system

Abstract

We provide the system. [Solution] A means of receiving personal preference information, An extraction means for extracting relevant activity information based on received preference information, A means of optimizing transportation that suggests an appropriate method of travel based on extracted activity information, An output means that outputs individually optimized activity information and movement methods, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to the decrease in the frequency of the elderly going out, there has been an increase in health risks and loneliness, and lack of information and inconvenience of transportation means have hindered going out. In order to solve these problems and enable the elderly to lead a healthier and more fulfilling life, it is important to promote participation in events and activities according to individual interests and concerns. However, in the current system, the provision of activity information optimal for individuals and the proposal of appropriate transportation means are not sufficiently carried out, so it is necessary to improve this.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that includes a receiving means for receiving individual preference information, and an extraction means for extracting relevant activity information based on the received preference information. Furthermore, it includes a transportation optimization means for suggesting appropriate transportation methods based on the extracted activity information, and an output means for outputting individually optimized activity information and transportation methods. This system makes it possible for elderly people to increase their motivation to participate in events and activities that align with their interests, thereby promoting their social participation.

[0006] "Receiving means" refers to devices or software applications that receive personal preference information from other devices or systems.

[0007] "Extraction means" refers to a method or apparatus for analyzing received preference information and extracting related activity information from a database or external source.

[0008] A "transportation optimization method" is a technical means that calculates and presents the most optimal method of travel for each individual based on extracted activity information.

[0009] "Output means" refers to a device or function that provides the user with generated event information and information on the most suitable mode of transportation. [Brief explanation of the drawing]

[0010] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0011] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0014] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0016] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages 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), or Bluetooth (registered trademark), etc.

[0017] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] As shown in Figure 1, the 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.

[0021] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0022] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0023] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0024] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0025] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0028] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0029] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0030] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0031] This invention is a system designed to encourage elderly people to go out, and it has the function of suggesting optimal events and activities based on individual preference information. This system consists of a user terminal, a server, and a generating AI model.

[0032] The user terminal is a device for users to input their interests and preferences, and a dedicated application is provided for it. By registering their hobbies and interests through the application, users can receive individually customized suggestions.

[0033] The server plays a central role in processing the user preference information it receives. After receiving preference information from the user terminal, the server passes the data to a generative AI model, which extracts the most suitable events and activities. The generative AI model in this system uses machine learning algorithms to analyze the user's hobbies and preferences and generates suggestions based on that analysis.

[0034] The generative AI model searches a database of local event information and activities related to the user's interests and returns relevant information to the server. In particular, it can make selections that are suitable for specific genres or geographical conditions that the user is interested in.

[0035] The server receives information from the generated AI model and generates a message to make suggestions to the user. These suggestions include detailed event information, along with the best mode of transportation from the user's place of residence to the event venue. The server refers to local public transportation data and calculates the optimal route, taking into account time and cost.

[0036] The user terminal ultimately displays the suggestion message received from the server to the user, encouraging participation. In this way, the present invention provides elderly people with easy access to events that match their interests and motivates them to go out.

[0037] As a concrete example, consider a user who is interested in "listening to music." This user registers this interest in the app, and then the server retrieves information on relevant concerts and music festivals. A generative AI model analyzes this information and selects the most suitable event based on the user's interests. Then, suggestions, including the best means of transportation, are sent to the user's device, allowing the user to easily decide whether or not to attend. Through this entire process, users can pursue their interests while also being encouraged to participate in social activities.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches a dedicated application and enters their interests and preferences. Here, they select hobby information such as "classical music" or "walking."

[0041] Step 2:

[0042] The device checks the entered interest data locally to verify that the information is correct. After verification, it converts the data to JSON format.

[0043] Step 3:

[0044] The terminal sends the converted preference information to the server. At this time, security protocols are used to ensure the safety of the data.

[0045] Step 4:

[0046] The server parses the received data and sends it to an AI model to generate database queries based on the user's interests and preferences.

[0047] Step 5:

[0048] The generative AI model accesses a database to search for relevant events and activities that match the user's preferences. This includes dates, locations, and categories.

[0049] Step 6:

[0050] The generative AI model returns event information selected based on preferences to the server. It also ranks the most suitable events that meet the specified criteria.

[0051] Step 7:

[0052] Based on the received event information, the server considers available transportation options and formulates the optimal travel route. It takes into account public transport timetables and car routes.

[0053] Step 8:

[0054] The server generates a suggestion message that integrates event details and transportation options, and sends it to the user's terminal.

[0055] Step 9:

[0056] The device displays the received suggestion message on the application screen and notifies the user.

[0057] Step 10:

[0058] Users can review the suggestions and choose to "attend" or "ignore" events that interest them. If they choose to attend, they can proceed with the reservation or application process.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] There is a lack of motivation to encourage older adults and individuals with specific hobbies to participate in social activities and events. Furthermore, they face challenges in easily accessing activities that interest them and finding suitable transportation options.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving personal hobby information, means for analyzing the received information and presenting relevant event information using a generation AI algorithm, and means for calculating the optimal route by referring to public transportation service data. This makes it easier for elderly people to participate in social activities based on their interests, and enables them to obtain event information and select means of transportation in an efficient and convenient manner.

[0064] A "device for receiving personal hobby information" is a device that allows an individual to input their hobbies and interests, and then collects that information.

[0065] A "device that converts data into a data format for communication" is a device that converts received information into a specific format for secure communication.

[0066] A "model that presents highly relevant event information using a generative AI algorithm" is a system equipped with machine learning technology that analyzes received hobby information and selects the most relevant events based on that information.

[0067] A "device for creating notifications that include detailed information and optimal means of transportation" is a device that organizes and prepares information in order to notify users of event details and the most suitable means of transportation associated with them.

[0068] A "computational mechanism that calculates the optimal route by referencing public transport service data" is a system that uses information from public transport to calculate the most efficient travel route to a destination.

[0069] A "device that displays the final notification to the user" is a device that visually provides the user with information generated from the server.

[0070] This invention is a system that promotes social participation for the elderly and individuals with specific hobbies. The system consists of a user terminal, a server, and a generative AI model.

[0071] The user terminal is a device for entering personal hobby information, and a dedicated application is provided for it. Through this app, users can input their interests and register their data. This ensures that the user's hobby information is collected appropriately. For example, if a user is interested in "listening to music," they can register that information through the app.

[0072] The server receives hobby information sent from user terminals and analyzes it. The analyzed data is passed to a generative AI model, which forms the basis for presenting highly relevant event information. The generative AI model utilizes machine learning algorithms to search for possible activities from a local event information database. This database operation is often implemented using programming languages ​​such as Python.

[0073] The event information selected by the generating AI model is sent back to the server. The server organizes this information and creates a notification message for the user. This message includes not only specific event details (date, time, location, cost, etc.) but also the best mode of transportation. Online map services such as Google® Maps API are used to calculate the best mode of transportation.

[0074] The user's device receives suggestion messages sent from the server and displays them to the user. Based on this information, the user can easily select events they want to attend and take further actions within the app as needed. For example, they can purchase tickets to attend the event they wish to participate in.

[0075] An example of a prompt might be: "Please suggest music events for senior citizens. The user's preference is for listening to music, and the geographical condition is the Tokyo area." This prompt serves as the basic data for the generative AI model to suggest appropriate events.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user launches a dedicated application on their device and enters information about their hobbies and interests. This information is entered as data indicating the user's hobbies and interests. The application converts the entered information into a standard data format such as JSON and prepares to send it to the server. Specifically, the user selects a hobby from several options on the screen and enters details as needed.

[0079] Step 2:

[0080] The terminal sends the entered hobby information to the server. This data reaches the server via the network and is entered as received data. The terminal uses the HTTPS protocol to securely transfer the information. Specifically, communication occurs simply by the application pressing the send button.

[0081] Step 3:

[0082] The server parses the JSON data received from the terminal and extracts the user's hobby information. This analysis identifies specific categories that indicate the user's interests. The server then prepares to pass the parsed information to the generating AI model. In this step, the server verifies the data's integrity by comparing it with past preference data recorded in the database.

[0083] Step 4:

[0084] The server inputs the analyzed data into the generative AI model and generates prompts to retrieve event information based on the user's interests. These prompts are used as input data for the generative AI model. Based on the received data, the generative AI model performs calculations to extract relevant events from the event information database and return that list to the server.

[0085] Step 5:

[0086] The server organizes the event information returned from the generated AI model and constructs a message to provide to the user. During message creation, the server uses external services such as the Google Maps API to calculate the optimal mode of transport and route, and includes this information in the message as well. The final output is a message that integrates the event information and the optimal route information.

[0087] Step 6:

[0088] The user's terminal receives suggestion messages sent from the server and displays them on the screen. Based on these suggestions, the user can select events of interest and decide whether or not to participate. Specifically, notifications on the terminal guide the user and display an interface to confirm the suggested content.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] There is a need to provide a system that can motivate elderly people and local residents to participate in activities and events that suit their preferences, thereby promoting going out. Conventional proposed systems have difficulty providing information directly related to users' interests in a timely manner, resulting in a problem of not promoting participation in social activities.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes receiving means for receiving personal preference information, extracting means for extracting relevant activity information based on the received preference information, and suggesting means for suggesting experiential events offered in the local area. This makes it possible for local residents to receive suggestions for activities and travel routes that match their interests.

[0094] "Personal preference information" refers to data about the interests and concerns of a specific individual, and is provided when a user registers their hobbies and interests.

[0095] "Receiving means" refers to a device or function for acquiring preference information from the user, and is primarily implemented on the user's terminal.

[0096] The "extraction method" is a function that selects activity information with specific relevance from the received preference information, and this is achieved using a generative AI model.

[0097] The "transportation optimization method" is a function that calculates the optimal mode of transportation to a proposed event, and is implemented using public transportation data.

[0098] The "recommendation method" is a function that selects and recommends experiential events held within a region based on the user's preference information.

[0099] "Output means" refers to a device or function that presents optimized activity information and movement methods to the user, and is implemented through a dedicated application.

[0100] The system for realizing this invention consists mainly of three main components: a user terminal, a server, and a generative AI model. The user first inputs their preference information through a dedicated smartphone application. This information is defined as "personal preference information" and includes limited interests and concerns. A receiving device on the terminal transmits this information to the server.

[0101] On the server, the received preference information is first extracted and passed to a generating AI model. The AI ​​model then analyzes this preference information to identify local events and experiences that best suit the user's interests. This process utilizes machine learning algorithms backed by large datasets and performs database searches to suggest local experience events. The server then uses the extracted activity information to construct a method that allows the user to easily participate. In doing so, the optimal travel route is calculated, taking traffic data into consideration.

[0102] The final suggestions are transferred to the user's terminal via an output device, displaying customized event information and transportation options based on the user's interests. These output messages include detailed event information and optimal transportation options using buses and trains. Specifically, if the user is interested in music, it will include concert information and suggestions for intermediate stops.

[0103] As a concrete example, suppose a user expresses interest in "listening to music" within the application. In this case, the server, through a generative AI model, selects local music concert information and makes suggestions considering available public transportation options.

