAI library to create a specific travel plan (e.g., "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine").

[0371] Input: Travel destination options

[0372] Output: Travel plan

[0373] Step 8:

[0374] Displaying the results

[0375] The server sends the generated itinerary to the device in JSON format.

[0376] The device visually displays the travel plan to the user, using HTML and CSS to present the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" in a user-friendly interface.

[0377] Input: Travel Plan

[0378] Output: A displayed itinerary

[0379] Step 9:

[0380] Gathering feedback

[0381] The server generates a question for the user to input feedback and sends it to the terminal, such as "Please give us your feedback on this plan."

[0382] The terminal displays the questions on the screen and allows the user to enter feedback.

[0383] The user inputs feedback on the provided plan (e.g., "I wish there was more time to view the flower fields").

[0384] The device sends user feedback to the server. The data is sent using a RESTful API.

[0385] The server stores the received feedback and registers it in a database to help improve the accuracy of the analysis means.

[0386] Input: User feedback

[0387] Output: Stored feedback data

[0388] The above are the specific processing steps of this system. We have clearly explained the detailed operations and data flow at each step and shown how the system can provide maximum support for user refreshment.

[0389] (Application example 2)

[0390] 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."

[0391] In today's busy lives, many people find it difficult to find the right way to relax, leading to the accumulation of stress and fatigue. In particular, there is a lack of objective indicators for determining which relaxation method is best for each individual. Therefore, there is a need for a method that can provide optimal relaxation content based on the user's psychological state and emotions, allowing them to effectively relax.

[0392] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, selection means for selecting optimal relaxation content based on the analysis results, generation means for generating a specific content plan based on the selected relaxation content, display means for displaying the generated content plan on the user terminal, emotion recognition means for analyzing the user's facial expressions, and feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal relaxation content based on the user's psychological state and emotions, thereby achieving a high level of refreshment.

[0393] "User" refers to an individual who uses the relaxation content.

[0394] "Mental state" refers to the state of mind that reflects the user's emotions and mood.

[0395] "Mood" refers to the temporary emotions or sensations that a user is experiencing at the time.

[0396] "Input means" refers to an interface for inputting the user's psychological state and mood.

[0397] "Data" refers to the input information on psychological state and mood.

[0398] "Analysis means" refers to a method for analyzing collected data using machine learning algorithms.

[0399] A "machine learning algorithm" refers to a calculation method that finds patterns based on data and makes predictions and judgments.

[0400] "Selection method" refers to a method for selecting the optimal relaxation content based on the analysis results.

[0401] "Relaxation content" refers to content such as music, videos, and meditation guides that are designed to promote refreshment and relaxation for users.

[0402] "Content plan" refers to a specific program or schedule created based on the selected relaxation content.

[0403] "Generation means" refers to a method for creating a specific content plan based on the selected relaxation content.

[0404] The "display means" refers to an interface for displaying the generated content plan on the user terminal.

[0405] "Emotion recognition means" refers to a method for analyzing a user's facial expression and recognizing their emotion.

[0406] "Feedback means" refers to a method for collecting opinions and impressions from users and using them to improve the accuracy of the analysis means.

[0407] "Generative AI model" refers to an algorithm or program that uses artificial intelligence to generate text or content.

[0408] A "prompt" refers to a formalized text of instructions or questions that are input to a generative AI model.

[0409] This invention is a system that proposes optimal relaxation content based on the user's psychological state and mood, and each of the means is specifically implemented as follows.

[0410] The server provides an input means for inputting the user's psychological state and mood. This means is realized by displaying questions in an interactive format on a device such as a smartphone or tablet. For example, questions such as "What is your stress level these days?" or "What is your favorite way to change your mood?" are displayed, and the user inputs their answers.

[0411] The input data is supplemented by the device's emotion recognition means, which uses the device's built-in camera and microphone to collect emotion data from the user's facial expressions and voice. For example, image data captured by the camera is analyzed using a face recognition library (e.g., OpenCV) to understand the user's emotional state. Voice data collected by the microphone is also analyzed using a voice emotion recognition library (e.g., Google Speech-to-Text API).

[0412] The collected psychological state, mood, and emotion data is sent to a server, where the server's analysis means analyzes the data using machine learning algorithms (e.g., TensorFlow, scikit-learn). The analysis results are used to understand the user's detailed psychological state and emotions.

[0413] Based on the analysis results, the server's selection means selects the most suitable relaxation content for the user from the database. At this time, a generative AI model (e.g., GPT-4) is used to generate a specific content plan based on the selected relaxation content. The generated prompt will have the following format:

[0414] "Stress levels are high these days, and for users who prefer nature videos, provide videos of the Alps, guided meditations with nature sounds, and relaxation videos for forest bathing."

[0415] The generated content plan is sent from the server to the device. The device visually displays this plan and provides the user with content that has a high relaxation effect. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" is displayed.

[0416] Furthermore, feedback is collected to gather information on user satisfaction and areas for improvement. For example, a question such as "Please provide feedback on this content" is displayed on the screen, and users respond by entering their feedback. The collected feedback is sent to the server and used to improve the accuracy of the analysis.

[0417] In this way, it is possible to propose optimal relaxation content based on the user's psychological state, mood, and emotions, thereby maximizing the refreshing effect on the user.

[0418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] The server generates questions for inputting the user's psychological state and mood and sends them to the terminal. The terminal displays the generated questions on the screen, and the user inputs answers through input fields. The input data includes information such as stress level and methods for changing mood. For example, it displays questions such as "What is your stress level these days?" and "What is your preferred method for changing mood?"

[0421] Input: Question generation and display

[0422] Output: User response data

[0423] Step 2:

[0424] The device uses a built-in camera and microphone to collect the user's facial expressions and voice. This data is then analyzed using emotion recognition techniques (e.g., OpenCV, Google Speech-to-Text API). For example, image data captured by the camera can be analyzed using a facial recognition library to identify the user's emotional state as "tired" or "stressed," etc.

[0425] Input: facial and voice data

[0426] Output: Emotional state data

[0427] Step 3:

[0428] The server receives and temporarily stores the psychological state, mood, and emotion data collected from the device. It then analyzes this data using machine learning algorithms (e.g., TensorFlow, scikit-learn). As a result of the analysis, a detailed model of the user's psychological state and emotions is obtained.

[0429] Input: Mental state, mood, and emotion data

[0430] Output: Analysis results (detailed psychological state and emotion model)

[0431] Step 4:

[0432] Based on the analysis results, the server's selection means selects the most suitable relaxation content. The selection process involves searching the database for relaxation content candidates that match the criteria. For example, based on the criteria of "high stress level" and "prefer nature videos," it may select relaxation videos of the Alps or forest bathing.

[0433] Input: Analysis results, relaxation content database

[0434] Output: Relaxation content candidates

[0435] Step 5:

[0436] Based on the selected relaxation content, the server's generation means will use a generative AI model (e.g., GPT-4) to generate a specific content plan, with the generated prompt text being something like: "For a user whose stress level has been high recently and who prefers nature videos, please provide videos of the Alps, meditation guides with nature sounds, and forest bathing relaxation videos."

[0437] Input: Relaxation content suggestions

[0438] Output: A concrete content plan

[0439] Step 6:

[0440] The generated content plan is sent from the server to the device, which then visually displays the plan to the user. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" are displayed on the device screen.

[0441] Input: Specific content plan

[0442] Output: A visual representation of the content plan

[0443] Step 7:

[0444] Users provide feedback on the provided relaxation content. The server receives feedback data from the device via the feedback means and uses it to improve the accuracy of the analysis means. For example, feedback such as "The video of the Alps was very good, but I wish the music volume was turned down a bit" can be collected and used to generate the next content plan.

[0445] Input: User feedback

[0446] Output: Collected feedback data

[0447] 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.

[0448] 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.

[0449] 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.

[0450] [Second embodiment]

[0451] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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."

[0463] The present invention is a system that uses AI to suggest optimal vacation destinations for busy modern people to refresh their minds and bodies. The system includes the following means.

[0464] 1. Entering user information

[0465] The server presents interactive questions to the user via the terminal to collect information about the user's psychological state and mood. For example, it might ask, "What is your stress level these days?"

[0466] The terminal displays these questions on the screen and receives answers entered by the user.

[0467] The user inputs information about their psychological state and mood and sends it to the server via their terminal.

[0468] 2. Data collection and analysis

[0469] The server collects and temporarily stores the received data.

[0470] The server uses machine learning algorithms to analyze the collected data and understand the user's detailed psychological state and mood.

[0471] 3. Travel destination recommendations

[0472] Based on the analysis results, the server selects the travel destination that best suits the user's requirements, including places rich in nature and places known for their refreshing effects.

[0473] The server uses generative AI technology to generate a specific travel plan based on the selected travel destination, including detailed information on tourist attractions, accommodations, and dining options.

[0474] 4. Displaying the results

[0475] The server transmits the generated travel destination and travel plan to the user terminal.

[0476] The terminal displays the received travel destination and plan on the screen so that the user can check it.

[0477] 5. Gathering User Feedback

[0478] The server displays a question on the terminal to prompt the user to input feedback.

[0479] The user inputs their opinions and suggestions for improvement regarding the proposed travel plan.

[0480] The terminal receives the user's feedback and transmits it to the server.

[0481] The server stores the collected feedback to help improve future suggestions.

[0482] Specific examples

[0483] As an example, consider a case where a user is experiencing moderate stress and prefers to enjoy nature.

[0484] 1. Example Questions and Answers

[0485] Server: "What's your stress level these days?"

[0486] User: "Medium"

[0487] Server: "What activities do you like?"

[0488] User: "Enjoying nature"

[0489] 2. Data collection and analysis

[0490] The server collects the user's responses and analyzes them using a machine learning algorithm. Based on the analysis results, it is determined that the user can expect a refreshing effect in a place rich in nature.

[0491] 3. Travel destination recommendations

[0492] The server selects "Furano in Hokkaido" as the travel destination based on the conditions.

[0493] The server generates travel plans such as "viewing flower fields, visiting hot springs, and enjoying local cuisine."

[0494] 4. Displaying the results

[0495] The server sends the generated travel plan to the terminal, and the terminal suggests to the user "viewing the flower fields and visiting hot springs in Furano, Hokkaido."

[0496] 5. Gathering Feedback

[0497] Server: "Please give us your feedback on this plan."

[0498] User: "I wish there was more time to admire the flower fields."

[0499] The server stores the user's feedback and uses it to improve the accuracy of the next suggestion.

[0500] As described above, the present invention is a system that can maximize the user's refreshing effect by providing optimal travel destinations and detailed travel plans based on the user's psychological state and mood.

[0501] The processing flow will be explained below.

[0502] Step 1:

[0503] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[0504] Step 2:

[0505] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[0506] Step 3:

[0507] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[0508] Step 4:

[0509] The terminal transmits the answer entered by the user to the server.

[0510] Step 5:

[0511] The server receives the response data sent from the terminal and temporarily stores it.

[0512] Step 6:

[0513] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[0514] Step 7:

[0515] The server analyzes the data using machine learning algorithms and applies models to understand the user's detailed psychological state and mood.

[0516] Step 8:

[0517] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, searching the database for potential travel destinations that match the criteria.

[0518] Step 9:

[0519] The server generates a specific travel plan based on the selected travel destinations, such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine."

[0520] Step 10:

[0521] The server transmits the generated travel plan to the user terminal.

[0522] Step 11:

[0523] The terminal visually displays the received travel destination and plan to the user, for example, "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[0524] Step 12:

[0525] The server generates a question prompting the user to input feedback and transmits it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[0526] Step 13:

[0527] The terminal displays a question on the screen prompting feedback input.

[0528] Step 14:

[0529] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0530] Step 15:

[0531] The terminal receives the user's feedback and transmits it to the server.

[0532] Step 16:

[0533] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0534] Example 1

[0535] 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."

[0536] Modern people often feel stressed in their busy daily lives and need to refresh their minds and bodies. However, finding a vacation destination that suits them is not easy. In addition, selecting a travel destination and creating a travel plan takes time and effort, so there is a need for a system that can suggest travel destinations that are more efficient, appropriate, and have a high refreshing effect.

[0537] 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.

[0538] In this invention, the server includes: an input means for inputting the user's psychological state and mood; a collection means for collecting the input psychological state and mood data and temporarily storing it in a database; an analysis means for analyzing the collected data using a machine learning algorithm to understand the user's detailed psychological state and mood; a selection means using a recommender system to select optimal travel destinations based on the analysis results; a generation means using a generative AI model to generate a specific travel plan based on the selected travel destinations; a display means for sending the generated travel plan to a user terminal and displaying it; a collection means for collecting feedback from users and storing the feedback data in a database; and a feedback analysis means for analyzing the collected feedback and using it to improve the accuracy of the analysis means. This enables busy modern people to efficiently and accurately find the optimal travel destination for themselves, maximizing the benefits of refreshing their mind and body.

[0539] "Input means" refers to a means by which a user inputs data relating to his or her own psychological state or mood.

[0540] "Collection means" refers to the means for receiving input data and feedback and temporarily storing them in a database.

[0541] A "database" is an information storage device for temporarily storing collected data and feedback.

[0542] The "analysis means" is a means for analyzing collected data using machine learning algorithms to understand the user's detailed psychological state and mood.

[0543] A "machine learning algorithm" is a mathematical technique for analyzing collected data and finding patterns and relationships.

[0544] A "recommender system" is a system that recommends suitable travel destinations to users based on analysis results.

[0545] The "selection means" is a means for selecting the optimal travel destination based on the analysis results using a recommender system.

[0546] A "generative AI model" is an artificial intelligence model that generates specific travel plans based on selected travel destinations.

[0547] "Generation means" refers to a means for creating a specific travel plan based on the selected travel destinations using a generative AI model.

[0548] The "display means" is a means for transmitting the generated travel plan to the user terminal and displaying it.

[0549] "Feedback" refers to opinions and information on improvements to travel plans provided by users.

[0550] The "feedback analysis means" is a means for analyzing collected feedback and using it to improve the accuracy of the analysis means.

[0551] The system of this invention proposes optimal travel destinations based on the user's psychological state and mood, creates specific travel plans, and provides them to the user. The various hardware and software required for this purpose are described below.

[0552] First, the server generates a question to accept user input and sends it to the device. This question generation is often done using a web server or backend API server running on the server. This API server uses JavaScript or Python. Examples of questions that can be generated include specific questions such as "What is your stress level these days?" and "What activities do you like to do?"

[0553] The device displays the questions received from the server using HTML and JavaScript. The user answers the questions and sends the answers to the server. For example, the user might enter answers such as "My stress level is moderate" or "I enjoy nature."

[0554] The server then temporarily stores the received user responses in a database (e.g., MySQL), and then uses a Python machine learning library (e.g., scikit-learn) to analyze the collected data and understand the user's detailed psychological state.

[0555] A recommender system is used to select the optimal travel destination based on the analysis results. Collaborative filtering algorithms are often used in recommender systems. Then, a generative AI model (e.g., GPT-4) is used to generate a specific travel plan based on the selected travel destination. The generative AI is input with a prompt sentence such as the following:

[0556] "If the user is experiencing moderate stress and enjoys nature, suggest the best travel plan."

[0557] The generated travel plan is sent from the server to the device and displayed on the device using HTML and JavaScript. The travel plan includes detailed information such as tourist spots, accommodations, and places to eat. For example, a plan called "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" may be generated.

