system

Abstract

We provide the system. [Solution] A means for collecting information about users' hobbies and preferences and generating a profile, A means of selecting and proposing events suitable for the user based on the collected information, A means of calculating and presenting the optimal mode of transportation for a proposed event, A means for generating messages to facilitate interaction with other users, A means of collecting feedback on the services provided and incorporating it into improvements for future proposals, A means of obtaining data from external event sources and displaying selected events based on user interests, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For the senior generation, active outings and social participation are important for maintaining health and social connections. However, many seniors cannot find activities or events that match their interests and concerns, resulting in a decrease in the frequency of outings, an increase in loneliness, and an increase in health risks. Also, outings may be hindered by inconvenient transportation and lack of information. The present invention aims to solve such problems.

Means for Solving the Problems

[0005] This invention provides a personalized event suggestion system for senior citizens. Specifically, it includes means for collecting information on the user's hobbies and preferences and generating a profile based on that information. Furthermore, it includes means for automatically selecting and suggesting events suitable for the user based on this profile. It also has a function to calculate and present the optimal means of transportation to the suggested events, thereby supporting the user in easily participating in events. In addition, it includes means for generating messages to promote interaction with other users and means for collecting feedback on the service and improving accuracy by reflecting it in future suggestions. Through this series of functions, the aim is to encourage seniors to go out and strengthen their social connections.

[0006] "Hobbies and preferences" refer to an individual's interest in specific activities or things, and are formed based on their individual experiences and values.

[0007] A "user profile" is a dataset that comprehensively summarizes information about a user, reflecting their interests, preferences, and behavioral history.

[0008] "Event selection" refers to the process of identifying and recommending appropriate activities and events related to the user's hobbies and preferences.

[0009] "Transportation" refers to methods or vehicles used to move people or goods to a specific location, and includes public transport and private vehicles.

[0010] "Promoting interaction" refers to actions and processes aimed at increasing communication and contact between people and deepening mutual relationships.

[0011] "Feedback collection" is the process of gathering opinions and evaluations from users about the services they have received, and is done to improve and optimize those services. [Brief explanation of the drawing]

[0012] [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]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered 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.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that proposes events tailored to the interests and preferences of senior users, thereby encouraging them to go out. This system consists of user terminals and a server, and is implemented in the following manner.

[0034] User data collection

[0035] The user terminal provides an interface for senior users to enter their profile information. This includes basic user information, past activities of interest, and a friends list. Users also provide detailed information about their hobbies and preferences through questionnaires.

[0036] Profile creation and updating

[0037] The server generates a profile for each user based on the collected data. This profile includes information representing the user's interests and preferences and is updated regularly. The server uses machine learning algorithms to improve the accuracy of the profile.

[0038] Event selection and proposal

[0039] The server matches user profiles with event information obtained from external sources (e.g., local event calendars or online platforms). Based on the user's interests, it selects suitable events and creates a prioritized list. Selected events are notified to the user's device, and detailed information is displayed.

[0040] Calculation of transportation methods

[0041] The server calculates the optimal transportation route based on the user's location and the selected event venue. Considering traffic information and public transport schedules, the recommended route is displayed on the user's device.

[0042] Proposal for social exchange

[0043] The server refers to the user's friend list and identifies friends who might be interested in the same event. It generates appropriate messages and encourages the user to invite their friends to the event through their device. This promotes social interaction.

[0044] Gathering and incorporating feedback

[0045] The user's terminal displays a feedback form for providing evaluations and opinions after participating in an event. The server collects this feedback and incorporates it into proposals for future events. This improves the accuracy of proposals and user satisfaction.

[0046] Specific example

[0047] Let's say user A is interested in music. The server collects data on music events and suggests nearby concerts to A. Furthermore, based on traffic information, it calculates the optimal route from A's home to the venue and suggests using the bus. The server also determines that A's friend B is also a music lover and generates an invitation message for them to attend together. After attending the event, A evaluates it from their device, and the server compiles the feedback to use for future improvements.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies and interests. The user enters their information, and the terminal sends this data to the server.

[0051] Step 2:

[0052] The server stores the received user data in a database and analyzes the user's interests. Machine learning algorithms are used to generate user profiles and identify categories of interest.

[0053] Step 3:

[0054] The server retrieves event information from external sources, including local event calendars and online platforms, which are regularly updated with new data.

[0055] Step 4:

[0056] The server matches the user profile with the retrieved event information and selects events that match the user's interests. It then scores the selected events and lists those with the highest priority.

[0057] Step 5:

[0058] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays a list containing the event details to the user.

[0059] Step 6:

[0060] When a user selects a suggested event, the server calculates the optimal transportation route based on the user's location and the event's location, taking into account public transportation schedules. The server then sends the calculated route information to the user's terminal.

[0061] Step 7:

[0062] The server checks the user's friend list and identifies friends who may share similar interests. The server generates a message for the user to invite their friends to the event and displays it on the user's device.

[0063] Step 8:

[0064] After participating in the event, the user's device displays a feedback form to receive ratings and opinions from the user. The user enters their feedback, and the device sends that information to the server.

[0065] Step 9:

[0066] The server records the received feedback in a database and uses it to improve future event selection and suggestion functions. The data contributes to updating the learning algorithm, improving the accuracy of the suggestions.

[0067] (Example 1)

[0068] 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."

[0069] For elderly users, there is a lack of support to stimulate their interests and passions in daily life and to promote going out and social interaction. Furthermore, the selection of transportation options is complex, making the provision of appropriate routes crucial. In addition, there is a challenge in the insufficient provision of personalized information that reflects feedback.

[0070] 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.

[0071] In this invention, the server includes means for collecting information on the preferences and interests of elderly users and generating a user profile; means for selecting and proposing the most suitable events for the user based on the collected information and event information obtained from external sources; and means for calculating and presenting the optimal travel route for the event proposal. This makes it possible to provide elderly users with personalized event proposals and convenient means of transportation.

[0072] "Elderly users" generally refer to users who belong to the age group of 65 years or older and have specific preferences or interests.

[0073] "Information about preferences and interests" means information related to activities, hobbies, or events that the user is particularly interested in.

[0074] A "user profile" is a data structure generated based on the preferences and interests of collected users, and is used to provide personalized information.

[0075] "External information sources" refer to information sources for obtaining event information relevant to the user, including local event calendars and online platforms.

[0076] "Event information" refers to information about the specific content and schedule of an event, obtained from external sources.

[0077] The "optimal travel route" refers to the most efficient route from the user's starting point to their destination, and is calculated taking into account the mode of transport and time of day.

[0078] "Communication messages" refer to messages containing information or invitations that are generated to facilitate interaction between users.

[0079] "Evaluation" refers to information regarding users' opinions and satisfaction levels after participating in an event.

[0080] "Feedback" refers to users' evaluations and suggestions for improvement regarding the services provided.

[0081] This invention aims to provide senior users with personalized event suggestions and efficient means of transportation. The system primarily consists of a server and user terminals.

[0082] The user first uses the device's interface to enter detailed information about their basic information and preferences. The device then sends this information to the server. The device includes tablets, smartphones, and other similar devices.

[0083] The servers run on the cloud and leverage high-performance computing resources. Built using programming languages ​​such as Python and Java (registered trademark), the servers implement machine learning algorithms to analyze user-submitted information and generate individual profiles. Generative AI models are used to improve the accuracy of these profiles.

[0084] The server utilizes web APIs and scraping techniques to retrieve event information from external sources. This allows it to collect information on local events and select appropriate events by matching them with the user's profile.

[0085] Furthermore, the server accesses a traffic database to obtain real-time traffic information. Based on this, it calculates the optimal travel route from the user's place of residence to the event venue and provides this information to the terminal.

[0086] For example, if user A enjoys music, the server collects information on music events held in the area and suggests the most suitable events for A. It also calculates the best mode of transportation from traffic information and presents A with a route from their home to the venue. Furthermore, it identifies friends from A's friend list who also have an interest in music, generates invitation messages for them to attend together, and notifies their device.

[0087] After participating in an event, users can provide feedback via their devices. The server analyzes this feedback and uses the data to improve the accuracy of future event suggestions.

[0088] An example of a prompt would be, "How do I generate a message suggesting a nearby concert to a senior who is interested in music, and inviting a friend?"

[0089] In this way, this system can encourage elderly users to participate in events based on their interests and provide opportunities for social interaction.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] Users enter their profile information on their devices. Specifically, users launch a dedicated application on their devices and enter basic information such as their date of birth, address, interests, and friends list. This input data is important for reflecting the user's preferences and interests, and the device plays the role of transmitting the entered information to the server.

[0093] Step 2:

[0094] The server generates a user profile based on the received profile information. The server's programs utilize machine learning techniques to organize the user's interests and preferences into a sophisticated data structure. The input to this process is the raw data provided by the user, and the output is the profile. The server uses the latest generative AI models to create a more personalized profile.

[0095] Step 3:

[0096] The server retrieves event information from external sources. Using APIs and web scraping techniques, it collects the latest information on events held in the region. The input to this process is raw event data obtained from external sources, and the output is organized event information. The server stores this information in a database.

[0097] Step 4:

[0098] The server matches user profiles with event information to select the most suitable event. The server utilizes a generative AI model to calculate the degree of event matching and prioritizes selecting the event that best matches the user's preferences. The input to this process is the user profile and event information, and the output is a list of selected events.

[0099] Step 5:

[0100] The server calculates the optimal travel route for the selected event. The server accesses an external traffic information database and analyzes transportation options based on the user's location and the event venue. This process considers public transport timetables and real-time traffic conditions. Inputs are the user's address and event location, and output are several recommended transportation routes.

[0101] Step 6:

[0102] The server generates messages to invite friends to facilitate social interaction between users. The server identifies friends from the user's friend list who might be interested in the event. The generation AI model creates appropriate invitations and suggestions and sends the messages via the device. The input is the friend list and event details, and the output is the generated invitation message.

[0103] Step 7:

[0104] After participating in an event, users provide evaluations and opinions through a feedback form on their devices. The device then sends this feedback to the server. The input is user feedback, and the output is data on the server that will be used to improve future event proposals. This feedback process allows the server to further improve the accuracy of future event proposals.

[0105] (Application Example 1)

[0106] 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."

[0107] To increase opportunities for seniors to go out and prevent social isolation, there is a need for a system that suggests events tailored to individual interests. However, existing systems lack the accuracy to accurately analyze user interests and provide appropriate events. Furthermore, they are insufficient in suggesting transportation options for event participation and in promoting social interaction.

[0108] 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.

[0109] In this invention, the server includes means for collecting information on the user's hobbies and preferences and generating a profile; means for selecting and proposing events suitable for the user based on the collected information; and means for calculating and presenting the optimal means of transportation to the proposed events. This makes it possible to provide event suggestions and optimal transportation routes based on the individual interests of elderly people, as well as to promote social interaction.

[0110] "Information about users' hobbies and preferences" refers to data on activities that users have shown interest in in the past, as well as detailed personal interest data collected through surveys.

[0111] A "profile" is an individual information model that reflects a user's hobbies, preferences, and interests.

[0112] "Methods for selecting and proposing events" refers to the process of matching collected user information with external event information to present gatherings and activities suitable for the user.

[0113] "Means for calculating and suggesting the optimal mode of transportation" refers to a function that analyzes available modes of transportation between the user's place of residence and destination, and provides the most efficient and convenient travel route.

[0114] "Means of generating messages to facilitate interaction with other users" refers to invitations and notifications created to encourage communication among users when participating in events or activities.

[0115] "Means of collecting feedback and incorporating it into improvements" refers to the process of compiling user evaluations and opinions to improve the accuracy and satisfaction of future events.

[0116] "External event information sources" refer to external information resources that provide event information, such as local event calendars and online platforms.

[0117] "Public transport information" refers to a dataset containing schedule and route information related to public transport.

[0118] The system for realizing this invention consists of a network-connected server and a user terminal. The server runs a program that collects information about the user's hobbies and preferences and generates and updates a profile. This profile undergoes processing to improve its accuracy using machine learning algorithms based on data acquired from the user terminal. A backend using a scripting language such as Python supports this processing.

[0119] The server periodically retrieves information from external event sources, matches it with the user's profile, selects appropriate events, and proposes them to the user's terminal. This involves using APIs to collect external data, and a frontend using JavaScript® and HTML visually presents the events to the user.

[0120] Furthermore, the server utilizes external services such as the Google Maps API to calculate the optimal transportation route from the user's location to the event venue and displays it on the user's device. This allows users to easily select from multiple travel options, including available public transportation.

[0121] Furthermore, the server generates messages for other users who may be attending the same event, based on the user's friend list, to encourage social interaction. Feedback provided by users after attending the event is collected and analyzed by the server to improve future proposals.

[0122] For example, if a user is interested in tennis and music, the server will suggest local tennis tournaments and music events. It can also suggest optimal travel routes using public transportation. After the event, user satisfaction and comments are collected as feedback to improve the system.

[0123] An example of a prompt message is: "If a 70-year-old user is interested in tennis and music, what events would you suggest? Also, please provide transportation options based on those events."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] Users input information about their hobbies and preferences through their device. This information includes past interests and specific examples of activities. The device transfers this data to the server, where it is entered as initial data for the user profile. Based on this input, a registration operation is performed to save it to the database.

[0127] Step 2:

[0128] The server generates a profile using the received user information. It applies machine learning algorithms to model the user's interests. It analyzes the input interest and activity data, generates interest clusters, and outputs this as the profile structure.

[0129] Step 3:

[0130] The server retrieves event data from external event sources via APIs. The retrieved data is then matched against the user's profile information, and events matching their interests are filtered. As a result of this filtering process, a list of appropriate events is generated and output.

[0131] Step 4:

[0132] The filtered event list is sent to the device and visually presented to the user. The device's interface displays event details, allowing the user to select events of interest.

[0133] Step 5:

[0134] The server uses the Google Maps API to calculate appropriate transportation options based on the user's location and the selected event venue. Input includes the user's location and event venue information. Based on this, it calculates public transport and car routes, and outputs the optimal route information.