[0104] An example of a prompt statement is as follows:

[0105] To encourage elderly people to go out, if a user's interest is "music," generate optimal suggestions based on information about nearby concerts and music festivals.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users input their preferences through a dedicated smartphone application. The information entered concerns the user's interests and concerns, and is prepared to be sent to the server after being entered into a form within the application. The input data here includes interests such as "listening to music" or "cooking classes."

[0109] Step 2:

[0110] The terminal transmits the input preference information to the server via a receiving device. The server receives this received data as input and performs initial data processing such as filtering and formatting. The output is preference information formatted into an analyzable format.

[0111] Step 3:

[0112] The server passes the received preference information to a generative AI model. The AI ​​model analyzes this data and extracts the activity and event information most relevant to the user's preferences. Here, data calculations are performed using machine learning algorithms, and a list of relevant events is generated as output.

[0113] Step 4:

[0114] The server retrieves local public transport data based on event information selected by the AI ​​model and calculates the optimal mode of transportation. The input is local transport data and event information, and the output is the optimal route and mode of transport. An optimization algorithm is used here based on distance, time, and cost.

[0115] Step 5:

[0116] The server generates a final suggestion message and sends it to the user's terminal. This message includes details such as event information, transportation options, and route guidance. The server combines this information and converts it into a user-friendly, customized format.

[0117] Step 6:

[0118] The user terminal displays suggestion messages received from the server, providing the user with information to help them choose the most suitable event to participate in. The output here is an interface that allows the user to easily decide whether or not to participate in an event. Specific actions such as push notifications and calendar integration also occur here.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a system that recognizes an individual's preferences and emotional state, and based on that, proposes optimal activity information and means of transportation. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0121] The user terminal provides an interface for receiving input information about the user's interests and concerns. This allows the user to input information (in voice, text, etc.) about their interests and current emotional state. The input information is then incorporated into the system as base data.

[0122] The server receives preference and emotion data transmitted from the user's terminal and performs data analysis. The generative AI model searches for relevant event information based on the user's hobbies and interests and selects the most suitable one. Furthermore, the emotion engine analyzes the input emotion information and selects activities that match the user's mood. For example, if the user is seeking relaxation, it will suggest events held in a quiet environment.

[0123] The emotion engine uses voice and text analysis technologies to determine the user's current emotions. The analysis results are reflected in the output of the generated AI model, which helps to improve the accuracy of the suggestions.

[0124] The server also calculates the optimal mode of transportation for the event information it suggests to the user. In doing so, it takes into account public transport and other modes of transport to provide a route that is easily accessible to the user.

[0125] The user terminal displays the final suggested information to the user, encouraging them to go out. For example, if the user is interested in a "concert" and is in a "happy" emotional state, the server suggests nearby music events and provides a plan that includes a route and time from the nearest train station as a means of transportation.

[0126] Thus, the present invention aims to encourage users to actively participate in activities and promote better social engagement through personalized event suggestions based on user preferences and emotions.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users launch a dedicated application and input their current emotional state, along with their interests, via voice or text. Input can be done using a microphone or keyboard.

[0130] Step 2:

[0131] The terminal converts the input interest and sentiment data into a digital format and extracts features. This data is then prepared for transmission to subsequent processes.

[0132] Step 3:

[0133] The terminal sends the converted data to the server. An encryption protocol is used for secure data transfer during this process.

[0134] Step 4:

[0135] The server analyzes the received preference and emotion information. This data is then passed to a generative AI model to retrieve personalized activity information.

[0136] Step 5:

[0137] The generative AI model extracts relevant events from a database based on the user's preferences and adjusts the suggested events to take emotional information into account. For example, if the user wants to relax, it will select a calming music event.

[0138] Step 6:

[0139] The emotion engine analyzes voice or text data to identify the user's emotions. It then identifies the most appropriate event type for that emotion and generates event suggestions.

[0140] Step 7:

[0141] The server devises the optimal mode of transportation based on the extracted event information. It calculates efficient travel routes using public transportation data.

[0142] Step 8:

[0143] The server creates a message integrating the generated event proposals and transportation options, and sends it to the user's terminal.

[0144] Step 9:

[0145] The device displays the received suggestion message to the user and notifies them using the application's notification function.

[0146] Step 10:

[0147] Users review the displayed suggestions and choose to "attend" or "ignore" events that interest them. Therefore, users can select the events that are best suited to them and increase their motivation to participate.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In modern society, there is a demand for promoting participation in activities based on diverse individual preferences and emotions, but there is a lack of systems that can flexibly respond to individual needs. In particular, it is difficult to provide personalized event suggestions that take into account the user's emotions and circumstances, and to optimize the means of transportation that accompany them. Conventional technologies have problems with accuracy and satisfaction when analyzing emotions and providing travel plans that take public transportation into consideration.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for receiving personal preference information and emotional data; means for analyzing the received preference information and emotional data to extract relevant activity information and select events that match the user's emotional state; and means for suggesting appropriate means of transportation based on the extracted and selected activity information and calculating an optimal travel route that takes public transportation into consideration. This makes it possible to provide highly accurate activity suggestions and efficient travel plans tailored to the needs of individual users.

[0153] A "receiving mechanism" is a function that acquires personal preference information and emotional data and receives it in a format that can be processed within the system.

[0154] The "analysis means" is a function that extracts relevant activity information based on received preference information and emotional data, and selects events that are appropriate to the user's emotional state.

[0155] The "transportation optimization method" is a function that calculates appropriate travel methods, including public transportation, based on extracted and selected activity information, and presents the optimal travel route.

[0156] "Output means" refers to a function that presents optimized activity information and movement methods to the user, and provides information visually or audibly.

[0157] "Emotional data" refers to information that indicates a user's current emotional state, and is collected in the form of audio or text.

[0158] A "generative AI model" is an AI technology used to select relevant event information based on user preference information and emotional data.

[0159] This invention provides a system that suggests optimal activity information and means of transportation based on an individual's preferences and emotional state. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0160] The user terminal provides an interface for inputting information about the user's interests, concerns, and emotional state. Users can input information in voice or text format, and this information forms the basis of the system's data. For example, a user might input, "I'm interested in rock concerts, and I feel like relaxing today."

[0161] The server receives preference information and emotion data transmitted from the user's terminal. This received data is analyzed by an emotion engine running on the server. The emotion engine uses speech recognition and text analysis technologies to determine the user's current emotions. The analysis results are processed by a generative AI model, which selects the most appropriate event information based on the user's emotions and preferences.

[0162] The generative AI model considers the user's emotional data and preferences, ranks relevant event information, and selects events that are suitable for the user. It also suggests the most appropriate mode of transportation to the location where the selected event will be held.

[0163] For example, if a user expresses interest in a "concert" and their emotional state is "happy," the system will search for quiet music events happening nearby and calculate a travel route using public transportation.

[0164] An example of a prompt related to this process would be: "I'm interested in music events, but I'd like to relax today. Do you have any recommendations?"

[0165] In this way, the present invention provides personalized event suggestions based on the user's preferences and emotions, supporting the user in actively participating in activities.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Users input their interests and emotional states using the interface of their user terminal. Specifically, they input preference information such as "I want to go to a music event" and emotional data such as "I feel happy today." The input information is stored on the terminal as digital data and used for subsequent processing.

[0169] Step 2:

[0170] The user terminal transmits collected preference information and emotional data to the server. The data is encrypted and securely sent over the network. The transmitted data is then stored on the server for subsequent analysis.

[0171] Step 3:

[0172] The server analyzes the received preference and emotion data. Here, the emotion engine operates, using speech recognition algorithms to quantify the emotion data and classifying the user's interests through text analysis. The analysis results in quantified emotional states and preference categories.

[0173] Step 4:

[0174] The server inputs data into the generative AI model based on the analysis results. The generative AI model searches the database for relevant event information based on the user's emotions and interests, and selects the most suitable events. A list of recommended events is generated as output.

[0175] Step 5:

[0176] The server proposes a suitable mode of transportation for the selected event. A transportation optimization system operates, calculating the optimal route using public transport data, taking into account the transmitted event information and the user's current location. The final output provides the route and estimated travel time.

[0177] Step 6:

[0178] The server sends the final suggested information to the user's terminal. The user's terminal displays the suggested event and mode of transportation on the screen and suggests actions to take. For example, it might say, "To get to the nearby jazz concert, you can take the bus and arrive in 20 minutes."

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] In today's information society, it is difficult for individuals to choose what is best suited to them from the vast amount of information and entertainment available. Furthermore, there are few systems that provide optimal entertainment information tailored to an individual's emotional state, along with suitable means of transportation, making it a challenge to enhance user satisfaction.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for receiving personal preference information and emotional state, means for extracting relevant entertainment information based on the received preference information and emotional state, and means for selecting the most suitable means of transportation based on the extracted entertainment information. This makes it possible to provide the user with optimal entertainment information and means of transportation tailored to their emotions and preferences in an integrated manner.

[0184] "Receiving means" refers to devices and technologies for acquiring an individual's preference information and emotional state.

[0185] "Extraction means" refers to a function that selects and extracts entertainment information related to the received preference information and emotional state.

[0186] "Transportation method selection means" refers to technology that determines the most optimal mode of transportation for a user to access entertainment information based on extracted entertainment information.

[0187] "Display means" means a device or method for visually conveying optimized entertainment information and directions to a user.

[0188] This invention begins with the user using a smart device to input personal preference information and emotional state. The user terminal is equipped with an emotion engine for recognizing emotions and can analyze the user's current emotional state through voice or text input. The received information is sent to a server.

[0189] The server analyzes preference information and emotional state based on the received data, in cooperation with the emotion engine. This analysis uses a generative AI model. This model learns the user's past behavior data and preferences and extracts suitable entertainment information. For example, if the user inputs emotional information such as "I'm a little tired," it will suggest content suitable for relaxation, such as quiet music or entertainment in a scenic location.

[0190] Based on the extracted information, the server calculates the optimal mode of transportation. The transportation selection mechanism utilizes public transport data to present the best route from the user's location to their destination. This data processing is achieved using public transport APIs and map services.

[0191] Finally, the server displays the calculation results on the user's terminal. The display method allows the user to easily see the selected entertainment information and how to access it. For example, suggestions might be displayed in response to a prompt such as, "I'm in the mood to relax. What music do you recommend?"

[0192] This system is implemented using software such as Python and TENSORFLOW®. Specifically, it utilizes smartphones and tablets, significantly improving user convenience.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user inputs their personal preferences and emotional state into a smart device. The input data is in voice or text format, and an emotion engine performs emotion analysis. The output obtained here is the user's preferences and their emotional state at that time.

[0196] Step 2:

[0197] The terminal sends the analysis results to the server. The server uses a generative AI model to analyze the received preference and emotion data. The data processing also considers the user's past preference data to extract the most relevant entertainment information. This output is a list of relevant entertainment information.

[0198] Step 3:

[0199] The server uses extracted entertainment information as input to calculate the optimal travel route using a means of transportation selection. This process obtains route data from a public transportation API and performs data calculations based on the user's location and destination. The output is information on the optimal travel route.

[0200] Step 4:

[0201] The server transmits the final entertainment and travel route information to the user's terminal. A display device presents this information to the user, introducing the content in a visually easy-to-understand format. This allows the user to prepare to enjoy the suggested entertainment.

[0202] This series of steps allows users to obtain optimal entertainment information and transportation options based on their emotional state and preferences.