[0558] Furthermore, the server collects feedback from users and stores it in a database. This feedback is used to improve the accuracy of the machine learning algorithm. An example of feedback could be a specific request such as "I would like more time to admire the flower fields."

[0559] In this way, by combining servers, terminals, generative AI models, and machine learning algorithms, the system can suggest optimal travel destinations and detailed travel plans to users, and improve the accuracy of the system by incorporating feedback.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] The server generates questions about the user's psychological state and mood and sends them to the device. The input is a question template pre-configured on the server, and the output is structured JSON-formatted question data. In operation, the server generates an API request and sends it to the device.

[0563] Step 2:

[0564] The terminal receives question data sent from the server and displays it on the screen using HTML and JavaScript. The input is JSON format data sent from the server, and the output is a question form displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[0565] Step 3:

[0566] The user enters answers to questions displayed on the terminal. The input is the user's answer, and the output is the answer data in JSON format that is sent by the terminal to the server. In operation, the user enters data into the form and presses the submit button.

[0567] Step 4:

[0568] The server receives the user's response data and temporarily stores it in a database. The input is the JSON formatted response data sent from the device, and the output is a record stored in the database. The server then validates the data and executes an INSERT query.

[0569] Step 5:

[0570] The server analyzes the stored data using a machine learning algorithm to evaluate the user's psychological state and mood. The input is the response data stored in the database, and the output is the user's psychological state data as the analysis result. In operation, the server runs a Python script and analyzes the data using a machine learning model.

[0571] Step 6:

[0572] Based on the analysis results, the server uses a recommender system to select the optimal travel destination. The input is the user's psychological state data as the analysis result, and the output is the selected travel destination data. In operation, the recommender algorithm compares the user data with the travel destination data to generate the optimal proposal.

[0573] Step 7:

[0574] The server uses a generative AI model to generate a specific travel plan based on the selected travel destination. The input is the selected travel destination data and a prompt, and the output is specific travel plan data. The prompt uses the following: "If the user is experiencing moderate stress and prefers to enjoy nature, please suggest the optimal travel plan."

[0575] Step 8:

[0576] The server sends the generated travel plan data to the device. The input is the travel plan data obtained from the generative AI model, and the output is the JSON-formatted travel plan data sent to the device. In operation, the server generates an API request and sends it to the device.

[0577] Step 9:

[0578] The device displays the received travel plan data on the screen using HTML and JavaScript. The input is the JSON-formatted travel plan data sent from the server, and the output is the travel plan displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[0579] Step 10:

[0580] The server sends feedback questions to the terminal and receives feedback from the user. The input is the feedback question as a prompt sentence, and the output is feedback data from the user. The question includes, "Please give us your feedback on this plan."

[0581] Step 11:

[0582] The user enters answers to feedback questions and sends them to the server. The input is the user's feedback, and the output is the JSON-formatted feedback data sent by the device to the server. In operation, the user enters data into the form and presses the submit button.

[0583] Step 12:

[0584] The server receives user feedback and stores it in a database. The input is the JSON-formatted feedback data sent from the device, and the output is a record stored in the database. The server validates the data and executes an INSERT query.

[0585] Step 13:

[0586] The server analyzes the feedback data to help improve the accuracy of the machine learning algorithm. The input is the feedback data stored in the database, and the output is an improved machine learning model. In operation, a new training dataset is created based on the feedback data, and the model is retrained.

[0587] (Application example 1)

[0588] 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."

[0589] In modern society, many people are seeking effective ways to reduce stress in their daily lives and refresh their minds and bodies. However, it is not easy to find a refreshing activity or plan that best suits each user's psychological state and mood. Furthermore, no system exists that efficiently and effectively suggests these refreshing activities or collects feedback. The present invention aims to solve these problems and provide a system that provides users with the optimal refreshing plan.

[0590] 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.

[0591] In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input psychological state and mood data and analyzing it using a machine learning algorithm, selection means for selecting an optimal refreshment plan based on the analysis results, generation means for generating a specific refreshment activity plan based on the selected plan, display means for displaying the generated refreshment activity plan on the user terminal, feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means, generation means for generating a detailed plan for refreshment activities by utilizing a generative AI model, and dialogue means for presenting questions to the user in an interactive format and allowing the user to input answers. This makes it possible to provide a refreshment plan optimal for the user's psychological state and mood and to collect feedback that is useful for improving the accuracy of suggestions.

[0592] Key Word Definitions

[0593] "Input means" refers to a technical element that provides an interface for inputting the user's psychological state and mood.

[0594] The "analysis means" is a technical element that collects input psychological state and mood data and analyzes it using machine learning algorithms.

[0595] The "selection means" is a technical element that selects the optimal refresh plan based on the analysis results.

[0596] The "generation means" is a technical element that generates a specific refreshment activity plan based on the selected plan.

[0597] The "display means" is a technical element that displays the generated refreshment activity plan on the user terminal.

[0598] "Feedback means" refers to a technical element that collects feedback from users and uses it to improve the accuracy of the analysis means.

[0599] "Generative AI Model" means an artificial intelligence model used to generate a detailed plan for refreshment activities.

[0600] "Dialogue means" refers to a technical element that presents questions to the user in an interactive format and provides an interface for the user to input answers.

[0601] This invention relates to an application that uses AI to propose optimal refreshment plans for modern people to refresh their minds and bodies. This system includes elements of a server, a terminal, and a user, and is implemented using the following means:

[0602] 1. Enter your user information:

[0603] The user inputs information about their psychological state and mood using a device such as a smartphone. The device provides an input means for transmitting this information to the server. Specifically, the device uses an interactive means in which questions are presented to the user in an interactive format and the user inputs answers.

[0604] 2. Data collection and analysis:

[0605] The server collects and temporarily stores the input data, and then uses an analytical tool to analyze the input data using machine learning algorithms. This analysis allows for a detailed understanding of the user's psychological state and mood.

[0606] 3. Select a refresh plan:

[0607] Based on the analysis results, the server uses a selection means to select an optimal refreshment plan, which includes a search means to search a database for potential activities with high refreshment effects.

[0608] 4. Generate a concrete refreshment activity plan:

[0609] The server uses a generating means for generating a specific refreshment activity plan based on the selected plan, where a generative AI model is utilized to generate a detailed plan for the refreshment activity, including specific information such as usage time, price, and location.

[0610] 5. View the generated plan:

[0611] The server transmits the generated refresh action plan to the terminal, which provides display means for displaying it to the user.

[0612] 6. Gathering Feedback:

[0613] The server provides a feedback mechanism for users to enter feedback after use, which is collected and stored to help improve future suggestions.

[0614] Examples:

[0615] If a user inputs "My recent stress level is medium" and "My favorite activity is relaxation," the server analyzes this information and selects "Spa & Massage" as the optimal refreshing activity. The server then generates a list of specific spa and massage shops and reservation information, and provides it to the user.

[0616] Example prompts for generative AI models:

[0617] User Answer:

[0618] Stress level: Medium

[0619] Favorite activity: Relaxation

[0620] prompt:

[0621] Based on the user's psychological state, please generate an optimal relaxation plan. Specifically, please include a list of spa and massage parlors available in brick-and-mortar locations, along with details such as opening hours and prices.

[0622] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0623] Step 1:

[0624] The server presents interactive questions to the user through the terminal and collects information about their psychological state and mood. At this time, the terminal receives the answers entered by the user and sends them to the server. An interactive means is used for input, and a question such as "What is your recent stress level?" is displayed. If the user answers "medium," the data is sent to the server.

[0625] Step 2:

[0626] The server collects and temporarily stores the received user response data. After sufficient data is collected, it analyzes the data using machine learning algorithms. Using the analytical means, calculations are performed to understand the user's detailed psychological state and mood. For example, if the stress level is "medium" and the preferred activity is "relaxation," the server will use this to evaluate the user's state.

[0627] Step 3:

[0628] The server selects the optimal refreshment plan based on the analysis results. At this time, it uses a selection method to search a database for activities with a high refreshing effect, such as spa or massage. The refreshment activity is selected based on the user's input psychological state and preferences.

[0629] Step 4:

[0630] The server generates a specific relaxation activity plan based on the selected relaxation plan. Using a generation means and a generative AI model, the server generates a detailed plan, including a list of specific spas and massage parlors, usage times, and prices. The plan is customized based on the user's input data.

[0631] Step 5:

[0632] The server sends the generated relaxation activity plan to the terminal, which then displays it to the user. Using the display means, the details of the specific relaxation plan are visually provided to the user. For example, a "list of spas and massages" is displayed, showing opening hours, prices, reservation information, etc.

[0633] Step 6:

[0634] After using the proposed refresh plan, the user inputs feedback through the terminal. The server receives and collects this feedback. The feedback collected from different users by the feedback means is used to improve the accuracy of the analysis means. For example, specific opinions such as "I'm satisfied with the plan" or "Something to improve is ____" are input.

[0635] Step 7:

[0636] The server stores the collected feedback in a database and uses it to improve the accuracy of future refreshment plan suggestions. This will make the suggestions for the next user more relevant and effective. For example, it will update the evaluation criteria for a new refreshment activity based on past feedback.

[0637] 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.

[0638] The present invention is a system that uses AI to suggest the best vacation destinations for busy modern people to refresh their minds and bodies. This system can be effectively implemented by including the following means.

[0639] 1. Entering user information

[0640] The server generates interactive questions and sends them to the device. These questions include questions about the user's psychological state and mood. For example, a question might be, "What is your stress level these days?"

[0641] The terminal displays these questions on the screen and allows the user to enter answers.

[0642] The user inputs information about his / her mental state and mood through the terminal and transmits it to the server.

[0643] 2. Use of emotion recognition engine

[0644] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice.

[0645] The server uses an emotion recognition engine to analyze the user's emotions from the collected data, and the analysis results are used to understand the user's detailed emotional state.

[0646] 3. Data collection and analysis

[0647] The server consolidates and temporarily stores the collected psychological state, mood, and emotion data.

[0648] The server preprocesses the combined data and converts it into a format suitable for input into machine learning algorithms.

[0649] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[0650] 4. Travel destination recommendations

[0651] Based on the analysis results, the server selects the travel destination that best suits the user's criteria. The selection process involves searching the database for potential travel destinations that match the criteria.

[0652] The server uses generative AI technology to generate a specific travel plan based on the selected destinations, such as a plan to view flower fields, visit hot springs, and enjoy local cuisine.

[0653] 5. Displaying the results

[0654] The server transmits the generated travel plan to the user terminal.

[0655] The terminal visually displays the received travel destination and plan to the user. For example, it may display "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[0656] 6. Gathering User Feedback

[0657] The server generates a question to prompt the user to input feedback and sends it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[0658] The terminal displays a question on the screen prompting feedback input.

[0659] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0660] The terminal receives the user's feedback and transmits it to the server.

[0661] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0662] Specific examples

[0663] As an example, consider a user who experiences moderate stress and enjoys nature.

[0664] 1. Example of Questions and Emotion Recognition

[0665] Server: "What's your stress level these days?"

[0666] User: "Medium"

[0667] The device uses an emotion recognition engine to analyze the user's facial expressions to determine whether they are smiling or tired, and acquires emotion data indicating "feeling tired."

[0668] 2. Data collection and analysis

[0669] The server collects the user's response data and emotion recognition data and analyzes them using a machine learning algorithm. For example, it analyzes the data based on the user's "moderate stress, likes to enjoy nature" and "feeling tired."

[0670] 3. Travel destination recommendations

[0671] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal plan for refreshing. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[0672] 4. Displaying the results

[0673] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[0674] 5. Gathering Feedback

[0675] Server: "Please give us your feedback on this plan."

[0676] User: "I wish there was more time to admire the flower fields."

[0677] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[0678] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[0682] Step 2:

[0683] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[0684] Step 3:

[0685] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[0686] Step 4:

[0687] The terminal transmits the answer entered by the user to the server.

[0688] Step 5:

[0689] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice, for example, to detect smiles and signs of fatigue.

[0690] Step 6:

[0691] The terminal transmits the collected emotion data to the server.

[0692] Step 7:

[0693] The server receives the response data and emotion data and temporarily stores them.

[0694] Step 8:

[0695] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[0696] Step 9:

[0697] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[0698] Step 10:

[0699] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, for example, searching a database for travel destinations that match criteria such as "places rich in nature" or "places with a high level of refreshing effect."

[0700] Step 11:

[0701] The server generates a specific travel plan based on the selected travel destinations. The plan includes tourist spots, accommodations, dining options, etc. For example, a plan called "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" can be created.

[0702] Step 12:

[0703] The server transmits the generated travel plan to the user terminal.

[0704] Step 13:

[0705] The device visually displays the received travel destination and plan to the user. For example, the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" is displayed on the screen.

[0706] Step 14:

[0707] The server generates a question to prompt the user to input feedback and sends it to the terminal. For example, the server generates a question such as "Please give us your feedback on this plan."

[0708] Step 15:

[0709] The terminal displays a question on the screen prompting feedback input.

[0710] Step 16:

[0711] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0712] Step 17:

[0713] The terminal receives the user's feedback and transmits it to the server.

[0714] Step 18:

[0715] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0716] Example 2

[0717] 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."

[0718] In modern society, many people find it difficult to find the perfect vacation destination to refresh their mind and body from their busy daily lives. In particular, conventional systems have not been able to fully grasp a user's psychological and emotional state and suggest appropriate travel destinations and specific travel plans based on that understanding. The present invention aims to provide a system that analyzes a user's psychological and emotional data and suggests optimal vacation destinations and travel plans.

[0719] 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 an input means for inputting the user's psychological state and mood, an analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, a collection means for collecting emotion data on the user's facial expressions and voice using a sensor, an emotion recognition means for analyzing the collected data, a selection means for selecting an optimal travel destination based on the analysis results, a generation means for generating a specific travel plan based on the selected travel destination, a display means for displaying the generated travel plan on the user terminal, and a feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal travel destinations and specific travel plans based on the user's detailed psychological and emotional state.

[0720] "Input means" refers to the means by which a user inputs his or her own psychological state or mood, and includes the terminal interface and forms.

[0721] "Analysis means" refers to devices or software that collect input data and analyze it using machine learning algorithms, etc.

[0722] "Collection means" refers to a mechanism for collecting emotional data such as the user's facial expressions and voice using sensors.

[0723] "Emotion recognition means" refers to the engine or algorithm that analyzes collected emotion data and determines the user's emotional state.

[0724] "Selection Method" refers to the algorithm or process used to select the most suitable travel destination based on the data obtained by the Analysis Method.

[0725] "Generator" means a process or algorithm for generating a specific itinerary based on selected travel destinations.

[0726] The "display means" refers to an interface or display device for visually displaying the generated travel plan on the user terminal.

[0727] "Feedback measures" refers to the processes and systems used to collect feedback from users and use that data to improve the accuracy of analytical measures.

[0728] The present invention is a system that proposes optimal travel destinations and travel plans based on the user's psychological state and mood. Specific embodiments of the system are described below.

[0729] Enter user information

[0730] The server generates a program that allows the user to input information about their psychological state and mood in the form of a question, and sends it to the device. This question might include, for example, "What is your stress level these days?" The device displays this question on the screen so that the user can input it. The user then inputs information about their psychological state and mood through the device and sends it to the server. This is done using Python's Flask framework and a RESTful API.

[0731] Use of emotion recognition engine

[0732] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice. WebRTC technology is used for this collection. The collected data is sent to a server and analyzed using, for example, Microsoft Azure's emotion recognition engine. This allows the user's emotional state to be analyzed.