[0135] Step 6:

[0136] The server consults the user's friends list and generates messages for other users who might be interested in similar events. These messages are automatically generated as invitations to participate and sent to the user's terminal as output.

[0137] Step 7:

[0138] After users participate in an event on their device, they provide feedback and evaluations through a feedback form. This feedback information is sent from the device to the server and analyzed to improve future event proposals. Based on the input feedback data, data analysis is performed to improve the accuracy of the proposal algorithm.

[0139] 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.

[0140] This invention provides an event suggestion system that takes into account not only the hobbies and preferences of senior users but also their emotional state. This system consists of a user terminal, a server, and an emotion engine, and is implemented in the following manner.

[0141] User profile generation and updating

[0142] The user terminal provides an interface for users to input information about their hobbies and interests. Users enter their information, and the terminal sends it to the server. The server analyzes this data using machine learning algorithms and generates a profile. The profile is updated periodically to reflect the user's changing interests and newly acquired information.

[0143] Analysis using an emotional engine

[0144] The server analyzes the user's emotions in real time through an emotion engine. This emotional state is determined based on physiological data collected from the device and the user's input. The server integrates the obtained emotional data into a user profile to gain a more comprehensive understanding of the user's current state.

[0145] Event selection and emotion-based adjustments

[0146] The server selects personalized events based on the user profile and emotional state. Event information is periodically retrieved from external sources and customized to the user's interests and emotions. In particular, emotional state influences event selection; for example, music events designed to relieve stress may be suggested.

[0147] Presenting the most suitable mode of transportation

[0148] The server calculates the optimal mode of transportation, taking into account the location of the proposed event and the user's place of residence. The selection of the transportation route is also appropriately adjusted according to the user's emotional state. The terminal presents the optimized transportation information received from the server to the user.

[0149] Promoting social interaction and feedback

[0150] The server utilizes the user's friend list to suggest interaction opportunities tailored to their emotional state. For example, it may determine that participating with friends would be effective in improving the user's mood. After participating in an event, the user's device facilitates feedback input, allowing the user to provide an evaluation of the service. The server analyzes this feedback and uses it to improve the system.

[0151] Specific example

[0152] The server, through its emotion engine, determines that user C has recently been feeling fatigued. In response, the server suggests a relaxing art exhibition for C. It also provides a short, local bus route to make it easily accessible. Furthermore, it generates a message encouraging C to invite their friend D, aiming for a more fulfilling experience. In this way, event suggestions are tailored to the user's needs.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies. The user enters their information, and the terminal sends that data to the server.

[0156] Step 2:

[0157] The server stores the received user data in a database and analyzes the user's interests. It uses algorithms to generate user profiles and identify interest categories.

[0158] Step 3:

[0159] The emotion engine uses biometric and interaction data from the user's device to evaluate the user's emotions in real time. For example, it analyzes the user's voice tone and keystroke patterns.

[0160] Step 4:

[0161] The server reflects the emotional state obtained from the emotion engine into the user profile. This integrated data is then used to select personalized events.

[0162] Step 5:

[0163] The server retrieves the latest event information from external sources and selects the most suitable event based on the user's interests and emotional state. For example, if the user is in an emotional state where they need to relax, it will suggest events such as yoga or art exhibitions.

[0164] Step 6:

[0165] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays the event details and related information.

[0166] Step 7:

[0167] When a user selects an event, the server calculates the optimal mode of transportation. Based on the user's emotional state, for example, it prioritizes comfortable public transport routes to allow for a relaxed journey. The calculation results are then notified to the user's device.

[0168] Step 8:

[0169] The server checks the user's friends list and identifies friends with similar interests. It then generates a message encouraging them to join together, taking their emotional state into consideration, and displays it on the user's device.

[0170] Step 9:

[0171] After participating in the event, the user's device displays a feedback form to collect ratings and opinions from the user. The user enters their impressions, and the device sends the feedback data to the server.

[0172] Step 10:

[0173] The server analyzes feedback data and uses it to select events and refine user profiles. This will result in more appropriate and accurate event suggestions in the future.

[0174] (Example 2)

[0175] 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".

[0176] There is a challenge in accurately understanding the hobbies, preferences, and emotional states of users, including the elderly, and proposing personalized activities to enrich their lifestyles. To solve this problem, it is necessary to collect and analyze diverse user data and make appropriate suggestions based on that data. However, conventional systems do not adequately analyze emotional states, and the personalized suggestions using that information are insufficient, making it difficult to improve user satisfaction.

[0177] 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.

[0178] In this invention, the server includes means for collecting information on the user's hobbies and interests and generating a user profile; means for analyzing the emotional state based on the collected information and the user's physiological data; and means for integrating the analyzed emotional state into the user profile to grasp the overall user state. This makes it possible to efficiently suggest events that are most suitable for the user and provide a place for interaction that is appropriate to their emotional state.

[0179] "User" refers to an individual who uses the system, and in this particular context, it refers to a person who provides information about their hobbies and emotional state, including elderly people.

[0180] "Hobbies and interests" refer to a user's sustained interest in specific activities or themes, and are elements that shape an individual's lifestyle and preferences.

[0181] A "user profile" refers to a collection of information that comprehensively represents a user's hobbies, interests, lifestyle, and emotional state based on collected data.

[0182] "Emotional state" refers to a state that reflects the user's psychological and physiological responses, and comprehensively represents their emotions and mood at that particular moment.

[0183] An "event" refers to an activity or event that takes place at a specific time and place, and is suggested to users based on their interests and emotional state.

[0184] "External information sources" refer to information resources and databases accessible from outside the system, and their role is to provide event information.

[0185] "Method of transportation" refers to the means and routes used by users to travel to the event venue, and includes optimized modes of transport.

[0186] "Communication" refers to messages and notifications sent from the system to encourage participation from other users or in proposed activities.

[0187] "Ratings" refer to feedback provided by users regarding the content of the services or events offered, and this information is used to improve the system.

[0188] This invention relates to a system that provides personalized event suggestions that take into account the user's hobbies, preferences, and emotional state. The system mainly consists of a user terminal, a server, and an emotion engine.

[0189] The user terminal provides an interface for users to input information about their hobbies and interests. Users use this interface to enter their own information, such as whether they are interested in painting or music, or whether they are available to attend events on weekends. The entered data is then sent from the terminal to the server.

[0190] After receiving this data, the server generates a user profile using machine learning algorithms. Specifically, software such as TENSORFLOW® is used for data analysis. The generated profile is periodically updated to reflect the user's changing interests.

[0191] Furthermore, the server uses an emotion engine to analyze the user's emotional state in real time based on physiological and input data. This analysis utilizes an emotion analysis API, and the results are integrated into the user profile to provide a more comprehensive understanding of the user's current situation.

[0192] The server selects appropriate events for the user based on their user profile and emotional state, retrieving them from external sources. To this end, it periodically retrieves event information from external databases and APIs. For example, it might suggest a relaxing art exhibition. In particular, events designed to reduce stress may also be selected.

[0193] The server uses the Google Maps API to calculate and present the best mode of transportation to the user based on the proposed event location and the user's place of residence. For example, it might display information such as, "You can reach the event venue in 30 minutes from the nearest bus stop."

[0194] Furthermore, the user's device will also include a means to generate and display messages to invite friends to the proposed event. This will increase opportunities for users to interact with other users and enjoy the event more.

[0195] After participating, users can provide feedback about the event via their device. The server receives this feedback and uses it to further improve the system.

[0196] For example, if the emotion engine analyzes that a user is stressed, the server will suggest relaxing activities for the user. The user will then receive the most suitable transportation through their device and arrangements will be made for them to participate comfortably.

[0197] Examples of prompts for a generative AI model:

[0198] "Please suggest events that will help senior users relieve stress. Choose events that their friends can also participate in."

[0199] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0200] Step 1:

[0201] The user terminal provides an interface for users to input information about their hobbies and interests. Through this interface, users input information such as "I'm interested in art exhibitions" or "I'm available on weekends." This input data is sent from the terminal to the server. Upon receiving the input from the terminal, the server accepts it as initial user data.

[0202] Step 2:

[0203] The server generates user profiles using machine learning algorithms based on the received user data. This process utilizes analytical software such as TensorFlow to cluster the data and create profiles. Specifically, user interests are categorized by theme and stored in a database. As a result, personalized profiles are generated for each user and used for future recommendations.

[0204] Step 3:

[0205] The server analyzes the user's emotional state in real time using an emotion engine, based on physiological data transmitted from the terminal and user input data. Using clues such as heart rate and input speed, it evaluates the user's state, including whether they are experiencing stress, using an emotion analysis API. This analysis result is integrated into the user profile, providing a more accurate reflection of the user's current situation.

[0206] Step 4:

[0207] Based on the generated profile and emotional state, the server retrieves event information from external databases and APIs and selects events appropriate for the user. In this process, it considers the impact of events on the emotional state and prioritizes those that promote relaxation. This selection generates a list of candidate events.

[0208] Step 5:

[0209] The server uses the Google Maps API to calculate the optimal mode of transportation based on the user's location and the location of selected events. It prioritizes comfort and selects routes that minimize travel time, providing the user with the best possible travel plan. The calculation results are sent to the device and displayed to the user.

[0210] Step 6:

[0211] The user's terminal generates and notifies messages to invite friends to the proposed event. This promotes interaction between users and increases their willingness to participate in the event. The server provides the infrastructure to facilitate this communication.

[0212] Step 7:

[0213] After participating in an event, users provide feedback via their device. This feedback is sent to the server and used to improve future event suggestions and system settings. User comments and ratings are recorded in a database and serve as foundational data to improve the accuracy of future recommendations.

[0214] (Application Example 2)

[0215] 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".

[0216] In modern society, there is a particular challenge in that it is difficult for the elderly to achieve social and emotional fulfillment. Therefore, there is a need to propose activities based on the individual interests and emotional states of the elderly and to promote social interaction. However, many existing systems do not adequately consider the emotional state and interests of users, and therefore fail to meet actual needs due to insufficient personalized suggestions. Consequently, there is a need for a system that can grasp users' hobbies, preferences, and emotions in real time and propose appropriate events and transportation options based on that information.

[0217] 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.

[0218] In this invention, the server includes means for collecting and analyzing information about the user's interests and emotions, means for selecting and proposing a suitable gathering for the user based on the analyzed information, and means for calculating and presenting the optimal means of transportation to the proposed gathering. This makes it possible to propose personalized events that meet the user's needs, thereby enabling support for elderly people to lead richer social lives.

[0219] A "user" is an individual who uses this system, and their interests and emotional information are the subject of collection and analysis.

[0220] "Interests" refer to specific activities or matters that users are interested in, and event suggestions are made based on these interests.

[0221] "Emotions" refer to the user's psychological or physiological state and are an important element in personalizing the suggested content.

[0222] A "gathering" refers to a specific event or activity suggested to the user, which is selected based on their interests and feelings.

[0223] "Transportation" refers to the means of getting around that a user will use to attend a proposed meeting, and it is presented after being optimized for that purpose.

[0224] "Analysis" is the process of processing collected data and modeling the user's interests and emotional state.

[0225] This invention is a system that provides individually optimized event suggestions and optimal transportation options based on the user's interests and emotions. It delivers personalized services through the coordinated functioning of a server, terminal, and user.

[0226] The server first receives data sent from the user's terminal. The user uses the terminal to input information about their interests and preferences. This data is collected on a cloud system (e.g., AWS® or Google Cloud). The collected data is processed into a data frame using the Python Pandas library. Then, it is modeled using machine learning libraries such as TensorFlow to generate a profile for each user.

[0227] Furthermore, the server also analyzes the user's emotional state. Physiological data from wearable devices (e.g., smartwatches) is used and collected in real time via Bluetooth. This data is used to perform emotion analysis on the device using TensorFlow Lite, and the results are sent to the server.

[0228] Based on user interest and sentiment data, the server retrieves the latest event information from external APIs (such as the Google Calendar API). It then uses Python to analyze the data and select the most suitable gathering for the user. The selected gathering is then notified to the user via their device.

[0229] The server then uses the Google Maps API to optimize the route and calculate the best mode of transportation to the proposed meeting. The optimal mode of transportation and route information are calculated and presented to the user's device.

[0230] The server also generates messages to invite friends to the proposed gathering, providing users with opportunities for social interaction. After attending the event, users can provide feedback via their device.

[0231] For example, if the server determines that a user's stress level is high, it might suggest a relaxing art exhibition. In this case, a route using nearby public transportation would be provided. The user would also receive a message encouraging them to attend with friends.

[0232] Examples of prompts generated using AI models include the following:

[0233] "Based on the user's recent emotional state, list the types of gatherings that can be suggested as stress relief."

[0234] "Based on the results of the emotional analysis, suggest the most suitable mode of transportation."

[0235] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0236] Step 1:

[0237] Users use their devices to input data about their interests and preferences. This input data is sent to a cloud server. The input data is processed into a dataframe format using the Pandas library. This generates the initial dataset for the user profile.

[0238] Step 2:

[0239] The server uses TensorFlow to perform analysis with a machine learning model when generating user profiles. This model clusters user interests based on collected data and builds individual profiles. Once a profile is generated, it is stored in a cloud-based database (e.g., AWS DynamoDB).

[0240] Step 3:

[0241] User emotional state data is collected in real time from wearable devices (such as smartwatches). This data is transmitted to the device via Bluetooth and then sent to a server. The server uses TensorFlow Lite to estimate the emotional state on the device. The input for this process is physiological data, and the output is the user's emotional state.

[0242] Step 4:

[0243] The server periodically retrieves event information from an external API (e.g., Google Calendar API) and analyzes the data using Python. Based on the user's profile and sentiment data, the server selects the most suitable gathering. The input is a list of events, and the output identifies customized gathering information for each user.

[0244] Step 5:

[0245] The server uses the Google Maps API to calculate the best mode of transportation to the proposed meeting. It takes the user's location and destination information as input and outputs the optimal route and transportation method. This route information is then displayed on the device and presented to the user.

[0246] Step 6:

[0247] After the event, users use their devices to input feedback about their experience. This data is sent to a server and recorded in a database to help improve future proposals. Based on this, we have the opportunity to incorporate actual user experiences into future proposals.

