system

Abstract

We provide the system. [Solution] An input device for inputting attribute information from the user, A processing device equipped with a generative model for analyzing attribute information received from the input device and extracting the user's potential preferences and activities, A display device that presents the activities extracted by the processing device and allows the user to select one, A support device for searching for relevant activity information based on the user's selection and providing it to the user, 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] In the senior layer, there is a problem of how to lead a fulfilling second life after retirement. In particular, when suddenly changing from a work - centered life, it is often the case that the direction of the new life is lost. Also, it is difficult to self - recognize potential interests, and it is a problem that time and effort are required to find appropriate activities and learning opportunities.

Means for Solving the Problems

[0005] This invention extracts the user's potential preferences and activities by acquiring user attribute information from an input device and analyzing this information using a generation model. The extracted activities are presented to the user through a display device, allowing the user to select activities that match their interests. Furthermore, the support device searches for and provides relevant information based on the selection, thereby providing concrete activity support, including learning programs and vocational training programs. This enables seniors to lead fulfilling second lives.

[0006] An "input device" is a device for receiving attribute information from a user, and in this invention, it is used by the user to input their preferences and professional experience.

[0007] "Attribute information" refers to personal information about the user, and in this invention, it includes hobbies, interests, and professional experience.

[0008] A "generative model" is a program that has an algorithm to analyze input attribute information and extract the user's potential preferences and activities.

[0009] A "processing device" is a device used to process attribute information and derive analysis results, and it is equipped with a generative model.

[0010] A "display device" is a device that presents information transmitted from a processing device to the user, and is used by the user to confirm the suggested activity.

[0011] A "support device" is a device that searches for relevant activity information based on the user's selection and provides specific support.

[0012] A "learning program" is an educational and training program designed to help users acquire new knowledge and skills.

[0013] A "vocational training program" is a training program designed to help users acquire new professional skills. [Brief explanation of the drawing]

[0014] [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]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to an activity suggestion platform for senior citizens using a system comprising an input device, a processing device, a display device, and a support device. This system allows users to input their personal attribute information in order to enrich their second life after retirement, and then suggests appropriate activities and learning opportunities based on that information.

[0036] The user first inputs attribute information such as their preferences and professional experience through a terminal. This information clarifies the user's interests and forms the basis for providing optimal suggestions. The input device transmits this information to the processing unit, which then prepares it for analysis.

[0037] The processing unit installed within the server analyzes the received attribute information using a generative model. The generative model uses sophisticated algorithms to extract the user's potential preferences and generate a suitable activity list. This list includes various activities based on the user's interests, such as hobbies, sports, acquiring new skills, and travel plans.

[0038] The generated activity list is presented to the user via a display device on a user-friendly interface. The user can choose the activity that best suits their interests from the displayed options. For example, if the user is interested in cooking and travel, suggestions such as taking a cooking class or a travel plan to learn about local cuisines will be presented.

[0039] Once the selection is confirmed, the terminal resends the information to the server, and the support device then searches for information to assist in carrying out the activity. The support device provides specific resources related to the selected activity, such as local activity groups, online communities, and start dates. In addition, opportunities to participate in learning programs and vocational training programs are also presented to the user.

[0040] Through the suggestions and support provided by this platform, users can more easily try new activities and gain direction to realize the second life they envision. Furthermore, the incentive function provided by the system helps users to continue learning and engaging in activities, thereby building a fulfilling life.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a device to input attribute information about their preferences and professional experience. For example, the user might input information such as "travel," "cooking," or "30 years of engineering experience."

[0044] Step 2:

[0045] The terminal formats the entered attribute information and sends it to the server. This formatted information includes user preferences and work experience.

[0046] Step 3:

[0047] The server starts analysis using a generative model based on the received attribute information. The generative model uses machine learning algorithms to identify the user's potential preferences and extract appropriate activities and interests.

[0048] Step 4:

[0049] The server creates a list of activities extracted by the generative model and prepares the most suitable suggestions for the user. This list includes hobbies and ways to acquire new skills, selected based on the user's attribute information.

[0050] Step 5:

[0051] The server sends the created activity list to the terminal.

[0052] Step 6:

[0053] The terminal displays the received activity list on the user interface for the user to review. The user interface is designed to be intuitive and easy to use.

[0054] Step 7:

[0055] Users select activities that interest them from the displayed suggestions. For example, they can choose to "participate in a cooking class" or "join a hiking group."

[0056] Step 8:

[0057] The terminal sends the user's selection to the server.

[0058] Step 9:

[0059] Based on the user's selection, the server uses assistive devices to search for relevant activity information. The assistive devices find necessary resources, schedules, online forums, and other information for the selected activity.

[0060] Step 10:

[0061] The server sends the searched activity information to the terminal and provides it to the user. This includes specific instructions on how to participate and information on reskilling courses.

[0062] Step 11:

[0063] The device displays received activity information to the user and prompts interaction. Based on this, the user can start an activity or join a community.

[0064] (Example 1)

[0065] 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."

[0066] For seniors after retirement, finding suitable activities and learning opportunities to lead a fulfilling second life is often difficult. There is a lack of automated methods to provide personalized suggestions based on individual hobbies, interests, and professional experience, resulting in users being unable to efficiently explore activities that best suit them.

[0067] 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.

[0068] In this invention, the server includes an information input means for collecting attribute information from a user, an information processing means having a generative model for analyzing the attribute information obtained from the information input means and generating the user's potential interests and activities, and a presentation means for displaying and making selectable the activities generated by the information processing means to the user. This makes it possible to provide optimal activity suggestions and related information based on the user's individual preferences.

[0069] "User" refers to an individual who inputs attribute information using the system and is eligible to receive activity suggestions.

[0070] "Attribute information" refers to data related to individual interests and backgrounds, such as users' preferences, work experience, and areas of interest.

[0071] "Information input means" refers to methods or devices for collecting attribute information from users, and specifically includes keyboards, touch panels, and the like.

[0072] A "generative model" refers to a model that uses machine learning algorithms to analyze user attribute information and identify and generate potential interests and activities.

[0073] "Information processing means" refers to technologies and devices that analyze input attribute information and use generative models to propose appropriate activities.

[0074] "Presentation means" refers to a method or device that displays and allows users to select activities generated by information processing means.

[0075] "Support measures" refer to methods or devices for searching for and providing relevant information based on the activities selected by the user.

[0076] This invention is a system that proposes activities and learning opportunities optimized for an individual based on the user's attribute information. This system comprises an input device, an information processing device, a display device, and a support device.