[0203] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0219] This invention is a system designed to encourage elderly people to go out, and it has the function of suggesting optimal events and activities based on individual preference information. This system consists of a user terminal, a server, and a generating AI model.

[0220] The user terminal is a device for users to input their interests and preferences, and a dedicated application is provided for it. By registering their hobbies and interests through the application, users can receive individually customized suggestions.

[0221] The server plays a central role in processing the user preference information it receives. After receiving preference information from the user terminal, the server passes the data to a generative AI model, which extracts the most suitable events and activities. The generative AI model in this system uses machine learning algorithms to analyze the user's hobbies and preferences and generates suggestions based on that analysis.

[0222] The generative AI model searches a database of local event information and activities related to the user's interests and returns relevant information to the server. In particular, it can make selections that are suitable for specific genres or geographical conditions that the user is interested in.

[0223] The server receives information from the generated AI model and generates a message to make suggestions to the user. These suggestions include detailed event information, along with the best mode of transportation from the user's place of residence to the event venue. The server refers to local public transportation data and calculates the optimal route, taking into account time and cost.

[0224] The user terminal ultimately displays the suggestion message received from the server to the user, encouraging participation. In this way, the present invention provides elderly people with easy access to events that match their interests and motivates them to go out.

[0225] As a concrete example, consider a user who is interested in "listening to music." This user registers this interest in the app, and then the server retrieves information on relevant concerts and music festivals. A generative AI model analyzes this information and selects the most suitable event based on the user's interests. Then, suggestions, including the best means of transportation, are sent to the user's device, allowing the user to easily decide whether or not to attend. Through this entire process, users can pursue their interests while also being encouraged to participate in social activities.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user launches a dedicated application and enters their interests and preferences. Here, they select hobby information such as "classical music" or "walking."

[0229] Step 2:

[0230] The device checks the entered interest data locally to verify that the information is correct. After verification, it converts the data to JSON format.

[0231] Step 3:

[0232] The terminal sends the converted preference information to the server. At this time, security protocols are used to ensure the safety of the data.

[0233] Step 4:

[0234] The server parses the received data and sends it to an AI model to generate database queries based on the user's interests and preferences.

[0235] Step 5:

[0236] The generative AI model accesses a database to search for relevant events and activities that match the user's preferences. This includes dates, locations, and categories.

[0237] Step 6:

[0238] The generative AI model returns event information selected based on preferences to the server. It also ranks the most suitable events that meet the specified criteria.

[0239] Step 7:

[0240] Based on the received event information, the server considers available transportation options and formulates the optimal travel route. It takes into account public transport timetables and car routes.

[0241] Step 8:

[0242] The server generates a suggestion message that integrates event details and transportation options, and sends it to the user's terminal.

[0243] Step 9:

[0244] The device displays the received suggestion message on the application screen and notifies the user.

[0245] Step 10:

[0246] Users can review the suggestions and choose to "attend" or "ignore" events that interest them. If they choose to attend, they can proceed with the reservation or application process.

[0247] (Example 1)

[0248] Next, we will describe Example 1. 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."

[0249] There is a lack of motivation to encourage older adults and individuals with specific hobbies to participate in social activities and events. Furthermore, they face challenges in easily accessing activities that interest them and finding suitable transportation options.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for receiving personal hobby information, means for analyzing the received information and presenting relevant event information using a generation AI algorithm, and means for calculating the optimal route by referring to public transportation service data. This makes it easier for elderly people to participate in social activities based on their interests, and enables them to obtain event information and select means of transportation in an efficient and convenient manner.

[0252] A "device for receiving personal hobby information" is a device that allows an individual to input their hobbies and interests, and then collects that information.

[0253] A "device that converts data into a data format for communication" is a device that converts received information into a specific format for secure communication.

[0254] A "model that presents highly relevant event information using a generative AI algorithm" is a system equipped with machine learning technology that analyzes received hobby information and selects the most relevant events based on that information.

[0255] A "device for creating notifications that include detailed information and optimal means of transportation" is a device that organizes and prepares information in order to notify users of event details and the most suitable means of transportation associated with them.

[0256] A "computational mechanism that calculates the optimal route by referencing public transport service data" is a system that uses information from public transport to calculate the most efficient travel route to a destination.

[0257] A "device that displays the final notification to the user" is a device that visually provides the user with information generated from the server.

[0258] This invention is a system that promotes social participation for the elderly and individuals with specific hobbies. The system consists of a user terminal, a server, and a generative AI model.

[0259] The user terminal is a device for entering personal hobby information, and a dedicated application is provided for it. Through this app, users can input their interests and register their data. This ensures that the user's hobby information is collected appropriately. For example, if a user is interested in "listening to music," they can register that information through the app.

[0260] The server receives hobby information sent from user terminals and analyzes it. The analyzed data is passed to a generative AI model, which forms the basis for presenting highly relevant event information. The generative AI model utilizes machine learning algorithms to search for possible activities from a local event information database. This database operation is often implemented using programming languages ​​such as Python.

[0261] The event information selected by the generating AI model is sent back to the server. The server organizes this information and creates a notification message for the user. This message includes not only specific event details (date, time, location, cost, etc.) but also the best mode of transportation. Online map services such as the Google Maps API are used to calculate the best mode of transportation.

[0262] The user's device receives suggestion messages sent from the server and displays them to the user. Based on this information, the user can easily select events they want to attend and take further actions within the app as needed. For example, they can purchase tickets to attend the event they wish to participate in.

[0263] An example of a prompt might be: "Please suggest music events for senior citizens. The user's preference is for listening to music, and the geographical condition is the Tokyo area." This prompt serves as the basic data for the generative AI model to suggest appropriate events.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user launches a dedicated application on their device and enters information about their hobbies and interests. This information is entered as data indicating the user's hobbies and interests. The application converts the entered information into a standard data format such as JSON and prepares to send it to the server. Specifically, the user selects a hobby from several options on the screen and enters details as needed.

[0267] Step 2:

[0268] The terminal sends the entered hobby information to the server. This data reaches the server via the network and is entered as received data. The terminal uses the HTTPS protocol to securely transfer the information. Specifically, communication occurs simply by the application pressing the send button.

[0269] Step 3:

[0270] The server parses the JSON data received from the terminal and extracts the user's hobby information. This analysis identifies specific categories that indicate the user's interests. The server then prepares to pass the parsed information to the generating AI model. In this step, the server verifies the data's integrity by comparing it with past preference data recorded in the database.

[0271] Step 4:

[0272] The server inputs the analyzed data into the generative AI model and generates prompts to retrieve event information based on the user's interests. These prompts are used as input data for the generative AI model. Based on the received data, the generative AI model performs calculations to extract relevant events from the event information database and return that list to the server.

[0273] Step 5:

[0274] The server organizes the event information returned from the generated AI model and constructs a message to provide to the user. During message creation, the server uses external services such as the Google Maps API to calculate the optimal mode of transport and route, and includes this information in the message as well. The final output is a message that integrates the event information and the optimal route information.

[0275] Step 6:

[0276] The user's terminal receives suggestion messages sent from the server and displays them on the screen. Based on these suggestions, the user can select events of interest and decide whether or not to participate. Specifically, notifications on the terminal guide the user and display an interface to confirm the suggested content.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] There is a need to provide a system that can motivate elderly people and local residents to participate in activities and events that suit their preferences, thereby promoting going out. Conventional proposed systems have difficulty providing information directly related to users' interests in a timely manner, resulting in a problem of not promoting participation in social activities.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes receiving means for receiving personal preference information, extracting means for extracting relevant activity information based on the received preference information, and suggesting means for suggesting experiential events offered in the local area. This makes it possible for local residents to receive suggestions for activities and travel routes that match their interests.

[0282] "Personal preference information" refers to data regarding the interests and concerns of a specific individual, and is information provided by a user registering their own hobbies and matters of concern.

[0283] "Receiving means" refers to a device or function for acquiring preference information from a user, and is mainly realized on a user terminal.

[0284] "Extracting means" is a function for selecting activity information with specific relevance from the received preference information, and is realized using a generative AI model.

[0285] "Transportation means optimization means" is a function for calculating the optimal means of transportation to a proposed event, and is realized using data of public transportation.

[0286] "Proposing means" is a function for selecting and recommending experience events held within a region based on a user's preference information.

[0287] "Output means" refers to a device or function for presenting optimized activity information and a method of movement to a user, and is realized through a dedicated application.

[0288] The system for realizing this invention mainly consists of three main components: a user terminal, a server, and a generative AI model. First, a user inputs their preference information through a dedicated smartphone application. This information is defined as "personal preference information" and includes limited interests and matters of concern. The receiving means on the terminal serves to transmit this information to the server.

[0289] On the server, the received preference information is first extracted and passed to a generating AI model. The AI ​​model then analyzes this preference information to identify local events and experiences that best suit the user's interests. This process utilizes machine learning algorithms backed by large datasets and performs database searches to suggest local experience events. The server then uses the extracted activity information to construct a method that allows the user to easily participate. In doing so, the optimal travel route is calculated, taking traffic data into consideration.

[0290] The final suggestions are transferred to the user's terminal via an output device, displaying customized event information and transportation options based on the user's interests. These output messages include detailed event information and optimal transportation options using buses and trains. Specifically, if the user is interested in music, it will include concert information and suggestions for intermediate stops.

[0291] As a concrete example, suppose a user expresses interest in "listening to music" within the application. In this case, the server, through a generative AI model, selects local music concert information and makes suggestions considering available public transportation options.

[0292] An example of a prompt statement is as follows:

[0293] To encourage elderly people to go out, if a user's interest is "music," generate optimal suggestions based on information about nearby concerts and music festivals.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] Users input their preferences through a dedicated smartphone application. The information entered concerns the user's interests and concerns, and is prepared to be sent to the server after being entered into a form within the application. The input data here includes interests such as "listening to music" or "cooking classes."

[0297] Step 2:

[0298] The terminal transmits the input preference information to the server via a receiving device. The server receives this received data as input and performs initial data processing such as filtering and formatting. The output is preference information formatted into an analyzable format.

[0299] Step 3:

[0300] The server passes the received preference information to a generative AI model. The AI ​​model analyzes this data and extracts the activity and event information most relevant to the user's preferences. Here, data calculations are performed using machine learning algorithms, and a list of relevant events is generated as output.

[0301] Step 4:

[0302] The server retrieves local public transport data based on event information selected by the AI ​​model and calculates the optimal mode of transportation. The input is local transport data and event information, and the output is the optimal route and mode of transport. An optimization algorithm is used here based on distance, time, and cost.

[0303] Step 5:

[0304] The server generates a final suggestion message and sends it to the user's terminal. This message includes details such as event information, transportation options, and route guidance. The server combines this information and converts it into a user-friendly, customized format.

[0305] Step 6:

[0306] The user terminal displays the proposed message received from the server and provides the user with information for optimal event participation. The output here is an interface that allows the user to easily operate and decide on event participation. Specific operations such as push notifications and calendar linking are also performed here.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0308] The present invention is a system that recognizes an individual's preferences and emotional state and proposes optimal activity information and means of transportation based thereon. In addition to the user terminal, the server, and the generation AI model, the system includes an emotion engine that recognizes the user's emotions.