[0248] Step 7:

[0249] The server also generates a message inviting friends and sends it to the user's device. The input is the user's friend list and sentiment data, and the output is the invitation message. This message plays a role in promoting social interaction.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] [Second Embodiment]

[0254] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0255] 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.

[0256] 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).

[0257] 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.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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".

[0266] This invention is a system that proposes events tailored to the interests and preferences of senior users, thereby encouraging them to go out. This system consists of user terminals and a server, and is implemented in the following manner.

[0267] User data collection

[0268] The user terminal provides an interface for senior users to enter their profile information. This includes basic user information, past activities of interest, and a friends list. Users also provide detailed information about their hobbies and preferences through questionnaires.

[0269] Profile creation and updating

[0270] The server generates a profile for each user based on the collected data. This profile includes information representing the user's interests and preferences and is updated regularly. The server uses machine learning algorithms to improve the accuracy of the profile.

[0271] Event selection and proposal

[0272] The server matches user profiles with event information obtained from external sources (e.g., local event calendars or online platforms). Based on the user's interests, it selects suitable events and creates a prioritized list. Selected events are notified to the user's device, and detailed information is displayed.

[0273] Calculation of transportation methods

[0274] The server calculates the optimal transportation route based on the user's location and the selected event venue. Considering traffic information and public transport schedules, the recommended route is displayed on the user's device.

[0275] Proposal for social exchange

[0276] The server refers to the user's friend list and identifies friends who might be interested in the same event. It generates appropriate messages and encourages the user to invite their friends to the event through their device. This promotes social interaction.

[0277] Gathering and incorporating feedback

[0278] The user's terminal displays a feedback form for providing evaluations and opinions after participating in an event. The server collects this feedback and incorporates it into proposals for future events. This improves the accuracy of proposals and user satisfaction.

[0279] Specific Example

[0280] Suppose user A is interested in music. The server collects data on music events and proposes a concert to be held nearby to user A. Furthermore, based on traffic information, it calculates the optimal route from home to the venue and proposes using the bus. Also, the server determines that user A's friend, user B, is also a music lover and generates an invitation message for them to participate together. After participating in the event, user A evaluates the event from the terminal, and the server aggregates the feedback for future use.

[0281] The following explains the process flow.

[0282] Step 1:

[0283] The user terminal displays an interface for the user to input basic profile information and data related to hobbies and interests. The user inputs their information, and the terminal sends this data to the server.

[0284] Step 2:

[0285] The server saves the received user data in the database and analyzes the user's interests and concerns. Using a machine learning algorithm, it generates a user profile and identifies the category of interests.

[0286] Step 3:

[0287] The server obtains event information from external sources. This includes the local event calendar and online platforms, and new data is updated regularly.

[0288] Step 4:

[0289] The server compares the user profile with the obtained event information, selects events that match the user's interests, scores the selected events, and lists those with high priority.

[0290] Step 5:

[0291] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays a list containing the event details to the user.

[0292] Step 6:

[0293] When a user selects a suggested event, the server calculates the optimal transportation route based on the user's location and the event's location, taking into account public transportation schedules. The server then sends the calculated route information to the user's terminal.

[0294] Step 7:

[0295] The server checks the user's friend list and identifies friends who may share similar interests. The server generates a message for the user to invite their friends to the event and displays it on the user's device.

[0296] Step 8:

[0297] After participating in the event, the user's device displays a feedback form to receive ratings and opinions from the user. The user enters their feedback, and the device sends that information to the server.

[0298] Step 9:

[0299] The server records the received feedback in a database and uses it to improve future event selection and suggestion functions. The data contributes to updating the learning algorithm, improving the accuracy of the suggestions.

[0300] (Example 1)

[0301] 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."

[0302] There is a problem that for elderly users, there is a lack of support to activate their interests and concerns in daily life and to promote going out and social interaction. Also, the selection of means of transportation is complex, and it is important to provide appropriate transportation routes. In addition, there is an issue that individualized information provision reflecting feedback is insufficient.

[0303] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0304] In this invention, the server includes means for collecting information on the preferences and interests of elderly users and generating a user profile, means for selecting and proposing the most suitable events for the user based on the collected information and the event information obtained from an external information source, and means for calculating and presenting the most optimal travel route for the event proposal. Thereby, it becomes possible to propose individualized events and provide highly convenient means of transportation for elderly users.

[0305] "Elderly users" generally refer to users belonging to the age group of 65 years old or above and having specific preferences and interests.

[0306] "Information on preferences and interests" means information related to activities, hobbies, or events that the user is particularly interested in.

[0307] "User profile" is a data structure generated based on the collected preferences and interests of the user and is used for individualized information provision.

[0308] "External information source" refers to an information provider for obtaining event information related to the user, including a local event calendar and an online platform.

[0309] "Event information" means information on the content and schedule of specific events obtained from an external information source.

[0310] The "optimal travel route" refers to the most efficient route from the user's starting point to their destination, and is calculated taking into account the mode of transport and time of day.

[0311] "Communication messages" refer to messages containing information or invitations that are generated to facilitate interaction between users.

[0312] "Evaluation" refers to information regarding users' opinions and satisfaction levels after participating in an event.

[0313] "Feedback" refers to users' evaluations and suggestions for improvement regarding the services provided.

[0314] This invention aims to provide senior users with personalized event suggestions and efficient means of transportation. The system primarily consists of a server and user terminals.

[0315] The user first uses the device's interface to enter detailed information about their basic information and preferences. The device then sends this information to the server. The device includes tablets, smartphones, and other similar devices.

[0316] The servers run on the cloud and leverage high-performance computing resources. Built using programming languages ​​such as Python and Java, the servers implement machine learning algorithms to analyze user-submitted information and generate individual profiles. Generative AI models are used to improve the accuracy of these profiles.

[0317] The server utilizes web APIs and scraping techniques to retrieve event information from external sources. This allows it to collect information on local events and select appropriate events by matching them with the user's profile.

[0318] Furthermore, the server accesses a traffic database to obtain real-time traffic information. Based on this, it calculates the optimal travel route from the user's place of residence to the event venue and provides this information to the terminal.

[0319] For example, if user A enjoys music, the server collects information on music events held in the area and suggests the most suitable events for A. It also calculates the best mode of transportation from traffic information and presents A with a route from their home to the venue. Furthermore, it identifies friends from A's friend list who also have an interest in music, generates invitation messages for them to attend together, and notifies their device.

[0320] After participating in an event, users can provide feedback via their devices. The server analyzes this feedback and uses the data to improve the accuracy of future event suggestions.

[0321] An example of a prompt would be, "How do I generate a message suggesting a nearby concert to a senior who is interested in music, and inviting a friend?"

[0322] In this way, this system can encourage elderly users to participate in events based on their interests and provide opportunities for social interaction.

[0323] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0324] Step 1:

[0325] Users enter their profile information on their devices. Specifically, users launch a dedicated application on their devices and enter basic information such as their date of birth, address, interests, and friends list. This input data is important for reflecting the user's preferences and interests, and the device plays the role of transmitting the entered information to the server.

[0326] Step 2:

[0327] The server generates a user profile based on the received profile information. The server's programs utilize machine learning techniques to organize the user's interests and preferences into a sophisticated data structure. The input to this process is the raw data provided by the user, and the output is the profile. The server uses the latest generative AI models to create a more personalized profile.

[0328] Step 3:

[0329] The server retrieves event information from external sources. Using APIs and web scraping techniques, it collects the latest information on events held in the region. The input to this process is raw event data obtained from external sources, and the output is organized event information. The server stores this information in a database.

[0330] Step 4:

[0331] The server matches user profiles with event information to select the most suitable event. The server utilizes a generative AI model to calculate the degree of event matching and prioritizes selecting the event that best matches the user's preferences. The input to this process is the user profile and event information, and the output is a list of selected events.

[0332] Step 5:

[0333] The server calculates the optimal travel route for the selected event. The server accesses an external traffic information database and analyzes transportation options based on the user's location and the event venue. This process considers public transport timetables and real-time traffic conditions. Inputs are the user's address and event location, and output are several recommended transportation routes.

[0334] Step 6:

[0335] The server generates messages to invite friends to facilitate social interaction between users. The server identifies friends from the user's friend list who might be interested in the event. The generation AI model creates appropriate invitations and suggestions and sends the messages via the device. The input is the friend list and event details, and the output is the generated invitation message.

[0336] Step 7:

[0337] After participating in an event, users provide evaluations and opinions through a feedback form on their devices. The device then sends this feedback to the server. The input is user feedback, and the output is data on the server that will be used to improve future event proposals. This feedback process allows the server to further improve the accuracy of future event proposals.

[0338] (Application Example 1)

[0339] 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."

[0340] To increase opportunities for seniors to go out and prevent social isolation, there is a need for a system that suggests events tailored to individual interests. However, existing systems lack the accuracy to accurately analyze user interests and provide appropriate events. Furthermore, they are insufficient in suggesting transportation options for event participation and in promoting social interaction.

[0341] 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.

[0342] In this invention, the server includes means for collecting information on the user's hobbies and preferences and generating a profile; means for selecting and proposing events suitable for the user based on the collected information; and means for calculating and presenting the optimal means of transportation to the proposed events. This makes it possible to provide event suggestions and optimal transportation routes based on the individual interests of elderly people, as well as to promote social interaction.

[0343] "Information about users' hobbies and preferences" refers to data on activities that users have shown interest in in the past, as well as detailed personal interest data collected through surveys.

[0344] A "profile" is an individual information model that reflects a user's hobbies, preferences, and interests.

[0345] "Methods for selecting and proposing events" refers to the process of matching collected user information with external event information to present gatherings and activities suitable for the user.

[0346] "Means for calculating and suggesting the optimal mode of transportation" refers to a function that analyzes available modes of transportation between the user's place of residence and destination, and provides the most efficient and convenient travel route.

[0347] "Means of generating messages to facilitate interaction with other users" refers to invitations and notifications created to encourage communication among users when participating in events or activities.

[0348] "Means of collecting feedback and incorporating it into improvements" refers to the process of compiling user evaluations and opinions to improve the accuracy and satisfaction of future events.

[0349] "External event information sources" refer to external information resources that provide event information, such as local event calendars and online platforms.

[0350] "Public transport information" refers to a dataset containing schedule and route information related to public transport.

[0351] The system for realizing this invention consists of a network-connected server and a user terminal. The server runs a program that collects information about the user's hobbies and preferences and generates and updates a profile. This profile undergoes processing to improve its accuracy using machine learning algorithms based on data acquired from the user terminal. A backend using a scripting language such as Python supports this processing.

[0352] The server periodically retrieves information from external event sources, matches it with the user's profile, selects appropriate events, and proposes them to the user's terminal. This involves using APIs to collect external data, and a frontend using JavaScript and HTML visually presents the events to the user.

[0353] Furthermore, the server utilizes external services such as the Google Maps API to calculate the optimal transportation route from the user's location to the event venue and displays it on the user's device. This allows users to easily select from multiple travel options, including available public transportation.

[0354] Furthermore, the server generates messages for other users who may be attending the same event, based on the user's friend list, to encourage social interaction. Feedback provided by users after attending the event is collected and analyzed by the server to improve future proposals.

[0355] For example, if a user is interested in tennis and music, the server will suggest local tennis tournaments and music events. It can also suggest optimal travel routes using public transportation. After the event, user satisfaction and comments are collected as feedback to improve the system.

[0356] An example of a prompt message is: "If a 70-year-old user is interested in tennis and music, what events would you suggest? Also, please provide transportation options based on those events."

[0357] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0358] Step 1:

[0359] Users input information about their hobbies and preferences through their device. This information includes past interests and specific examples of activities. The device transfers this data to the server, where it is entered as initial data for the user profile. Based on this input, a registration operation is performed to save it to the database.

[0360] Step 2:

[0361] The server generates a profile using the received user information. It applies machine learning algorithms to model the user's interests. It analyzes the input interest and activity data, generates interest clusters, and outputs this as the profile structure.

[0362] Step 3:

[0363] The server retrieves event data from external event sources via APIs. The retrieved data is then matched against the user's profile information, and events matching their interests are filtered. As a result of this filtering process, a list of appropriate events is generated and output.

[0364] Step 4:

[0365] The filtered event list is sent to the device and visually presented to the user. The device's interface displays event details, allowing the user to select events of interest.

[0366] Step 5:

[0367] The server uses the Google Maps API to calculate appropriate transportation options based on the user's location and the selected event venue. Input includes the user's location and event venue information. Based on this, it calculates public transport and car routes, and outputs the optimal route information.

[0368] Step 6:

[0369] The server consults the user's friends list and generates messages for other users who might be interested in similar events. These messages are automatically generated as invitations to participate and sent to the user's terminal as output.

[0370] Step 7:

[0371] After users participate in an event on their device, they provide feedback and evaluations through a feedback form. This feedback information is sent from the device to the server and analyzed to improve future event proposals. Based on the input feedback data, data analysis is performed to improve the accuracy of the proposal algorithm.

[0372] 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.

[0373] This invention provides an event suggestion system that takes into account not only the hobbies and preferences of senior users but also their emotional state. This system consists of a user terminal, a server, and an emotion engine, and is implemented in the following manner.

[0374] User profile generation and updating

[0375] The user terminal provides an interface for users to input information about their hobbies and interests. Users enter their information, and the terminal sends it to the server. The server analyzes this data using machine learning algorithms and generates a profile. The profile is updated periodically to reflect the user's changing interests and newly acquired information.

[0376] Analysis using an emotional engine

[0377] The server analyzes the user's emotions in real time through an emotion engine. This emotional state is determined based on physiological data collected from the device and the user's input. The server integrates the obtained emotional data into a user profile to gain a more comprehensive understanding of the user's current state.

[0378] Event selection and emotion-based adjustments

[0379] The server selects personalized events based on the user profile and emotional state. Event information is periodically retrieved from external sources and customized to the user's interests and emotions. In particular, emotional state influences event selection; for example, music events designed to relieve stress may be suggested.

[0380] Presenting the most suitable mode of transportation

[0381] The server calculates the optimal mode of transportation, taking into account the location of the proposed event and the user's place of residence. The selection of the transportation route is also appropriately adjusted according to the user's emotional state. The terminal presents the optimized transportation information received from the server to the user.