[0077] Users access the system through a terminal and first input attribute information such as their preferences and work experience. Specifically, they use input devices such as keyboards and touchscreens. By collecting this information, it is possible to clearly understand the user's interests and concerns.

[0078] An information processing device installed within the server analyzes this attribute information using a generative AI model. The generative AI model utilizes machine learning algorithms to extract the user's potential interests and preferences. This process generates a list of activities tailored to the user. The activity list includes hobbies, sports, new skill acquisition, and travel plans based on the user's interests.

[0079] The generated activity list is presented to the user on a user-friendly interface via a display device. Users can browse this interface and select the activity that best matches their interests from the presented options. For example, a user interested in cooking and travel might be offered a cooking class or a travel plan to learn about cuisines from around the country.

[0080] When a user selects a specific activity, that information is retransmitted to the server via the terminal. Based on this information, the support device searches for and provides specific resources related to the selected activity. These include local activity groups, online communities, and event schedules. Opportunities to participate in learning programs and vocational training programs are also provided.

[0081] As a concrete example, if a user expresses a desire to enrich their life after retirement, the prompt "Please suggest suitable activities for retirement based on my hobbies and interests" is entered into the generating AI model. Based on this prompt, the system proposes a specific activity plan based on the user's attribute information.

[0082] This invention makes it possible for users to easily try new activities and gain direction for enjoying a fulfilling second life.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users log in to the system via their device and enter attribute information to clarify their preferences, work experience, and current interests. The entered data is collected in the form of text or multiple-choice options and temporarily stored on the device. Specifically, users enter information into on-screen forms using a keyboard or touch panel.

[0086] Step 2:

[0087] The terminal sends the entered attribute information to the server. Protocols are applied to ensure data accuracy and security during this process. The transmitted data is stored in a database on the server and prepared for analysis. Specifically, the terminal sends data to the server using encrypted communication and performs error checking.

[0088] Step 3:

[0089] The server begins data analysis using a generative AI model based on the received attribute information. Here, machine learning algorithms extract the user's latent preferences. The input attribute information is pattern-recognized within the model, and a list of activities best suited to the user is output. The generated list includes suggestions for hobbies and new skills. Specifically, the server executes an analysis job to generate the activity list.

[0090] Step 4:

[0091] The server sends the generated activity list to the terminal. The terminal displays the received list in a user-friendly interface. The user uses this interface to select the activity that interests them most from the provided activities. Specifically, the terminal renders the information as visual elements on the screen and waits for the user's selection.

[0092] Step 5:

[0093] When a user selects a specific activity, that information is sent back from the terminal to the server. Based on this selection, the server uses assistive devices to search for and collect additional relevant information. Specifically, local activity groups, online communities, and event schedules are searched for and prepared to be provided to the user. The process involves the server generating database queries based on the selection and collecting relevant information.

[0094] Step 6:

[0095] Based on information obtained from the support device, the server provides the user with the resources necessary to carry out detailed activities. This includes links to learning programs and vocational training programs. These suggestions are displayed sequentially to the user on the terminal, and can be accessed immediately through the relevant links. Specifically, the system provides the user with the ability to review detailed information and choose to proceed to the next step.

[0096] (Application Example 1)

[0097] 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."

[0098] In modern society, for seniors to enrich their lives after retirement, it is crucial to find activities that suit their preferences and health condition. However, with so much information available, making the optimal choice is not easy. Therefore, there is a need for support systems that enable seniors to efficiently find and participate in activities that match their interests.

[0099] 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.

[0100] In this invention, the server includes an information input means for inputting attribute information from the user, an information processing means equipped with a generation algorithm for analyzing the attribute information received from the information input means and extracting the user's potential preferences and activities, and an information display means for presenting the activities extracted by the information processing means and allowing the user to select one. This makes it possible for the user to intuitively select and participate in activities that suit their hobbies and health condition.

[0101] An "information input means" is an interface device that allows a user to input their own attribute information into the system.

[0102] An "information processing device" is a device that analyzes the received user attribute information and extracts the user's potential preferences and activities using a generation algorithm.

[0103] A "generative algorithm" is a computational method that uses machine learning to suggest the most suitable activities for a user based on attribute information provided by that user.

[0104] An "information display means" is a device that visually presents extracted activity information as a user interface, allowing the user to make a selection.

[0105] A "support device" is a device that searches for relevant activity information based on the user's selection and provides it to the user.

[0106] A "visual display means" is a device that visually displays information so that users can intuitively and easily understand and select activities.

[0107] The "analysis means" is a device that uses machine learning models to identify local communities and events based on user attribute information and to make recommendations.

[0108] In implementing this invention, first, the user inputs attribute information such as their preferences and health status using a terminal. In response, the server receives this attribute information through an information input means. Next, an information processing means installed in the server executes a generation algorithm to analyze the received attribute information. This algorithm is built using a general machine learning library (e.g., TENSORFLOW® or PyTorch) and is used to reveal the user's latent preferences.

[0109] The analyzed information is optimized on the server and extracted as an activity list to be presented to the user. Next, the information display means visually presents this activity list, representing it on an interface that allows the user to intuitively operate it. For example, by referencing Google® Material Design or Apple's Human Interface Guidelines, an environment that is easy for the user to see and operate is provided.

[0110] When a user selects a specific activity, that selection information is sent back to the server. Based on the user's selection, the server's support system searches the internet for various relevant information, such as local communities, online events, and educational programs, and provides it to the user. This system makes it easy for users to find activities that interest them and obtain resources to participate.

[0111] For example, if a male user in his 70s operates a device and inputs that he is interested in "travel" and "DIY," the platform will suggest opportunities to participate in local travel communities and DIY workshops. An example of a related prompt would be: "A man in his 70s, interested in travel and DIY, looking for nearby events." This prompt allows the generative AI model to function properly and provide the user with valuable information.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user uses a terminal to input attribute information about their preferences and health status. This input is sent to the server via the terminal's information input device. The input data is sent to the server in a well-formed format (such as JSON or XML) through the terminal's input device.

[0115] Step 2:

[0116] The server's information processing mechanism uses a generative AI model to analyze the received attribute information. This model is trained using machine learning algorithms (such as TensorFlow or PyTorch) and extracts the user's latent preferences based on the input data. This generates a user profile and outputs indicators showing which activities are suitable.