[0733] Data collection and analysis

[0734] The server collects user input data and emotion recognition data and temporarily stores them in MongoDB. The server preprocesses this data and converts it into a format suitable for input into machine learning algorithms, using the Python Pandas library. The preprocessed data is then analyzed using Scikit-learn, and a model is applied to understand the user's detailed psychological and emotional state.

[0735] Travel destination recommendations

[0736] Based on the analysis results, the server selects the travel destination that best suits the criteria. SQL queries are used to search for travel destination candidates from the MySQL database. Based on the selected travel destinations, the server uses GPT-4, a generative AI technology, to generate a specific travel plan. For example, a plan such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" may be generated.

[0737] Displaying the results

[0738] The server sends the generated travel plan to the user's device. The data is sent in JSON format. The device then displays the received travel plan on the screen using HTML and CSS to visually display it to the user.

[0739] Collecting User Feedback

[0740] The server generates a question to prompt the user to enter feedback and sends it to the device. For example, a question could be, "Please give us your feedback on this plan." The device displays this question on the screen and allows the user to enter feedback. The user enters their opinions and suggestions for improvement regarding the provided plan, and the device sends this feedback to the server. The server saves the received feedback and registers it in a database to improve the accuracy of future proposals.

[0741] Specific examples

[0742] For example, consider the case where a user experiences moderate stress and enjoys enjoying nature.

[0743] 1. Example of Questions and Emotion Recognition

[0744] Server: "What's your stress level these days?"

[0745] User: "Medium"

[0746] The device uses a webcam to analyze the user's facial expressions, such as smiles and tiredness, using an emotion recognition engine, and obtains emotional data such as "feeling tired."

[0747] 2. Data collection and analysis

[0748] The server collects user response data and emotion recognition data and analyzes them using Scikit-learn. The analysis is based on the data "moderate stress, prefers enjoying nature" and "feeling tired."

[0749] 3. Travel destination recommendations

[0750] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal refreshment plan using GPT-4. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[0751] 4. Displaying the results

[0752] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[0753] 5. Gathering Feedback

[0754] Server: "Please give us your feedback on this plan."

[0755] User: "I wish there was more time to admire the flower fields."

[0756] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[0757] The following prompt is used as an example of a prompt sentence:

[0758] "Please suggest a travel plan that would be ideal for a Hokkaido resident who is moderately stressed, enjoys nature, and feels a bit tired. Please include viewing flower fields and visiting hot springs."

[0759] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[0760] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0761] Step 1:

[0762] Enter user information

[0763] The server generates questions to inquire about the user's state of mind and mood, such as "What is your stress level these days?"

[0764] The device receives questions from the server and displays them on the screen, specifically by rendering a question form using HTML and JavaScript.

[0765] The user inputs the answer to the question into the terminal, for example, "moderate stress."

[0766] The device sends the user's answers to the server. This data is sent using a RESTful API.

[0767] Input: User's state of mind or mood (e.g., "moderate stress")

[0768] Output: Collected response data

[0769] Step 2:

[0770] Collecting Emotional Data

[0771] The device uses a camera and microphone to collect the user's facial expressions and voice, and this operation uses WebRTC technology.

[0772] The terminal sends the collected data to the server. This data is encoded in BASE64 format, for example, and sent via a POST request.

[0773] Input: User's facial expression data, voice data

[0774] Output: Collected emotion data

[0775] Step 3:

[0776] Emotional Data Analysis

[0777] The server then sends the received emotional data to a Microsoft Azure emotion recognition engine, which analyzes the data and determines the user's emotional state, such as "feeling tired."

[0778] Input: Collected emotion data

[0779] Output: Parsed emotional state (e.g., "Feeling tired")

[0780] Step 4:

[0781] Data collection and preprocessing

[0782] The server combines the user response data with the analyzed emotion data and temporarily stores it in MongoDB.

[0783] The server preprocesses the aggregated data and converts it into a format suitable for machine learning algorithms, using the Python Pandas library for this preprocessing.

[0784] Input: User response data and emotion data

[0785] Output: Preprocessed data format

[0786] Step 5:

[0787] Data analysis

[0788] The server inputs the preprocessed data into a Scikit-learn machine learning model to analyze the user's detailed psychological and emotional state.

[0789] Input: Preprocessed data format

[0790] Output: Analysis result (e.g., "I am moderately stressed and enjoy nature.")

[0791] Step 6:

[0792] Selecting a travel destination

[0793] The server then searches the MySQL database for the best travel destination based on the analysis results, using an SQL query to identify potential travel destinations such as "Furano, Hokkaido."

[0794] Input: Analysis results

[0795] Output: Travel destination candidates (e.g. "Furano, Hokkaido")

[0796] Step 7:

[0797] Generate a travel plan

[0798] The server generates a travel plan using GPT-4 based on the selected travel destinations. It uses the Python OpenAI library to create a specific travel plan (e.g., "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine").

[0799] Input: Travel destination options

[0800] Output: Travel plan

[0801] Step 8:

[0802] Displaying the results

[0803] The server sends the generated itinerary to the device in JSON format.

[0804] The device visually displays the travel plan to the user, using HTML and CSS to present the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" in a user-friendly interface.

[0805] Input: Travel Plan

[0806] Output: A displayed itinerary

[0807] Step 9:

[0808] Gathering feedback

[0809] The server generates a question for the user to input feedback and sends it to the terminal, such as "Please give us your feedback on this plan."

[0810] The terminal displays the questions on the screen and allows the user to enter feedback.

[0811] The user inputs feedback on the provided plan (e.g., "I wish there was more time to view the flower fields").

[0812] The device sends user feedback to the server. The data is sent using a RESTful API.

[0813] The server stores the received feedback and registers it in a database to help improve the accuracy of the analysis means.

[0814] Input: User feedback

[0815] Output: Stored feedback data

[0816] The above are the specific processing steps of this system. We have clearly explained the detailed operations and data flow at each step and shown how the system can provide maximum support for user refreshment.

[0817] (Application example 2)

[0818] 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."

[0819] In today's busy lives, many people find it difficult to find the right way to relax, leading to the accumulation of stress and fatigue. In particular, there is a lack of objective indicators for determining which relaxation method is best for each individual. Therefore, there is a need for a method that can provide optimal relaxation content based on the user's psychological state and emotions, allowing them to effectively relax.

[0820] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, selection means for selecting optimal relaxation content based on the analysis results, generation means for generating a specific content plan based on the selected relaxation content, display means for displaying the generated content plan on the user terminal, emotion recognition means for analyzing the user's facial expressions, and feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal relaxation content based on the user's psychological state and emotions, thereby achieving a high level of refreshment.

[0821] "User" refers to an individual who uses the relaxation content.

[0822] "Mental state" refers to the state of mind that reflects the user's emotions and mood.

[0823] "Mood" refers to the temporary emotions or sensations that a user is experiencing at the time.

[0824] "Input means" refers to an interface for inputting the user's psychological state and mood.

[0825] "Data" refers to the input information on psychological state and mood.

[0826] "Analysis means" refers to a method for analyzing collected data using machine learning algorithms.

[0827] A "machine learning algorithm" refers to a calculation method that finds patterns based on data and makes predictions and judgments.

[0828] "Selection method" refers to a method for selecting the optimal relaxation content based on the analysis results.

[0829] "Relaxation content" refers to content such as music, videos, and meditation guides that are designed to promote refreshment and relaxation for users.

[0830] "Content plan" refers to a specific program or schedule created based on the selected relaxation content.

[0831] "Generation means" refers to a method for creating a specific content plan based on the selected relaxation content.

[0832] The "display means" refers to an interface for displaying the generated content plan on the user terminal.

[0833] "Emotion recognition means" refers to a method for analyzing a user's facial expression and recognizing their emotion.

[0834] "Feedback means" refers to a method for collecting opinions and impressions from users and using them to improve the accuracy of the analysis means.

[0835] "Generative AI model" refers to an algorithm or program that uses artificial intelligence to generate text or content.

[0836] A "prompt" refers to a formalized text of instructions or questions that are input to a generative AI model.

[0837] This invention is a system that proposes optimal relaxation content based on the user's psychological state and mood, and each of the means is specifically implemented as follows.

[0838] The server provides an input means for inputting the user's psychological state and mood. This means is realized by displaying questions in an interactive format on a device such as a smartphone or tablet. For example, questions such as "What is your stress level these days?" or "What is your favorite way to change your mood?" are displayed, and the user inputs their answers.

[0839] The input data is supplemented by the device's emotion recognition means, which uses the device's built-in camera and microphone to collect emotion data from the user's facial expressions and voice. For example, image data captured by the camera is analyzed using a face recognition library (e.g., OpenCV) to understand the user's emotional state. Voice data collected by the microphone is also analyzed using a voice emotion recognition library (e.g., Google Speech-to-Text API).

[0840] The collected psychological state, mood, and emotion data is sent to a server, where the server's analysis means analyzes the data using machine learning algorithms (e.g., TensorFlow, scikit-learn). The analysis results are used to understand the user's detailed psychological state and emotions.

[0841] Based on the analysis results, the server's selection means selects the most suitable relaxation content for the user from the database. At this time, a generative AI model (e.g., GPT-4) is used to generate a specific content plan based on the selected relaxation content. The generated prompt will have the following format:

[0842] "Stress levels are high these days, and for users who prefer nature videos, provide videos of the Alps, guided meditations with nature sounds, and relaxation videos for forest bathing."

[0843] The generated content plan is sent from the server to the device. The device visually displays this plan and provides the user with content that has a high relaxation effect. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" is displayed.

[0844] Furthermore, feedback is collected to gather information on user satisfaction and areas for improvement. For example, a question such as "Please provide feedback on this content" is displayed on the screen, and users respond by entering their feedback. The collected feedback is sent to the server and used to improve the accuracy of the analysis.

[0845] In this way, it is possible to propose optimal relaxation content based on the user's psychological state, mood, and emotions, thereby maximizing the refreshing effect on the user.

[0846] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0847] Step 1:

[0848] The server generates questions for inputting the user's psychological state and mood and sends them to the terminal. The terminal displays the generated questions on the screen, and the user inputs answers through input fields. The input data includes information such as stress level and methods for changing mood. For example, it displays questions such as "What is your stress level these days?" and "What is your preferred method for changing mood?"

[0849] Input: Question generation and display

[0850] Output: User response data

[0851] Step 2:

[0852] The device uses a built-in camera and microphone to collect the user's facial expressions and voice. This data is then analyzed using emotion recognition techniques (e.g., OpenCV, Google Speech-to-Text API). For example, image data captured by the camera can be analyzed using a facial recognition library to identify the user's emotional state as "tired" or "stressed," etc.

[0853] Input: facial and voice data

[0854] Output: Emotional state data

[0855] Step 3:

[0856] The server receives and temporarily stores the psychological state, mood, and emotion data collected from the device. It then analyzes this data using machine learning algorithms (e.g., TensorFlow, scikit-learn). As a result of the analysis, a detailed model of the user's psychological state and emotions is obtained.

[0857] Input: Mental state, mood, and emotion data

[0858] Output: Analysis results (detailed psychological state and emotion model)

[0859] Step 4:

[0860] Based on the analysis results, the server's selection means selects the most suitable relaxation content. The selection process involves searching the database for relaxation content candidates that match the criteria. For example, based on the criteria of "high stress level" and "prefer nature videos," it may select relaxation videos of the Alps or forest bathing.

[0861] Input: Analysis results, relaxation content database

[0862] Output: Relaxation content candidates

[0863] Step 5:

[0864] Based on the selected relaxation content, the server's generation means will use a generative AI model (e.g., GPT-4) to generate a specific content plan, with the generated prompt text being something like: "For a user whose stress level has been high recently and who prefers nature videos, please provide videos of the Alps, meditation guides with nature sounds, and forest bathing relaxation videos."

[0865] Input: Relaxation content suggestions

[0866] Output: A concrete content plan

[0867] Step 6:

[0868] The generated content plan is sent from the server to the device, which then visually displays the plan to the user. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" are displayed on the device screen.

[0869] Input: Specific content plan

[0870] Output: A visual representation of the content plan

[0871] Step 7:

[0872] Users provide feedback on the provided relaxation content. The server receives feedback data from the device via the feedback means and uses it to improve the accuracy of the analysis means. For example, feedback such as "The video of the Alps was very good, but I wish the music volume was turned down a bit" can be collected and used to generate the next content plan.

[0873] Input: User feedback

[0874] Output: Collected feedback data

[0875] 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.

[0876] 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.

[0877] 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.

[0878] [Third embodiment]

[0879] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0880] 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.

[0881] 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).

[0882] 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.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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."

[0891] The present invention is a system that uses AI to suggest optimal vacation destinations for busy modern people to refresh their minds and bodies. The system includes the following means.

[0892] 1. Entering user information

[0893] The server presents interactive questions to the user via the terminal to collect information about the user's psychological state and mood. For example, it might ask, "What is your stress level these days?"

[0894] The terminal displays these questions on the screen and receives answers entered by the user.

[0895] The user inputs information about their psychological state and mood and sends it to the server via their terminal.

[0896] 2. Data collection and analysis

[0897] The server collects and temporarily stores the received data.

[0898] The server uses machine learning algorithms to analyze the collected data and understand the user's detailed psychological state and mood.

[0899] 3. Travel destination recommendations

[0900] Based on the analysis results, the server selects the travel destination that best suits the user's requirements, including places rich in nature and places known for their refreshing effects.

[0901] The server uses generative AI technology to generate a specific travel plan based on the selected travel destination, including detailed information on tourist attractions, accommodations, and dining options.

[0902] 4. Displaying the results

[0903] The server transmits the generated travel destination and travel plan to the user terminal.

[0904] The terminal displays the received travel destination and plan on the screen so that the user can check it.

[0905] 5. Gathering User Feedback

[0906] The server displays a question on the terminal to prompt the user to input feedback.

[0907] The user inputs their opinions and suggestions for improvement regarding the proposed travel plan.

[0908] The terminal receives the user's feedback and transmits it to the server.

[0909] The server stores the collected feedback to help improve future suggestions.

[0910] Specific examples

[0911] As an example, consider a case where a user is experiencing moderate stress and prefers to enjoy nature.

[0912] 1. Example Questions and Answers

[0913] Server: "What's your stress level these days?"

[0914] User: "Medium"

[0915] Server: "What activities do you like?"

[0916] User: "Enjoying nature"

[0917] 2. Data collection and analysis

[0918] The server collects the user's responses and analyzes them using a machine learning algorithm. Based on the analysis results, it is determined that the user can expect a refreshing effect in a place rich in nature.

[0919] 3. Travel destination recommendations

[0920] The server selects "Furano in Hokkaido" as the travel destination based on the conditions.

[0921] The server generates travel plans such as "viewing flower fields, visiting hot springs, and enjoying local cuisine."

[0922] 4. Displaying the results

[0923] The server sends the generated travel plan to the terminal, and the terminal suggests to the user "viewing the flower fields and visiting hot springs in Furano, Hokkaido."

[0924] 5. Gathering Feedback

[0925] Server: "Please give us your feedback on this plan."

[0926] User: "I wish there was more time to admire the flower fields."

[0927] The server stores the user's feedback and uses it to improve the accuracy of the next suggestion.

[0928] As described above, the present invention is a system that can maximize the user's refreshing effect by providing optimal travel destinations and detailed travel plans based on the user's psychological state and mood.