[0309] The user terminal provides an interface for receiving input of information regarding the user's interests and concerns. Thereby, the user can input information (such as in voice or text form) about their interests and current emotional state. The input information is incorporated into the system as base data.

[0310] The server receives the preference information and emotional data transmitted from the user terminal and performs data analysis. The generation AI model searches for relevant event information based on the user's hobbies and interests and makes the most suitable selection therefrom. Furthermore, the emotion engine analyzes the input emotional information and selects activities that match the user's mood. For example, if the user is seeking relaxation, an event held in a quiet environment is proposed.

[0311] The emotion engine uses voice analysis and text analysis techniques to determine the user's current emotions. The analysis results are reflected in the output of the generation AI model and play a role in enhancing the accuracy of the proposed content.

[0312] The server also calculates the optimal mode of transportation for the event information it suggests to the user. In doing so, it takes into account public transport and other modes of transport to provide a route that is easily accessible to the user.

[0313] The user terminal displays the final suggested information to the user, encouraging them to go out. For example, if the user is interested in a "concert" and is in a "happy" emotional state, the server suggests nearby music events and provides a plan that includes a route and time from the nearest train station as a means of transportation.

[0314] Thus, the present invention aims to encourage users to actively participate in activities and promote better social engagement through personalized event suggestions based on user preferences and emotions.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] Users launch a dedicated application and input their current emotional state, along with their interests, via voice or text. Input can be done using a microphone or keyboard.

[0318] Step 2:

[0319] The terminal converts the input interest and sentiment data into a digital format and extracts features. This data is then prepared for transmission to subsequent processes.

[0320] Step 3:

[0321] The terminal sends the converted data to the server. An encryption protocol is used for secure data transfer during this process.

[0322] Step 4:

[0323] The server analyzes the received preference and emotion information. This data is then passed to a generative AI model to retrieve personalized activity information.

[0324] Step 5:

[0325] The generative AI model extracts relevant events from a database based on the user's preferences and adjusts the suggested events to take emotional information into account. For example, if the user wants to relax, it will select a calming music event.

[0326] Step 6:

[0327] The emotion engine analyzes voice or text data to identify the user's emotions. It then identifies the most appropriate event type for that emotion and generates event suggestions.

[0328] Step 7:

[0329] The server devises the optimal mode of transportation based on the extracted event information. It calculates efficient travel routes using public transportation data.

[0330] Step 8:

[0331] The server creates a message integrating the generated event proposals and transportation options, and sends it to the user's terminal.

[0332] Step 9:

[0333] The device displays the received suggestion message to the user and notifies them using the application's notification function.

[0334] Step 10:

[0335] Users review the displayed suggestions and choose to "attend" or "ignore" events that interest them. Therefore, users can select the events that are best suited to them and increase their motivation to participate.

[0336] (Example 2)

[0337] Next, we will describe Example 2. 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".

[0338] In modern society, there is a demand for promoting participation in activities based on diverse individual preferences and emotions, but there is a lack of systems that can flexibly respond to individual needs. In particular, it is difficult to provide personalized event suggestions that take into account the user's emotions and circumstances, and to optimize the means of transportation that accompany them. Conventional technologies have problems with accuracy and satisfaction when analyzing emotions and providing travel plans that take public transportation into consideration.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes means for receiving personal preference information and emotional data; means for analyzing the received preference information and emotional data to extract relevant activity information and select events that match the user's emotional state; and means for suggesting appropriate means of transportation based on the extracted and selected activity information and calculating an optimal travel route that takes public transportation into consideration. This makes it possible to provide highly accurate activity suggestions and efficient travel plans tailored to the needs of individual users.

[0341] A "receiving mechanism" is a function that acquires personal preference information and emotional data and receives it in a format that can be processed within the system.

[0342] The "analysis means" is a function that extracts relevant activity information based on received preference information and emotional data, and selects events that are appropriate to the user's emotional state.

[0343] The "transportation optimization method" is a function that calculates appropriate travel methods, including public transportation, based on extracted and selected activity information, and presents the optimal travel route.

[0344] "Output means" refers to a function that presents optimized activity information and movement methods to the user, and provides information visually or audibly.

[0345] "Emotional data" refers to information that indicates a user's current emotional state, and is collected in the form of audio or text.

[0346] A "generative AI model" is an AI technology used to select relevant event information based on user preference information and emotional data.

[0347] This invention provides a system that suggests optimal activity information and means of transportation based on an individual's preferences and emotional state. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0348] The user terminal provides an interface for inputting information about the user's interests, concerns, and emotional state. Users can input information in voice or text format, and this information forms the basis of the system's data. For example, a user might input, "I'm interested in rock concerts, and I feel like relaxing today."

[0349] The server receives preference information and emotion data transmitted from the user's terminal. This received data is analyzed by an emotion engine running on the server. The emotion engine uses speech recognition and text analysis technologies to determine the user's current emotions. The analysis results are processed by a generative AI model, which selects the most appropriate event information based on the user's emotions and preferences.

[0350] The generative AI model considers the user's emotional data and preferences, ranks relevant event information, and selects events that are suitable for the user. It also suggests the most appropriate mode of transportation to the location where the selected event will be held.

[0351] For example, if a user expresses interest in a "concert" and their emotional state is "happy," the system will search for quiet music events happening nearby and calculate a travel route using public transportation.

[0352] An example of a prompt related to this process would be: "I'm interested in music events, but I'd like to relax today. Do you have any recommendations?"

[0353] In this way, the present invention provides personalized event suggestions based on the user's preferences and emotions, supporting the user in actively participating in activities.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] Users input their interests and emotional states using the interface of their user terminal. Specifically, they input preference information such as "I want to go to a music event" and emotional data such as "I feel happy today." The input information is stored on the terminal as digital data and used for subsequent processing.

[0357] Step 2:

[0358] The user terminal transmits collected preference information and emotional data to the server. The data is encrypted and securely sent over the network. The transmitted data is then stored on the server for subsequent analysis.

[0359] Step 3:

[0360] The server analyzes the received preference and emotion data. Here, the emotion engine operates, using speech recognition algorithms to quantify the emotion data and classifying the user's interests through text analysis. The analysis results in quantified emotional states and preference categories.

[0361] Step 4:

[0362] The server inputs data into the generative AI model based on the analysis results. The generative AI model searches the database for relevant event information based on the user's emotions and interests, and selects the most suitable events. A list of recommended events is generated as output.

[0363] Step 5:

[0364] The server proposes a suitable mode of transportation for the selected event. A transportation optimization system operates, calculating the optimal route using public transport data, taking into account the transmitted event information and the user's current location. The final output provides the route and estimated travel time.

[0365] Step 6:

[0366] The server sends the final suggested information to the user's terminal. The user's terminal displays the suggested event and mode of transportation on the screen and suggests actions to take. For example, it might say, "To get to the nearby jazz concert, you can take the bus and arrive in 20 minutes."

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0369] In today's information society, it is difficult for individuals to choose what is best suited to them from the vast amount of information and entertainment available. Furthermore, there are few systems that provide optimal entertainment information tailored to an individual's emotional state, along with suitable means of transportation, making it a challenge to enhance user satisfaction.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for receiving personal preference information and emotional state, means for extracting relevant entertainment information based on the received preference information and emotional state, and means for selecting the most suitable means of transportation based on the extracted entertainment information. This makes it possible to provide the user with optimal entertainment information and means of transportation tailored to their emotions and preferences in an integrated manner.

[0372] "Receiving means" refers to devices and technologies for acquiring an individual's preference information and emotional state.

[0373] "Extraction means" refers to a function that selects and extracts entertainment information related to the received preference information and emotional state.

[0374] "Transportation method selection means" refers to technology that determines the most optimal mode of transportation for a user to access entertainment information based on extracted entertainment information.

[0375] "Display means" means a device or method for visually conveying optimized entertainment information and directions to a user.

[0376] This invention begins with the user using a smart device to input personal preference information and emotional state. The user terminal is equipped with an emotion engine for recognizing emotions and can analyze the user's current emotional state through voice or text input. The received information is sent to a server.

[0377] The server analyzes preference information and emotional state based on the received data, in cooperation with the emotion engine. This analysis uses a generative AI model. This model learns the user's past behavior data and preferences and extracts suitable entertainment information. For example, if the user inputs emotional information such as "I'm a little tired," it will suggest content suitable for relaxation, such as quiet music or entertainment in a scenic location.

[0378] Based on the extracted information, the server calculates the optimal mode of transportation. The transportation selection mechanism utilizes public transport data to present the best route from the user's location to their destination. This data processing is achieved using public transport APIs and map services.

[0379] Finally, the server displays the calculation results on the user's terminal. The display method allows the user to easily see the selected entertainment information and how to access it. For example, suggestions might be displayed in response to a prompt such as, "I'm in the mood to relax. What music do you recommend?"

[0380] This system is implemented using software such as Python and TensorFlow. Specifically, it utilizes smartphones and tablets, significantly improving user convenience.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The user inputs their personal preferences and emotional state into a smart device. The input data is in voice or text format, and an emotion engine performs emotion analysis. The output obtained here is the user's preferences and their emotional state at that time.

[0384] Step 2:

[0385] The terminal sends the analysis results to the server. The server uses a generative AI model to analyze the received preference and emotion data. The data processing also considers the user's past preference data to extract the most relevant entertainment information. This output is a list of relevant entertainment information.

[0386] Step 3:

[0387] The server uses extracted entertainment information as input to calculate the optimal travel route using a means of transportation selection. This process obtains route data from a public transportation API and performs data calculations based on the user's location and destination. The output is information on the optimal travel route.

[0388] Step 4:

[0389] The server transmits the final entertainment and travel route information to the user's terminal. A display device presents this information to the user, introducing the content in a visually easy-to-understand format. This allows the user to prepare to enjoy the suggested entertainment.

[0390] This series of steps allows users to obtain optimal entertainment information and transportation options based on their emotional state and preferences.

[0391] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0392] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0407] This invention is a system designed to encourage elderly people to go out, and it has the function of suggesting optimal events and activities based on individual preference information. This system consists of a user terminal, a server, and a generating AI model.

[0408] The user terminal is a device for users to input their interests and preferences, and a dedicated application is provided for it. By registering their hobbies and interests through the application, users can receive individually customized suggestions.

[0409] The server plays a central role in processing the user preference information it receives. After receiving preference information from the user terminal, the server passes the data to a generative AI model, which extracts the most suitable events and activities. The generative AI model in this system uses machine learning algorithms to analyze the user's hobbies and preferences and generates suggestions based on that analysis.

[0410] The generative AI model searches a database of local event information and activities related to the user's interests and returns relevant information to the server. In particular, it can make selections that are suitable for specific genres or geographical conditions that the user is interested in.

[0411] The server receives information from the generated AI model and generates a message to make suggestions to the user. These suggestions include detailed event information, along with the best mode of transportation from the user's place of residence to the event venue. The server refers to local public transportation data and calculates the optimal route, taking into account time and cost.

[0412] The user terminal ultimately displays the suggestion message received from the server to the user, encouraging participation. In this way, the present invention provides elderly people with easy access to events that match their interests and motivates them to go out.