[0117] Step 3:

[0118] The server creates an optimal activity list based on the analysis results. This activity list is transmitted to the terminal using an information display device. Here, the generated data is converted into a human-readable format and organized so that it can be displayed in a format that is easy for the user to understand intuitively.

[0119] Step 4:

[0120] The user reviews the activity list displayed on their device and selects the activities they are interested in. The selection results are sent back from the device to the server, and the selection information is saved as a log.

[0121] Step 5:

[0122] The server uses support tools to collect additional information related to the user's selection. Specifically, it searches the internet for local communities, online events, and relevant learning materials. The search results are organized and provided to the user's device in an easily accessible format.

[0123] Step 6:

[0124] Based on the information provided on the device, users prepare to participate in their chosen activity. At this stage, support information (e.g., links for registration and contact information) is displayed on the device as needed, allowing users to take action.

[0125] 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.

[0126] This invention relates to a system that recognizes a user's attribute information and emotions and makes optimal activity suggestions based on that information. The system comprises an input device, a processing device, a display device, a support device, and an emotion engine, and provides useful guidelines for enriching the user's second life.

[0127] Users first use a device to input attribute information such as their preferences and work experience. In addition, the emotion engine acquires emotional data from facial expressions, tone of voice, and input patterns to analyze the user's emotional state during input and activity selection. This emotional data is key to evaluating the user's motivation and level of interest.

[0128] The processing unit on the server uses a generative model to comprehensively analyze user attribute information and emotional data. This process generates a list of activities that take into account the user's current emotional state and past psychological tendencies. For example, if a user expresses interest in "travel" and "cooking," but their current emotional state indicates they are seeking a relaxing experience, a quiet cooking-related workshop will be suggested.

[0129] The suggested list of activities is presented to the user via a display device. The user selects activities of interest from the provided options, and their selection history and associated sentiment data are stored in the system to inform future suggestions.

[0130] Based on user selections, the assistive device searches for relevant activity information and provides specific actionable information. The device displays details such as stores, groups, and learning programs related to the activity, helping the user to smoothly begin the activity. Furthermore, the emotion engine continuously collects user emotional feedback and measures activity satisfaction, which helps to provide more accurate suggestions.

[0131] Thus, the present invention provides a comprehensive system that combines user emotions and attribute data, supporting users in enriching their second lives emotionally.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users input attribute information about their preferences and work experience through their device. The input data includes specific information such as "I like traveling" or "I worked as an engineer for 30 years."

[0135] Step 2:

[0136] The terminal formats the input attribute information and simultaneously collects the user's emotional state using an emotion engine. The emotion engine detects emotions from the user's facial expressions and tone of voice and sends the analyzed data to the server.

[0137] Step 3:

[0138] Based on the received attribute information and sentiment data, the server uses a generative model to extract the user's potential preferences and suitable activities. The generative model also takes sentiment data into consideration to create activity suggestions that match the user's current mood.

[0139] Step 4:

[0140] The server sends the generated activity list to the terminal. The activity list includes multiple suggestions that match the user's interests and are appropriate to their current emotional state.

[0141] Step 5:

[0142] The device displays the received activity list on the user interface for the user to review. For example, relaxing activities related to cooking or travel might be presented.

[0143] Step 6:

[0144] The user selects an activity of interest from the provided suggestions. During the activity selection process, the user's emotional state is also detected.

[0145] Step 7:

[0146] The device then sends the user's selected activity and their emotional data back to the server.

[0147] Step 8:

[0148] Based on the user's selection, the server uses assistive devices to search for relevant activity information. This includes detailed information for carrying out the selected activity, such as participation procedures, schedules, and relevant community information.

[0149] Step 9:

[0150] The server sends activity information to the terminal and provides it to the user.

[0151] Step 10:

[0152] The device displays received information to the user, supporting a smooth start to activities. The emotion engine continuously records the user's emotional feedback and uses it to improve the accuracy of future suggestions.

[0153] (Example 2)

[0154] 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".

[0155] In modern society, there is a demand for users to select activities that are appropriate to their individual lifestyles and interests. However, it is difficult for users to efficiently select the activities they desire from a variety of options, and it is particularly challenging to respond quickly to changes in their emotions and interests. Conventional systems fail to make suggestions that adequately consider the user's emotional state, resulting in decreased user satisfaction. Therefore, the challenge is to provide a system that automatically suggests the optimal activity based on the user's attribute information and emotional data.

[0156] 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.

[0157] In this invention, the server includes data input means for acquiring attribute information and emotional data from the user, information processing means for analyzing the attribute information and emotional data obtained using a generative model, and information display means for presenting and allowing the user to select activities based on the analysis results. This makes it possible to suggest activities that reflect changes in the user's attributes and emotions.

[0158] "Data input means" refers to a device or method for obtaining attribute information and sentiment data from a user.

[0159] "Information processing means" refers to a device or method for analyzing acquired attribute information and sentiment data, and for making activity suggestions to the user using a generative model.

[0160] A "generative model" is an algorithm or program that automatically generates optimal activity suggestions using a user's past selection history, attribute information, and sentiment data.

[0161] "Information display means" refers to a device or method that visually presents activities to the user based on the analysis results of information processing means.

[0162] "Guidance means" refers to a device or method for searching for and providing detailed information related to a selected activity based on the user's choice.

[0163] "Data collection means" refers to a device or method for collecting users' emotional feedback, evaluating their satisfaction with the activity, and improving future suggestions.

[0164] This invention relates to a system that provides optimal activity suggestions based on user attribute information and sentiment data. The system includes data input means, information processing means, information display means, guidance means, and data collection means.

[0165] The user first uses a device to input attribute information such as their hobbies and work experience. This device is equipped with a camera and microphone, and emotional data is acquired through the user's facial expressions and tone of voice. Through this process, the user's current emotional state is evaluated.

[0166] The server receives attribute information and sentiment data transmitted from the terminal and analyzes it through information processing tools. This analysis uses a generative AI model, which automatically generates activity suggestions that reflect the user's past selection history and emotional state. For example, if a user's interests are "travel" and "cooking" and they desire a relaxing experience, the server might suggest quiet activities such as cooking classes.

[0167] The generated activity suggestions are presented to the user through an information display device. The user can select an activity that interests them from this list. Furthermore, the terminal sends a request to the server based on the selected activity, and the server, via a guidance device, searches for and provides the user with detailed information about the relevant activity. This allows the user to obtain specific steps to begin the activity.

[0168] Furthermore, the system records users' emotional feedback using data collection methods and evaluates their satisfaction with the activity. This information is used to make more effective suggestions for future activities.