[0382] Promoting social interaction and feedback

[0383] The server utilizes the user's friend list to suggest interaction opportunities tailored to their emotional state. For example, it may determine that participating with friends would be effective in improving the user's mood. After participating in an event, the user's device facilitates feedback input, allowing the user to provide an evaluation of the service. The server analyzes this feedback and uses it to improve the system.

[0384] Specific example

[0385] The server, through its emotion engine, determines that user C has recently been feeling fatigued. In response, the server suggests a relaxing art exhibition for C. It also provides a short, local bus route to make it easily accessible. Furthermore, it generates a message encouraging C to invite their friend D, aiming for a more fulfilling experience. In this way, event suggestions are tailored to the user's needs.

[0386] The following describes the processing flow.

[0387] Step 1:

[0388] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies. The user enters their information, and the terminal sends that data to the server.

[0389] Step 2:

[0390] The server stores the received user data in a database and analyzes the user's interests. It uses algorithms to generate user profiles and identify interest categories.

[0391] Step 3:

[0392] The emotion engine uses biometric and interaction data from the user's device to evaluate the user's emotions in real time. For example, it analyzes the user's voice tone and keystroke patterns.

[0393] Step 4:

[0394] The server reflects the emotional state obtained from the emotion engine into the user profile. This integrated data is then used to select personalized events.

[0395] Step 5:

[0396] The server retrieves the latest event information from external sources and selects the most suitable event based on the user's interests and emotional state. For example, if the user is in an emotional state where they need to relax, it will suggest events such as yoga or art exhibitions.

[0397] Step 6:

[0398] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays the event details and related information.

[0399] Step 7:

[0400] When a user selects an event, the server calculates the optimal mode of transportation. Based on the user's emotional state, for example, it prioritizes comfortable public transport routes to allow for a relaxed journey. The calculation results are then notified to the user's device.

[0401] Step 8:

[0402] The server checks the user's friends list and identifies friends with similar interests. It then generates a message encouraging them to join together, taking their emotional state into consideration, and displays it on the user's device.

[0403] Step 9:

[0404] After participating in the event, the user's device displays a feedback form to collect ratings and opinions from the user. The user enters their impressions, and the device sends the feedback data to the server.

[0405] Step 10:

[0406] The server analyzes feedback data and uses it to select events and refine user profiles. This will result in more appropriate and accurate event suggestions in the future.

[0407] (Example 2)

[0408] 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".

[0409] There is a challenge in accurately understanding the hobbies, preferences, and emotional states of users, including the elderly, and proposing personalized activities to enrich their lifestyles. To solve this problem, it is necessary to collect and analyze diverse user data and make appropriate suggestions based on that data. However, conventional systems do not adequately analyze emotional states, and the personalized suggestions using that information are insufficient, making it difficult to improve user satisfaction.

[0410] 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.

[0411] In this invention, the server includes means for collecting information on the user's hobbies and interests and generating a user profile; means for analyzing the emotional state based on the collected information and the user's physiological data; and means for integrating the analyzed emotional state into the user profile to grasp the overall user state. This makes it possible to efficiently suggest events that are most suitable for the user and provide a place for interaction that is appropriate to their emotional state.

[0412] "User" refers to an individual who uses the system, and in this particular context, it refers to a person who provides information about their hobbies and emotional state, including elderly people.

[0413] "Hobbies and interests" refer to a user's sustained interest in specific activities or themes, and are elements that shape an individual's lifestyle and preferences.

[0414] A "user profile" refers to a collection of information that comprehensively represents a user's hobbies, interests, lifestyle, and emotional state based on collected data.

[0415] "Emotional state" refers to a state that reflects the user's psychological and physiological responses, and comprehensively represents their emotions and mood at that particular moment.

[0416] An "event" refers to an activity or event that takes place at a specific time and place, and is suggested to users based on their interests and emotional state.

[0417] "External information sources" refer to information resources and databases accessible from outside the system, and their role is to provide event information.

[0418] "Method of transportation" refers to the means and routes used by users to travel to the event venue, and includes optimized modes of transport.

[0419] "Communication" refers to messages and notifications sent from the system to encourage participation from other users or in proposed activities.

[0420] "Ratings" refer to feedback provided by users regarding the content of the services or events offered, and this information is used to improve the system.

[0421] This invention relates to a system that provides personalized event suggestions that take into account the user's hobbies, preferences, and emotional state. The system mainly consists of a user terminal, a server, and an emotion engine.

[0422] The user terminal provides an interface for users to input information about their hobbies and interests. Users use this interface to enter their own information, such as whether they are interested in painting or music, or whether they are available to attend events on weekends. The entered data is then sent from the terminal to the server.

[0423] After receiving this data, the server generates a user profile using machine learning algorithms. Specifically, software such as TensorFlow is used for data analysis. The generated profile is periodically updated to reflect the user's changing interests.

[0424] Furthermore, the server uses an emotion engine to analyze the user's emotional state in real time based on physiological and input data. This analysis utilizes an emotion analysis API, and the results are integrated into the user profile to provide a more comprehensive understanding of the user's current situation.

[0425] The server selects appropriate events for the user based on their user profile and emotional state, retrieving them from external sources. To this end, it periodically retrieves event information from external databases and APIs. For example, it might suggest a relaxing art exhibition. In particular, events designed to reduce stress may also be selected.

[0426] The server uses the Google Maps API to calculate and present the best mode of transportation to the user based on the proposed event location and the user's place of residence. For example, it might display information such as, "You can reach the event venue in 30 minutes from the nearest bus stop."

[0427] Furthermore, the user's device will also include a means to generate and display messages to invite friends to the proposed event. This will increase opportunities for users to interact with other users and enjoy the event more.

[0428] After participating, users can provide feedback about the event via their device. The server receives this feedback and uses it to further improve the system.

[0429] For example, if the emotion engine analyzes that a user is stressed, the server will suggest relaxing activities for the user. The user will then receive the most suitable transportation through their device and arrangements will be made for them to participate comfortably.

[0430] Examples of prompts for a generative AI model:

[0431] "Please suggest events that will help senior users relieve stress. Choose events that their friends can also participate in."

[0432] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0433] Step 1:

[0434] The user terminal provides an interface for users to input information about their hobbies and interests. Through this interface, users input information such as "I'm interested in art exhibitions" or "I'm available on weekends." This input data is sent from the terminal to the server. Upon receiving the input from the terminal, the server accepts it as initial user data.

[0435] Step 2:

[0436] The server generates user profiles using machine learning algorithms based on the received user data. This process utilizes analytical software such as TensorFlow to cluster the data and create profiles. Specifically, user interests are categorized by theme and stored in a database. As a result, personalized profiles are generated for each user and used for future recommendations.

[0437] Step 3:

[0438] The server analyzes the user's emotional state in real time using an emotion engine, based on physiological data transmitted from the terminal and user input data. Using clues such as heart rate and input speed, it evaluates the user's state, including whether they are experiencing stress, using an emotion analysis API. This analysis result is integrated into the user profile, providing a more accurate reflection of the user's current situation.

[0439] Step 4:

[0440] Based on the generated profile and emotional state, the server retrieves event information from external databases and APIs and selects events appropriate for the user. In this process, it considers the impact of events on the emotional state and prioritizes those that promote relaxation. This selection generates a list of candidate events.

[0441] Step 5:

[0442] The server uses the Google Maps API to calculate the optimal mode of transportation based on the user's location and the location of selected events. It prioritizes comfort and selects routes that minimize travel time, providing the user with the best possible travel plan. The calculation results are sent to the device and displayed to the user.

[0443] Step 6:

[0444] The user's terminal generates and notifies messages to invite friends to the proposed event. This promotes interaction between users and increases their willingness to participate in the event. The server provides the infrastructure to facilitate this communication.

[0445] Step 7:

[0446] After participating in an event, users provide feedback via their device. This feedback is sent to the server and used to improve future event suggestions and system settings. User comments and ratings are recorded in a database and serve as foundational data to improve the accuracy of future recommendations.

[0447] (Application Example 2)

[0448] 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 as the "terminal".

[0449] In modern society, there is a particular challenge in that it is difficult for the elderly to achieve social and emotional fulfillment. Therefore, there is a need to propose activities based on the individual interests and emotional states of the elderly and to promote social interaction. However, many existing systems do not adequately consider the emotional state and interests of users, and therefore fail to meet actual needs due to insufficient personalized suggestions. Consequently, there is a need for a system that can grasp users' hobbies, preferences, and emotions in real time and propose appropriate events and transportation options based on that information.

[0450] 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.

[0451] In this invention, the server includes means for collecting and analyzing information about the user's interests and emotions, means for selecting and proposing a suitable gathering for the user based on the analyzed information, and means for calculating and presenting the optimal means of transportation to the proposed gathering. This makes it possible to propose personalized events that meet the user's needs, thereby enabling support for elderly people to lead richer social lives.

[0452] A "user" is an individual who uses this system, and their interests and emotional information are the subject of collection and analysis.

[0453] "Interests" refer to specific activities or matters that users are interested in, and event suggestions are made based on these interests.

[0454] "Emotions" refer to the user's psychological or physiological state and are an important element in personalizing the suggested content.

[0455] A "gathering" refers to a specific event or activity suggested to the user, which is selected based on their interests and feelings.

[0456] "Transportation" refers to the means of getting around that a user will use to attend a proposed meeting, and it is presented after being optimized for that purpose.

[0457] "Analysis" is the process of processing collected data and modeling the user's interests and emotional state.

[0458] This invention is a system that provides individually optimized event suggestions and optimal transportation options based on the user's interests and emotions. It delivers personalized services through the coordinated functioning of a server, terminal, and user.

[0459] The server first receives data sent from the user's terminal. The user uses the terminal to input information about their interests and preferences. This data is collected on a cloud system (e.g., AWS or Google Cloud). The collected data is processed into a data frame using the Python Pandas library. Then, it is modeled using machine learning libraries such as TensorFlow to generate a profile for each user.

[0460] Furthermore, the server also analyzes the user's emotional state. Physiological data from wearable devices (e.g., smartwatches) is used and collected in real time via Bluetooth. This data is used to perform emotion analysis on the device using TensorFlow Lite, and the results are sent to the server.

[0461] Based on user interest and sentiment data, the server retrieves the latest event information from external APIs (such as the Google Calendar API). It then uses Python to analyze the data and select the most suitable gathering for the user. The selected gathering is then notified to the user via their device.

[0462] The server then uses the Google Maps API to optimize the route and calculate the best mode of transportation to the proposed meeting. The optimal mode of transportation and route information are calculated and presented to the user's device.

[0463] The server also generates messages to invite friends to the proposed gathering, providing users with opportunities for social interaction. After attending the event, users can provide feedback via their device.

[0464] For example, if the server determines that a user's stress level is high, it might suggest a relaxing art exhibition. In this case, a route using nearby public transportation would be provided. The user would also receive a message encouraging them to attend with friends.

[0465] Examples of prompts generated using AI models include the following:

[0466] "Based on the user's recent emotional state, list the types of gatherings that can be suggested as stress relief."

[0467] "Based on the results of the emotional analysis, suggest the most suitable mode of transportation."

[0468] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0469] Step 1:

[0470] Users use their devices to input data about their interests and preferences. This input data is sent to a cloud server. The input data is processed into a dataframe format using the Pandas library. This generates the initial dataset for the user profile.

[0471] Step 2:

[0472] The server uses TensorFlow to perform analysis with a machine learning model when generating user profiles. This model clusters user interests based on collected data and builds individual profiles. Once a profile is generated, it is stored in a cloud-based database (e.g., AWS DynamoDB).

[0473] Step 3:

[0474] User emotional state data is collected in real time from wearable devices (such as smartwatches). This data is transmitted to the device via Bluetooth and then sent to a server. The server uses TensorFlow Lite to estimate the emotional state on the device. The input for this process is physiological data, and the output is the user's emotional state.

[0475] Step 4:

[0476] The server periodically retrieves event information from an external API (e.g., Google Calendar API) and analyzes the data using Python. Based on the user's profile and sentiment data, the server selects the most suitable gathering. The input is a list of events, and the output identifies customized gathering information for each user.

[0477] Step 5:

[0478] The server uses the Google Maps API to calculate the best mode of transportation to the proposed meeting. It takes the user's location and destination information as input and outputs the optimal route and transportation method. This route information is then displayed on the device and presented to the user.

[0479] Step 6:

[0480] After the event, users use their devices to input feedback about their experience. This data is sent to a server and recorded in a database to help improve future proposals. Based on this, we have the opportunity to incorporate actual user experiences into future proposals.

[0481] Step 7:

[0482] The server also generates a message inviting friends and sends it to the user's device. The input is the user's friend list and sentiment data, and the output is the invitation message. This message plays a role in promoting social interaction.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] [Third Embodiment]

[0487] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0488] 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.

[0489] 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).

[0490] 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.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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".

[0499] This invention is a system that proposes events tailored to the interests and preferences of senior users, thereby encouraging them to go out. This system consists of user terminals and a server, and is implemented in the following manner.

[0500] User data collection

[0501] The user terminal provides an interface for senior users to enter their profile information. This includes basic user information, past activities of interest, and a friends list. Users also provide detailed information about their hobbies and preferences through questionnaires.

[0502] Profile creation and updating

[0503] The server generates a profile for each user based on the collected data. This profile includes information representing the user's interests and preferences and is updated regularly. The server uses machine learning algorithms to improve the accuracy of the profile.

[0504] Event selection and proposal

[0505] The server matches user profiles with event information obtained from external sources (e.g., local event calendars or online platforms). Based on the user's interests, it selects suitable events and creates a prioritized list. Selected events are notified to the user's device, and detailed information is displayed.

[0506] Calculation of transportation methods

[0507] The server calculates the optimal transportation route based on the user's location and the selected event venue. Considering traffic information and public transport schedules, the recommended route is displayed on the user's device.

[0508] Proposal for social exchange

[0509] The server refers to the user's friend list and identifies friends who might be interested in the same event. It generates appropriate messages and encourages the user to invite their friends to the event through their device. This promotes social interaction.