[0413] As a concrete example, consider a user who is interested in "listening to music." This user registers this interest in the app, and then the server retrieves information on relevant concerts and music festivals. A generative AI model analyzes this information and selects the most suitable event based on the user's interests. Then, suggestions, including the best means of transportation, are sent to the user's device, allowing the user to easily decide whether or not to attend. Through this entire process, users can pursue their interests while also being encouraged to participate in social activities.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user launches a dedicated application and enters their interests and preferences. Here, they select hobby information such as "classical music" or "walking."

[0417] Step 2:

[0418] The device checks the entered interest data locally to verify that the information is correct. After verification, it converts the data to JSON format.

[0419] Step 3:

[0420] The terminal sends the converted preference information to the server. At this time, security protocols are used to ensure the safety of the data.

[0421] Step 4:

[0422] The server parses the received data and sends it to an AI model to generate database queries based on the user's interests and preferences.

[0423] Step 5:

[0424] The generative AI model accesses a database to search for relevant events and activities that match the user's preferences. This includes dates, locations, and categories.

[0425] Step 6:

[0426] The generative AI model returns event information selected based on preferences to the server. It also ranks the most suitable events that meet the specified criteria.

[0427] Step 7:

[0428] Based on the received event information, the server considers available transportation options and formulates the optimal travel route. It takes into account public transport timetables and car routes.

[0429] Step 8:

[0430] The server generates a suggestion message that integrates event details and transportation options, and sends it to the user's terminal.

[0431] Step 9:

[0432] The device displays the received suggestion message on the application screen and notifies the user.

[0433] Step 10:

[0434] Users can review the suggestions and choose to "attend" or "ignore" events that interest them. If they choose to attend, they can proceed with the reservation or application process.

[0435] (Example 1)

[0436] Next, we will describe Example 1. 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."

[0437] There is a lack of motivation to encourage older adults and individuals with specific hobbies to participate in social activities and events. Furthermore, they face challenges in easily accessing activities that interest them and finding suitable transportation options.

[0438] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0439] In this invention, the server includes means for receiving personal hobby information, means for analyzing the received information and presenting relevant event information using a generation AI algorithm, and means for calculating the optimal route by referring to public transportation service data. This makes it easier for elderly people to participate in social activities based on their interests, and enables them to obtain event information and select means of transportation in an efficient and convenient manner.

[0440] A "device for receiving personal hobby information" is a device that allows an individual to input their hobbies and interests, and then collects that information.

[0441] A "device that converts data into a data format for communication" is a device that converts received information into a specific format for secure communication.

[0442] A "model that presents highly relevant event information using a generative AI algorithm" is a system equipped with machine learning technology that analyzes received hobby information and selects the most relevant events based on that information.

[0443] A "device for creating notifications that include detailed information and optimal means of transportation" is a device that organizes and prepares information in order to notify users of event details and the most suitable means of transportation associated with them.

[0444] A "computational mechanism that calculates the optimal route by referencing public transport service data" is a system that uses information from public transport to calculate the most efficient travel route to a destination.

[0445] A "device that displays the final notification to the user" is a device that visually provides the user with information generated from the server.

[0446] This invention is a system that promotes social participation for the elderly and individuals with specific hobbies. The system consists of a user terminal, a server, and a generative AI model.

[0447] The user terminal is a device for entering personal hobby information, and a dedicated application is provided for it. Through this app, users can input their interests and register their data. This ensures that the user's hobby information is collected appropriately. For example, if a user is interested in "listening to music," they can register that information through the app.

[0448] The server receives hobby information sent from user terminals and analyzes it. The analyzed data is passed to a generative AI model, which forms the basis for presenting highly relevant event information. The generative AI model utilizes machine learning algorithms to search for possible activities from a local event information database. This database operation is often implemented using programming languages ​​such as Python.

[0449] The event information selected by the generating AI model is sent back to the server. The server organizes this information and creates a notification message for the user. This message includes not only specific event details (date, time, location, cost, etc.) but also the best mode of transportation. Online map services such as the Google Maps API are used to calculate the best mode of transportation.

[0450] The user's device receives suggestion messages sent from the server and displays them to the user. Based on this information, the user can easily select events they want to attend and take further actions within the app as needed. For example, they can purchase tickets to attend the event they wish to participate in.

[0451] An example of a prompt might be: "Please suggest music events for senior citizens. The user's preference is for listening to music, and the geographical condition is the Tokyo area." This prompt serves as the basic data for the generative AI model to suggest appropriate events.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The user launches a dedicated application on their device and enters information about their hobbies and interests. This information is entered as data indicating the user's hobbies and interests. The application converts the entered information into a standard data format such as JSON and prepares to send it to the server. Specifically, the user selects a hobby from several options on the screen and enters details as needed.

[0455] Step 2:

[0456] The terminal sends the entered hobby information to the server. This data reaches the server via the network and is entered as received data. The terminal uses the HTTPS protocol to securely transfer the information. Specifically, communication occurs simply by the application pressing the send button.

[0457] Step 3:

[0458] The server parses the JSON data received from the terminal and extracts the user's hobby information. This analysis identifies specific categories that indicate the user's interests. The server then prepares to pass the parsed information to the generating AI model. In this step, the server verifies the data's integrity by comparing it with past preference data recorded in the database.

[0459] Step 4:

[0460] The server inputs the analyzed data into the generative AI model and generates prompts to retrieve event information based on the user's interests. These prompts are used as input data for the generative AI model. Based on the received data, the generative AI model performs calculations to extract relevant events from the event information database and return that list to the server.

[0461] Step 5:

[0462] The server organizes the event information returned from the generated AI model and constructs a message to provide to the user. During message creation, the server uses external services such as the Google Maps API to calculate the optimal mode of transport and route, and includes this information in the message as well. The final output is a message that integrates the event information and the optimal route information.

[0463] Step 6:

[0464] The user's terminal receives suggestion messages sent from the server and displays them on the screen. Based on these suggestions, the user can select events of interest and decide whether or not to participate. Specifically, notifications on the terminal guide the user and display an interface to confirm the suggested content.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, 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."

[0467] There is a need to provide a system that can motivate elderly people and local residents to participate in activities and events that suit their preferences, thereby promoting going out. Conventional proposed systems have difficulty providing information directly related to users' interests in a timely manner, resulting in a problem of not promoting participation in social activities.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes receiving means for receiving personal preference information, extracting means for extracting relevant activity information based on the received preference information, and suggesting means for suggesting experiential events offered in the local area. This makes it possible for local residents to receive suggestions for activities and travel routes that match their interests.

[0470] "Personal preference information" refers to data about the interests and concerns of a specific individual, and is provided when a user registers their hobbies and interests.

[0471] "Receiving means" refers to a device or function for acquiring preference information from the user, and is primarily implemented on the user's terminal.

[0472] The "extraction method" is a function that selects activity information with specific relevance from the received preference information, and this is achieved using a generative AI model.

[0473] The "transportation optimization method" is a function that calculates the optimal mode of transportation to a proposed event, and is implemented using public transportation data.

[0474] The "recommendation method" is a function that selects and recommends experiential events held within a region based on the user's preference information.

[0475] "Output means" refers to a device or function that presents optimized activity information and movement methods to the user, and is implemented through a dedicated application.

[0476] The system for realizing this invention consists mainly of three main components: a user terminal, a server, and a generative AI model. The user first inputs their preference information through a dedicated smartphone application. This information is defined as "personal preference information" and includes limited interests and concerns. A receiving device on the terminal transmits this information to the server.

[0477] On the server, the received preference information is first extracted and passed to a generating AI model. The AI ​​model then analyzes this preference information to identify local events and experiences that best suit the user's interests. This process utilizes machine learning algorithms backed by large datasets and performs database searches to suggest local experience events. The server then uses the extracted activity information to construct a method that allows the user to easily participate. In doing so, the optimal travel route is calculated, taking traffic data into consideration.

[0478] The final suggestions are transferred to the user's terminal via an output device, displaying customized event information and transportation options based on the user's interests. These output messages include detailed event information and optimal transportation options using buses and trains. Specifically, if the user is interested in music, it will include concert information and suggestions for intermediate stops.

[0479] As a concrete example, suppose a user expresses interest in "listening to music" within the application. In this case, the server, through a generative AI model, selects local music concert information and makes suggestions considering available public transportation options.

[0480] An example of a prompt statement is as follows:

[0481] To encourage elderly people to go out, if a user's interest is "music," generate optimal suggestions based on information about nearby concerts and music festivals.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] Users input their preferences through a dedicated smartphone application. The information entered concerns the user's interests and concerns, and is prepared to be sent to the server after being entered into a form within the application. The input data here includes interests such as "listening to music" or "cooking classes."

[0485] Step 2:

[0486] The terminal transmits the input preference information to the server via a receiving device. The server receives this received data as input and performs initial data processing such as filtering and formatting. The output is preference information formatted into an analyzable format.

[0487] Step 3:

[0488] The server passes the received preference information to a generative AI model. The AI ​​model analyzes this data and extracts the activity and event information most relevant to the user's preferences. Here, data calculations are performed using machine learning algorithms, and a list of relevant events is generated as output.

[0489] Step 4:

[0490] The server retrieves local public transport data based on event information selected by the AI ​​model and calculates the optimal mode of transportation. The input is local transport data and event information, and the output is the optimal route and mode of transport. An optimization algorithm is used here based on distance, time, and cost.

[0491] Step 5:

[0492] The server generates a final suggestion message and sends it to the user's terminal. This message includes details such as event information, transportation options, and route guidance. The server combines this information and converts it into a user-friendly, customized format.

[0493] Step 6:

[0494] The user terminal displays suggestion messages received from the server, providing the user with information to help them choose the most suitable event to participate in. The output here is an interface that allows the user to easily decide whether or not to participate in an event. Specific actions such as push notifications and calendar integration also occur here.

[0495] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0496] This invention is a system that recognizes an individual's preferences and emotional state, and based on that, proposes optimal activity information and means of transportation. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0497] The user terminal provides an interface for receiving input information about the user's interests and concerns. This allows the user to input information (in voice, text, etc.) about their interests and current emotional state. The input information is then incorporated into the system as base data.

[0498] The server receives preference and emotion data transmitted from the user's terminal and performs data analysis. The generative AI model searches for relevant event information based on the user's hobbies and interests and selects the most suitable one. Furthermore, the emotion engine analyzes the input emotion information and selects activities that match the user's mood. For example, if the user is seeking relaxation, it will suggest events held in a quiet environment.

[0499] The emotion engine uses voice and text analysis technologies to determine the user's current emotions. The analysis results are reflected in the output of the generated AI model, which helps to improve the accuracy of the suggestions.

[0500] The server also calculates the optimal mode of transportation for the event information it suggests to the user. In doing so, it takes into account public transport and other modes of transport to provide a route that is easily accessible to the user.

[0501] The user terminal displays the final suggested information to the user, encouraging them to go out. For example, if the user is interested in a "concert" and is in a "happy" emotional state, the server suggests nearby music events and provides a plan that includes a route and time from the nearest train station as a means of transportation.

[0502] Thus, the present invention aims to encourage users to actively participate in activities and promote better social engagement through personalized event suggestions based on user preferences and emotions.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] Users launch a dedicated application and input their current emotional state, along with their interests, via voice or text. Input can be done using a microphone or keyboard.