[0169] An example of a prompt message is as follows: "User attributes: Hobbies include traveling, professional experience is teaching, current emotional state is desiring a relaxing experience." Based on such prompts, the generative AI model will suggest appropriate activities.

[0170] The hardware requires user terminals (PCs or smartphones) and a central server, while the software consists of an emotion engine for sentiment analysis and a generative AI model for suggesting activities.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user inputs their attribute information through the terminal. The terminal captures information such as hobbies and work experience provided by the user using a data entry mechanism. As a result of the data entry, the user's attribute information is obtained.

[0174] Step 2:

[0175] The device uses its camera and microphone to collect emotional data from the user's facial expressions and tone of voice. An emotion engine analyzes this information to identify the user's current emotional state. Based on the emotional data analysis of the input, the user's emotional state is output.

[0176] Step 3:

[0177] The terminal sends collected attribute information and sentiment data to the server. The server uses information processing tools to input this data into a generative AI model and generate prompt sentences. Based on these prompt sentences, the generative AI model performs data calculations to suggest activities appropriate for the user. The resulting output is a list of activity suggestions for the user.

[0178] Step 4:

[0179] The server sends a list of generated activity suggestions to the terminal. The terminal uses an information display device to present this list to the user. The user selects activities of interest from the displayed suggestions. The user's selection is used in the next step.

[0180] Step 5:

[0181] Based on the user's selection, the terminal sends a request to the server, which uses guidance tools to search for and provide detailed information about the relevant activity. This may include, for example, information about the location and time of the selected activity. The retrieved information is sent to the terminal and presented to the user.

[0182] Step 6:

[0183] After a user participates in an activity, emotional feedback is collected through the device and sent to the server. The server uses data collection tools to analyze this feedback and evaluate user satisfaction. This feedback data is used to improve future activity suggestions.

[0184] (Application Example 2)

[0185] 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".

[0186] In modern society, there is a demand for systems that propose personalized activities based on each individual's emotional state and attribute data, thereby enriching their lifestyles. However, existing systems are insufficient in making suggestions based on emotional state, making it difficult to increase user emotional satisfaction. Therefore, a new method is needed to effectively utilize emotional data and propose activities that are optimal for the user.

[0187] 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.

[0188] In this invention, the server includes an input means for inputting attribute data and emotional state from the user; a processing means equipped with a generative model for analyzing the attribute data and emotional state received from the input means and extracting the user's potential preferences and activities; and a display means for optimizing the activities extracted by the processing means based on the user's emotional state and allowing the user to select them. This enables personalized activity suggestions that take the user's emotional state into consideration.

[0189] 1. "Attribute data" refers to information that indicates individual characteristics and backgrounds, such as user preferences, work experience, and past activity history.

[0190] 2. "Emotional state" refers to the user's psychological or emotional condition, including emotions such as joy, sadness, excitement, and stress.

[0191] 3. "Input means" refers to an interface for receiving attribute data and emotional states from the user, and typically consists of sensors or user interface devices.

[0192] 4. "Processing means" refers to a computer or the core computing device of a system that analyzes received attribute data and emotional states and uses a generative model to recommend the most suitable activity to the user.

[0193] 5. A "generative model" is a data analysis algorithm used to suggest personalized activities based on user attribute data and emotional states.

[0194] 6. "Display means" refers to a device for visually or audibly presenting activity suggestions optimized by the processing means to the user.

[0195] The system for realizing this invention is accessible to users on a daily basis using devices such as smartphones and tablets. These devices are equipped with input devices such as cameras, microphones, and touchscreens, which are used to collect user attribute data and emotional states. Users provide information about their hobbies, work experience, and current mood to the device through these input devices.

[0196] The server features a processing program utilizing the Python language and TensorFlow to analyze input data. This processing program uses a generative model to analyze attribute data and emotional states obtained from the user. Data sent from the terminal is processed on the server, and personalized activity suggestions are generated for each user.

[0197] Furthermore, the generated activity suggestions are provided to the user through various display methods. For example, they can be visually presented on a smartphone screen or verbally guided through a voice assistant. The activity suggestions are tailored to the user's emotional state; for instance, based on information such as "You need a relaxing activity today," nearby relaxing events or locations can be suggested.

[0198] For example, if a busy business person is feeling stressed, the system might suggest relaxation classes or events in a park. This would involve a prompt message such as, "Create activity suggestions that can help users relax when they are feeling overwhelmed. Suggest events or locations that are easily accessible from their current location."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal accepts input from the user. The user uses the terminal's camera, microphone, and touchscreen to input attribute data (e.g., hobbies, work experience) and current emotional state (e.g., stress level, relaxation level). The input data is processed on the terminal and formatted for transmission to the server.

[0202] Step 2:

[0203] The server receives attribute data and emotional state transmitted from the terminal. The server runs a processing program to analyze this data, preprocesses it (e.g., denoising and normalizing), and then organizes it as input for a generative AI model.

[0204] Step 3:

[0205] The server uses a generative AI model to generate personalized activity suggestions based on the user's attribute data and emotional state. This process involves comparison with similar past datasets and analysis of user trends. The generative model uses prompts (e.g., "Please create activity suggestions that will help me relax") to extract the most suitable activity options.

[0206] Step 4:

[0207] The generated activity suggestions are sent from the server to the terminal. The terminal optimizes the content as screen displays and audio guidance to present these suggestions to the user. The user confirms and selects the suggested activities through visual or auditory means.

[0208] Step 5:

[0209] Based on the activity selected by the user, the device requests detailed information related to that activity from the server. The server uses support tools to search for the necessary information (e.g., the location and time of an event) and provides it to the device.

[0210] Step 6:

[0211] The information obtained on the client side is used to guide users to participate in activities. The terminal manages the user's scheduling and displays map and navigation information. User feedback at this stage (e.g., satisfaction with the activity) is also collected and sent to the server, where it is incorporated into future suggestions.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention relates to an activity suggestion platform for senior citizens using a system comprising an input device, a processing device, a display device, and a support device. This system allows users to input their personal attribute information in order to enrich their second life after retirement, and then suggests appropriate activities and learning opportunities based on that information.

[0229] The user first inputs attribute information such as their preferences and professional experience through a terminal. This information clarifies the user's interests and forms the basis for providing optimal suggestions. The input device transmits this information to the processing unit, which then prepares it for analysis.