[0929] The processing flow will be explained below.

[0930] Step 1:

[0931] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[0932] Step 2:

[0933] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[0934] Step 3:

[0935] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[0936] Step 4:

[0937] The terminal transmits the answer entered by the user to the server.

[0938] Step 5:

[0939] The server receives the response data sent from the terminal and temporarily stores it.

[0940] Step 6:

[0941] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[0942] Step 7:

[0943] The server analyzes the data using machine learning algorithms and applies models to understand the user's detailed psychological state and mood.

[0944] Step 8:

[0945] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, searching the database for potential travel destinations that match the criteria.

[0946] Step 9:

[0947] The server generates a specific travel plan based on the selected travel destinations, such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine."

[0948] Step 10:

[0949] The server transmits the generated travel plan to the user terminal.

[0950] Step 11:

[0951] The terminal visually displays the received travel destination and plan to the user, for example, "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[0952] Step 12:

[0953] The server generates a question prompting the user to input feedback and transmits it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[0954] Step 13:

[0955] The terminal displays a question on the screen prompting feedback input.

[0956] Step 14:

[0957] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[0958] Step 15:

[0959] The terminal receives the user's feedback and transmits it to the server.

[0960] Step 16:

[0961] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[0962] Example 1

[0963] 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."

[0964] Modern people often feel stressed in their busy daily lives and need to refresh their minds and bodies. However, finding a vacation destination that suits them is not easy. In addition, selecting a travel destination and creating a travel plan takes time and effort, so there is a need for a system that can suggest travel destinations that are more efficient, appropriate, and have a high refreshing effect.

[0965] 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.

[0966] In this invention, the server includes: an input means for inputting the user's psychological state and mood; a collection means for collecting the input psychological state and mood data and temporarily storing it in a database; an analysis means for analyzing the collected data using a machine learning algorithm to understand the user's detailed psychological state and mood; a selection means using a recommender system to select optimal travel destinations based on the analysis results; a generation means using a generative AI model to generate a specific travel plan based on the selected travel destinations; a display means for sending the generated travel plan to a user terminal and displaying it; a collection means for collecting feedback from users and storing the feedback data in a database; and a feedback analysis means for analyzing the collected feedback and using it to improve the accuracy of the analysis means. This enables busy modern people to efficiently and accurately find the optimal travel destination for themselves, maximizing the benefits of refreshing their mind and body.

[0967] "Input means" refers to a means by which a user inputs data relating to his or her own psychological state or mood.

[0968] "Collection means" refers to the means for receiving input data and feedback and temporarily storing them in a database.

[0969] A "database" is an information storage device for temporarily storing collected data and feedback.

[0970] The "analysis means" is a means for analyzing collected data using machine learning algorithms to understand the user's detailed psychological state and mood.

[0971] A "machine learning algorithm" is a mathematical technique for analyzing collected data and finding patterns and relationships.

[0972] A "recommender system" is a system that recommends suitable travel destinations to users based on analysis results.

[0973] The "selection means" is a means for selecting the optimal travel destination based on the analysis results using a recommender system.

[0974] A "generative AI model" is an artificial intelligence model that generates specific travel plans based on selected travel destinations.

[0975] "Generation means" refers to a means for creating a specific travel plan based on the selected travel destinations using a generative AI model.

[0976] The "display means" is a means for transmitting the generated travel plan to the user terminal and displaying it.

[0977] "Feedback" refers to opinions and information on improvements to travel plans provided by users.

[0978] The "feedback analysis means" is a means for analyzing collected feedback and using it to improve the accuracy of the analysis means.

[0979] The system of this invention proposes optimal travel destinations based on the user's psychological state and mood, creates specific travel plans, and provides them to the user. The various hardware and software required for this purpose are described below.

[0980] First, the server generates a question to accept user input and sends it to the device. This question generation is often done using a web server or backend API server running on the server. This API server uses JavaScript or Python. Examples of questions that can be generated include specific questions such as "What is your stress level these days?" and "What activities do you like to do?"

[0981] The device displays the questions received from the server using HTML and JavaScript. The user answers the questions and sends the answers to the server. For example, the user might enter answers such as "My stress level is moderate" or "I enjoy nature."

[0982] The server then temporarily stores the received user responses in a database (e.g., MySQL), and then uses a Python machine learning library (e.g., scikit-learn) to analyze the collected data and understand the user's detailed psychological state.

[0983] A recommender system is used to select the optimal travel destination based on the analysis results. Collaborative filtering algorithms are often used in recommender systems. Then, a generative AI model (e.g., GPT-4) is used to generate a specific travel plan based on the selected travel destination. The generative AI is input with a prompt sentence such as the following:

[0984] "If the user is experiencing moderate stress and enjoys nature, suggest the best travel plan."

[0985] The generated travel plan is sent from the server to the device and displayed on the device using HTML and JavaScript. The travel plan includes detailed information such as tourist spots, accommodations, and places to eat. For example, a plan called "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" may be generated.

[0986] Furthermore, the server collects feedback from users and stores it in a database. This feedback is used to improve the accuracy of the machine learning algorithm. An example of feedback could be a specific request such as "I would like more time to admire the flower fields."

[0987] In this way, by combining servers, terminals, generative AI models, and machine learning algorithms, the system can suggest optimal travel destinations and detailed travel plans to users, and improve the accuracy of the system by incorporating feedback.

[0988] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0989] Step 1:

[0990] The server generates questions about the user's psychological state and mood and sends them to the device. The input is a question template pre-configured on the server, and the output is structured JSON-formatted question data. In operation, the server generates an API request and sends it to the device.

[0991] Step 2:

[0992] The terminal receives question data sent from the server and displays it on the screen using HTML and JavaScript. The input is JSON format data sent from the server, and the output is a question form displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[0993] Step 3:

[0994] The user enters answers to questions displayed on the terminal. The input is the user's answer, and the output is the answer data in JSON format that is sent by the terminal to the server. In operation, the user enters data into the form and presses the submit button.

[0995] Step 4:

[0996] The server receives the user's response data and temporarily stores it in a database. The input is the JSON formatted response data sent from the device, and the output is a record stored in the database. The server then validates the data and executes an INSERT query.

[0997] Step 5:

[0998] The server analyzes the stored data using a machine learning algorithm to evaluate the user's psychological state and mood. The input is the response data stored in the database, and the output is the user's psychological state data as the analysis result. In operation, the server runs a Python script and analyzes the data using a machine learning model.

[0999] Step 6:

[1000] Based on the analysis results, the server uses a recommender system to select the optimal travel destination. The input is the user's psychological state data as the analysis result, and the output is the selected travel destination data. In operation, the recommender algorithm compares the user data with the travel destination data to generate the optimal proposal.

[1001] Step 7:

[1002] The server uses a generative AI model to generate a specific travel plan based on the selected travel destination. The input is the selected travel destination data and a prompt, and the output is specific travel plan data. The prompt uses the following: "If the user is experiencing moderate stress and prefers to enjoy nature, please suggest the optimal travel plan."

[1003] Step 8:

[1004] The server sends the generated travel plan data to the device. The input is the travel plan data obtained from the generative AI model, and the output is the JSON-formatted travel plan data sent to the device. In operation, the server generates an API request and sends it to the device.

[1005] Step 9:

[1006] The device displays the received travel plan data on the screen using HTML and JavaScript. The input is the JSON-formatted travel plan data sent from the server, and the output is the travel plan displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[1007] Step 10:

[1008] The server sends feedback questions to the terminal and receives feedback from the user. The input is the feedback question as a prompt sentence, and the output is feedback data from the user. The question includes, "Please give us your feedback on this plan."

[1009] Step 11:

[1010] The user enters answers to feedback questions and sends them to the server. The input is the user's feedback, and the output is the JSON-formatted feedback data sent by the device to the server. In operation, the user enters data into the form and presses the submit button.

[1011] Step 12:

[1012] The server receives user feedback and stores it in a database. The input is the JSON-formatted feedback data sent from the device, and the output is a record stored in the database. The server validates the data and executes an INSERT query.

[1013] Step 13:

[1014] The server analyzes the feedback data to help improve the accuracy of the machine learning algorithm. The input is the feedback data stored in the database, and the output is an improved machine learning model. In operation, a new training dataset is created based on the feedback data, and the model is retrained.

[1015] (Application example 1)

[1016] 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."

[1017] In modern society, many people are seeking effective ways to reduce stress in their daily lives and refresh their minds and bodies. However, it is not easy to find a refreshing activity or plan that best suits each user's psychological state and mood. Furthermore, no system exists that efficiently and effectively suggests these refreshing activities or collects feedback. The present invention aims to solve these problems and provide a system that provides users with the optimal refreshing plan.

[1018] 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.

[1019] In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input psychological state and mood data and analyzing it using a machine learning algorithm, selection means for selecting an optimal refreshment plan based on the analysis results, generation means for generating a specific refreshment activity plan based on the selected plan, display means for displaying the generated refreshment activity plan on the user terminal, feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means, generation means for generating a detailed plan for refreshment activities by utilizing a generative AI model, and dialogue means for presenting questions to the user in an interactive format and allowing the user to input answers. This makes it possible to provide a refreshment plan optimal for the user's psychological state and mood and to collect feedback that is useful for improving the accuracy of suggestions.

[1020] Key Word Definitions

[1021] "Input means" refers to a technical element that provides an interface for inputting the user's psychological state and mood.

[1022] The "analysis means" is a technical element that collects input psychological state and mood data and analyzes it using machine learning algorithms.

[1023] The "selection means" is a technical element that selects the optimal refresh plan based on the analysis results.

[1024] The "generation means" is a technical element that generates a specific refreshment activity plan based on the selected plan.

[1025] The "display means" is a technical element that displays the generated refreshment activity plan on the user terminal.

[1026] "Feedback means" refers to a technical element that collects feedback from users and uses it to improve the accuracy of the analysis means.

[1027] "Generative AI Model" means an artificial intelligence model used to generate a detailed plan for refreshment activities.

[1028] "Dialogue means" refers to a technical element that presents questions to the user in an interactive format and provides an interface for the user to input answers.

[1029] This invention relates to an application that uses AI to propose optimal refreshment plans for modern people to refresh their minds and bodies. This system includes elements of a server, a terminal, and a user, and is implemented using the following means:

[1030] 1. Enter your user information:

[1031] The user inputs information about their psychological state and mood using a device such as a smartphone. The device provides an input means for transmitting this information to the server. Specifically, the device uses an interactive means in which questions are presented to the user in an interactive format and the user inputs answers.

[1032] 2. Data collection and analysis:

[1033] The server collects and temporarily stores the input data, and then uses an analytical tool to analyze the input data using machine learning algorithms. This analysis allows for a detailed understanding of the user's psychological state and mood.

[1034] 3. Select a refresh plan:

[1035] Based on the analysis results, the server uses a selection means to select an optimal refreshment plan, which includes a search means to search a database for potential activities with high refreshment effects.

[1036] 4. Generate a concrete refreshment activity plan:

[1037] The server uses a generating means for generating a specific refreshment activity plan based on the selected plan, where a generative AI model is utilized to generate a detailed plan for the refreshment activity, including specific information such as usage time, price, and location.

[1038] 5. View the generated plan:

[1039] The server transmits the generated refresh action plan to the terminal, which provides display means for displaying it to the user.

[1040] 6. Gathering Feedback:

[1041] The server provides a feedback mechanism for users to enter feedback after use, which is collected and stored to help improve future suggestions.

[1042] Examples:

[1043] If a user inputs "My recent stress level is medium" and "My favorite activity is relaxation," the server analyzes this information and selects "Spa & Massage" as the optimal refreshing activity. The server then generates a list of specific spa and massage shops and reservation information, and provides it to the user.

[1044] Example prompts for generative AI models:

[1045] User Answer:

[1046] Stress level: Medium

[1047] Favorite activity: Relaxation

[1048] prompt:

[1049] Based on the user's psychological state, please generate an optimal relaxation plan. Specifically, please include a list of spa and massage parlors available in brick-and-mortar locations, along with details such as opening hours and prices.

[1050] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1051] Step 1:

[1052] The server presents interactive questions to the user through the terminal and collects information about their psychological state and mood. At this time, the terminal receives the answers entered by the user and sends them to the server. An interactive means is used for input, and a question such as "What is your recent stress level?" is displayed. If the user answers "medium," the data is sent to the server.

[1053] Step 2:

[1054] The server collects and temporarily stores the received user response data. After sufficient data is collected, it analyzes the data using machine learning algorithms. Using the analytical means, calculations are performed to understand the user's detailed psychological state and mood. For example, if the stress level is "medium" and the preferred activity is "relaxation," the server will use this to evaluate the user's state.

[1055] Step 3:

[1056] The server selects the optimal refreshment plan based on the analysis results. At this time, it uses a selection method to search a database for activities with a high refreshing effect, such as spa or massage. The refreshment activity is selected based on the user's input psychological state and preferences.

[1057] Step 4:

[1058] The server generates a specific relaxation activity plan based on the selected relaxation plan. Using a generation means and a generative AI model, the server generates a detailed plan, including a list of specific spas and massage parlors, usage times, and prices. The plan is customized based on the user's input data.

[1059] Step 5:

[1060] The server sends the generated relaxation activity plan to the terminal, which then displays it to the user. Using the display means, the details of the specific relaxation plan are visually provided to the user. For example, a "list of spas and massages" is displayed, showing opening hours, prices, reservation information, etc.

[1061] Step 6:

[1062] After using the proposed refresh plan, the user inputs feedback through the terminal. The server receives and collects this feedback. The feedback collected from different users by the feedback means is used to improve the accuracy of the analysis means. For example, specific opinions such as "I'm satisfied with the plan" or "Something to improve is ____" are input.

[1063] Step 7:

[1064] The server stores the collected feedback in a database and uses it to improve the accuracy of future refreshment plan suggestions. This will make the suggestions for the next user more relevant and effective. For example, it will update the evaluation criteria for a new refreshment activity based on past feedback.

[1065] 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.

[1066] The present invention is a system that uses AI to suggest the best vacation destinations for busy modern people to refresh their minds and bodies. This system can be effectively implemented by including the following means.

[1067] 1. Entering user information

[1068] The server generates interactive questions and sends them to the device. These questions include questions about the user's psychological state and mood. For example, a question might be, "What is your stress level these days?"

[1069] The terminal displays these questions on the screen and allows the user to enter answers.

[1070] The user inputs information about his / her mental state and mood through the terminal and transmits it to the server.

[1071] 2. Use of emotion recognition engine

[1072] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice.

[1073] The server uses an emotion recognition engine to analyze the user's emotions from the collected data, and the analysis results are used to understand the user's detailed emotional state.

[1074] 3. Data collection and analysis

[1075] The server consolidates and temporarily stores the collected psychological state, mood, and emotion data.

[1076] The server preprocesses the combined data and converts it into a format suitable for input into machine learning algorithms.

[1077] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[1078] 4. Travel destination recommendations

[1079] Based on the analysis results, the server selects the travel destination that best suits the user's criteria. The selection process involves searching the database for potential travel destinations that match the criteria.

[1080] The server uses generative AI technology to generate a specific travel plan based on the selected destinations, such as a plan to view flower fields, visit hot springs, and enjoy local cuisine.

[1081] 5. Displaying the results

[1082] The server transmits the generated travel plan to the user terminal.