[0510] Gathering and incorporating feedback

[0511] The user's terminal displays a feedback form for providing evaluations and opinions after participating in an event. The server collects this feedback and incorporates it into proposals for future events. This improves the accuracy of proposals and user satisfaction.

[0512] Specific example

[0513] Let's say user A is interested in music. The server collects data on music events and suggests nearby concerts to A. Furthermore, based on traffic information, it calculates the optimal route from A's home to the venue and suggests using the bus. The server also determines that A's friend B is also a music lover and generates an invitation message for them to attend together. After attending the event, A evaluates it from their device, and the server compiles the feedback to use for future improvements.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies and interests. The user enters their information, and the terminal sends this data to the server.

[0517] Step 2:

[0518] The server stores the received user data in a database and analyzes the user's interests. Machine learning algorithms are used to generate user profiles and identify categories of interest.

[0519] Step 3:

[0520] The server retrieves event information from external sources, including local event calendars and online platforms, which are regularly updated with new data.

[0521] Step 4:

[0522] The server matches the user profile with the retrieved event information and selects events that match the user's interests. It then scores the selected events and lists those with the highest priority.

[0523] Step 5:

[0524] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays a list containing the event details to the user.

[0525] Step 6:

[0526] When a user selects a suggested event, the server calculates the optimal transportation route based on the user's location and the event's location, taking into account public transportation schedules. The server then sends the calculated route information to the user's terminal.

[0527] Step 7:

[0528] The server checks the user's friend list and identifies friends who may share similar interests. The server generates a message for the user to invite their friends to the event and displays it on the user's device.

[0529] Step 8:

[0530] After participating in the event, the user's device displays a feedback form to receive ratings and opinions from the user. The user enters their feedback, and the device sends that information to the server.

[0531] Step 9:

[0532] The server records the received feedback in a database and uses it to improve future event selection and suggestion functions. The data contributes to updating the learning algorithm, improving the accuracy of the suggestions.

[0533] (Example 1)

[0534] 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."

[0535] For elderly users, there is a lack of support to stimulate their interests and passions in daily life and to promote going out and social interaction. Furthermore, the selection of transportation options is complex, making the provision of appropriate routes crucial. In addition, there is a challenge in the insufficient provision of personalized information that reflects feedback.

[0536] 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.

[0537] In this invention, the server includes means for collecting information on the preferences and interests of elderly users and generating a user profile; means for selecting and proposing the most suitable events for the user based on the collected information and event information obtained from external sources; and means for calculating and presenting the optimal travel route for the event proposal. This makes it possible to provide elderly users with personalized event proposals and convenient means of transportation.

[0538] "Elderly users" generally refer to users who belong to the age group of 65 years or older and have specific preferences or interests.

[0539] "Information about preferences and interests" means information related to activities, hobbies, or events that the user is particularly interested in.

[0540] A "user profile" is a data structure generated based on the preferences and interests of collected users, and is used to provide personalized information.

[0541] "External information sources" refer to information sources for obtaining event information relevant to the user, including local event calendars and online platforms.

[0542] "Event information" refers to information about the specific content and schedule of an event, obtained from external sources.

[0543] The "optimal travel route" refers to the most efficient route from the user's starting point to their destination, and is calculated taking into account the mode of transport and time of day.

[0544] "Communication messages" refer to messages containing information or invitations that are generated to facilitate interaction between users.

[0545] "Evaluation" refers to information regarding users' opinions and satisfaction levels after participating in an event.

[0546] "Feedback" refers to users' evaluations and suggestions for improvement regarding the services provided.

[0547] This invention aims to provide senior users with personalized event suggestions and efficient means of transportation. The system primarily consists of a server and user terminals.

[0548] The user first uses the device's interface to enter detailed information about their basic information and preferences. The device then sends this information to the server. The device includes tablets, smartphones, and other similar devices.

[0549] The servers run on the cloud and leverage high-performance computing resources. Built using programming languages ​​such as Python and Java, the servers implement machine learning algorithms to analyze user-submitted information and generate individual profiles. Generative AI models are used to improve the accuracy of these profiles.

[0550] The server utilizes web APIs and scraping techniques to retrieve event information from external sources. This allows it to collect information on local events and select appropriate events by matching them with the user's profile.

[0551] Furthermore, the server accesses a traffic database to obtain real-time traffic information. Based on this, it calculates the optimal travel route from the user's place of residence to the event venue and provides this information to the terminal.

[0552] For example, if user A enjoys music, the server collects information on music events held in the area and suggests the most suitable events for A. It also calculates the best mode of transportation from traffic information and presents A with a route from their home to the venue. Furthermore, it identifies friends from A's friend list who also have an interest in music, generates invitation messages for them to attend together, and notifies their device.

[0553] After participating in an event, users can provide feedback via their devices. The server analyzes this feedback and uses the data to improve the accuracy of future event suggestions.

[0554] An example of a prompt would be, "How do I generate a message suggesting a nearby concert to a senior who is interested in music, and inviting a friend?"

[0555] In this way, this system can encourage elderly users to participate in events based on their interests and provide opportunities for social interaction.

[0556] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0557] Step 1:

[0558] Users enter their profile information on their devices. Specifically, users launch a dedicated application on their devices and enter basic information such as their date of birth, address, interests, and friends list. This input data is important for reflecting the user's preferences and interests, and the device plays the role of transmitting the entered information to the server.

[0559] Step 2:

[0560] The server generates a user profile based on the received profile information. The server's programs utilize machine learning techniques to organize the user's interests and preferences into a sophisticated data structure. The input to this process is the raw data provided by the user, and the output is the profile. The server uses the latest generative AI models to create a more personalized profile.

[0561] Step 3:

[0562] The server retrieves event information from external sources. Using APIs and web scraping techniques, it collects the latest information on events held in the region. The input to this process is raw event data obtained from external sources, and the output is organized event information. The server stores this information in a database.

[0563] Step 4:

[0564] The server matches user profiles with event information to select the most suitable event. The server utilizes a generative AI model to calculate the degree of event matching and prioritizes selecting the event that best matches the user's preferences. The input to this process is the user profile and event information, and the output is a list of selected events.

[0565] Step 5:

[0566] The server calculates the optimal travel route for the selected event. The server accesses an external traffic information database and analyzes transportation options based on the user's location and the event venue. This process considers public transport timetables and real-time traffic conditions. Inputs are the user's address and event location, and output are several recommended transportation routes.

[0567] Step 6:

[0568] The server generates messages to invite friends to facilitate social interaction between users. The server identifies friends from the user's friend list who might be interested in the event. The generation AI model creates appropriate invitations and suggestions and sends the messages via the device. The input is the friend list and event details, and the output is the generated invitation message.

[0569] Step 7:

[0570] After participating in an event, users provide evaluations and opinions through a feedback form on their devices. The device then sends this feedback to the server. The input is user feedback, and the output is data on the server that will be used to improve future event proposals. This feedback process allows the server to further improve the accuracy of future event proposals.

[0571] (Application Example 1)

[0572] 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."

[0573] To increase opportunities for seniors to go out and prevent social isolation, there is a need for a system that suggests events tailored to individual interests. However, existing systems lack the accuracy to accurately analyze user interests and provide appropriate events. Furthermore, they are insufficient in suggesting transportation options for event participation and in promoting social interaction.

[0574] 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.

[0575] In this invention, the server includes means for collecting information on the user's hobbies and preferences and generating a profile; means for selecting and proposing events suitable for the user based on the collected information; and means for calculating and presenting the optimal means of transportation to the proposed events. This makes it possible to provide event suggestions and optimal transportation routes based on the individual interests of elderly people, as well as to promote social interaction.

[0576] "Information about users' hobbies and preferences" refers to data on activities that users have shown interest in in the past, as well as detailed personal interest data collected through surveys.

[0577] A "profile" is an individual information model that reflects a user's hobbies, preferences, and interests.

[0578] "Methods for selecting and proposing events" refers to the process of matching collected user information with external event information to present gatherings and activities suitable for the user.

[0579] "Means for calculating and suggesting the optimal mode of transportation" refers to a function that analyzes available modes of transportation between the user's place of residence and destination, and provides the most efficient and convenient travel route.

[0580] "Means of generating messages to facilitate interaction with other users" refers to invitations and notifications created to encourage communication among users when participating in events or activities.

[0581] "Means of collecting feedback and incorporating it into improvements" refers to the process of compiling user evaluations and opinions to improve the accuracy and satisfaction of future events.

[0582] "External event information sources" refer to external information resources that provide event information, such as local event calendars and online platforms.

[0583] "Public transport information" refers to a dataset containing schedule and route information related to public transport.

[0584] The system for realizing this invention consists of a network-connected server and a user terminal. The server runs a program that collects information about the user's hobbies and preferences and generates and updates a profile. This profile undergoes processing to improve its accuracy using machine learning algorithms based on data acquired from the user terminal. A backend using a scripting language such as Python supports this processing.

[0585] The server periodically retrieves information from external event sources, matches it with the user's profile, selects appropriate events, and proposes them to the user's terminal. This involves using APIs to collect external data, and a frontend using JavaScript and HTML visually presents the events to the user.

[0586] Furthermore, the server utilizes external services such as the Google Maps API to calculate the optimal transportation route from the user's location to the event venue and displays it on the user's device. This allows users to easily select from multiple travel options, including available public transportation.

[0587] Furthermore, the server generates messages for other users who may be attending the same event, based on the user's friend list, to encourage social interaction. Feedback provided by users after attending the event is collected and analyzed by the server to improve future proposals.

[0588] For example, if a user is interested in tennis and music, the server will suggest local tennis tournaments and music events. It can also suggest optimal travel routes using public transportation. After the event, user satisfaction and comments are collected as feedback to improve the system.

[0589] An example of a prompt message is: "If a 70-year-old user is interested in tennis and music, what events would you suggest? Also, please provide transportation options based on those events."

[0590] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0591] Step 1:

[0592] Users input information about their hobbies and preferences through their device. This information includes past interests and specific examples of activities. The device transfers this data to the server, where it is entered as initial data for the user profile. Based on this input, a registration operation is performed to save it to the database.

[0593] Step 2:

[0594] The server generates a profile using the received user information. It applies machine learning algorithms to model the user's interests. It analyzes the input interest and activity data, generates interest clusters, and outputs this as the profile structure.

[0595] Step 3:

[0596] The server retrieves event data from external event sources via APIs. The retrieved data is then matched against the user's profile information, and events matching their interests are filtered. As a result of this filtering process, a list of appropriate events is generated and output.

[0597] Step 4:

[0598] The filtered event list is sent to the device and visually presented to the user. The device's interface displays event details, allowing the user to select events of interest.

[0599] Step 5:

[0600] The server uses the Google Maps API to calculate appropriate transportation options based on the user's location and the selected event venue. Input includes the user's location and event venue information. Based on this, it calculates public transport and car routes, and outputs the optimal route information.

[0601] Step 6:

[0602] The server consults the user's friends list and generates messages for other users who might be interested in similar events. These messages are automatically generated as invitations to participate and sent to the user's terminal as output.

[0603] Step 7:

[0604] After users participate in an event on their device, they provide feedback and evaluations through a feedback form. This feedback information is sent from the device to the server and analyzed to improve future event proposals. Based on the input feedback data, data analysis is performed to improve the accuracy of the proposal algorithm.

[0605] 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.

[0606] This invention provides an event suggestion system that takes into account not only the hobbies and preferences of senior users but also their emotional state. This system consists of a user terminal, a server, and an emotion engine, and is implemented in the following manner.

[0607] User profile generation and updating

[0608] The user terminal provides an interface for users to input information about their hobbies and interests. Users enter their information, and the terminal sends it to the server. The server analyzes this data using machine learning algorithms and generates a profile. The profile is updated periodically to reflect the user's changing interests and newly acquired information.

[0609] Analysis using an emotional engine

[0610] The server analyzes the user's emotions in real time through an emotion engine. This emotional state is determined based on physiological data collected from the device and the user's input. The server integrates the obtained emotional data into a user profile to gain a more comprehensive understanding of the user's current state.

[0611] Event selection and emotion-based adjustments

[0612] The server selects personalized events based on the user profile and emotional state. Event information is periodically retrieved from external sources and customized to the user's interests and emotions. In particular, emotional state influences event selection; for example, music events designed to relieve stress may be suggested.

[0613] Presenting the most suitable mode of transportation

[0614] The server calculates the optimal mode of transportation, taking into account the location of the proposed event and the user's place of residence. The selection of the transportation route is also appropriately adjusted according to the user's emotional state. The terminal presents the optimized transportation information received from the server to the user.

[0615] Promoting social interaction and feedback

[0616] The server utilizes the user's friend list to suggest interaction opportunities tailored to their emotional state. For example, it may determine that participating with friends would be effective in improving the user's mood. After participating in an event, the user's device facilitates feedback input, allowing the user to provide an evaluation of the service. The server analyzes this feedback and uses it to improve the system.

[0617] Specific example

[0618] The server, through its emotion engine, determines that user C has recently been feeling fatigued. In response, the server suggests a relaxing art exhibition for C. It also provides a short, local bus route to make it easily accessible. Furthermore, it generates a message encouraging C to invite their friend D, aiming for a more fulfilling experience. In this way, event suggestions are tailored to the user's needs.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies. The user enters their information, and the terminal sends that data to the server.

[0622] Step 2:

[0623] The server stores the received user data in a database and analyzes the user's interests. It uses algorithms to generate user profiles and identify interest categories.

[0624] Step 3:

[0625] The emotion engine uses biometric and interaction data from the user's device to evaluate the user's emotions in real time. For example, it analyzes the user's voice tone and keystroke patterns.

[0626] Step 4:

[0627] The server reflects the emotional state obtained from the emotion engine into the user profile. This integrated data is then used to select personalized events.

[0628] Step 5:

[0629] The server retrieves the latest event information from external sources and selects the most suitable event based on the user's interests and emotional state. For example, if the user is in an emotional state where they need to relax, it will suggest events such as yoga or art exhibitions.

[0630] Step 6:

[0631] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays the event details and related information.