[0230] The processing unit installed within the server analyzes the received attribute information using a generative model. The generative model uses sophisticated algorithms to extract the user's potential preferences and generate a suitable activity list. This list includes various activities based on the user's interests, such as hobbies, sports, acquiring new skills, and travel plans.

[0231] The generated activity list is presented to the user via a display device on a user-friendly interface. The user can choose the activity that best suits their interests from the displayed options. For example, if the user is interested in cooking and travel, suggestions such as taking a cooking class or a travel plan to learn about local cuisines will be presented.

[0232] Once the selection is confirmed, the terminal resends the information to the server, and the support device then searches for information to assist in carrying out the activity. The support device provides specific resources related to the selected activity, such as local activity groups, online communities, and start dates. In addition, opportunities to participate in learning programs and vocational training programs are also presented to the user.

[0233] Through the suggestions and support provided by this platform, users can more easily try new activities and gain direction to realize the second life they envision. Furthermore, the incentive function provided by the system helps users to continue learning and engaging in activities, thereby building a fulfilling life.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user uses a device to input attribute information about their preferences and professional experience. For example, the user might input information such as "travel," "cooking," or "30 years of engineering experience."

[0237] Step 2:

[0238] The terminal formats the entered attribute information and sends it to the server. This formatted information includes user preferences and work experience.

[0239] Step 3:

[0240] The server starts analysis using a generative model based on the received attribute information. The generative model uses machine learning algorithms to identify the user's potential preferences and extract appropriate activities and interests.

[0241] Step 4:

[0242] The server creates a list of activities extracted by the generative model and prepares the most suitable suggestions for the user. This list includes hobbies and ways to acquire new skills, selected based on the user's attribute information.

[0243] Step 5:

[0244] The server sends the created activity list to the terminal.

[0245] Step 6:

[0246] The terminal displays the received activity list on the user interface for the user to review. The user interface is designed to be intuitive and easy to use.

[0247] Step 7:

[0248] Users select activities that interest them from the displayed suggestions. For example, they can choose to "participate in a cooking class" or "join a hiking group."

[0249] Step 8:

[0250] The terminal sends the user's selection to the server.

[0251] Step 9:

[0252] Based on the user's selection, the server uses assistive devices to search for relevant activity information. The assistive devices find necessary resources, schedules, online forums, and other information for the selected activity.

[0253] Step 10:

[0254] The server sends the searched activity information to the terminal and provides it to the user. This includes specific instructions on how to participate and information on reskilling courses.

[0255] Step 11:

[0256] The device displays received activity information to the user and prompts interaction. Based on this, the user can start an activity or join a community.

[0257] (Example 1)

[0258] 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."

[0259] For seniors after retirement, finding suitable activities and learning opportunities to lead a fulfilling second life is often difficult. There is a lack of automated methods to provide personalized suggestions based on individual hobbies, interests, and professional experience, resulting in users being unable to efficiently explore activities that best suit them.

[0260] 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.

[0261] In this invention, the server includes an information input means for collecting attribute information from a user, an information processing means having a generative model for analyzing the attribute information obtained from the information input means and generating the user's potential interests and activities, and a presentation means for displaying and making selectable the activities generated by the information processing means to the user. This makes it possible to provide optimal activity suggestions and related information based on the user's individual preferences.

[0262] "User" refers to an individual who inputs attribute information using the system and is eligible to receive activity suggestions.

[0263] "Attribute information" refers to data related to individual interests and backgrounds, such as users' preferences, work experience, and areas of interest.

[0264] "Information input means" refers to methods or devices for collecting attribute information from users, and specifically includes keyboards, touch panels, and the like.

[0265] A "generative model" refers to a model that uses machine learning algorithms to analyze user attribute information and identify and generate potential interests and activities.

[0266] "Information processing means" refers to technologies and devices that analyze input attribute information and use generative models to propose appropriate activities.

[0267] "Presentation means" refers to a method or device that displays and allows users to select activities generated by information processing means.

[0268] "Support measures" refer to methods or devices for searching for and providing relevant information based on the activities selected by the user.

[0269] This invention is a system that proposes activities and learning opportunities optimized for an individual based on the user's attribute information. This system comprises an input device, an information processing device, a display device, and a support device.

[0270] Users access the system through a terminal and first input attribute information such as their preferences and work experience. Specifically, they use input devices such as keyboards and touchscreens. By collecting this information, it is possible to clearly understand the user's interests and concerns.

[0271] An information processing device installed within the server analyzes this attribute information using a generative AI model. The generative AI model utilizes machine learning algorithms to extract the user's potential interests and preferences. This process generates a list of activities tailored to the user. The activity list includes hobbies, sports, new skill acquisition, and travel plans based on the user's interests.

[0272] The generated activity list is presented to the user on a user-friendly interface via a display device. Users can browse this interface and select the activity that best matches their interests from the presented options. For example, a user interested in cooking and travel might be offered a cooking class or a travel plan to learn about cuisines from around the country.

[0273] When a user selects a specific activity, that information is retransmitted to the server via the terminal. Based on this information, the support device searches for and provides specific resources related to the selected activity. These include local activity groups, online communities, and event schedules. Opportunities to participate in learning programs and vocational training programs are also provided.

[0274] As a concrete example, if a user expresses a desire to enrich their life after retirement, the prompt "Please suggest suitable activities for retirement based on my hobbies and interests" is entered into the generating AI model. Based on this prompt, the system proposes a specific activity plan based on the user's attribute information.

[0275] This invention makes it possible for users to easily try new activities and gain direction for enjoying a fulfilling second life.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] Users log in to the system via their device and enter attribute information to clarify their preferences, work experience, and current interests. The entered data is collected in the form of text or multiple-choice options and temporarily stored on the device. Specifically, users enter information into on-screen forms using a keyboard or touch panel.

[0279] Step 2:

[0280] The terminal sends the entered attribute information to the server. Protocols are applied to ensure data accuracy and security during this process. The transmitted data is stored in a database on the server and prepared for analysis. Specifically, the terminal sends data to the server using encrypted communication and performs error checking.

[0281] Step 3:

[0282] The server begins data analysis using a generative AI model based on the received attribute information. Here, machine learning algorithms extract the user's latent preferences. The input attribute information is pattern-recognized within the model, and a list of activities best suited to the user is output. The generated list includes suggestions for hobbies and new skills. Specifically, the server executes an analysis job to generate the activity list.

[0283] Step 4:

[0284] The server sends the generated activity list to the terminal. The terminal displays the received list in a user-friendly interface. The user uses this interface to select the most interesting activity from the provided activities. Specifically, the terminal renders the information as visual elements on the screen and waits for the user's selection operation.