[0506] Step 2:

[0507] The terminal converts the input interest and sentiment data into a digital format and extracts features. This data is then prepared for transmission to subsequent processes.

[0508] Step 3:

[0509] The terminal sends the converted data to the server. An encryption protocol is used for secure data transfer during this process.

[0510] Step 4:

[0511] The server analyzes the received preference and emotion information. This data is then passed to a generative AI model to retrieve personalized activity information.

[0512] Step 5:

[0513] The generative AI model extracts relevant events from a database based on the user's preferences and adjusts the suggested events to take emotional information into account. For example, if the user wants to relax, it will select a calming music event.

[0514] Step 6:

[0515] The emotion engine analyzes voice or text data to identify the user's emotions. It then identifies the most appropriate event type for that emotion and generates event suggestions.

[0516] Step 7:

[0517] The server devises the optimal mode of transportation based on the extracted event information. It calculates efficient travel routes using public transportation data.

[0518] Step 8:

[0519] The server creates a message integrating the generated event proposals and transportation options, and sends it to the user's terminal.

[0520] Step 9:

[0521] The device displays the received suggestion message to the user and notifies them using the application's notification function.

[0522] Step 10:

[0523] Users review the displayed suggestions and choose to "attend" or "ignore" events that interest them. Therefore, users can select the events that are best suited to them and increase their motivation to participate.

[0524] (Example 2)

[0525] Next, we will describe Example 2. 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."

[0526] In modern society, there is a demand for promoting participation in activities based on diverse individual preferences and emotions, but there is a lack of systems that can flexibly respond to individual needs. In particular, it is difficult to provide personalized event suggestions that take into account the user's emotions and circumstances, and to optimize the means of transportation that accompany them. Conventional technologies have problems with accuracy and satisfaction when analyzing emotions and providing travel plans that take public transportation into consideration.

[0527] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0528] In this invention, the server includes means for receiving personal preference information and emotional data; means for analyzing the received preference information and emotional data to extract relevant activity information and select events that match the user's emotional state; and means for suggesting appropriate means of transportation based on the extracted and selected activity information and calculating an optimal travel route that takes public transportation into consideration. This makes it possible to provide highly accurate activity suggestions and efficient travel plans tailored to the needs of individual users.

[0529] A "receiving mechanism" is a function that acquires personal preference information and emotional data and receives it in a format that can be processed within the system.

[0530] The "analysis means" is a function that extracts relevant activity information based on received preference information and emotional data, and selects events that are appropriate to the user's emotional state.

[0531] The "transportation optimization method" is a function that calculates appropriate travel methods, including public transportation, based on extracted and selected activity information, and presents the optimal travel route.

[0532] "Output means" refers to a function that presents optimized activity information and movement methods to the user, and provides information visually or audibly.

[0533] "Emotional data" refers to information that indicates a user's current emotional state, and is collected in the form of audio or text.

[0534] A "generative AI model" is an AI technology used to select relevant event information based on user preference information and emotional data.

[0535] This invention provides a system that suggests optimal activity information and means of transportation based on an individual's preferences and emotional state. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0536] The user terminal provides an interface for inputting information about the user's interests, concerns, and emotional state. Users can input information in voice or text format, and this information forms the basis of the system's data. For example, a user might input, "I'm interested in rock concerts, and I feel like relaxing today."

[0537] The server receives preference information and emotion data transmitted from the user's terminal. This received data is analyzed by an emotion engine running on the server. The emotion engine uses speech recognition and text analysis technologies to determine the user's current emotions. The analysis results are processed by a generative AI model, which selects the most appropriate event information based on the user's emotions and preferences.

[0538] The generative AI model considers the user's emotional data and preferences, ranks relevant event information, and selects events that are suitable for the user. It also suggests the most appropriate mode of transportation to the location where the selected event will be held.

[0539] For example, if a user expresses interest in a "concert" and their emotional state is "happy," the system will search for quiet music events happening nearby and calculate a travel route using public transportation.

[0540] An example of a prompt related to this process would be: "I'm interested in music events, but I'd like to relax today. Do you have any recommendations?"

[0541] In this way, the present invention provides personalized event suggestions based on the user's preferences and emotions, supporting the user in actively participating in activities.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] Users input their interests and emotional states using the interface of their user terminal. Specifically, they input preference information such as "I want to go to a music event" and emotional data such as "I feel happy today." The input information is stored on the terminal as digital data and used for subsequent processing.

[0545] Step 2:

[0546] The user terminal transmits collected preference information and emotional data to the server. The data is encrypted and securely sent over the network. The transmitted data is then stored on the server for subsequent analysis.

[0547] Step 3:

[0548] The server analyzes the received preference and emotion data. Here, the emotion engine operates, using speech recognition algorithms to quantify the emotion data and classifying the user's interests through text analysis. The analysis results in quantified emotional states and preference categories.

[0549] Step 4:

[0550] The server inputs data into the generative AI model based on the analysis results. The generative AI model searches the database for relevant event information based on the user's emotions and interests, and selects the most suitable events. A list of recommended events is generated as output.

[0551] Step 5:

[0552] The server proposes a suitable mode of transportation for the selected event. A transportation optimization system operates, calculating the optimal route using public transport data, taking into account the transmitted event information and the user's current location. The final output provides the route and estimated travel time.

[0553] Step 6:

[0554] The server sends the final suggested information to the user's terminal. The user's terminal displays the suggested event and mode of transportation on the screen and suggests actions to take. For example, it might say, "To get to the nearby jazz concert, you can take the bus and arrive in 20 minutes."

[0555] (Application Example 2)

[0556] Next, we will explain application example 2. In the following explanation, 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."

[0557] In today's information society, it is difficult for individuals to choose what is best suited to them from the vast amount of information and entertainment available. Furthermore, there are few systems that provide optimal entertainment information tailored to an individual's emotional state, along with suitable means of transportation, making it a challenge to enhance user satisfaction.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0559] In this invention, the server includes means for receiving personal preference information and emotional state, means for extracting relevant entertainment information based on the received preference information and emotional state, and means for selecting the most suitable means of transportation based on the extracted entertainment information. This makes it possible to provide the user with optimal entertainment information and means of transportation tailored to their emotions and preferences in an integrated manner.

[0560] "Receiving means" refers to devices and technologies for acquiring an individual's preference information and emotional state.

[0561] "Extraction means" refers to a function that selects and extracts entertainment information related to the received preference information and emotional state.

[0562] "Transportation method selection means" refers to technology that determines the most optimal mode of transportation for a user to access entertainment information based on extracted entertainment information.

[0563] "Display means" means a device or method for visually conveying optimized entertainment information and directions to a user.

[0564] This invention begins with the user using a smart device to input personal preference information and emotional state. The user terminal is equipped with an emotion engine for recognizing emotions and can analyze the user's current emotional state through voice or text input. The received information is sent to a server.

[0565] The server analyzes preference information and emotional state based on the received data, in cooperation with the emotion engine. This analysis uses a generative AI model. This model learns the user's past behavior data and preferences and extracts suitable entertainment information. For example, if the user inputs emotional information such as "I'm a little tired," it will suggest content suitable for relaxation, such as quiet music or entertainment in a scenic location.

[0566] Based on the extracted information, the server calculates the optimal mode of transportation. The transportation selection mechanism utilizes public transport data to present the best route from the user's location to their destination. This data processing is achieved using public transport APIs and map services.

[0567] Finally, the server displays the calculation results on the user's terminal. The display method allows the user to easily see the selected entertainment information and how to access it. For example, suggestions might be displayed in response to a prompt such as, "I'm in the mood to relax. What music do you recommend?"

[0568] This system is implemented using software such as Python and TensorFlow. Specifically, it utilizes smartphones and tablets, significantly improving user convenience.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The user inputs their personal preferences and emotional state into a smart device. The input data is in voice or text format, and an emotion engine performs emotion analysis. The output obtained here is the user's preferences and their emotional state at that time.

[0572] Step 2:

[0573] The terminal sends the analysis results to the server. The server uses a generative AI model to analyze the received preference and emotion data. The data processing also considers the user's past preference data to extract the most relevant entertainment information. This output is a list of relevant entertainment information.

[0574] Step 3:

[0575] The server uses extracted entertainment information as input to calculate the optimal travel route using a means of transportation selection. This process obtains route data from a public transportation API and performs data calculations based on the user's location and destination. The output is information on the optimal travel route.

[0576] Step 4:

[0577] The server transmits the final entertainment and travel route information to the user's terminal. A display device presents this information to the user, introducing the content in a visually easy-to-understand format. This allows the user to prepare to enjoy the suggested entertainment.

[0578] This series of steps allows users to obtain optimal entertainment information and transportation options based on their emotional state and preferences.

[0579] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0580] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0581] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] As shown in Figure 7, the 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.

[0585] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0586] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0587] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0588] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0589] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0590] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0591] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0592] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0593] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0594] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0595] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0596] This invention is a system designed to encourage elderly people to go out, and it has the function of suggesting optimal events and activities based on individual preference information. This system consists of a user terminal, a server, and a generating AI model.

[0597] The user terminal is a device for users to input their interests and preferences, and a dedicated application is provided for it. By registering their hobbies and interests through the application, users can receive individually customized suggestions.

[0598] The server plays a central role in processing the user preference information it receives. After receiving preference information from the user terminal, the server passes the data to a generative AI model, which extracts the most suitable events and activities. The generative AI model in this system uses machine learning algorithms to analyze the user's hobbies and preferences and generates suggestions based on that analysis.

[0599] The generative AI model searches a database of local event information and activities related to the user's interests and returns relevant information to the server. In particular, it can make selections that are suitable for specific genres or geographical conditions that the user is interested in.

[0600] The server receives information from the generated AI model and generates a message to make suggestions to the user. These suggestions include detailed event information, along with the best mode of transportation from the user's place of residence to the event venue. The server refers to local public transportation data and calculates the optimal route, taking into account time and cost.

[0601] The user terminal ultimately displays the suggestion message received from the server to the user, encouraging participation. In this way, the present invention provides elderly people with easy access to events that match their interests and motivates them to go out.

[0602] As a concrete example, consider a user who is interested in "listening to music." This user registers this interest in the app, and then the server retrieves information on relevant concerts and music festivals. A generative AI model analyzes this information and selects the most suitable event based on the user's interests. Then, suggestions, including the best means of transportation, are sent to the user's device, allowing the user to easily decide whether or not to attend. Through this entire process, users can pursue their interests while also being encouraged to participate in social activities.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user launches a dedicated application and enters their interests and preferences. Here, they select hobby information such as "classical music" or "walking."

[0606] Step 2:

[0607] The device checks the entered interest data locally to verify that the information is correct. After verification, it converts the data to JSON format.

[0608] Step 3:

[0609] The terminal sends the converted preference information to the server. At this time, security protocols are used to ensure the safety of the data.

[0610] Step 4:

[0611] The server parses the received data and sends it to an AI model to generate database queries based on the user's interests and preferences.

[0612] Step 5:

[0613] The generative AI model accesses a database to search for relevant events and activities that match the user's preferences. This includes dates, locations, and categories.

[0614] Step 6:

[0615] The generative AI model returns event information selected based on preferences to the server. It also ranks the most suitable events that meet the specified criteria.

[0616] Step 7:

[0617] Based on the received event information, the server considers available transportation options and formulates the optimal travel route. It takes into account public transport timetables and car routes.