[1083] The terminal visually displays the received travel destination and plan to the user. For example, it may display "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[1084] 6. Gathering User Feedback

[1085] The server generates a question to prompt the user to input feedback and sends it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[1086] The terminal displays a question on the screen prompting feedback input.

[1087] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[1088] The terminal receives the user's feedback and transmits it to the server.

[1089] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[1090] Specific examples

[1091] As an example, consider a user who experiences moderate stress and enjoys nature.

[1092] 1. Example of Questions and Emotion Recognition

[1093] Server: "What's your stress level these days?"

[1094] User: "Medium"

[1095] The device uses an emotion recognition engine to analyze the user's facial expressions to determine whether they are smiling or tired, and acquires emotion data indicating "feeling tired."

[1096] 2. Data collection and analysis

[1097] The server collects the user's response data and emotion recognition data and analyzes them using a machine learning algorithm. For example, it analyzes the data based on the user's "moderate stress, likes to enjoy nature" and "feeling tired."

[1098] 3. Travel destination recommendations

[1099] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal plan for refreshing. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[1100] 4. Displaying the results

[1101] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[1102] 5. Gathering Feedback

[1103] Server: "Please give us your feedback on this plan."

[1104] User: "I wish there was more time to admire the flower fields."

[1105] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[1106] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[1110] Step 2:

[1111] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[1112] Step 3:

[1113] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[1114] Step 4:

[1115] The terminal transmits the answer entered by the user to the server.

[1116] Step 5:

[1117] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice, for example, to detect smiles and signs of fatigue.

[1118] Step 6:

[1119] The terminal transmits the collected emotion data to the server.

[1120] Step 7:

[1121] The server receives the response data and emotion data and temporarily stores them.

[1122] Step 8:

[1123] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[1124] Step 9:

[1125] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[1126] Step 10:

[1127] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, for example, searching a database for travel destinations that match criteria such as "places rich in nature" or "places with a high level of refreshing effect."

[1128] Step 11:

[1129] The server generates a specific travel plan based on the selected travel destinations. The plan includes tourist spots, accommodations, dining options, etc. For example, a plan called "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" can be created.

[1130] Step 12:

[1131] The server transmits the generated travel plan to the user terminal.

[1132] Step 13:

[1133] The device visually displays the received travel destination and plan to the user. For example, the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" is displayed on the screen.

[1134] Step 14:

[1135] The server generates a question to prompt the user to input feedback and sends it to the terminal. For example, the server generates a question such as "Please give us your feedback on this plan."

[1136] Step 15:

[1137] The terminal displays a question on the screen prompting feedback input.

[1138] Step 16:

[1139] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[1140] Step 17:

[1141] The terminal receives the user's feedback and transmits it to the server.

[1142] Step 18:

[1143] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[1144] Example 2

[1145] 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."

[1146] In modern society, many people find it difficult to find the perfect vacation destination to refresh their mind and body from their busy daily lives. In particular, conventional systems have not been able to fully grasp a user's psychological and emotional state and suggest appropriate travel destinations and specific travel plans based on that understanding. The present invention aims to provide a system that analyzes a user's psychological and emotional data and suggests optimal vacation destinations and travel plans.

[1147] 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 an input means for inputting the user's psychological state and mood, an analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, a collection means for collecting emotion data on the user's facial expressions and voice using a sensor, an emotion recognition means for analyzing the collected data, a selection means for selecting an optimal travel destination based on the analysis results, a generation means for generating a specific travel plan based on the selected travel destination, a display means for displaying the generated travel plan on the user terminal, and a feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal travel destinations and specific travel plans based on the user's detailed psychological and emotional state.

[1148] "Input means" refers to the means by which a user inputs his or her own psychological state or mood, and includes the terminal interface and forms.

[1149] "Analysis means" refers to devices or software that collect input data and analyze it using machine learning algorithms, etc.

[1150] "Collection means" refers to a mechanism for collecting emotional data such as the user's facial expressions and voice using sensors.

[1151] "Emotion recognition means" refers to the engine or algorithm that analyzes collected emotion data and determines the user's emotional state.

[1152] "Selection Method" refers to the algorithm or process used to select the most suitable travel destination based on the data obtained by the Analysis Method.

[1153] "Generator" means a process or algorithm for generating a specific itinerary based on selected travel destinations.

[1154] The "display means" refers to an interface or display device for visually displaying the generated travel plan on the user terminal.

[1155] "Feedback measures" refers to the processes and systems used to collect feedback from users and use that data to improve the accuracy of analytical measures.

[1156] The present invention is a system that proposes optimal travel destinations and travel plans based on the user's psychological state and mood. Specific embodiments of the system are described below.

[1157] Enter user information

[1158] The server generates a program that allows the user to input information about their psychological state and mood in the form of a question, and sends it to the device. This question might include, for example, "What is your stress level these days?" The device displays this question on the screen so that the user can input it. The user then inputs information about their psychological state and mood through the device and sends it to the server. This is done using Python's Flask framework and a RESTful API.

[1159] Use of emotion recognition engine

[1160] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice. WebRTC technology is used for this collection. The collected data is sent to a server and analyzed using, for example, Microsoft Azure's emotion recognition engine. This allows the user's emotional state to be analyzed.

[1161] Data collection and analysis

[1162] The server collects user input data and emotion recognition data and temporarily stores them in MongoDB. The server preprocesses this data and converts it into a format suitable for input into machine learning algorithms, using the Python Pandas library. The preprocessed data is then analyzed using Scikit-learn, and a model is applied to understand the user's detailed psychological and emotional state.

[1163] Travel destination recommendations

[1164] Based on the analysis results, the server selects the travel destination that best suits the criteria. SQL queries are used to search for travel destination candidates from the MySQL database. Based on the selected travel destinations, the server uses GPT-4, a generative AI technology, to generate a specific travel plan. For example, a plan such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" may be generated.

[1165] Displaying the results

[1166] The server sends the generated travel plan to the user's device. The data is sent in JSON format. The device then displays the received travel plan on the screen using HTML and CSS to visually display it to the user.

[1167] Collecting User Feedback

[1168] The server generates a question to prompt the user to enter feedback and sends it to the device. For example, a question could be, "Please give us your feedback on this plan." The device displays this question on the screen and allows the user to enter feedback. The user enters their opinions and suggestions for improvement regarding the provided plan, and the device sends this feedback to the server. The server saves the received feedback and registers it in a database to improve the accuracy of future proposals.

[1169] Specific examples

[1170] For example, consider the case where a user experiences moderate stress and enjoys enjoying nature.

[1171] 1. Example of Questions and Emotion Recognition

[1172] Server: "What's your stress level these days?"

[1173] User: "Medium"

[1174] The device uses a webcam to analyze the user's facial expressions, such as smiles and tiredness, using an emotion recognition engine, and obtains emotional data such as "feeling tired."

[1175] 2. Data collection and analysis

[1176] The server collects user response data and emotion recognition data and analyzes them using Scikit-learn. The analysis is based on the data "moderate stress, prefers enjoying nature" and "feeling tired."

[1177] 3. Travel destination recommendations

[1178] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal refreshment plan using GPT-4. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[1179] 4. Displaying the results

[1180] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[1181] 5. Gathering Feedback

[1182] Server: "Please give us your feedback on this plan."

[1183] User: "I wish there was more time to admire the flower fields."

[1184] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[1185] The following prompt is used as an example of a prompt sentence:

[1186] "Please suggest a travel plan that would be ideal for a Hokkaido resident who is moderately stressed, enjoys nature, and feels a bit tired. Please include viewing flower fields and visiting hot springs."

[1187] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[1188] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1189] Step 1:

[1190] Enter user information

[1191] The server generates questions to inquire about the user's state of mind and mood, such as "What is your stress level these days?"

[1192] The device receives questions from the server and displays them on the screen, specifically by rendering a question form using HTML and JavaScript.

[1193] The user inputs the answer to the question into the terminal, for example, "moderate stress."

[1194] The device sends the user's answers to the server. This data is sent using a RESTful API.

[1195] Input: User's state of mind or mood (e.g., "moderate stress")

[1196] Output: Collected response data

[1197] Step 2:

[1198] Collecting Emotional Data

[1199] The device uses a camera and microphone to collect the user's facial expressions and voice, and this operation uses WebRTC technology.

[1200] The terminal sends the collected data to the server. This data is encoded in BASE64 format, for example, and sent via a POST request.

[1201] Input: User's facial expression data, voice data

[1202] Output: Collected emotion data

[1203] Step 3:

[1204] Emotional Data Analysis

[1205] The server then sends the received emotional data to a Microsoft Azure emotion recognition engine, which analyzes the data and determines the user's emotional state, such as "feeling tired."

[1206] Input: Collected emotion data

[1207] Output: Parsed emotional state (e.g., "Feeling tired")

[1208] Step 4:

[1209] Data collection and preprocessing

[1210] The server combines the user response data with the analyzed emotion data and temporarily stores it in MongoDB.

[1211] The server preprocesses the aggregated data and converts it into a format suitable for machine learning algorithms, using the Python Pandas library for this preprocessing.

[1212] Input: User response data and emotion data

[1213] Output: Preprocessed data format

[1214] Step 5:

[1215] Data analysis

[1216] The server inputs the preprocessed data into a Scikit-learn machine learning model to analyze the user's detailed psychological and emotional state.

[1217] Input: Preprocessed data format

[1218] Output: Analysis result (e.g., "I am moderately stressed and enjoy nature.")

[1219] Step 6:

[1220] Selecting a travel destination

[1221] The server then searches the MySQL database for the best travel destination based on the analysis results, using an SQL query to identify potential travel destinations such as "Furano, Hokkaido."

[1222] Input: Analysis results

[1223] Output: Travel destination candidates (e.g. "Furano, Hokkaido")

[1224] Step 7:

[1225] Generate a travel plan

[1226] The server generates a travel plan using GPT-4 based on the selected travel destinations. It uses the Python OpenAI library to create a specific travel plan (e.g., "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine").

[1227] Input: Travel destination options

[1228] Output: Travel plan

[1229] Step 8:

[1230] Displaying the results

[1231] The server sends the generated itinerary to the device in JSON format.

[1232] The device visually displays the travel plan to the user, using HTML and CSS to present the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" in a user-friendly interface.

[1233] Input: Travel Plan

[1234] Output: A displayed itinerary

[1235] Step 9:

[1236] Gathering feedback

[1237] The server generates a question for the user to input feedback and sends it to the terminal, such as "Please give us your feedback on this plan."

[1238] The terminal displays the questions on the screen and allows the user to enter feedback.

[1239] The user inputs feedback on the provided plan (e.g., "I wish there was more time to view the flower fields").

[1240] The device sends user feedback to the server. The data is sent using a RESTful API.

[1241] The server stores the received feedback and registers it in a database to help improve the accuracy of the analysis means.

[1242] Input: User feedback

[1243] Output: Stored feedback data

[1244] The above are the specific processing steps of this system. We have clearly explained the detailed operations and data flow at each step and shown how the system can provide maximum support for user refreshment.

[1245] (Application example 2)

[1246] 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."

[1247] In today's busy lives, many people find it difficult to find the right way to relax, leading to the accumulation of stress and fatigue. In particular, there is a lack of objective indicators for determining which relaxation method is best for each individual. Therefore, there is a need for a method that can provide optimal relaxation content based on the user's psychological state and emotions, allowing them to effectively relax.

[1248] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, selection means for selecting optimal relaxation content based on the analysis results, generation means for generating a specific content plan based on the selected relaxation content, display means for displaying the generated content plan on the user terminal, emotion recognition means for analyzing the user's facial expressions, and feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal relaxation content based on the user's psychological state and emotions, thereby achieving a high level of refreshment.

[1249] "User" refers to an individual who uses the relaxation content.

[1250] "Mental state" refers to the state of mind that reflects the user's emotions and mood.

[1251] "Mood" refers to the temporary emotions or sensations that a user is experiencing at the time.

[1252] "Input means" refers to an interface for inputting the user's psychological state and mood.

[1253] "Data" refers to the input information on psychological state and mood.

[1254] "Analysis means" refers to a method for analyzing collected data using machine learning algorithms.

[1255] A "machine learning algorithm" refers to a calculation method that finds patterns based on data and makes predictions and judgments.

[1256] "Selection method" refers to a method for selecting the optimal relaxation content based on the analysis results.

[1257] "Relaxation content" refers to content such as music, videos, and meditation guides that are designed to promote refreshment and relaxation for users.

[1258] "Content plan" refers to a specific program or schedule created based on the selected relaxation content.

[1259] "Generation means" refers to a method for creating a specific content plan based on the selected relaxation content.

[1260] The "display means" refers to an interface for displaying the generated content plan on the user terminal.

[1261] "Emotion recognition means" refers to a method for analyzing a user's facial expression and recognizing their emotion.

[1262] "Feedback means" refers to a method for collecting opinions and impressions from users and using them to improve the accuracy of the analysis means.

[1263] "Generative AI model" refers to an algorithm or program that uses artificial intelligence to generate text or content.

[1264] A "prompt" refers to a formalized text of instructions or questions that are input to a generative AI model.

[1265] This invention is a system that proposes optimal relaxation content based on the user's psychological state and mood, and each of the means is specifically implemented as follows.

[1266] The server provides an input means for inputting the user's psychological state and mood. This means is realized by displaying questions in an interactive format on a device such as a smartphone or tablet. For example, questions such as "What is your stress level these days?" or "What is your favorite way to change your mood?" are displayed, and the user inputs their answers.

[1267] The input data is supplemented by the device's emotion recognition means, which uses the device's built-in camera and microphone to collect emotion data from the user's facial expressions and voice. For example, image data captured by the camera is analyzed using a face recognition library (e.g., OpenCV) to understand the user's emotional state. Voice data collected by the microphone is also analyzed using a voice emotion recognition library (e.g., Google Speech-to-Text API).

[1268] The collected psychological state, mood, and emotion data is sent to a server, where the server's analysis means analyzes the data using machine learning algorithms (e.g., TensorFlow, scikit-learn). The analysis results are used to understand the user's detailed psychological state and emotions.

[1269] Based on the analysis results, the server's selection means selects the most suitable relaxation content for the user from the database. At this time, a generative AI model (e.g., GPT-4) is used to generate a specific content plan based on the selected relaxation content. The generated prompt will have the following format:

[1270] "Stress levels are high these days, and for users who prefer nature videos, provide videos of the Alps, guided meditations with nature sounds, and relaxation videos for forest bathing."

[1271] The generated content plan is sent from the server to the device. The device visually displays this plan and provides the user with content that has a high relaxation effect. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" is displayed.

[1272] Furthermore, feedback is collected to gather information on user satisfaction and areas for improvement. For example, a question such as "Please provide feedback on this content" is displayed on the screen, and users respond by entering their feedback. The collected feedback is sent to the server and used to improve the accuracy of the analysis.

[1273] In this way, it is possible to propose optimal relaxation content based on the user's psychological state, mood, and emotions, thereby maximizing the refreshing effect on the user.

[1274] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1275] Step 1:

[1276] The server generates questions for inputting the user's psychological state and mood and sends them to the terminal. The terminal displays the generated questions on the screen, and the user inputs answers through input fields. The input data includes information such as stress level and methods for changing mood. For example, it displays questions such as "What is your stress level these days?" and "What is your preferred method for changing mood?"