[0285] Step 5:

[0286] When the user selects a specific activity, the information is sent from the terminal to the server again. Based on this selection information, the server uses the support device to search for and collect relevant additional information. Specifically, local activity groups, online communities, various event schedules, etc. are searched and preparations for providing to the user are completed. As an operation, the server generates a database query based on the selection information and collects relevant information.

[0287] Step 6:

[0288] Based on the information obtained from the support device, the server provides the user with the resources necessary for detailed activity implementation. At this time, links to learning programs and vocational training programs are also included. These proposals are sequentially displayed to the user on the terminal and can be immediately accessed through the relevant links. Specifically, a function is provided for the user to view the detailed information and make a selection to proceed to the next step.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". ]>

[0291] In modern society, for seniors to enrich their lives after retirement, it is crucial to find activities that suit their preferences and health condition. However, with so much information available, making the optimal choice is not easy. Therefore, there is a need for support systems that enable seniors to efficiently find and participate in activities that match their interests.

[0292] 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.

[0293] In this invention, the server includes an information input means for inputting attribute information from the user, an information processing means equipped with a generation algorithm for analyzing the attribute information received from the information input means and extracting the user's potential preferences and activities, and an information display means for presenting the activities extracted by the information processing means and allowing the user to select one. This makes it possible for the user to intuitively select and participate in activities that suit their hobbies and health condition.

[0294] An "information input means" is an interface device that allows a user to input their own attribute information into the system.

[0295] An "information processing device" is a device that analyzes the received user attribute information and extracts the user's potential preferences and activities using a generation algorithm.

[0296] A "generative algorithm" is a computational method that uses machine learning to suggest the most suitable activities for a user based on attribute information provided by that user.

[0297] An "information display means" is a device that visually presents extracted activity information as a user interface, allowing the user to make a selection.

[0298] A "support device" is a device that searches for relevant activity information based on the user's selection and provides it to the user.

[0299] A "visual display means" is a device that visually displays information so that users can intuitively and easily understand and select activities.

[0300] The "analysis means" is a device that uses machine learning models to identify local communities and events based on user attribute information and to make recommendations.

[0301] In implementing this invention, first, the user inputs attribute information such as their preferences and health status using a terminal. In response, the server receives this attribute information through an information input means. Next, an information processing means installed on the server executes a generation algorithm to analyze the received attribute information. This algorithm is built using a common machine learning library (e.g., TensorFlow or PyTorch) and is used to reveal the user's latent preferences.

[0302] The analyzed information is optimized on the server and extracted as an activity list to be presented to the user. Next, the information display means visually presents this activity list, representing it on an interface that allows the user to intuitively interact with it. For example, by referencing Google Material Design or Apple's Human Interface Guidelines, an environment that is easy for the user to see and operate is provided.

[0303] When a user selects a specific activity, that selection information is sent back to the server. Based on the user's selection, the server's support system searches the internet for various relevant information, such as local communities, online events, and educational programs, and provides it to the user. This system makes it easy for users to find activities that interest them and obtain resources to participate.

[0304] As a specific example, for instance, when a male user in his 70s operates a terminal and inputs that he is interested in "travel" and "DIY", this platform proposes opportunities to participate in local travel communities and DIY workshops. Also, examples of related prompt texts are as follows: "A male in his 70s, interested in travel and DIY, and wants to find nearby events". With this prompt, it becomes possible for the generative AI model to operate appropriately and provide valuable information to the user.

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The user uses the terminal to input attribute information regarding their interests and health status. This input is sent to the server by the information input means of the terminal. Through the input device of the terminal, the input data is sent to the server in a structured format (such as JSON or XML format).

[0308] Step 2:

[0309] The information processing means of the server uses a generative AI model to analyze the received attribute information. This model is trained using a machine learning algorithm (such as TensorFlow or PyTorch) and extracts the user's potential preferences based on the input data. As a result, a user profile is generated and an indicator showing which activities are suitable is output.

[0310] Step 3:

[0311] Based on the analysis results, the server creates an optimal activity list. This activity list is sent to the terminal using the information display means. Here, the generated data is converted into a form that is easy for humans to recognize and organized so that it is displayed in a form that is intuitive and easy for the user to understand.

[0312] Step 4:

[0313] The user reviews the activity list displayed on their device and selects the activities they are interested in. The selection results are sent back from the device to the server, and the selection information is saved as a log.

[0314] Step 5:

[0315] The server uses support tools to collect additional information related to the user's selection. Specifically, it searches the internet for local communities, online events, and relevant learning materials. The search results are organized and provided to the user's device in an easily accessible format.

[0316] Step 6:

[0317] Based on the information provided on the device, users prepare to participate in their chosen activity. At this stage, support information (e.g., links for registration and contact information) is displayed on the device as needed, allowing users to take action.

[0318] 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.

[0319] This invention relates to a system that recognizes a user's attribute information and emotions and makes optimal activity suggestions based on that information. The system comprises an input device, a processing device, a display device, a support device, and an emotion engine, and provides useful guidelines for enriching the user's second life.

[0320] Users first use a device to input attribute information such as their preferences and work experience. In addition, the emotion engine acquires emotional data from facial expressions, tone of voice, and input patterns to analyze the user's emotional state during input and activity selection. This emotional data is key to evaluating the user's motivation and level of interest.

[0321] The processing unit on the server uses a generative model to comprehensively analyze user attribute information and emotional data. This process generates a list of activities that take into account the user's current emotional state and past psychological tendencies. For example, if a user expresses interest in "travel" and "cooking," but their current emotional state indicates they are seeking a relaxing experience, a quiet cooking-related workshop will be suggested.

[0322] The suggested list of activities is presented to the user via a display device. The user selects activities of interest from the provided options, and their selection history and associated sentiment data are stored in the system to inform future suggestions.

[0323] Based on user selections, the assistive device searches for relevant activity information and provides specific actionable information. The device displays details such as stores, groups, and learning programs related to the activity, helping the user to smoothly begin the activity. Furthermore, the emotion engine continuously collects user emotional feedback and measures activity satisfaction, which helps to provide more accurate suggestions.

[0324] Thus, the present invention provides a comprehensive system that combines user emotions and attribute data, supporting users in enriching their second lives emotionally.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] Users input attribute information about their preferences and work experience through their device. The input data includes specific information such as "I like traveling" or "I worked as an engineer for 30 years."