[0632] Step 7:

[0633] When a user selects an event, the server calculates the optimal mode of transportation. Based on the user's emotional state, for example, it prioritizes comfortable public transport routes to allow for a relaxed journey. The calculation results are then notified to the user's device.

[0634] Step 8:

[0635] The server checks the user's friends list and identifies friends with similar interests. It then generates a message encouraging them to join together, taking their emotional state into consideration, and displays it on the user's device.

[0636] Step 9:

[0637] After participating in the event, the user's device displays a feedback form to collect ratings and opinions from the user. The user enters their impressions, and the device sends the feedback data to the server.

[0638] Step 10:

[0639] The server analyzes feedback data and uses it to select events and refine user profiles. This will result in more appropriate and accurate event suggestions in the future.

[0640] (Example 2)

[0641] 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."

[0642] There is a challenge in accurately understanding the hobbies, preferences, and emotional states of users, including the elderly, and proposing personalized activities to enrich their lifestyles. To solve this problem, it is necessary to collect and analyze diverse user data and make appropriate suggestions based on that data. However, conventional systems do not adequately analyze emotional states, and the personalized suggestions using that information are insufficient, making it difficult to improve user satisfaction.

[0643] 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.

[0644] In this invention, the server includes means for collecting information on the user's hobbies and interests and generating a user profile; means for analyzing the emotional state based on the collected information and the user's physiological data; and means for integrating the analyzed emotional state into the user profile to grasp the overall user state. This makes it possible to efficiently suggest events that are most suitable for the user and provide a place for interaction that is appropriate to their emotional state.

[0645] "User" refers to an individual who uses the system, and in this particular context, it refers to a person who provides information about their hobbies and emotional state, including elderly people.

[0646] "Hobbies and interests" refer to a user's sustained interest in specific activities or themes, and are elements that shape an individual's lifestyle and preferences.

[0647] A "user profile" refers to a collection of information that comprehensively represents a user's hobbies, interests, lifestyle, and emotional state based on collected data.

[0648] "Emotional state" refers to a state that reflects the user's psychological and physiological responses, and comprehensively represents their emotions and mood at that particular moment.

[0649] An "event" refers to an activity or event that takes place at a specific time and place, and is suggested to users based on their interests and emotional state.

[0650] "External information sources" refer to information resources and databases accessible from outside the system, and their role is to provide event information.

[0651] "Method of transportation" refers to the means and routes used by users to travel to the event venue, and includes optimized modes of transport.

[0652] "Communication" refers to messages and notifications sent from the system to encourage participation from other users or in proposed activities.

[0653] "Ratings" refer to feedback provided by users regarding the content of the services or events offered, and this information is used to improve the system.

[0654] This invention relates to a system that provides personalized event suggestions that take into account the user's hobbies, preferences, and emotional state. The system mainly consists of a user terminal, a server, and an emotion engine.

[0655] The user terminal provides an interface for users to input information about their hobbies and interests. Users use this interface to enter their own information, such as whether they are interested in painting or music, or whether they are available to attend events on weekends. The entered data is then sent from the terminal to the server.

[0656] After receiving this data, the server generates a user profile using machine learning algorithms. Specifically, software such as TensorFlow is used for data analysis. The generated profile is periodically updated to reflect the user's changing interests.

[0657] Furthermore, the server uses an emotion engine to analyze the user's emotional state in real time based on physiological and input data. This analysis utilizes an emotion analysis API, and the results are integrated into the user profile to provide a more comprehensive understanding of the user's current situation.

[0658] The server selects appropriate events for the user based on their user profile and emotional state, retrieving them from external sources. To this end, it periodically retrieves event information from external databases and APIs. For example, it might suggest a relaxing art exhibition. In particular, events designed to reduce stress may also be selected.

[0659] The server uses the Google Maps API to calculate and present the best mode of transportation to the user based on the proposed event location and the user's place of residence. For example, it might display information such as, "You can reach the event venue in 30 minutes from the nearest bus stop."

[0660] Furthermore, the user's device will also include a means to generate and display messages to invite friends to the proposed event. This will increase opportunities for users to interact with other users and enjoy the event more.

[0661] After participating, users can provide feedback about the event via their device. The server receives this feedback and uses it to further improve the system.

[0662] For example, if the emotion engine analyzes that a user is stressed, the server will suggest relaxing activities for the user. The user will then receive the most suitable transportation through their device and arrangements will be made for them to participate comfortably.

[0663] Examples of prompts for a generative AI model:

[0664] "Please suggest events that will help senior users relieve stress. Choose events that their friends can also participate in."

[0665] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0666] Step 1:

[0667] The user terminal provides an interface for users to input information about their hobbies and interests. Through this interface, users input information such as "I'm interested in art exhibitions" or "I'm available on weekends." This input data is sent from the terminal to the server. Upon receiving the input from the terminal, the server accepts it as initial user data.

[0668] Step 2:

[0669] The server generates user profiles using machine learning algorithms based on the received user data. This process utilizes analytical software such as TensorFlow to cluster the data and create profiles. Specifically, user interests are categorized by theme and stored in a database. As a result, personalized profiles are generated for each user and used for future recommendations.

[0670] Step 3:

[0671] The server analyzes the user's emotional state in real time using an emotion engine, based on physiological data transmitted from the terminal and user input data. Using clues such as heart rate and input speed, it evaluates the user's state, including whether they are experiencing stress, using an emotion analysis API. This analysis result is integrated into the user profile, providing a more accurate reflection of the user's current situation.

[0672] Step 4:

[0673] Based on the generated profile and emotional state, the server retrieves event information from external databases and APIs and selects events appropriate for the user. In this process, it considers the impact of events on the emotional state and prioritizes those that promote relaxation. This selection generates a list of candidate events.

[0674] Step 5:

[0675] The server uses the Google Maps API to calculate the optimal mode of transportation based on the user's location and the location of selected events. It prioritizes comfort and selects routes that minimize travel time, providing the user with the best possible travel plan. The calculation results are sent to the device and displayed to the user.

[0676] Step 6:

[0677] The user's terminal generates and notifies messages to invite friends to the proposed event. This promotes interaction between users and increases their willingness to participate in the event. The server provides the infrastructure to facilitate this communication.

[0678] Step 7:

[0679] After participating in an event, users provide feedback via their device. This feedback is sent to the server and used to improve future event suggestions and system settings. User comments and ratings are recorded in a database and serve as foundational data to improve the accuracy of future recommendations.

[0680] (Application Example 2)

[0681] 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."

[0682] In modern society, there is a particular challenge in that it is difficult for the elderly to achieve social and emotional fulfillment. Therefore, there is a need to propose activities based on the individual interests and emotional states of the elderly and to promote social interaction. However, many existing systems do not adequately consider the emotional state and interests of users, and therefore fail to meet actual needs due to insufficient personalized suggestions. Consequently, there is a need for a system that can grasp users' hobbies, preferences, and emotions in real time and propose appropriate events and transportation options based on that information.

[0683] 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.

[0684] In this invention, the server includes means for collecting and analyzing information about the user's interests and emotions, means for selecting and proposing a suitable gathering for the user based on the analyzed information, and means for calculating and presenting the optimal means of transportation to the proposed gathering. This makes it possible to propose personalized events that meet the user's needs, thereby enabling support for elderly people to lead richer social lives.

[0685] A "user" is an individual who uses this system, and their interests and emotional information are the subject of collection and analysis.

[0686] "Interests" refer to specific activities or matters that users are interested in, and event suggestions are made based on these interests.

[0687] "Emotions" refer to the user's psychological or physiological state and are an important element in personalizing the suggested content.

[0688] A "gathering" refers to a specific event or activity suggested to the user, which is selected based on their interests and feelings.

[0689] "Transportation" refers to the means of getting around that a user will use to attend a proposed meeting, and it is presented after being optimized for that purpose.

[0690] "Analysis" is the process of processing collected data and modeling the user's interests and emotional state.

[0691] This invention is a system that provides individually optimized event suggestions and optimal transportation options based on the user's interests and emotions. It delivers personalized services through the coordinated functioning of a server, terminal, and user.

[0692] The server first receives data sent from the user's terminal. The user uses the terminal to input information about their interests and preferences. This data is collected on a cloud system (e.g., AWS or Google Cloud). The collected data is processed into a data frame using the Python Pandas library. Then, it is modeled using machine learning libraries such as TensorFlow to generate a profile for each user.

[0693] Furthermore, the server also analyzes the user's emotional state. Physiological data from wearable devices (e.g., smartwatches) is used and collected in real time via Bluetooth. This data is used to perform emotion analysis on the device using TensorFlow Lite, and the results are sent to the server.

[0694] Based on user interest and sentiment data, the server retrieves the latest event information from external APIs (such as the Google Calendar API). It then uses Python to analyze the data and select the most suitable gathering for the user. The selected gathering is then notified to the user via their device.

[0695] The server then uses the Google Maps API to optimize the route and calculate the best mode of transportation to the proposed meeting. The optimal mode of transportation and route information are calculated and presented to the user's device.

[0696] The server also generates messages to invite friends to the proposed gathering, providing users with opportunities for social interaction. After attending the event, users can provide feedback via their device.

[0697] For example, if the server determines that a user's stress level is high, it might suggest a relaxing art exhibition. In this case, a route using nearby public transportation would be provided. The user would also receive a message encouraging them to attend with friends.

[0698] Examples of prompts generated using AI models include the following:

[0699] "Based on the user's recent emotional state, list the types of gatherings that can be suggested as stress relief."

[0700] "Based on the results of the emotional analysis, suggest the most suitable mode of transportation."

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] Users use their devices to input data about their interests and preferences. This input data is sent to a cloud server. The input data is processed into a dataframe format using the Pandas library. This generates the initial dataset for the user profile.

[0704] Step 2:

[0705] The server uses TensorFlow to perform analysis with a machine learning model when generating user profiles. This model clusters user interests based on collected data and builds individual profiles. Once a profile is generated, it is stored in a cloud-based database (e.g., AWS DynamoDB).

[0706] Step 3:

[0707] User emotional state data is collected in real time from wearable devices (such as smartwatches). This data is transmitted to the device via Bluetooth and then sent to a server. The server uses TensorFlow Lite to estimate the emotional state on the device. The input for this process is physiological data, and the output is the user's emotional state.

[0708] Step 4:

[0709] The server periodically retrieves event information from an external API (e.g., Google Calendar API) and analyzes the data using Python. Based on the user's profile and sentiment data, the server selects the most suitable gathering. The input is a list of events, and the output identifies customized gathering information for each user.

[0710] Step 5:

[0711] The server uses the Google Maps API to calculate the best mode of transportation to the proposed meeting. It takes the user's location and destination information as input and outputs the optimal route and transportation method. This route information is then displayed on the device and presented to the user.

[0712] Step 6:

[0713] After the event, users use their devices to input feedback about their experience. This data is sent to a server and recorded in a database to help improve future proposals. Based on this, we have the opportunity to incorporate actual user experiences into future proposals.

[0714] Step 7:

[0715] The server also generates a message inviting friends and sends it to the user's device. The input is the user's friend list and sentiment data, and the output is the invitation message. This message plays a role in promoting social interaction.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] [Fourth Embodiment]

[0720] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0721] 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.

[0722] 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).

[0723] 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.

[0724] 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.

[0725] 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).

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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".

[0733] This invention is a system that proposes events tailored to the interests and preferences of senior users, thereby encouraging them to go out. This system consists of user terminals and a server, and is implemented in the following manner.

[0734] User data collection

[0735] The user terminal provides an interface for senior users to enter their profile information. This includes basic user information, past activities of interest, and a friends list. Users also provide detailed information about their hobbies and preferences through questionnaires.

[0736] Profile creation and updating

[0737] The server generates a profile for each user based on the collected data. This profile includes information representing the user's interests and preferences and is updated regularly. The server uses machine learning algorithms to improve the accuracy of the profile.

[0738] Event selection and proposal

[0739] The server matches user profiles with event information obtained from external sources (e.g., local event calendars or online platforms). Based on the user's interests, it selects suitable events and creates a prioritized list. Selected events are notified to the user's device, and detailed information is displayed.

[0740] Calculation of transportation methods

[0741] The server calculates the optimal transportation route based on the user's location and the selected event venue. Considering traffic information and public transport schedules, the recommended route is displayed on the user's device.

[0742] Proposal for social exchange

[0743] The server refers to the user's friend list and identifies friends who might be interested in the same event. It generates appropriate messages and encourages the user to invite their friends to the event through their device. This promotes social interaction.

[0744] Gathering and incorporating feedback

[0745] The user's terminal displays a feedback form for providing evaluations and opinions after participating in an event. The server collects this feedback and incorporates it into proposals for future events. This improves the accuracy of proposals and user satisfaction.

[0746] Specific example

[0747] Let's say user A is interested in music. The server collects data on music events and suggests nearby concerts to A. Furthermore, based on traffic information, it calculates the optimal route from A's home to the venue and suggests using the bus. The server also determines that A's friend B is also a music lover and generates an invitation message for them to attend together. After attending the event, A evaluates it from their device, and the server compiles the feedback to use for future improvements.

[0748] The following describes the processing flow.

[0749] Step 1:

[0750] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies and interests. The user enters their information, and the terminal sends this data to the server.

[0751] Step 2:

[0752] The server stores the received user data in a database and analyzes the user's interests. Machine learning algorithms are used to generate user profiles and identify categories of interest.

[0753] Step 3:

[0754] The server retrieves event information from external sources, including local event calendars and online platforms, which are regularly updated with new data.

[0755] Step 4:

[0756] The server matches the user profile with the retrieved event information and selects events that match the user's interests. It then scores the selected events and lists those with the highest priority.

[0757] Step 5:

[0758] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays a list containing the event details to the user.

[0759] Step 6:

[0760] When a user selects a suggested event, the server calculates the optimal transportation route based on the user's location and the event's location, taking into account public transportation schedules. The server then sends the calculated route information to the user's terminal.

[0761] Step 7:

[0762] The server checks the user's friend list and identifies friends who may share similar interests. The server generates a message for the user to invite their friends to the event and displays it on the user's device.