[1277] Input: Question generation and display

[1278] Output: User response data

[1279] Step 2:

[1280] The device uses a built-in camera and microphone to collect the user's facial expressions and voice. This data is then analyzed using emotion recognition techniques (e.g., OpenCV, Google Speech-to-Text API). For example, image data captured by the camera can be analyzed using a facial recognition library to identify the user's emotional state as "tired" or "stressed," etc.

[1281] Input: facial and voice data

[1282] Output: Emotional state data

[1283] Step 3:

[1284] The server receives and temporarily stores the psychological state, mood, and emotion data collected from the device. It then analyzes this data using machine learning algorithms (e.g., TensorFlow, scikit-learn). As a result of the analysis, a detailed model of the user's psychological state and emotions is obtained.

[1285] Input: Mental state, mood, and emotion data

[1286] Output: Analysis results (detailed psychological state and emotion model)

[1287] Step 4:

[1288] Based on the analysis results, the server's selection means selects the most suitable relaxation content. The selection process involves searching the database for relaxation content candidates that match the criteria. For example, based on the criteria of "high stress level" and "prefer nature videos," it may select relaxation videos of the Alps or forest bathing.

[1289] Input: Analysis results, relaxation content database

[1290] Output: Relaxation content candidates

[1291] Step 5:

[1292] Based on the selected relaxation content, the server's generation means will use a generative AI model (e.g., GPT-4) to generate a specific content plan, with the generated prompt text being something like: "For a user whose stress level has been high recently and who prefers nature videos, please provide videos of the Alps, meditation guides with nature sounds, and forest bathing relaxation videos."

[1293] Input: Relaxation content suggestions

[1294] Output: A concrete content plan

[1295] Step 6:

[1296] The generated content plan is sent from the server to the device, which then visually displays the plan to the user. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" are displayed on the device screen.

[1297] Input: Specific content plan

[1298] Output: A visual representation of the content plan

[1299] Step 7:

[1300] Users provide feedback on the provided relaxation content. The server receives feedback data from the device via the feedback means and uses it to improve the accuracy of the analysis means. For example, feedback such as "The video of the Alps was very good, but I wish the music volume was turned down a bit" can be collected and used to generate the next content plan.

[1301] Input: User feedback

[1302] Output: Collected feedback data

[1303] 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.

[1304] 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.

[1305] 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.

[1306] [Fourth embodiment]

[1307] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1308] 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.

[1309] 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).

[1310] 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.

[1311] 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.

[1312] 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).

[1313] 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.

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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."

[1320] The present invention is a system that uses AI to suggest optimal vacation destinations for busy modern people to refresh their minds and bodies. The system includes the following means.

[1321] 1. Entering user information

[1322] The server presents interactive questions to the user via the terminal to collect information about the user's psychological state and mood. For example, it might ask, "What is your stress level these days?"

[1323] The terminal displays these questions on the screen and receives answers entered by the user.

[1324] The user inputs information about their psychological state and mood and sends it to the server via their terminal.

[1325] 2. Data collection and analysis

[1326] The server collects and temporarily stores the received data.

[1327] The server uses machine learning algorithms to analyze the collected data and understand the user's detailed psychological state and mood.

[1328] 3. Travel destination recommendations

[1329] Based on the analysis results, the server selects the travel destination that best suits the user's requirements, including places rich in nature and places known for their refreshing effects.

[1330] The server uses generative AI technology to generate a specific travel plan based on the selected travel destination, including detailed information on tourist attractions, accommodations, and dining options.

[1331] 4. Displaying the results

[1332] The server transmits the generated travel destination and travel plan to the user terminal.

[1333] The terminal displays the received travel destination and plan on the screen so that the user can check it.

[1334] 5. Gathering User Feedback

[1335] The server displays a question on the terminal to prompt the user to input feedback.

[1336] The user inputs their opinions and suggestions for improvement regarding the proposed travel plan.

[1337] The terminal receives the user's feedback and transmits it to the server.

[1338] The server stores the collected feedback to help improve future suggestions.

[1339] Specific examples

[1340] As an example, consider a case where a user is experiencing moderate stress and prefers to enjoy nature.

[1341] 1. Example Questions and Answers

[1342] Server: "What's your stress level these days?"

[1343] User: "Medium"

[1344] Server: "What activities do you like?"

[1345] User: "Enjoying nature"

[1346] 2. Data collection and analysis

[1347] The server collects the user's responses and analyzes them using a machine learning algorithm. Based on the analysis results, it is determined that the user can expect a refreshing effect in a place rich in nature.

[1348] 3. Travel destination recommendations

[1349] The server selects "Furano in Hokkaido" as the travel destination based on the conditions.

[1350] The server generates travel plans such as "viewing flower fields, visiting hot springs, and enjoying local cuisine."

[1351] 4. Displaying the results

[1352] The server sends the generated travel plan to the terminal, and the terminal suggests to the user "viewing the flower fields and visiting hot springs in Furano, Hokkaido."

[1353] 5. Gathering Feedback

[1354] Server: "Please give us your feedback on this plan."

[1355] User: "I wish there was more time to admire the flower fields."

[1356] The server stores the user's feedback and uses it to improve the accuracy of the next suggestion.

[1357] As described above, the present invention is a system that can maximize the user's refreshing effect by providing optimal travel destinations and detailed travel plans based on the user's psychological state and mood.

[1358] The processing flow will be explained below.

[1359] Step 1:

[1360] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[1361] Step 2:

[1362] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[1363] Step 3:

[1364] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[1365] Step 4:

[1366] The terminal transmits the answer entered by the user to the server.

[1367] Step 5:

[1368] The server receives the response data sent from the terminal and temporarily stores it.

[1369] Step 6:

[1370] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[1371] Step 7:

[1372] The server analyzes the data using machine learning algorithms and applies models to understand the user's detailed psychological state and mood.

[1373] Step 8:

[1374] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, searching the database for potential travel destinations that match the criteria.

[1375] Step 9:

[1376] The server generates a specific travel plan based on the selected travel destinations, such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine."

[1377] Step 10:

[1378] The server transmits the generated travel plan to the user terminal.

[1379] Step 11:

[1380] The terminal visually displays the received travel destination and plan to the user, for example, "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[1381] Step 12:

[1382] The server generates a question prompting the user to input feedback and transmits it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[1383] Step 13:

[1384] The terminal displays a question on the screen prompting feedback input.

[1385] Step 14:

[1386] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[1387] Step 15:

[1388] The terminal receives the user's feedback and transmits it to the server.

[1389] Step 16:

[1390] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[1391] Example 1

[1392] 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."

[1393] Modern people often feel stressed in their busy daily lives and need to refresh their minds and bodies. However, finding a vacation destination that suits them is not easy. In addition, selecting a travel destination and creating a travel plan takes time and effort, so there is a need for a system that can suggest travel destinations that are more efficient, appropriate, and have a high refreshing effect.

[1394] 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.

[1395] In this invention, the server includes: an input means for inputting the user's psychological state and mood; a collection means for collecting the input psychological state and mood data and temporarily storing it in a database; an analysis means for analyzing the collected data using a machine learning algorithm to understand the user's detailed psychological state and mood; a selection means using a recommender system to select optimal travel destinations based on the analysis results; a generation means using a generative AI model to generate a specific travel plan based on the selected travel destinations; a display means for sending the generated travel plan to a user terminal and displaying it; a collection means for collecting feedback from users and storing the feedback data in a database; and a feedback analysis means for analyzing the collected feedback and using it to improve the accuracy of the analysis means. This enables busy modern people to efficiently and accurately find the optimal travel destination for themselves, maximizing the benefits of refreshing their mind and body.

[1396] "Input means" refers to a means by which a user inputs data relating to his or her own psychological state or mood.

[1397] "Collection means" refers to the means for receiving input data and feedback and temporarily storing them in a database.

[1398] A "database" is an information storage device for temporarily storing collected data and feedback.

[1399] The "analysis means" is a means for analyzing collected data using machine learning algorithms to understand the user's detailed psychological state and mood.

[1400] A "machine learning algorithm" is a mathematical technique for analyzing collected data and finding patterns and relationships.

[1401] A "recommender system" is a system that recommends suitable travel destinations to users based on analysis results.

[1402] The "selection means" is a means for selecting the optimal travel destination based on the analysis results using a recommender system.

[1403] A "generative AI model" is an artificial intelligence model that generates specific travel plans based on selected travel destinations.

[1404] "Generation means" refers to a means for creating a specific travel plan based on the selected travel destinations using a generative AI model.

[1405] The "display means" is a means for transmitting the generated travel plan to the user terminal and displaying it.

[1406] "Feedback" refers to opinions and information on improvements to travel plans provided by users.

[1407] The "feedback analysis means" is a means for analyzing collected feedback and using it to improve the accuracy of the analysis means.

[1408] The system of this invention proposes optimal travel destinations based on the user's psychological state and mood, creates specific travel plans, and provides them to the user. The various hardware and software required for this purpose are described below.

[1409] First, the server generates a question to accept user input and sends it to the device. This question generation is often done using a web server or backend API server running on the server. This API server uses JavaScript or Python. Examples of questions that can be generated include specific questions such as "What is your stress level these days?" and "What activities do you like to do?"

[1410] The device displays the questions received from the server using HTML and JavaScript. The user answers the questions and sends the answers to the server. For example, the user might enter answers such as "My stress level is moderate" or "I enjoy nature."

[1411] The server then temporarily stores the received user responses in a database (e.g., MySQL), and then uses a Python machine learning library (e.g., scikit-learn) to analyze the collected data and understand the user's detailed psychological state.

[1412] A recommender system is used to select the optimal travel destination based on the analysis results. Collaborative filtering algorithms are often used in recommender systems. Then, a generative AI model (e.g., GPT-4) is used to generate a specific travel plan based on the selected travel destination. The generative AI is input with a prompt sentence such as the following:

[1413] "If the user is experiencing moderate stress and enjoys nature, suggest the best travel plan."

[1414] The generated travel plan is sent from the server to the device and displayed on the device using HTML and JavaScript. The travel plan includes detailed information such as tourist spots, accommodations, and places to eat. For example, a plan called "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" may be generated.

[1415] Furthermore, the server collects feedback from users and stores it in a database. This feedback is used to improve the accuracy of the machine learning algorithm. An example of feedback could be a specific request such as "I would like more time to admire the flower fields."

[1416] In this way, by combining servers, terminals, generative AI models, and machine learning algorithms, the system can suggest optimal travel destinations and detailed travel plans to users, and improve the accuracy of the system by incorporating feedback.

[1417] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1418] Step 1:

[1419] The server generates questions about the user's psychological state and mood and sends them to the device. The input is a question template pre-configured on the server, and the output is structured JSON-formatted question data. In operation, the server generates an API request and sends it to the device.

[1420] Step 2:

[1421] The terminal receives question data sent from the server and displays it on the screen using HTML and JavaScript. The input is JSON format data sent from the server, and the output is a question form displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[1422] Step 3:

[1423] The user enters answers to questions displayed on the terminal. The input is the user's answer, and the output is the answer data in JSON format that is sent by the terminal to the server. In operation, the user enters data into the form and presses the submit button.

[1424] Step 4:

[1425] The server receives the user's response data and temporarily stores it in a database. The input is the JSON formatted response data sent from the device, and the output is a record stored in the database. The server then validates the data and executes an INSERT query.

[1426] Step 5:

[1427] The server analyzes the stored data using a machine learning algorithm to evaluate the user's psychological state and mood. The input is the response data stored in the database, and the output is the user's psychological state data as the analysis result. In operation, the server runs a Python script and analyzes the data using a machine learning model.

[1428] Step 6:

[1429] Based on the analysis results, the server uses a recommender system to select the optimal travel destination. The input is the user's psychological state data as the analysis result, and the output is the selected travel destination data. In operation, the recommender algorithm compares the user data with the travel destination data to generate the optimal proposal.

[1430] Step 7:

[1431] The server uses a generative AI model to generate a specific travel plan based on the selected travel destination. The input is the selected travel destination data and a prompt, and the output is specific travel plan data. The prompt uses the following: "If the user is experiencing moderate stress and prefers to enjoy nature, please suggest the optimal travel plan."

[1432] Step 8:

[1433] The server sends the generated travel plan data to the device. The input is the travel plan data obtained from the generative AI model, and the output is the JSON-formatted travel plan data sent to the device. In operation, the server generates an API request and sends it to the device.

[1434] Step 9:

[1435] The device displays the received travel plan data on the screen using HTML and JavaScript. The input is the JSON-formatted travel plan data sent from the server, and the output is the travel plan displayed on the user's screen. In operation, JavaScript parses the JSON data and updates the HTML document.

[1436] Step 10:

[1437] The server sends feedback questions to the terminal and receives feedback from the user. The input is the feedback question as a prompt sentence, and the output is feedback data from the user. The question includes, "Please give us your feedback on this plan."

[1438] Step 11:

[1439] The user enters answers to feedback questions and sends them to the server. The input is the user's feedback, and the output is the JSON-formatted feedback data sent by the device to the server. In operation, the user enters data into the form and presses the submit button.

[1440] Step 12:

[1441] The server receives user feedback and stores it in a database. The input is the JSON-formatted feedback data sent from the device, and the output is a record stored in the database. The server validates the data and executes an INSERT query.

[1442] Step 13:

[1443] The server analyzes the feedback data to help improve the accuracy of the machine learning algorithm. The input is the feedback data stored in the database, and the output is an improved machine learning model. In operation, a new training dataset is created based on the feedback data, and the model is retrained.

[1444] (Application example 1)

[1445] 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."

[1446] In modern society, many people are seeking effective ways to reduce stress in their daily lives and refresh their minds and bodies. However, it is not easy to find a refreshing activity or plan that best suits each user's psychological state and mood. Furthermore, no system exists that efficiently and effectively suggests these refreshing activities or collects feedback. The present invention aims to solve these problems and provide a system that provides users with the optimal refreshing plan.

[1447] 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.

[1448] In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input psychological state and mood data and analyzing it using a machine learning algorithm, selection means for selecting an optimal refreshment plan based on the analysis results, generation means for generating a specific refreshment activity plan based on the selected plan, display means for displaying the generated refreshment activity plan on the user terminal, feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means, generation means for generating a detailed plan for refreshment activities by utilizing a generative AI model, and dialogue means for presenting questions to the user in an interactive format and allowing the user to input answers. This makes it possible to provide a refreshment plan optimal for the user's psychological state and mood and to collect feedback that is useful for improving the accuracy of suggestions.

[1449] Key Word Definitions

[1450] "Input means" refers to a technical element that provides an interface for inputting the user's psychological state and mood.

[1451] The "analysis means" is a technical element that collects input psychological state and mood data and analyzes it using machine learning algorithms.

[1452] The "selection means" is a technical element that selects the optimal refresh plan based on the analysis results.

[1453] The "generation means" is a technical element that generates a specific refreshment activity plan based on the selected plan.

[1454] The "display means" is a technical element that displays the generated refreshment activity plan on the user terminal.

[1455] "Feedback means" refers to a technical element that collects feedback from users and uses it to improve the accuracy of the analysis means.

[1456] "Generative AI Model" means an artificial intelligence model used to generate a detailed plan for refreshment activities.

[1457] "Dialogue means" refers to a technical element that presents questions to the user in an interactive format and provides an interface for the user to input answers.

[1458] This invention relates to an application that uses AI to propose optimal refreshment plans for modern people to refresh their minds and bodies. This system includes elements of a server, a terminal, and a user, and is implemented using the following means:

[1459] 1. Enter your user information:

[1460] The user inputs information about their psychological state and mood using a device such as a smartphone. The device provides an input means for transmitting this information to the server. Specifically, the device uses an interactive means in which questions are presented to the user in an interactive format and the user inputs answers.