[0328] Step 2:

[0329] The terminal formats the input attribute information and simultaneously collects the user's emotional state using an emotion engine. The emotion engine detects emotions from the user's facial expressions and tone of voice and sends the analyzed data to the server.

[0330] Step 3:

[0331] Based on the received attribute information and sentiment data, the server uses a generative model to extract the user's potential preferences and suitable activities. The generative model also takes sentiment data into consideration to create activity suggestions that match the user's current mood.

[0332] Step 4:

[0333] The server sends the generated activity list to the terminal. The activity list includes multiple suggestions that match the user's interests and are appropriate to their current emotional state.

[0334] Step 5:

[0335] The device displays the received activity list on the user interface for the user to review. For example, relaxing activities related to cooking or travel might be presented.

[0336] Step 6:

[0337] The user selects an activity of interest from the provided suggestions. During the activity selection process, the user's emotional state is also detected.

[0338] Step 7:

[0339] The device then sends the user's selected activity and their emotional data back to the server.

[0340] Step 8:

[0341] Based on the user's selection, the server uses assistive devices to search for relevant activity information. This includes detailed information for carrying out the selected activity, such as participation procedures, schedules, and relevant community information.

[0342] Step 9:

[0343] The server sends activity information to the terminal and provides it to the user.

[0344] Step 10:

[0345] The device displays received information to the user, supporting a smooth start to activities. The emotion engine continuously records the user's emotional feedback and uses it to improve the accuracy of future suggestions.

[0346] (Example 2)

[0347] 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".

[0348] In modern society, there is a demand for users to select activities that are appropriate to their individual lifestyles and interests. However, it is difficult for users to efficiently select the activities they desire from a variety of options, and it is particularly challenging to respond quickly to changes in their emotions and interests. Conventional systems fail to make suggestions that adequately consider the user's emotional state, resulting in decreased user satisfaction. Therefore, the challenge is to provide a system that automatically suggests the optimal activity based on the user's attribute information and emotional data.

[0349] 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.

[0350] In this invention, the server includes data input means for acquiring attribute information and emotional data from the user, information processing means for analyzing the attribute information and emotional data obtained using a generative model, and information display means for presenting and allowing the user to select activities based on the analysis results. This makes it possible to suggest activities that reflect changes in the user's attributes and emotions.

[0351] "Data input means" refers to a device or method for obtaining attribute information and sentiment data from a user.

[0352] "Information processing means" refers to a device or method for analyzing acquired attribute information and sentiment data, and for making activity suggestions to the user using a generative model.

[0353] A "generative model" is an algorithm or program that automatically generates optimal activity suggestions using a user's past selection history, attribute information, and sentiment data.

[0354] "Information display means" refers to a device or method that visually presents activities to the user based on the analysis results of information processing means.

[0355] "Guidance means" refers to a device or method for searching for and providing detailed information related to a selected activity based on the user's choice.

[0356] "Data collection means" refers to a device or method for collecting users' emotional feedback, evaluating their satisfaction with the activity, and improving future suggestions.

[0357] This invention relates to a system that provides optimal activity suggestions based on user attribute information and sentiment data. The system includes data input means, information processing means, information display means, guidance means, and data collection means.

[0358] The user first uses a device to input attribute information such as their hobbies and work experience. This device is equipped with a camera and microphone, and emotional data is acquired through the user's facial expressions and tone of voice. Through this process, the user's current emotional state is evaluated.

[0359] The server receives attribute information and sentiment data transmitted from the terminal and analyzes it through information processing tools. This analysis uses a generative AI model, which automatically generates activity suggestions that reflect the user's past selection history and emotional state. For example, if a user's interests are "travel" and "cooking" and they desire a relaxing experience, the server might suggest quiet activities such as cooking classes.

[0360] The generated activity suggestions are presented to the user through an information display device. The user can select an activity that interests them from this list. Furthermore, the terminal sends a request to the server based on the selected activity, and the server, via a guidance device, searches for and provides the user with detailed information about the relevant activity. This allows the user to obtain specific steps to begin the activity.

[0361] Furthermore, the system records users' emotional feedback using data collection methods and evaluates their satisfaction with the activity. This information is used to make more effective suggestions for future activities.

[0362] An example of a prompt message is as follows: "User attributes: Hobbies include traveling, professional experience is teaching, current emotional state is desiring a relaxing experience." Based on such prompts, the generative AI model will suggest appropriate activities.

[0363] The hardware requires user terminals (PCs or smartphones) and a central server, while the software consists of an emotion engine for sentiment analysis and a generative AI model for suggesting activities.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user inputs their attribute information through the terminal. The terminal captures information such as hobbies and work experience provided by the user using a data entry mechanism. As a result of the data entry, the user's attribute information is obtained.

[0367] Step 2:

[0368] The device uses its camera and microphone to collect emotional data from the user's facial expressions and tone of voice. An emotion engine analyzes this information to identify the user's current emotional state. Based on the emotional data analysis of the input, the user's emotional state is output.

[0369] Step 3:

[0370] The terminal sends collected attribute information and sentiment data to the server. The server uses information processing tools to input this data into a generative AI model and generate prompt sentences. Based on these prompt sentences, the generative AI model performs data calculations to suggest activities appropriate for the user. The resulting output is a list of activity suggestions for the user.

[0371] Step 4:

[0372] The server sends a list of generated activity suggestions to the terminal. The terminal uses an information display device to present this list to the user. The user selects activities of interest from the displayed suggestions. The user's selection is used in the next step.

[0373] Step 5:

[0374] Based on the user's selection, the terminal sends a request to the server, which uses guidance tools to search for and provide detailed information about the relevant activity. This may include, for example, information about the location and time of the selected activity. The retrieved information is sent to the terminal and presented to the user.

[0375] Step 6:

[0376] After a user participates in an activity, emotional feedback is collected through the device and sent to the server. The server uses data collection tools to analyze this feedback and evaluate user satisfaction. This feedback data is used to improve future activity suggestions.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] In modern society, there is a demand for systems that propose personalized activities based on each individual's emotional state and attribute data, thereby enriching their lifestyles. However, existing systems are insufficient in making suggestions based on emotional state, making it difficult to increase user emotional satisfaction. Therefore, a new method is needed to effectively utilize emotional data and propose activities that are optimal for the user.

[0380] 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.

[0381] In this invention, the server includes an input means for inputting attribute data and emotional state from the user; a processing means equipped with a generative model for analyzing the attribute data and emotional state received from the input means and extracting the user's potential preferences and activities; and a display means for optimizing the activities extracted by the processing means based on the user's emotional state and allowing the user to select them. This enables personalized activity suggestions that take the user's emotional state into consideration.