[0618] Step 8:

[0619] The server generates a suggestion message that integrates event details and transportation options, and sends it to the user's terminal.

[0620] Step 9:

[0621] The device displays the received suggestion message on the application screen and notifies the user.

[0622] Step 10:

[0623] Users can review the suggestions and choose to "attend" or "ignore" events that interest them. If they choose to attend, they can proceed with the reservation or application process.

[0624] (Example 1)

[0625] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] There is a lack of motivation to encourage older adults and individuals with specific hobbies to participate in social activities and events. Furthermore, they face challenges in easily accessing activities that interest them and finding suitable transportation options.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0628] In this invention, the server includes means for receiving personal hobby information, means for analyzing the received information and presenting relevant event information using a generation AI algorithm, and means for calculating the optimal route by referring to public transportation service data. This makes it easier for elderly people to participate in social activities based on their interests, and enables them to obtain event information and select means of transportation in an efficient and convenient manner.

[0629] A "device for receiving personal hobby information" is a device that allows an individual to input their hobbies and interests, and then collects that information.

[0630] A "device that converts data into a data format for communication" is a device that converts received information into a specific format for secure communication.

[0631] A "model that presents highly relevant event information using a generative AI algorithm" is a system equipped with machine learning technology that analyzes received hobby information and selects the most relevant events based on that information.

[0632] A "device for creating notifications that include detailed information and optimal means of transportation" is a device that organizes and prepares information in order to notify users of event details and the most suitable means of transportation associated with them.

[0633] A "computational mechanism that calculates the optimal route by referencing public transport service data" is a system that uses information from public transport to calculate the most efficient travel route to a destination.

[0634] A "device that displays the final notification to the user" is a device that visually provides the user with information generated from the server.

[0635] This invention is a system that promotes social participation for the elderly and individuals with specific hobbies. The system consists of a user terminal, a server, and a generative AI model.

[0636] The user terminal is a device for entering personal hobby information, and a dedicated application is provided for it. Through this app, users can input their interests and register their data. This ensures that the user's hobby information is collected appropriately. For example, if a user is interested in "listening to music," they can register that information through the app.

[0637] The server receives hobby information sent from user terminals and analyzes it. The analyzed data is passed to a generative AI model, which forms the basis for presenting highly relevant event information. The generative AI model utilizes machine learning algorithms to search for possible activities from a local event information database. This database operation is often implemented using programming languages ​​such as Python.

[0638] The event information selected by the generating AI model is sent back to the server. The server organizes this information and creates a notification message for the user. This message includes not only specific event details (date, time, location, cost, etc.) but also the best mode of transportation. Online map services such as the Google Maps API are used to calculate the best mode of transportation.

[0639] The user's device receives suggestion messages sent from the server and displays them to the user. Based on this information, the user can easily select events they want to attend and take further actions within the app as needed. For example, they can purchase tickets to attend the event they wish to participate in.

[0640] An example of a prompt might be: "Please suggest music events for senior citizens. The user's preference is for listening to music, and the geographical condition is the Tokyo area." This prompt serves as the basic data for the generative AI model to suggest appropriate events.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user launches a dedicated application on their device and enters information about their hobbies and interests. This information is entered as data indicating the user's hobbies and interests. The application converts the entered information into a standard data format such as JSON and prepares to send it to the server. Specifically, the user selects a hobby from several options on the screen and enters details as needed.

[0644] Step 2:

[0645] The terminal sends the entered hobby information to the server. This data reaches the server via the network and is entered as received data. The terminal uses the HTTPS protocol to securely transfer the information. Specifically, communication occurs simply by the application pressing the send button.

[0646] Step 3:

[0647] The server parses the JSON data received from the terminal and extracts the user's hobby information. This analysis identifies specific categories that indicate the user's interests. The server then prepares to pass the parsed information to the generating AI model. In this step, the server verifies the data's integrity by comparing it with past preference data recorded in the database.

[0648] Step 4:

[0649] The server inputs the analyzed data into the generative AI model and generates prompts to retrieve event information based on the user's interests. These prompts are used as input data for the generative AI model. Based on the received data, the generative AI model performs calculations to extract relevant events from the event information database and return that list to the server.

[0650] Step 5:

[0651] The server organizes the event information returned from the generated AI model and constructs a message to provide to the user. During message creation, the server uses external services such as the Google Maps API to calculate the optimal mode of transport and route, and includes this information in the message as well. The final output is a message that integrates the event information and the optimal route information.

[0652] Step 6:

[0653] The user's terminal receives suggestion messages sent from the server and displays them on the screen. Based on these suggestions, the user can select events of interest and decide whether or not to participate. Specifically, notifications on the terminal guide the user and display an interface to confirm the suggested content.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] There is a need to provide a system that can motivate elderly people and local residents to participate in activities and events that suit their preferences, thereby promoting going out. Conventional proposed systems have difficulty providing information directly related to users' interests in a timely manner, resulting in a problem of not promoting participation in social activities.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes receiving means for receiving personal preference information, extracting means for extracting relevant activity information based on the received preference information, and suggesting means for suggesting experiential events offered in the local area. This makes it possible for local residents to receive suggestions for activities and travel routes that match their interests.

[0659] "Personal preference information" refers to data about the interests and concerns of a specific individual, and is provided when a user registers their hobbies and interests.

[0660] "Receiving means" refers to a device or function for acquiring preference information from the user, and is primarily implemented on the user's terminal.

[0661] The "extraction method" is a function that selects activity information with specific relevance from the received preference information, and this is achieved using a generative AI model.

[0662] The "transportation optimization method" is a function that calculates the optimal mode of transportation to a proposed event, and is implemented using public transportation data.

[0663] The "recommendation method" is a function that selects and recommends experiential events held within a region based on the user's preference information.

[0664] "Output means" refers to a device or function that presents optimized activity information and movement methods to the user, and is implemented through a dedicated application.

[0665] The system for realizing this invention consists mainly of three main components: a user terminal, a server, and a generative AI model. The user first inputs their preference information through a dedicated smartphone application. This information is defined as "personal preference information" and includes limited interests and concerns. A receiving device on the terminal transmits this information to the server.

[0666] On the server, the received preference information is first extracted and passed to a generating AI model. The AI ​​model then analyzes this preference information to identify local events and experiences that best suit the user's interests. This process utilizes machine learning algorithms backed by large datasets and performs database searches to suggest local experience events. The server then uses the extracted activity information to construct a method that allows the user to easily participate. In doing so, the optimal travel route is calculated, taking traffic data into consideration.

[0667] The final suggestions are transferred to the user's terminal via an output device, displaying customized event information and transportation options based on the user's interests. These output messages include detailed event information and optimal transportation options using buses and trains. Specifically, if the user is interested in music, it will include concert information and suggestions for intermediate stops.

[0668] As a concrete example, suppose a user expresses interest in "listening to music" within the application. In this case, the server, through a generative AI model, selects local music concert information and makes suggestions considering available public transportation options.

[0669] An example of a prompt statement is as follows:

[0670] To encourage elderly people to go out, if a user's interest is "music," generate optimal suggestions based on information about nearby concerts and music festivals.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] Users input their preferences through a dedicated smartphone application. The information entered concerns the user's interests and concerns, and is prepared to be sent to the server after being entered into a form within the application. The input data here includes interests such as "listening to music" or "cooking classes."

[0674] Step 2:

[0675] The terminal transmits the input preference information to the server via a receiving device. The server receives this received data as input and performs initial data processing such as filtering and formatting. The output is preference information formatted into an analyzable format.

[0676] Step 3:

[0677] The server passes the received preference information to a generative AI model. The AI ​​model analyzes this data and extracts the activity and event information most relevant to the user's preferences. Here, data calculations are performed using machine learning algorithms, and a list of relevant events is generated as output.

[0678] Step 4:

[0679] The server retrieves local public transport data based on event information selected by the AI ​​model and calculates the optimal mode of transportation. The input is local transport data and event information, and the output is the optimal route and mode of transport. An optimization algorithm is used here based on distance, time, and cost.

[0680] Step 5:

[0681] The server generates a final suggestion message and sends it to the user's terminal. This message includes details such as event information, transportation options, and route guidance. The server combines this information and converts it into a user-friendly, customized format.

[0682] Step 6:

[0683] The user terminal displays suggestion messages received from the server, providing the user with information to help them choose the most suitable event to participate in. The output here is an interface that allows the user to easily decide whether or not to participate in an event. Specific actions such as push notifications and calendar integration also occur here.

[0684] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0685] This invention is a system that recognizes an individual's preferences and emotional state, and based on that, proposes optimal activity information and means of transportation. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0686] The user terminal provides an interface for receiving input information about the user's interests and concerns. This allows the user to input information (in voice, text, etc.) about their interests and current emotional state. The input information is then incorporated into the system as base data.

[0687] The server receives preference and emotion data transmitted from the user's terminal and performs data analysis. The generative AI model searches for relevant event information based on the user's hobbies and interests and selects the most suitable one. Furthermore, the emotion engine analyzes the input emotion information and selects activities that match the user's mood. For example, if the user is seeking relaxation, it will suggest events held in a quiet environment.

[0688] The emotion engine uses voice and text analysis technologies to determine the user's current emotions. The analysis results are reflected in the output of the generated AI model, which helps to improve the accuracy of the suggestions.

[0689] The server also calculates the optimal mode of transportation for the event information it suggests to the user. In doing so, it takes into account public transport and other modes of transport to provide a route that is easily accessible to the user.

[0690] The user terminal displays the final suggested information to the user, encouraging them to go out. For example, if the user is interested in a "concert" and is in a "happy" emotional state, the server suggests nearby music events and provides a plan that includes a route and time from the nearest train station as a means of transportation.

[0691] Thus, the present invention aims to encourage users to actively participate in activities and promote better social engagement through personalized event suggestions based on user preferences and emotions.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] Users launch a dedicated application and input their current emotional state, along with their interests, via voice or text. Input can be done using a microphone or keyboard.

[0695] Step 2:

[0696] The terminal converts the input interest and sentiment data into a digital format and extracts features. This data is then prepared for transmission to subsequent processes.

[0697] Step 3:

[0698] The terminal sends the converted data to the server. An encryption protocol is used for secure data transfer during this process.

[0699] Step 4:

[0700] The server analyzes the received preference and emotion information. This data is then passed to a generative AI model to retrieve personalized activity information.

[0701] Step 5:

[0702] The generative AI model extracts relevant events from a database based on the user's preferences and adjusts the suggested events to take emotional information into account. For example, if the user wants to relax, it will select a calming music event.

[0703] Step 6:

[0704] The emotion engine analyzes voice or text data to identify the user's emotions. It then identifies the most appropriate event type for that emotion and generates event suggestions.

[0705] Step 7:

[0706] The server devises the optimal mode of transportation based on the extracted event information. It calculates efficient travel routes using public transportation data.

[0707] Step 8:

[0708] The server creates a message integrating the generated event proposals and transportation options, and sends it to the user's terminal.

[0709] Step 9:

[0710] The device displays the received suggestion message to the user and notifies them using the application's notification function.

[0711] Step 10:

[0712] Users review the displayed suggestions and choose to "attend" or "ignore" events that interest them. Therefore, users can select the events that are best suited to them and increase their motivation to participate.