[1461] 2. Data collection and analysis:

[1462] The server collects and temporarily stores the input data, and then uses an analytical tool to analyze the input data using machine learning algorithms. This analysis allows for a detailed understanding of the user's psychological state and mood.

[1463] 3. Select a refresh plan:

[1464] Based on the analysis results, the server uses a selection means to select an optimal refreshment plan, which includes a search means to search a database for potential activities with high refreshment effects.

[1465] 4. Generate a concrete refreshment activity plan:

[1466] The server uses a generating means for generating a specific refreshment activity plan based on the selected plan, where a generative AI model is utilized to generate a detailed plan for the refreshment activity, including specific information such as usage time, price, and location.

[1467] 5. View the generated plan:

[1468] The server transmits the generated refresh action plan to the terminal, which provides display means for displaying it to the user.

[1469] 6. Gathering Feedback:

[1470] The server provides a feedback mechanism for users to enter feedback after use, which is collected and stored to help improve future suggestions.

[1471] Examples:

[1472] If a user inputs "My recent stress level is medium" and "My favorite activity is relaxation," the server analyzes this information and selects "Spa & Massage" as the optimal refreshing activity. The server then generates a list of specific spa and massage shops and reservation information, and provides it to the user.

[1473] Example prompts for generative AI models:

[1474] User Answer:

[1475] Stress level: Medium

[1476] Favorite activity: Relaxation

[1477] prompt:

[1478] Based on the user's psychological state, please generate an optimal relaxation plan. Specifically, please include a list of spa and massage parlors available in brick-and-mortar locations, along with details such as opening hours and prices.

[1479] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1480] Step 1:

[1481] The server presents interactive questions to the user through the terminal and collects information about their psychological state and mood. At this time, the terminal receives the answers entered by the user and sends them to the server. An interactive means is used for input, and a question such as "What is your recent stress level?" is displayed. If the user answers "medium," the data is sent to the server.

[1482] Step 2:

[1483] The server collects and temporarily stores the received user response data. After sufficient data is collected, it analyzes the data using machine learning algorithms. Using the analytical means, calculations are performed to understand the user's detailed psychological state and mood. For example, if the stress level is "medium" and the preferred activity is "relaxation," the server will use this to evaluate the user's state.

[1484] Step 3:

[1485] The server selects the optimal refreshment plan based on the analysis results. At this time, it uses a selection method to search a database for activities with a high refreshing effect, such as spa or massage. The refreshment activity is selected based on the user's input psychological state and preferences.

[1486] Step 4:

[1487] The server generates a specific relaxation activity plan based on the selected relaxation plan. Using a generation means and a generative AI model, the server generates a detailed plan, including a list of specific spas and massage parlors, usage times, and prices. The plan is customized based on the user's input data.

[1488] Step 5:

[1489] The server sends the generated relaxation activity plan to the terminal, which then displays it to the user. Using the display means, the details of the specific relaxation plan are visually provided to the user. For example, a "list of spas and massages" is displayed, showing opening hours, prices, reservation information, etc.

[1490] Step 6:

[1491] After using the proposed refresh plan, the user inputs feedback through the terminal. The server receives and collects this feedback. The feedback collected from different users by the feedback means is used to improve the accuracy of the analysis means. For example, specific opinions such as "I'm satisfied with the plan" or "Something to improve is ____" are input.

[1492] Step 7:

[1493] The server stores the collected feedback in a database and uses it to improve the accuracy of future refreshment plan suggestions. This will make the suggestions for the next user more relevant and effective. For example, it will update the evaluation criteria for a new refreshment activity based on past feedback.

[1494] 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.

[1495] The present invention is a system that uses AI to suggest the best vacation destinations for busy modern people to refresh their minds and bodies. This system can be effectively implemented by including the following means.

[1496] 1. Entering user information

[1497] The server generates interactive questions and sends them to the device. These questions include questions about the user's psychological state and mood. For example, a question might be, "What is your stress level these days?"

[1498] The terminal displays these questions on the screen and allows the user to enter answers.

[1499] The user inputs information about his / her mental state and mood through the terminal and transmits it to the server.

[1500] 2. Use of emotion recognition engine

[1501] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice.

[1502] The server uses an emotion recognition engine to analyze the user's emotions from the collected data, and the analysis results are used to understand the user's detailed emotional state.

[1503] 3. Data collection and analysis

[1504] The server consolidates and temporarily stores the collected psychological state, mood, and emotion data.

[1505] The server preprocesses the combined data and converts it into a format suitable for input into machine learning algorithms.

[1506] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[1507] 4. Travel destination recommendations

[1508] Based on the analysis results, the server selects the travel destination that best suits the user's criteria. The selection process involves searching the database for potential travel destinations that match the criteria.

[1509] The server uses generative AI technology to generate a specific travel plan based on the selected destinations, such as a plan to view flower fields, visit hot springs, and enjoy local cuisine.

[1510] 5. Displaying the results

[1511] The server transmits the generated travel plan to the user terminal.

[1512] The terminal visually displays the received travel destination and plan to the user. For example, it may display "Viewing flower fields and visiting hot springs in Furano, Hokkaido."

[1513] 6. Gathering User Feedback

[1514] The server generates a question to prompt the user to input feedback and sends it to the terminal, for example, a question such as "Please give us your feedback on this plan."

[1515] The terminal displays a question on the screen prompting feedback input.

[1516] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[1517] The terminal receives the user's feedback and transmits it to the server.

[1518] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[1519] Specific examples

[1520] As an example, consider a user who experiences moderate stress and enjoys nature.

[1521] 1. Example of Questions and Emotion Recognition

[1522] Server: "What's your stress level these days?"

[1523] User: "Medium"

[1524] The device uses an emotion recognition engine to analyze the user's facial expressions to determine whether they are smiling or tired, and acquires emotion data indicating "feeling tired."

[1525] 2. Data collection and analysis

[1526] The server collects the user's response data and emotion recognition data and analyzes them using a machine learning algorithm. For example, it analyzes the data based on the user's "moderate stress, likes to enjoy nature" and "feeling tired."

[1527] 3. Travel destination recommendations

[1528] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal plan for refreshing. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[1529] 4. Displaying the results

[1530] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing the flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[1531] 5. Gathering Feedback

[1532] Server: "Please give us your feedback on this plan."

[1533] User: "I wish there was more time to admire the flower fields."

[1534] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[1535] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[1536] The processing flow will be explained below.

[1537] Step 1:

[1538] The server generates questions to understand the user's psychological state and mood and sends them to the device. For example, it generates a question such as, "What is your stress level these days?"

[1539] Step 2:

[1540] The device displays the question from the server on the screen, along with answer options (e.g., low, medium, high).

[1541] Step 3:

[1542] The user inputs an answer to the question displayed on the screen of the terminal. For example, the user inputs "Stress level: medium."

[1543] Step 4:

[1544] The terminal transmits the answer entered by the user to the server.

[1545] Step 5:

[1546] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice, for example, to detect smiles and signs of fatigue.

[1547] Step 6:

[1548] The terminal transmits the collected emotion data to the server.

[1549] Step 7:

[1550] The server receives the response data and emotion data and temporarily stores them.

[1551] Step 8:

[1552] The server preprocesses the received data and converts it into a format suitable for input to machine learning algorithms, for example, into numerical or categorical data.

[1553] Step 9:

[1554] The server uses machine learning algorithms to analyze the data and apply models to understand the user's detailed mental and emotional state.

[1555] Step 10:

[1556] Based on the analysis results, the server selects the travel destination that best suits the user's criteria, for example, searching a database for travel destinations that match criteria such as "places rich in nature" or "places with a high level of refreshing effect."

[1557] Step 11:

[1558] The server generates a specific travel plan based on the selected travel destinations. The plan includes tourist spots, accommodations, dining options, etc. For example, a plan called "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" can be created.

[1559] Step 12:

[1560] The server transmits the generated travel plan to the user terminal.

[1561] Step 13:

[1562] The device visually displays the received travel destination and plan to the user. For example, the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" is displayed on the screen.

[1563] Step 14:

[1564] The server generates a question to prompt the user to input feedback and sends it to the terminal. For example, the server generates a question such as "Please give us your feedback on this plan."

[1565] Step 15:

[1566] The terminal displays a question on the screen prompting feedback input.

[1567] Step 16:

[1568] The user can input their opinions and suggestions for improvement regarding the provided plan. For example, they can input feedback such as "I would like more time to view the flower fields."

[1569] Step 17:

[1570] The terminal receives the user's feedback and transmits it to the server.

[1571] Step 18:

[1572] The server stores the received feedback and registers it in a database to improve the accuracy of suggestions next time.

[1573] Example 2

[1574] 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."

[1575] In modern society, many people find it difficult to find the perfect vacation destination to refresh their mind and body from their busy daily lives. In particular, conventional systems have not been able to fully grasp a user's psychological and emotional state and suggest appropriate travel destinations and specific travel plans based on that understanding. The present invention aims to provide a system that analyzes a user's psychological and emotional data and suggests optimal vacation destinations and travel plans.

[1576] 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 an input means for inputting the user's psychological state and mood, an analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, a collection means for collecting emotion data on the user's facial expressions and voice using a sensor, an emotion recognition means for analyzing the collected data, a selection means for selecting an optimal travel destination based on the analysis results, a generation means for generating a specific travel plan based on the selected travel destination, a display means for displaying the generated travel plan on the user terminal, and a feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal travel destinations and specific travel plans based on the user's detailed psychological and emotional state.

[1577] "Input means" refers to the means by which a user inputs his or her own psychological state or mood, and includes the terminal interface and forms.

[1578] "Analysis means" refers to devices or software that collect input data and analyze it using machine learning algorithms, etc.

[1579] "Collection means" refers to a mechanism for collecting emotional data such as the user's facial expressions and voice using sensors.

[1580] "Emotion recognition means" refers to the engine or algorithm that analyzes collected emotion data and determines the user's emotional state.

[1581] "Selection Method" refers to the algorithm or process used to select the most suitable travel destination based on the data obtained by the Analysis Method.

[1582] "Generator" means a process or algorithm for generating a specific itinerary based on selected travel destinations.

[1583] The "display means" refers to an interface or display device for visually displaying the generated travel plan on the user terminal.

[1584] "Feedback measures" refers to the processes and systems used to collect feedback from users and use that data to improve the accuracy of analytical measures.

[1585] The present invention is a system that proposes optimal travel destinations and travel plans based on the user's psychological state and mood. Specific embodiments of the system are described below.

[1586] Enter user information

[1587] The server generates a program that allows the user to input information about their psychological state and mood in the form of a question, and sends it to the device. This question might include, for example, "What is your stress level these days?" The device displays this question on the screen so that the user can input it. The user then inputs information about their psychological state and mood through the device and sends it to the server. This is done using Python's Flask framework and a RESTful API.

[1588] Use of emotion recognition engine

[1589] The device uses sensors such as a camera and microphone to collect emotional data from the user's facial expressions and voice. WebRTC technology is used for this collection. The collected data is sent to a server and analyzed using, for example, Microsoft Azure's emotion recognition engine. This allows the user's emotional state to be analyzed.

[1590] Data collection and analysis

[1591] The server collects user input data and emotion recognition data and temporarily stores them in MongoDB. The server preprocesses this data and converts it into a format suitable for input into machine learning algorithms, using the Python Pandas library. The preprocessed data is then analyzed using Scikit-learn, and a model is applied to understand the user's detailed psychological and emotional state.

[1592] Travel destination recommendations

[1593] Based on the analysis results, the server selects the travel destination that best suits the criteria. SQL queries are used to search for travel destination candidates from the MySQL database. Based on the selected travel destinations, the server uses GPT-4, a generative AI technology, to generate a specific travel plan. For example, a plan such as "viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine" may be generated.

[1594] Displaying the results

[1595] The server sends the generated travel plan to the user's device. The data is sent in JSON format. The device then displays the received travel plan on the screen using HTML and CSS to visually display it to the user.

[1596] Collecting User Feedback

[1597] The server generates a question to prompt the user to enter feedback and sends it to the device. For example, a question could be, "Please give us your feedback on this plan." The device displays this question on the screen and allows the user to enter feedback. The user enters their opinions and suggestions for improvement regarding the provided plan, and the device sends this feedback to the server. The server saves the received feedback and registers it in a database to improve the accuracy of future proposals.

[1598] Specific examples

[1599] For example, consider the case where a user experiences moderate stress and enjoys enjoying nature.

[1600] 1. Example of Questions and Emotion Recognition

[1601] Server: "What's your stress level these days?"

[1602] User: "Medium"

[1603] The device uses a webcam to analyze the user's facial expressions, such as smiles and tiredness, using an emotion recognition engine, and obtains emotional data such as "feeling tired."

[1604] 2. Data collection and analysis

[1605] The server collects user response data and emotion recognition data and analyzes them using Scikit-learn. The analysis is based on the data "moderate stress, prefers enjoying nature" and "feeling tired."

[1606] 3. Travel destination recommendations

[1607] The server selects "Furano in Hokkaido" as a travel destination based on the conditions and generates the optimal refreshment plan using GPT-4. For example, it suggests "viewing the flower fields of Furano, touring hot springs, and enjoying local cuisine."

[1608] 4. Displaying the results

[1609] The server transmits the generated travel plan to the terminal, and the terminal displays "Viewing flower fields and visiting hot springs in Furano, Hokkaido" to the user.

[1610] 5. Gathering Feedback

[1611] Server: "Please give us your feedback on this plan."

[1612] User: "I wish there was more time to admire the flower fields."

[1613] The device sends user feedback to the server, which stores the data to improve the accuracy of suggestions next time.

[1614] The following prompt is used as an example of a prompt sentence:

[1615] "Please suggest a travel plan that would be ideal for a Hokkaido resident who is moderately stressed, enjoys nature, and feels a bit tired. Please include viewing flower fields and visiting hot springs."

[1616] As described above, the present invention is a system that combines the user's psychological state and mood, as well as an emotion recognition engine, to provide optimal travel destinations and detailed travel plans, thereby maximizing the user's refreshing effect.

[1617] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1618] Step 1:

[1619] Enter user information

[1620] The server generates questions to inquire about the user's state of mind and mood, such as "What is your stress level these days?"

[1621] The device receives questions from the server and displays them on the screen, specifically by rendering a question form using HTML and JavaScript.

[1622] The user inputs the answer to the question into the terminal, for example, "moderate stress."

[1623] The device sends the user's answers to the server. This data is sent using a RESTful API.

[1624] Input: User's state of mind or mood (e.g., "moderate stress")

[1625] Output: Collected response data

[1626] Step 2:

[1627] Collecting Emotional Data

[1628] The device uses a camera and microphone to collect the user's facial expressions and voice, and this operation uses WebRTC technology.

[1629] The terminal sends the collected data to the server. This data is encoded in BASE64 format, for example, and sent via a POST request.

[1630] Input: User's facial expression data, voice data

[1631] Output: Collected emotion data

[1632] Step 3:

[1633] Emotional Data Analysis

[1634] The server then sends the received emotional data to a Microsoft Azure emotion recognition engine, which analyzes the data and determines the user's emotional state, such as "feeling tired."