[0763] Step 8:

[0764] After participating in the event, the user's device displays a feedback form to receive ratings and opinions from the user. The user enters their feedback, and the device sends that information to the server.

[0765] Step 9:

[0766] The server records the received feedback in a database and uses it to improve future event selection and suggestion functions. The data contributes to updating the learning algorithm, improving the accuracy of the suggestions.

[0767] (Example 1)

[0768] 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".

[0769] For elderly users, there is a lack of support to stimulate their interests and passions in daily life and to promote going out and social interaction. Furthermore, the selection of transportation options is complex, making the provision of appropriate routes crucial. In addition, there is a challenge in the insufficient provision of personalized information that reflects feedback.

[0770] 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.

[0771] In this invention, the server includes means for collecting information on the preferences and interests of elderly users and generating a user profile; means for selecting and proposing the most suitable events for the user based on the collected information and event information obtained from external sources; and means for calculating and presenting the optimal travel route for the event proposal. This makes it possible to provide elderly users with personalized event proposals and convenient means of transportation.

[0772] "Elderly users" generally refer to users who belong to the age group of 65 years or older and have specific preferences or interests.

[0773] "Information about preferences and interests" means information related to activities, hobbies, or events that the user is particularly interested in.

[0774] A "user profile" is a data structure generated based on the preferences and interests of collected users, and is used to provide personalized information.

[0775] "External information sources" refer to information sources for obtaining event information relevant to the user, including local event calendars and online platforms.

[0776] "Event information" refers to information about the specific content and schedule of an event, obtained from external sources.

[0777] The "optimal travel route" refers to the most efficient route from the user's starting point to their destination, and is calculated taking into account the mode of transport and time of day.

[0778] "Communication messages" refer to messages containing information or invitations that are generated to facilitate interaction between users.

[0779] "Evaluation" refers to information regarding users' opinions and satisfaction levels after participating in an event.

[0780] "Feedback" refers to users' evaluations and suggestions for improvement regarding the services provided.

[0781] This invention aims to provide senior users with personalized event suggestions and efficient means of transportation. The system primarily consists of a server and user terminals.

[0782] The user first uses the device's interface to enter detailed information about their basic information and preferences. The device then sends this information to the server. The device includes tablets, smartphones, and other similar devices.

[0783] The servers run on the cloud and leverage high-performance computing resources. Built using programming languages ​​such as Python and Java, the servers implement machine learning algorithms to analyze user-submitted information and generate individual profiles. Generative AI models are used to improve the accuracy of these profiles.

[0784] The server utilizes web APIs and scraping techniques to retrieve event information from external sources. This allows it to collect information on local events and select appropriate events by matching them with the user's profile.

[0785] Furthermore, the server accesses a traffic database to obtain real-time traffic information. Based on this, it calculates the optimal travel route from the user's place of residence to the event venue and provides this information to the terminal.

[0786] For example, if user A enjoys music, the server collects information on music events held in the area and suggests the most suitable events for A. It also calculates the best mode of transportation from traffic information and presents A with a route from their home to the venue. Furthermore, it identifies friends from A's friend list who also have an interest in music, generates invitation messages for them to attend together, and notifies their device.

[0787] After participating in an event, users can provide feedback via their devices. The server analyzes this feedback and uses the data to improve the accuracy of future event suggestions.

[0788] An example of a prompt would be, "How do I generate a message suggesting a nearby concert to a senior who is interested in music, and inviting a friend?"

[0789] In this way, this system can encourage elderly users to participate in events based on their interests and provide opportunities for social interaction.

[0790] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0791] Step 1:

[0792] Users enter their profile information on their devices. Specifically, users launch a dedicated application on their devices and enter basic information such as their date of birth, address, interests, and friends list. This input data is important for reflecting the user's preferences and interests, and the device plays the role of transmitting the entered information to the server.

[0793] Step 2:

[0794] The server generates a user profile based on the received profile information. The server's programs utilize machine learning techniques to organize the user's interests and preferences into a sophisticated data structure. The input to this process is the raw data provided by the user, and the output is the profile. The server uses the latest generative AI models to create a more personalized profile.

[0795] Step 3:

[0796] The server retrieves event information from external sources. Using APIs and web scraping techniques, it collects the latest information on events held in the region. The input to this process is raw event data obtained from external sources, and the output is organized event information. The server stores this information in a database.

[0797] Step 4:

[0798] The server matches user profiles with event information to select the most suitable event. The server utilizes a generative AI model to calculate the degree of event matching and prioritizes selecting the event that best matches the user's preferences. The input to this process is the user profile and event information, and the output is a list of selected events.

[0799] Step 5:

[0800] The server calculates the optimal travel route for the selected event. The server accesses an external traffic information database and analyzes transportation options based on the user's location and the event venue. This process considers public transport timetables and real-time traffic conditions. Inputs are the user's address and event location, and output are several recommended transportation routes.

[0801] Step 6:

[0802] The server generates messages to invite friends to facilitate social interaction between users. The server identifies friends from the user's friend list who might be interested in the event. The generation AI model creates appropriate invitations and suggestions and sends the messages via the device. The input is the friend list and event details, and the output is the generated invitation message.

[0803] Step 7:

[0804] After participating in an event, users provide evaluations and opinions through a feedback form on their devices. The device then sends this feedback to the server. The input is user feedback, and the output is data on the server that will be used to improve future event proposals. This feedback process allows the server to further improve the accuracy of future event proposals.

[0805] (Application Example 1)

[0806] 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".

[0807] To increase opportunities for seniors to go out and prevent social isolation, there is a need for a system that suggests events tailored to individual interests. However, existing systems lack the accuracy to accurately analyze user interests and provide appropriate events. Furthermore, they are insufficient in suggesting transportation options for event participation and in promoting social interaction.

[0808] 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.

[0809] In this invention, the server includes means for collecting information on the user's hobbies and preferences and generating a profile; means for selecting and proposing events suitable for the user based on the collected information; and means for calculating and presenting the optimal means of transportation to the proposed events. This makes it possible to provide event suggestions and optimal transportation routes based on the individual interests of elderly people, as well as to promote social interaction.

[0810] "Information about users' hobbies and preferences" refers to data on activities that users have shown interest in in the past, as well as detailed personal interest data collected through surveys.

[0811] A "profile" is an individual information model that reflects a user's hobbies, preferences, and interests.

[0812] "Methods for selecting and proposing events" refers to the process of matching collected user information with external event information to present gatherings and activities suitable for the user.

[0813] "Means for calculating and suggesting the optimal mode of transportation" refers to a function that analyzes available modes of transportation between the user's place of residence and destination, and provides the most efficient and convenient travel route.

[0814] "Means of generating messages to facilitate interaction with other users" refers to invitations and notifications created to encourage communication among users when participating in events or activities.

[0815] "Means of collecting feedback and incorporating it into improvements" refers to the process of compiling user evaluations and opinions to improve the accuracy and satisfaction of future events.

[0816] "External event information sources" refer to external information resources that provide event information, such as local event calendars and online platforms.

[0817] "Public transport information" refers to a dataset containing schedule and route information related to public transport.

[0818] The system for realizing this invention consists of a network-connected server and a user terminal. The server runs a program that collects information about the user's hobbies and preferences and generates and updates a profile. This profile undergoes processing to improve its accuracy using machine learning algorithms based on data acquired from the user terminal. A backend using a scripting language such as Python supports this processing.

[0819] The server periodically retrieves information from external event sources, matches it with the user's profile, selects appropriate events, and proposes them to the user's terminal. This involves using APIs to collect external data, and a frontend using JavaScript and HTML visually presents the events to the user.

[0820] Furthermore, the server utilizes external services such as the Google Maps API to calculate the optimal transportation route from the user's location to the event venue and displays it on the user's device. This allows users to easily select from multiple travel options, including available public transportation.

[0821] Furthermore, the server generates messages for other users who may be attending the same event, based on the user's friend list, to encourage social interaction. Feedback provided by users after attending the event is collected and analyzed by the server to improve future proposals.

[0822] For example, if a user is interested in tennis and music, the server will suggest local tennis tournaments and music events. It can also suggest optimal travel routes using public transportation. After the event, user satisfaction and comments are collected as feedback to improve the system.

[0823] An example of a prompt message is: "If a 70-year-old user is interested in tennis and music, what events would you suggest? Also, please provide transportation options based on those events."

[0824] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0825] Step 1:

[0826] Users input information about their hobbies and preferences through their device. This information includes past interests and specific examples of activities. The device transfers this data to the server, where it is entered as initial data for the user profile. Based on this input, a registration operation is performed to save it to the database.

[0827] Step 2:

[0828] The server generates a profile using the received user information. It applies machine learning algorithms to model the user's interests. It analyzes the input interest and activity data, generates interest clusters, and outputs this as the profile structure.

[0829] Step 3:

[0830] The server retrieves event data from external event sources via APIs. The retrieved data is then matched against the user's profile information, and events matching their interests are filtered. As a result of this filtering process, a list of appropriate events is generated and output.

[0831] Step 4:

[0832] The filtered event list is sent to the device and visually presented to the user. The device's interface displays event details, allowing the user to select events of interest.

[0833] Step 5:

[0834] The server uses the Google Maps API to calculate appropriate transportation options based on the user's location and the selected event venue. Input includes the user's location and event venue information. Based on this, it calculates public transport and car routes, and outputs the optimal route information.

[0835] Step 6:

[0836] The server consults the user's friends list and generates messages for other users who might be interested in similar events. These messages are automatically generated as invitations to participate and sent to the user's terminal as output.

[0837] Step 7:

[0838] After users participate in an event on their device, they provide feedback and evaluations through a feedback form. This feedback information is sent from the device to the server and analyzed to improve future event proposals. Based on the input feedback data, data analysis is performed to improve the accuracy of the proposal algorithm.

[0839] 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.

[0840] This invention provides an event suggestion system that takes into account not only the hobbies and preferences of senior users but also their emotional state. This system consists of a user terminal, a server, and an emotion engine, and is implemented in the following manner.

[0841] User profile generation and updating

[0842] The user terminal provides an interface for users to input information about their hobbies and interests. Users enter their information, and the terminal sends it to the server. The server analyzes this data using machine learning algorithms and generates a profile. The profile is updated periodically to reflect the user's changing interests and newly acquired information.

[0843] Analysis using an emotional engine

[0844] The server analyzes the user's emotions in real time through an emotion engine. This emotional state is determined based on physiological data collected from the device and the user's input. The server integrates the obtained emotional data into a user profile to gain a more comprehensive understanding of the user's current state.

[0845] Event selection and emotion-based adjustments

[0846] The server selects personalized events based on the user profile and emotional state. Event information is periodically retrieved from external sources and customized to the user's interests and emotions. In particular, emotional state influences event selection; for example, music events designed to relieve stress may be suggested.

[0847] Presenting the most suitable mode of transportation

[0848] The server calculates the optimal mode of transportation, taking into account the location of the proposed event and the user's place of residence. The selection of the transportation route is also appropriately adjusted according to the user's emotional state. The terminal presents the optimized transportation information received from the server to the user.

[0849] Promoting social interaction and feedback

[0850] The server utilizes the user's friend list to suggest interaction opportunities tailored to their emotional state. For example, it may determine that participating with friends would be effective in improving the user's mood. After participating in an event, the user's device facilitates feedback input, allowing the user to provide an evaluation of the service. The server analyzes this feedback and uses it to improve the system.

[0851] Specific example

[0852] The server, through its emotion engine, determines that user C has recently been feeling fatigued. In response, the server suggests a relaxing art exhibition for C. It also provides a short, local bus route to make it easily accessible. Furthermore, it generates a message encouraging C to invite their friend D, aiming for a more fulfilling experience. In this way, event suggestions are tailored to the user's needs.

[0853] The following describes the processing flow.

[0854] Step 1:

[0855] The user terminal displays an interface for inputting basic profile information and data related to the user's hobbies. The user enters their information, and the terminal sends that data to the server.

[0856] Step 2:

[0857] The server stores the received user data in a database and analyzes the user's interests. It uses algorithms to generate user profiles and identify interest categories.

[0858] Step 3:

[0859] The emotion engine uses biometric and interaction data from the user's device to evaluate the user's emotions in real time. For example, it analyzes the user's voice tone and keystroke patterns.

[0860] Step 4:

[0861] The server reflects the emotional state obtained from the emotion engine into the user profile. This integrated data is then used to select personalized events.

[0862] Step 5:

[0863] The server retrieves the latest event information from external sources and selects the most suitable event based on the user's interests and emotional state. For example, if the user is in an emotional state where they need to relax, it will suggest events such as yoga or art exhibitions.

[0864] Step 6:

[0865] The server sends the selected event information to the user's terminal and notifies the user. The terminal displays the event details and related information.

[0866] Step 7:

[0867] When a user selects an event, the server calculates the optimal mode of transportation. Based on the user's emotional state, for example, it prioritizes comfortable public transport routes to allow for a relaxed journey. The calculation results are then notified to the user's device.

[0868] Step 8:

[0869] The server checks the user's friends list and identifies friends with similar interests. It then generates a message encouraging them to join together, taking their emotional state into consideration, and displays it on the user's device.

[0870] Step 9:

[0871] After participating in the event, the user's device displays a feedback form to collect ratings and opinions from the user. The user enters their impressions, and the device sends the feedback data to the server.

[0872] Step 10:

[0873] The server analyzes feedback data and uses it to select events and refine user profiles. This will result in more appropriate and accurate event suggestions in the future.

[0874] (Example 2)

[0875] 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".

[0876] There is a challenge in accurately understanding the hobbies, preferences, and emotional states of users, including the elderly, and proposing personalized activities to enrich their lifestyles. To solve this problem, it is necessary to collect and analyze diverse user data and make appropriate suggestions based on that data. However, conventional systems do not adequately analyze emotional states, and the personalized suggestions using that information are insufficient, making it difficult to improve user satisfaction.

[0877] 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.

[0878] In this invention, the server includes means for collecting information on the user's hobbies and interests and generating a user profile; means for analyzing the emotional state based on the collected information and the user's physiological data; and means for integrating the analyzed emotional state into the user profile to grasp the overall user state. This makes it possible to efficiently suggest events that are most suitable for the user and provide a place for interaction that is appropriate to their emotional state.