[0713] (Example 2)

[0714] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] In modern society, there is a demand for promoting participation in activities based on diverse individual preferences and emotions, but there is a lack of systems that can flexibly respond to individual needs. In particular, it is difficult to provide personalized event suggestions that take into account the user's emotions and circumstances, and to optimize the means of transportation that accompany them. Conventional technologies have problems with accuracy and satisfaction when analyzing emotions and providing travel plans that take public transportation into consideration.

[0716] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0717] In this invention, the server includes means for receiving personal preference information and emotional data; means for analyzing the received preference information and emotional data to extract relevant activity information and select events that match the user's emotional state; and means for suggesting appropriate means of transportation based on the extracted and selected activity information and calculating an optimal travel route that takes public transportation into consideration. This makes it possible to provide highly accurate activity suggestions and efficient travel plans tailored to the needs of individual users.

[0718] A "receiving mechanism" is a function that acquires personal preference information and emotional data and receives it in a format that can be processed within the system.

[0719] The "analysis means" is a function that extracts relevant activity information based on received preference information and emotional data, and selects events that are appropriate to the user's emotional state.

[0720] The "transportation optimization method" is a function that calculates appropriate travel methods, including public transportation, based on extracted and selected activity information, and presents the optimal travel route.

[0721] "Output means" refers to a function that presents optimized activity information and movement methods to the user, and provides information visually or audibly.

[0722] "Emotional data" refers to information that indicates a user's current emotional state, and is collected in the form of audio or text.

[0723] A "generative AI model" is an AI technology used to select relevant event information based on user preference information and emotional data.

[0724] This invention provides a system that suggests optimal activity information and means of transportation based on an individual's preferences and emotional state. The system includes a user terminal, a server, and a generative AI model, as well as an emotion engine that recognizes the user's emotions.

[0725] The user terminal provides an interface for inputting information about the user's interests, concerns, and emotional state. Users can input information in voice or text format, and this information forms the basis of the system's data. For example, a user might input, "I'm interested in rock concerts, and I feel like relaxing today."

[0726] The server receives preference information and emotion data transmitted from the user's terminal. This received data is analyzed by an emotion engine running on the server. The emotion engine uses speech recognition and text analysis technologies to determine the user's current emotions. The analysis results are processed by a generative AI model, which selects the most appropriate event information based on the user's emotions and preferences.

[0727] The generative AI model considers the user's emotional data and preferences, ranks relevant event information, and selects events that are suitable for the user. It also suggests the most appropriate mode of transportation to the location where the selected event will be held.

[0728] For example, if a user expresses interest in a "concert" and their emotional state is "happy," the system will search for quiet music events happening nearby and calculate a travel route using public transportation.

[0729] An example of a prompt related to this process would be: "I'm interested in music events, but I'd like to relax today. Do you have any recommendations?"

[0730] In this way, the present invention provides personalized event suggestions based on the user's preferences and emotions, supporting the user in actively participating in activities.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] Users input their interests and emotional states using the interface of their user terminal. Specifically, they input preference information such as "I want to go to a music event" and emotional data such as "I feel happy today." The input information is stored on the terminal as digital data and used for subsequent processing.

[0734] Step 2:

[0735] The user terminal transmits collected preference information and emotional data to the server. The data is encrypted and securely sent over the network. The transmitted data is then stored on the server for subsequent analysis.

[0736] Step 3:

[0737] The server analyzes the received preference and emotion data. Here, the emotion engine operates, using speech recognition algorithms to quantify the emotion data and classifying the user's interests through text analysis. The analysis results in quantified emotional states and preference categories.

[0738] Step 4:

[0739] The server inputs data into the generative AI model based on the analysis results. The generative AI model searches the database for relevant event information based on the user's emotions and interests, and selects the most suitable events. A list of recommended events is generated as output.

[0740] Step 5:

[0741] The server proposes a suitable mode of transportation for the selected event. A transportation optimization system operates, calculating the optimal route using public transport data, taking into account the transmitted event information and the user's current location. The final output provides the route and estimated travel time.

[0742] Step 6:

[0743] The server sends the final suggested information to the user's terminal. The user's terminal displays the suggested event and mode of transportation on the screen and suggests actions to take. For example, it might say, "To get to the nearby jazz concert, you can take the bus and arrive in 20 minutes."

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] In today's information society, it is difficult for individuals to choose what is best suited to them from the vast amount of information and entertainment available. Furthermore, there are few systems that provide optimal entertainment information tailored to an individual's emotional state, along with suitable means of transportation, making it a challenge to enhance user satisfaction.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for receiving personal preference information and emotional state, means for extracting relevant entertainment information based on the received preference information and emotional state, and means for selecting the most suitable means of transportation based on the extracted entertainment information. This makes it possible to provide the user with optimal entertainment information and means of transportation tailored to their emotions and preferences in an integrated manner.

[0749] "Receiving means" refers to devices and technologies for acquiring an individual's preference information and emotional state.

[0750] "Extraction means" refers to a function that selects and extracts entertainment information related to the received preference information and emotional state.

[0751] "Transportation method selection means" refers to technology that determines the most optimal mode of transportation for a user to access entertainment information based on extracted entertainment information.

[0752] "Display means" means a device or method for visually conveying optimized entertainment information and directions to a user.

[0753] This invention begins with the user using a smart device to input personal preference information and emotional state. The user terminal is equipped with an emotion engine for recognizing emotions and can analyze the user's current emotional state through voice or text input. The received information is sent to a server.

[0754] The server analyzes preference information and emotional state based on the received data, in cooperation with the emotion engine. This analysis uses a generative AI model. This model learns the user's past behavior data and preferences and extracts suitable entertainment information. For example, if the user inputs emotional information such as "I'm a little tired," it will suggest content suitable for relaxation, such as quiet music or entertainment in a scenic location.

[0755] Based on the extracted information, the server calculates the optimal mode of transportation. The transportation selection mechanism utilizes public transport data to present the best route from the user's location to their destination. This data processing is achieved using public transport APIs and map services.

[0756] Finally, the server displays the calculation results on the user's terminal. The display method allows the user to easily see the selected entertainment information and how to access it. For example, suggestions might be displayed in response to a prompt such as, "I'm in the mood to relax. What music do you recommend?"

[0757] This system is implemented using software such as Python and TensorFlow. Specifically, it utilizes smartphones and tablets, significantly improving user convenience.

[0758] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0759] Step 1:

[0760] The user inputs their personal preferences and emotional state into a smart device. The input data is in voice or text format, and an emotion engine performs emotion analysis. The output obtained here is the user's preferences and their emotional state at that time.

[0761] Step 2:

[0762] The terminal sends the analysis results to the server. The server uses a generative AI model to analyze the received preference and emotion data. The data processing also considers the user's past preference data to extract the most relevant entertainment information. This output is a list of relevant entertainment information.

[0763] Step 3:

[0764] The server uses extracted entertainment information as input to calculate the optimal travel route using a means of transportation selection. This process obtains route data from a public transportation API and performs data calculations based on the user's location and destination. The output is information on the optimal travel route.

[0765] Step 4:

[0766] The server transmits the final entertainment and travel route information to the user's terminal. A display device presents this information to the user, introducing the content in a visually easy-to-understand format. This allows the user to prepare to enjoy the suggested entertainment.

[0767] This series of steps allows users to obtain optimal entertainment information and transportation options based on their emotional state and preferences.

[0768] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0769] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0770] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0771] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0772] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0773] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0774] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0775] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0776] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0777] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0778] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0779] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0780] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0781] 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.

[0782] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0783] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0784] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0785] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0786] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0787] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0788] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] A means of receiving personal preference information,

[0792] An extraction means for extracting relevant activity information based on received preference information,

[0793] A means of optimizing transportation that suggests an appropriate method of travel based on extracted activity information,

[0794] An output means that outputs individually optimized activity information and movement methods,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, further comprising a learning means for analyzing preference information received by the receiving means to learn the user's preferences.

[0798] (Claim 3)

[0799] The system according to claim 1, characterized in that the transportation optimization means calculates the optimal travel route using public transportation data.

[0800] "Example 1"

[0801] (Claim 1)

[0802] A device that receives personal hobby information,

[0803] A device that converts received hobby information into a data format and transmits it,

[0804] A model that analyzes data and presents highly relevant event information using a generative AI algorithm,

[0805] A device that organizes the presented event information and creates a notification that includes detailed information and the most suitable means of transportation,

[0806] A computing mechanism that calculates the optimal route by referring to public transport service data,

[0807] The device that displays the final notification to the user,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising a function that analyzes hobby information received by the receiving device and learns the user's hobbies through machine learning.

[0811] (Claim 3)

[0812] The system according to claim 1, characterized in that the computing mechanism optimizes the means of transportation using information from an online map service.

[0813] "Application Example 1"

[0814] (Claim 1)

[0815] A means of receiving personal preference information,

[0816] An extraction means for extracting relevant activity information based on received preference information,

[0817] A means of optimizing transportation that suggests an appropriate method of travel based on extracted activity information,

[0818] A proposal method for suggesting experiential events to be offered in local spaces,

[0819] An output means that outputs individually optimized activity information and movement methods,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising a learning means that analyzes preference information received by the receiving means to learn the user's preferences and generates information on real-world experience events.

[0823] (Claim 3)

[0824] The system according to claim 1, characterized in that the transportation optimization means calculates the optimal travel route using public transportation data and combines it with local experiential events.

[0825] "Example 2 of combining an emotion engine"

[0826] (Claim 1)

[0827] A receiving means for receiving personal preference information and emotional data,

[0828] An analytical means that analyzes received preference information and emotional data to extract relevant activity information and selects events that match the user's emotional state,

[0829] A means of optimizing transportation that suggests appropriate means of transportation based on extracted and selected activity information and calculates the optimal travel route considering public transportation,

[0830] An output means that outputs optimized activity information and movement methods to the user,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, characterized in that the analysis means analyzes an individual's emotional data using voice analysis and text analysis technologies and selects activity information using a generative AI model.

[0834] (Claim 3)

[0835] The system according to claim 1, characterized in that the means of transportation optimization means proposes the optimal travel route from the user's current location to their destination using an algorithm that processes traffic information in real time.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] A receiving means for receiving personal preference information and emotional state,

[0839] An extraction means for extracting relevant entertainment information based on received preference information and emotional state,

[0840] A means of selecting the most suitable means of transportation based on extracted entertainment information,

[0841] A display means that outputs optimized entertainment information and means of transportation,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising a learning means for analyzing preference information and emotional states received by the receiving means and learning the user's interests.

[0845] (Claim 3)

[0846] The system according to claim 1, characterized in that the means of transportation selection means calculates the optimal travel route using public transportation data. [Explanation of Symbols]

[0847] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A receiving means for receiving personal preference information, An extraction means for extracting relevant activity information based on received preference information, A means of optimizing transportation that suggests an appropriate method of travel based on extracted activity information, An output means that outputs individually optimized activity information and movement methods, A system that includes this.

2. The system according to claim 1, further comprising a learning means for analyzing preference information received by the receiving means to learn the user's preferences.

3. The system according to claim 1, characterized in that the transportation optimization means calculates the optimal travel route using public transportation data.

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