[1635] Input: Collected emotion data

[1636] Output: Parsed emotional state (e.g., "Feeling tired")

[1637] Step 4:

[1638] Data collection and preprocessing

[1639] The server combines the user response data with the analyzed emotion data and temporarily stores it in MongoDB.

[1640] The server preprocesses the aggregated data and converts it into a format suitable for machine learning algorithms, using the Python Pandas library for this preprocessing.

[1641] Input: User response data and emotion data

[1642] Output: Preprocessed data format

[1643] Step 5:

[1644] Data analysis

[1645] The server inputs the preprocessed data into a Scikit-learn machine learning model to analyze the user's detailed psychological and emotional state.

[1646] Input: Preprocessed data format

[1647] Output: Analysis result (e.g., "I am moderately stressed and enjoy nature.")

[1648] Step 6:

[1649] Selecting a travel destination

[1650] The server then searches the MySQL database for the best travel destination based on the analysis results, using an SQL query to identify potential travel destinations such as "Furano, Hokkaido."

[1651] Input: Analysis results

[1652] Output: Travel destination candidates (e.g. "Furano, Hokkaido")

[1653] Step 7:

[1654] Generate a travel plan

[1655] The server generates a travel plan using GPT-4 based on the selected travel destinations. It uses the Python OpenAI library to create a specific travel plan (e.g., "Viewing the flower fields of Furano, visiting hot springs, and enjoying local cuisine").

[1656] Input: Travel destination options

[1657] Output: Travel plan

[1658] Step 8:

[1659] Displaying the results

[1660] The server sends the generated itinerary to the device in JSON format.

[1661] The device visually displays the travel plan to the user, using HTML and CSS to present the content "Viewing flower fields and visiting hot springs in Furano, Hokkaido" in a user-friendly interface.

[1662] Input: Travel Plan

[1663] Output: A displayed itinerary

[1664] Step 9:

[1665] Gathering feedback

[1666] The server generates a question for the user to input feedback and sends it to the terminal, such as "Please give us your feedback on this plan."

[1667] The terminal displays the questions on the screen and allows the user to enter feedback.

[1668] The user inputs feedback on the provided plan (e.g., "I wish there was more time to view the flower fields").

[1669] The device sends user feedback to the server. The data is sent using a RESTful API.

[1670] The server stores the received feedback and registers it in a database to help improve the accuracy of the analysis means.

[1671] Input: User feedback

[1672] Output: Stored feedback data

[1673] The above are the specific processing steps of this system. We have clearly explained the detailed operations and data flow at each step and shown how the system can provide maximum support for user refreshment.

[1674] (Application example 2)

[1675] 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."

[1676] In today's busy lives, many people find it difficult to find the right way to relax, leading to the accumulation of stress and fatigue. In particular, there is a lack of objective indicators for determining which relaxation method is best for each individual. Therefore, there is a need for a method that can provide optimal relaxation content based on the user's psychological state and emotions, allowing them to effectively relax.

[1677] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for inputting the user's psychological state and mood, analysis means for collecting the input data on the psychological state and mood and analyzing it using a machine learning algorithm, selection means for selecting optimal relaxation content based on the analysis results, generation means for generating a specific content plan based on the selected relaxation content, display means for displaying the generated content plan on the user terminal, emotion recognition means for analyzing the user's facial expressions, and feedback means for collecting feedback from the user and using it to improve the accuracy of the analysis means. This makes it possible to provide optimal relaxation content based on the user's psychological state and emotions, thereby achieving a high level of refreshment.

[1678] "User" refers to an individual who uses the relaxation content.

[1679] "Mental state" refers to the state of mind that reflects the user's emotions and mood.

[1680] "Mood" refers to the temporary emotions or sensations that a user is experiencing at the time.

[1681] "Input means" refers to an interface for inputting the user's psychological state and mood.

[1682] "Data" refers to the input information on psychological state and mood.

[1683] "Analysis means" refers to a method for analyzing collected data using machine learning algorithms.

[1684] A "machine learning algorithm" refers to a calculation method that finds patterns based on data and makes predictions and judgments.

[1685] "Selection method" refers to a method for selecting the optimal relaxation content based on the analysis results.

[1686] "Relaxation content" refers to content such as music, videos, and meditation guides that are designed to promote refreshment and relaxation for users.

[1687] "Content plan" refers to a specific program or schedule created based on the selected relaxation content.

[1688] "Generation means" refers to a method for creating a specific content plan based on the selected relaxation content.

[1689] The "display means" refers to an interface for displaying the generated content plan on the user terminal.

[1690] "Emotion recognition means" refers to a method for analyzing a user's facial expression and recognizing their emotion.

[1691] "Feedback means" refers to a method for collecting opinions and impressions from users and using them to improve the accuracy of the analysis means.

[1692] "Generative AI model" refers to an algorithm or program that uses artificial intelligence to generate text or content.

[1693] A "prompt" refers to a formalized text of instructions or questions that are input to a generative AI model.

[1694] This invention is a system that proposes optimal relaxation content based on the user's psychological state and mood, and each of the means is specifically implemented as follows.

[1695] The server provides an input means for inputting the user's psychological state and mood. This means is realized by displaying questions in an interactive format on a device such as a smartphone or tablet. For example, questions such as "What is your stress level these days?" or "What is your favorite way to change your mood?" are displayed, and the user inputs their answers.

[1696] The input data is supplemented by the device's emotion recognition means, which uses the device's built-in camera and microphone to collect emotion data from the user's facial expressions and voice. For example, image data captured by the camera is analyzed using a face recognition library (e.g., OpenCV) to understand the user's emotional state. Voice data collected by the microphone is also analyzed using a voice emotion recognition library (e.g., Google Speech-to-Text API).

[1697] The collected psychological state, mood, and emotion data is sent to a server, where the server's analysis means analyzes the data using machine learning algorithms (e.g., TensorFlow, scikit-learn). The analysis results are used to understand the user's detailed psychological state and emotions.

[1698] Based on the analysis results, the server's selection means selects the most suitable relaxation content for the user from the database. At this time, a generative AI model (e.g., GPT-4) is used to generate a specific content plan based on the selected relaxation content. The generated prompt will have the following format:

[1699] "Stress levels are high these days, and for users who prefer nature videos, provide videos of the Alps, guided meditations with nature sounds, and relaxation videos for forest bathing."

[1700] The generated content plan is sent from the server to the device. The device visually displays this plan and provides the user with content that has a high relaxation effect. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" is displayed.

[1701] Furthermore, feedback is collected to gather information on user satisfaction and areas for improvement. For example, a question such as "Please provide feedback on this content" is displayed on the screen, and users respond by entering their feedback. The collected feedback is sent to the server and used to improve the accuracy of the analysis.

[1702] In this way, it is possible to propose optimal relaxation content based on the user's psychological state, mood, and emotions, thereby maximizing the refreshing effect on the user.

[1703] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1704] Step 1:

[1705] The server generates questions for inputting the user's psychological state and mood and sends them to the terminal. The terminal displays the generated questions on the screen, and the user inputs answers through input fields. The input data includes information such as stress level and methods for changing mood. For example, it displays questions such as "What is your stress level these days?" and "What is your preferred method for changing mood?"

[1706] Input: Question generation and display

[1707] Output: User response data

[1708] Step 2:

[1709] The device uses a built-in camera and microphone to collect the user's facial expressions and voice. This data is then analyzed using emotion recognition techniques (e.g., OpenCV, Google Speech-to-Text API). For example, image data captured by the camera can be analyzed using a facial recognition library to identify the user's emotional state as "tired" or "stressed," etc.

[1710] Input: facial and voice data

[1711] Output: Emotional state data

[1712] Step 3:

[1713] The server receives and temporarily stores the psychological state, mood, and emotion data collected from the device. It then analyzes this data using machine learning algorithms (e.g., TensorFlow, scikit-learn). As a result of the analysis, a detailed model of the user's psychological state and emotions is obtained.

[1714] Input: Mental state, mood, and emotion data

[1715] Output: Analysis results (detailed psychological state and emotion model)

[1716] Step 4:

[1717] Based on the analysis results, the server's selection means selects the most suitable relaxation content. The selection process involves searching the database for relaxation content candidates that match the criteria. For example, based on the criteria of "high stress level" and "prefer nature videos," it may select relaxation videos of the Alps or forest bathing.

[1718] Input: Analysis results, relaxation content database

[1719] Output: Relaxation content candidates

[1720] Step 5:

[1721] Based on the selected relaxation content, the server's generation means will use a generative AI model (e.g., GPT-4) to generate a specific content plan, with the generated prompt text being something like: "For a user whose stress level has been high recently and who prefers nature videos, please provide videos of the Alps, meditation guides with nature sounds, and forest bathing relaxation videos."

[1722] Input: Relaxation content suggestions

[1723] Output: A concrete content plan

[1724] Step 6:

[1725] The generated content plan is sent from the server to the device, which then visually displays the plan to the user. For example, specific content such as "video of the Alps," "meditation guide with nature sounds," and "forest bathing relaxation video" are displayed on the device screen.

[1726] Input: Specific content plan

[1727] Output: A visual representation of the content plan

[1728] Step 7:

[1729] Users provide feedback on the provided relaxation content. The server receives feedback data from the device via the feedback means and uses it to improve the accuracy of the analysis means. For example, feedback such as "The video of the Alps was very good, but I wish the music volume was turned down a bit" can be collected and used to generate the next content plan.

[1730] Input: User feedback

[1731] Output: Collected feedback data

[1732] 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.

[1733] 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.

[1734] 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 robot 414.

[1735] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1736] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1737] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1738] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1739] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1740] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1741] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1742] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1743] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1744] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1745] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1746] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1747] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1748] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1749] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1750] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1751] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1752] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1753] The following is further disclosed regarding the above embodiment.

[1754] (Claim 1)

[1755] an input means for inputting the user's mental state and mood;

[1756] An analysis method that collects input psychological state and mood data and analyzes it using machine learning algorithms;

[1757] A selection means for selecting the optimal travel destination based on the analysis results;

[1758] A generating means for generating a specific travel plan based on the selected travel destination;

[1759] a display means for displaying the generated travel plan on a user terminal;

[1760] A feedback mechanism for collecting feedback from users and using it to improve the accuracy of the analysis mechanism;

[1761] A system including:

[1762] (Claim 2)

[1763] 10. The system according to claim 1, further comprising an interactive means for presenting questions to a user in an interactive format and for the user to input answers.

[1764] (Claim 3)

[1765] 2. The system according to claim 1, further comprising a search means for searching a database for travel destination candidates with a high refreshing effect based on the user's psychological state and mood.

[1766] "Example 1"

[1767] (Claim 1)

[1768] an input means for inputting the user's mental state and mood;

[1769] A collection means for collecting input psychological state and mood data and temporarily storing it in a database;

[1770] The collected data is analyzed using machine learning algorithms to understand the user's detailed psychological state and mood.

[1771] A selection method using a recommender system that selects the optimal travel destination based on the analysis results;

[1772] A generation means using a generative AI model to generate a specific travel plan based on the selected travel destination;

[1773] a display means for transmitting the generated travel plan to a user terminal and displaying it;

[1774] a collection means for collecting feedback from users and storing the feedback data in a database;

[1775] a feedback analysis means for analyzing the collected feedback and for improving the accuracy of the analysis means;

[1776] A system including:

[1777] (Claim 2)

[1778] 10. The system according to claim 1, further comprising an interactive means for presenting questions to a user in an interactive format and for the user to input answers.

[1779] (Claim 3)

[1780] 2. The system according to claim 1, further comprising a search means for searching a database for travel destination candidates with a high refreshing effect based on the user's psychological state and mood.

[1781] "Application Example 1"

[1782] Adding the distinctive features of the application examples to the original claims

[1783] (Claim 1)

[1784] an input means for inputting the user's mental state and mood;

[1785] An analysis method that collects input psychological state and mood data and analyzes it using machine learning algorithms;

[1786] A selection means for selecting an optimal refresh plan based on the analysis results;

[1787] A generating means for generating a specific refreshment activity plan based on the selected plan;

[1788] a display means for displaying the generated refreshment activity plan on a user terminal;

[1789] A feedback mechanism for collecting feedback from users and using it to improve the accuracy of the analysis mechanism;

[1790] A generating means for generating a detailed plan for refreshment activities by utilizing a generative AI model;

[1791] an interactive means for presenting questions to a user in an interactive format and for the user to input answers;

[1792] A system including:

[1793] (Claim 2)

[1794] 2. The system according to claim 1, further comprising a search means for searching a database for potential activities with a high refreshing effect based on the user's psychological state and mood.

[1795] (Claim 3)

[1796] The system of claim 1, wherein the detailed plan of the refreshment activity generated using the generative AI model includes specific information such as usage time, price, and location.

[1797] "Example 2: Combining Emotion Engines"

[1798] (Claim 1)

[1799] an input means for inputting the user's mental state and mood;

[1800] An analysis method that collects input psychological state and mood data and analyzes it using machine learning algorithms;

[1801] A collection means for collecting emotion data by using a sensor to measure a user's facial expression and voice;

[1802] an emotion recognition means for analyzing the collected data;

[1803] A selection means for selecting the optimal travel destination based on the analysis results;

[1804] A generating means for generating a specific travel plan based on the selected travel destination;

[1805] a display means for displaying the generated travel plan on a user terminal;

[1806] A feedback mechanism for collecting feedback from users and using it to improve the accuracy of the analysis mechanism;

[1807] A system including:

[1808] (Claim 2)

[1809] 10. The system according to claim 1, further comprising an interactive means for presenting questions to a user in an interactive format and for the user to input answers.

[1810] (Claim 3)

[1811] 2. The system according to claim 1, further comprising a search means for searching a database for travel destination candidates with a high refreshing effect based on the user's psychological state and mood.

[1812] "Application example 2 when combining emotion engines"

[1813] (Claim 1)

[1814] an input means for inputting the user's mental state and mood;

[1815] An analysis method that collects input psychological state and mood data and analyzes it using machine learning algorithms;

[1816] a selection means for selecting optimal relaxation content based on the analysis results;

[1817] A generating means for generating a specific content plan based on the selected relaxation content;

[1818] a display means for displaying the generated content plan on a user terminal;

[1819] An emotion recognition means for analyzing a user's facial expression;

[1820] A feedback mechanism for collecting feedback from users and using it to improve the accuracy of the analysis mechanism;

[1821] A system including:

[1822] (Claim 2)

[1823] 10. The system according to claim 1, further comprising an interactive means for presenting questions to a user in an interactive format and for the user to input answers.

[1824] (Claim 3)

[1825] 2. The system according to claim 1, further comprising a search means for searching a database for content candidates with a high refreshing effect based on the psychological state and mood of the user.

[1826] (Claim 4)

[1827] 10. The system of claim 1, further comprising a generating means for generating specific prompt sentences based on the selected relaxation content using a generative AI model. [Explanation of symbols]

[1828] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting the user's mental state and mood; An analysis method that collects input psychological state and mood data and analyzes it using machine learning algorithms; A selection means for selecting the optimal travel destination based on the analysis results; A generating means for generating a specific travel plan based on the selected travel destination; a display means for displaying the generated travel plan on a user terminal; A feedback mechanism for collecting feedback from users and using it to improve the accuracy of the analysis mechanism; A system including:

2. 2. The system according to claim 1, further comprising an interactive means for presenting questions to a user in an interactive format and for the user to input answers.

3. 2. The system according to claim 1, further comprising a search means for searching a database for travel destination candidates with a high refreshing effect based on the user's psychological state and mood.

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