[0879] "User" refers to an individual who uses the system, and in this particular context, it refers to a person who provides information about their hobbies and emotional state, including elderly people.

[0880] "Hobbies and interests" refer to a user's sustained interest in specific activities or themes, and are elements that shape an individual's lifestyle and preferences.

[0881] A "user profile" refers to a collection of information that comprehensively represents a user's hobbies, interests, lifestyle, and emotional state based on collected data.

[0882] "Emotional state" refers to a state that reflects the user's psychological and physiological responses, and comprehensively represents their emotions and mood at that particular moment.

[0883] An "event" refers to an activity or event that takes place at a specific time and place, and is suggested to users based on their interests and emotional state.

[0884] "External information sources" refer to information resources and databases accessible from outside the system, and their role is to provide event information.

[0885] "Method of transportation" refers to the means and routes used by users to travel to the event venue, and includes optimized modes of transport.

[0886] "Communication" refers to messages and notifications sent from the system to encourage participation from other users or in proposed activities.

[0887] "Ratings" refer to feedback provided by users regarding the content of the services or events offered, and this information is used to improve the system.

[0888] This invention relates to a system that provides personalized event suggestions that take into account the user's hobbies, preferences, and emotional state. The system mainly consists of a user terminal, a server, and an emotion engine.

[0889] The user terminal provides an interface for users to input information about their hobbies and interests. Users use this interface to enter their own information, such as whether they are interested in painting or music, or whether they are available to attend events on weekends. The entered data is then sent from the terminal to the server.

[0890] After receiving this data, the server generates a user profile using machine learning algorithms. Specifically, software such as TensorFlow is used for data analysis. The generated profile is periodically updated to reflect the user's changing interests.

[0891] Furthermore, the server uses an emotion engine to analyze the user's emotional state in real time based on physiological and input data. This analysis utilizes an emotion analysis API, and the results are integrated into the user profile to provide a more comprehensive understanding of the user's current situation.

[0892] The server selects appropriate events for the user based on their user profile and emotional state, retrieving them from external sources. To this end, it periodically retrieves event information from external databases and APIs. For example, it might suggest a relaxing art exhibition. In particular, events designed to reduce stress may also be selected.

[0893] The server uses the Google Maps API to calculate and present the best mode of transportation to the user based on the proposed event location and the user's place of residence. For example, it might display information such as, "You can reach the event venue in 30 minutes from the nearest bus stop."

[0894] Furthermore, the user's device will also include a means to generate and display messages to invite friends to the proposed event. This will increase opportunities for users to interact with other users and enjoy the event more.

[0895] After participating, users can provide feedback about the event via their device. The server receives this feedback and uses it to further improve the system.

[0896] For example, if the emotion engine analyzes that a user is stressed, the server will suggest relaxing activities for the user. The user will then receive the most suitable transportation through their device and arrangements will be made for them to participate comfortably.

[0897] Examples of prompts for a generative AI model:

[0898] "Please suggest events that will help senior users relieve stress. Choose events that their friends can also participate in."

[0899] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0900] Step 1:

[0901] The user terminal provides an interface for users to input information about their hobbies and interests. Through this interface, users input information such as "I'm interested in art exhibitions" or "I'm available on weekends." This input data is sent from the terminal to the server. Upon receiving the input from the terminal, the server accepts it as initial user data.

[0902] Step 2:

[0903] The server generates user profiles using machine learning algorithms based on the received user data. This process utilizes analytical software such as TensorFlow to cluster the data and create profiles. Specifically, user interests are categorized by theme and stored in a database. As a result, personalized profiles are generated for each user and used for future recommendations.

[0904] Step 3:

[0905] The server analyzes the user's emotional state in real time using an emotion engine, based on physiological data transmitted from the terminal and user input data. Using clues such as heart rate and input speed, it evaluates the user's state, including whether they are experiencing stress, using an emotion analysis API. This analysis result is integrated into the user profile, providing a more accurate reflection of the user's current situation.

[0906] Step 4:

[0907] Based on the generated profile and emotional state, the server retrieves event information from external databases and APIs and selects events appropriate for the user. In this process, it considers the impact of events on the emotional state and prioritizes those that promote relaxation. This selection generates a list of candidate events.

[0908] Step 5:

[0909] The server uses the Google Maps API to calculate the optimal mode of transportation based on the user's location and the location of selected events. It prioritizes comfort and selects routes that minimize travel time, providing the user with the best possible travel plan. The calculation results are sent to the device and displayed to the user.

[0910] Step 6:

[0911] The user's terminal generates and notifies messages to invite friends to the proposed event. This promotes interaction between users and increases their willingness to participate in the event. The server provides the infrastructure to facilitate this communication.

[0912] Step 7:

[0913] After participating in an event, users provide feedback via their device. This feedback is sent to the server and used to improve future event suggestions and system settings. User comments and ratings are recorded in a database and serve as foundational data to improve the accuracy of future recommendations.

[0914] (Application Example 2)

[0915] 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".

[0916] In modern society, there is a particular challenge in that it is difficult for the elderly to achieve social and emotional fulfillment. Therefore, there is a need to propose activities based on the individual interests and emotional states of the elderly and to promote social interaction. However, many existing systems do not adequately consider the emotional state and interests of users, and therefore fail to meet actual needs due to insufficient personalized suggestions. Consequently, there is a need for a system that can grasp users' hobbies, preferences, and emotions in real time and propose appropriate events and transportation options based on that information.

[0917] 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.

[0918] In this invention, the server includes means for collecting and analyzing information about the user's interests and emotions, means for selecting and proposing a suitable gathering for the user based on the analyzed information, and means for calculating and presenting the optimal means of transportation to the proposed gathering. This makes it possible to propose personalized events that meet the user's needs, thereby enabling support for elderly people to lead richer social lives.

[0919] A "user" is an individual who uses this system, and their interests and emotional information are the subject of collection and analysis.

[0920] "Interests" refer to specific activities or matters that users are interested in, and event suggestions are made based on these interests.

[0921] "Emotions" refer to the user's psychological or physiological state and are an important element in personalizing the suggested content.

[0922] A "gathering" refers to a specific event or activity suggested to the user, which is selected based on their interests and feelings.

[0923] "Transportation" refers to the means of getting around that a user will use to attend a proposed meeting, and it is presented after being optimized for that purpose.

[0924] "Analysis" is the process of processing collected data and modeling the user's interests and emotional state.

[0925] This invention is a system that provides individually optimized event suggestions and optimal transportation options based on the user's interests and emotions. It delivers personalized services through the coordinated functioning of a server, terminal, and user.

[0926] The server first receives data sent from the user's terminal. The user uses the terminal to input information about their interests and preferences. This data is collected on a cloud system (e.g., AWS or Google Cloud). The collected data is processed into a data frame using the Python Pandas library. Then, it is modeled using machine learning libraries such as TensorFlow to generate a profile for each user.

[0927] Furthermore, the server also analyzes the user's emotional state. Physiological data from wearable devices (e.g., smartwatches) is used and collected in real time via Bluetooth. This data is used to perform emotion analysis on the device using TensorFlow Lite, and the results are sent to the server.

[0928] Based on user interest and sentiment data, the server retrieves the latest event information from external APIs (such as the Google Calendar API). It then uses Python to analyze the data and select the most suitable gathering for the user. The selected gathering is then notified to the user via their device.

[0929] The server then uses the Google Maps API to optimize the route and calculate the best mode of transportation to the proposed meeting. The optimal mode of transportation and route information are calculated and presented to the user's device.

[0930] The server also generates messages to invite friends to the proposed gathering, providing users with opportunities for social interaction. After attending the event, users can provide feedback via their device.

[0931] For example, if the server determines that a user's stress level is high, it might suggest a relaxing art exhibition. In this case, a route using nearby public transportation would be provided. The user would also receive a message encouraging them to attend with friends.

[0932] Examples of prompts generated using AI models include the following:

[0933] "Based on the user's recent emotional state, list the types of gatherings that can be suggested as stress relief."

[0934] "Based on the results of the emotional analysis, suggest the most suitable mode of transportation."

[0935] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0936] Step 1:

[0937] Users use their devices to input data about their interests and preferences. This input data is sent to a cloud server. The input data is processed into a data frame format using the Pandas library. This generates the initial dataset for the user profile.

[0938] Step 2:

[0939] The server uses TensorFlow to perform analysis with a machine learning model when generating user profiles. This model clusters user interests based on collected data and builds individual profiles. Once a profile is generated, it is stored in a cloud-based database (e.g., AWS DynamoDB).

[0940] Step 3:

[0941] User emotional state data is collected in real time from wearable devices (such as smartwatches). This data is transmitted to the device via Bluetooth and then sent to a server. The server uses TensorFlow Lite to estimate the emotional state on the device. The input for this process is physiological data, and the output is the user's emotional state.

[0942] Step 4:

[0943] The server periodically retrieves event information from an external API (e.g., Google Calendar API) and analyzes the data using Python. Based on the user's profile and sentiment data, the server selects the most suitable gathering. The input is a list of events, and the output identifies customized gathering information for each user.

[0944] Step 5:

[0945] The server uses the Google Maps API to calculate the best mode of transportation to the proposed meeting. It takes the user's location and destination information as input and outputs the optimal route and transportation method. This route information is then displayed on the device and presented to the user.

[0946] Step 6:

[0947] After the event, users use their devices to input feedback about their experience. This data is sent to a server and recorded in a database to help improve future proposals. Based on this, we have the opportunity to incorporate actual user experiences into future proposals.

[0948] Step 7:

[0949] The server also generates a message inviting friends and sends it to the user's device. The input is the user's friend list and sentiment data, and the output is the invitation message. This message plays a role in promoting social interaction.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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."

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] The following is further disclosed regarding the embodiments described above.

[0972] (Claim 1)

[0973] A means for collecting information about users' hobbies and preferences and generating a profile,

[0974] A means of selecting and proposing events suitable for the user based on the collected information,

[0975] A means for calculating and presenting the optimal means of transportation for a proposed event,

[0976] A means for generating messages to facilitate interaction with other users,

[0977] A means of collecting feedback on the services provided and incorporating it into improvements for future proposals,

[0978] A system that includes this.

[0979] (Claim 2)

[0980] The system according to claim 1, which models user interests and preferences using clustering technology.

[0981] (Claim 3)

[0982] The system according to claim 1, which periodically obtains event information from an external source and selects events based on that information.

[0983] "Example 1"

[0984] (Claim 1)

[0985] A means for collecting information on the preferences and interests of elderly users and generating user profiles,

[0986] A means of selecting and proposing the most suitable events for users based on collected information and event information obtained from external sources,

[0987] A means of calculating and presenting the optimal travel route for an event proposal,

[0988] A means for generating communication messages to promote social interaction with other users,

[0989] A means of collecting feedback on the activities provided and reflecting it in future proposals,

[0990] A system that includes this.

[0991] (Claim 2)

[0992] The system according to claim 1, which models the interests and concerns of elderly users using machine learning techniques.

[0993] (Claim 3)

[0994] The system according to claim 1, which periodically acquires event information from an external information source and selects events based on that information.

[0995] "Application Example 1"

[0996] (Claim 1)

[0997] A means for collecting information about users' hobbies and preferences and generating a profile,

[0998] A means of selecting and proposing events suitable for the user based on the collected information,

[0999] A means of calculating and presenting the optimal mode of transportation for a proposed event,

[1000] A means for generating messages to facilitate interaction with other users,

[1001] A means of collecting feedback on the services provided and incorporating it into improvements for future proposals,

[1002] A means of obtaining data from external event sources and displaying selected events based on user interests,

[1003] A system that includes this.

[1004] (Claim 2)

[1005] The system according to claim 1, which models user interests and preferences using classification technology.

[1006] (Claim 3)

[1007] The system according to claim 1, which has a function to propose the optimal route using public transport information.

[1008] "Example 2 of combining an emotion engine"

[1009] (Claim 1)

[1010] A means for collecting information about a user's hobbies and interests and generating a user profile,

[1011] A means of analyzing emotional states based on collected information and user physiological data,

[1012] A means of integrating the analyzed emotional state into the user profile to grasp the comprehensive user state,

[1013] A means of selecting and proposing events suitable for the user based on the user's profile and emotional state, using information obtained from external sources,

[1014] A means for calculating and presenting the optimal method of transportation for the proposed event,

[1015] A means for generating communications to facilitate interaction with other users,

[1016] A means of collecting feedback on the provided features and reflecting it in improving future proposals,

[1017] A system that includes this.

[1018] (Claim 2)

[1019] The system according to claim 1, which models users' interests and preferences using a grouping technique.

[1020] (Claim 3)

[1021] The system according to claim 1, which automatically obtains event information from an external source and selects events based on that information.

[1022] "Application example 2 when combining with an emotional engine"

[1023] (Claim 1)

[1024] A means for collecting and analyzing information about users' interests and emotions,

[1025] A means of selecting and suggesting a meeting suitable for the user based on the analyzed information,

[1026] A means of calculating and presenting the optimal means of transportation to the proposed meeting,

[1027] A means for generating communication content that promotes social interaction with other users,

[1028] A means of collecting feedback on the services provided and reflecting it in improving future proposals,

[1029] A system that includes this.

[1030] (Claim 2)

[1031] The system according to claim 1, which models the user's preferences and emotional state using machine learning technology and personalizes the suggested content.

[1032] (Claim 3)

[1033] The system according to claim 1, which periodically acquires meeting information from an external data source and selects meetings based on that information. [Explanation of Symbols]

[1034] 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 means for collecting information about users' hobbies and preferences and generating a profile, A means of selecting and proposing events suitable for the user based on the collected information, A means of calculating and presenting the optimal mode of transportation for a proposed event, A means for generating messages to facilitate interaction with other users, A means of collecting feedback on the services provided and incorporating it into improvements for future proposals, A means of obtaining data from external event sources and displaying selected events based on user interests, A system that includes this.

2. The system according to claim 1, which models user interests and preferences using classification technology.

3. The system according to claim 1, which has a function to propose the optimal route using public transport information.

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