[0382] 1. "Attribute data" refers to information that indicates individual characteristics and backgrounds, such as user preferences, work experience, and past activity history.

[0383] 2. "Emotional state" refers to the user's psychological or emotional condition, including emotions such as joy, sadness, excitement, and stress.

[0384] 3. "Input means" refers to an interface for receiving attribute data and emotional states from the user, and typically consists of sensors or user interface devices.

[0385] 4. "Processing means" refers to a computer or the core computing device of a system that analyzes received attribute data and emotional states and uses a generative model to recommend the most suitable activity to the user.

[0386] 5. A "generative model" is a data analysis algorithm used to suggest personalized activities based on user attribute data and emotional states.

[0387] 6. "Display means" refers to a device for visually or audibly presenting activity suggestions optimized by the processing means to the user.

[0388] The system for realizing this invention is accessible to users on a daily basis using devices such as smartphones and tablets. These devices are equipped with input devices such as cameras, microphones, and touchscreens, which are used to collect user attribute data and emotional states. Users provide information about their hobbies, work experience, and current mood to the device through these input devices.

[0389] The server features a processing program utilizing the Python language and TensorFlow to analyze input data. This processing program uses a generative model to analyze attribute data and emotional states obtained from the user. Data sent from the terminal is processed on the server, and personalized activity suggestions are generated for each user.

[0390] Furthermore, the generated activity suggestions are provided to the user through various display methods. For example, they can be visually presented on a smartphone screen or verbally guided through a voice assistant. The activity suggestions are tailored to the user's emotional state; for instance, based on information such as "You need a relaxing activity today," nearby relaxing events or locations can be suggested.

[0391] For example, if a busy business person is feeling stressed, the system might suggest relaxation classes or events in a park. This would involve a prompt message such as, "Create activity suggestions that can help users relax when they are feeling overwhelmed. Suggest events or locations that are easily accessible from their current location."

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The terminal accepts input from the user. The user uses the terminal's camera, microphone, and touchscreen to input attribute data (e.g., hobbies, work experience) and current emotional state (e.g., stress level, relaxation level). The input data is processed on the terminal and formatted for transmission to the server.

[0395] Step 2:

[0396] The server receives attribute data and emotional state transmitted from the terminal. The server runs a processing program to analyze this data, preprocesses it (e.g., denoising and normalizing), and then organizes it as input for a generative AI model.

[0397] Step 3:

[0398] The server uses a generative AI model to generate personalized activity suggestions based on the user's attribute data and emotional state. This process involves comparison with similar past datasets and analysis of user trends. The generative model uses prompts (e.g., "Please create activity suggestions that will help me relax") to extract the most suitable activity options.

[0399] Step 4:

[0400] The generated activity suggestions are sent from the server to the terminal. The terminal optimizes the content as screen displays and audio guidance to present these suggestions to the user. The user confirms and selects the suggested activities through visual or auditory means.

[0401] Step 5:

[0402] Based on the activity selected by the user, the device requests detailed information related to that activity from the server. The server uses support tools to search for the necessary information (e.g., the location and time of an event) and provides it to the device.

[0403] Step 6:

[0404] The information obtained on the client side is used to guide users to participate in activities. The terminal manages the user's scheduling and displays map and navigation information. User feedback at this stage (e.g., satisfaction with the activity) is also collected and sent to the server, where it is incorporated into future suggestions.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention relates to an activity suggestion platform for senior citizens using a system comprising an input device, a processing device, a display device, and a support device. This system allows users to input their personal attribute information in order to enrich their second life after retirement, and then suggests appropriate activities and learning opportunities based on that information.

[0422] The user first inputs attribute information such as their preferences and professional experience through a terminal. This information clarifies the user's interests and forms the basis for providing optimal suggestions. The input device transmits this information to the processing unit, which then prepares it for analysis.

[0423] The processing unit installed within the server analyzes the received attribute information using a generative model. The generative model uses sophisticated algorithms to extract the user's potential preferences and generate a suitable activity list. This list includes various activities based on the user's interests, such as hobbies, sports, acquiring new skills, and travel plans.

[0424] The generated activity list is presented to the user via a display device on a user-friendly interface. The user can choose the activity that best suits their interests from the displayed options. For example, if the user is interested in cooking and travel, suggestions such as taking a cooking class or a travel plan to learn about local cuisines will be presented.

[0425] Once the selection is confirmed, the terminal resends the information to the server, and the support device then searches for information to assist in carrying out the activity. The support device provides specific resources related to the selected activity, such as local activity groups, online communities, and start dates. In addition, opportunities to participate in learning programs and vocational training programs are also presented to the user.

[0426] Through the suggestions and support provided by this platform, users can more easily try new activities and gain direction to realize the second life they envision. Furthermore, the incentive function provided by the system helps users to continue learning and engaging in activities, thereby building a fulfilling life.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The user uses a device to input attribute information about their preferences and professional experience. For example, the user might input information such as "travel," "cooking," or "30 years of engineering experience."

[0430] Step 2:

[0431] The terminal formats the entered attribute information and sends it to the server. This formatted information includes user preferences and work experience.

[0432] Step 3:

[0433] The server starts analysis using a generative model based on the received attribute information. The generative model uses machine learning algorithms to identify the user's potential preferences and extract appropriate activities and interests.

[0434] Step 4:

[0435] The server creates a list of activities extracted by the generative model and prepares the most suitable suggestions for the user. This list includes hobbies and ways to acquire new skills, selected based on the user's attribute information.

[0436] Step 5:

[0437] The server sends the created activity list to the terminal.

[0438] Step 6:

[0439] The terminal displays the received activity list on the user interface for the user to review. The user interface is designed to be intuitive and easy to use.

[0440] Step 7:

[0441] Users select activities that interest them from the displayed suggestions. For example, they can choose to "participate in a cooking class" or "join a hiking group."

[0442] Step 8:

[0443] The terminal sends the user's selection to the server.

[0444] Step 9:

[0445] Based on the user's selection, the server uses assistive devices to search for relevant activity information. The assistive devices find necessary resources, schedules, online forums, and other information for the selected activity.

[0446] Step 10:

[0447] The server sends the searched activity information to the terminal and provides it to the user. This includes specific instructions on how to participate and information on reskilling courses.

[0448] Step 11:

[0449] The device displays received activity information to the user and prompts interaction. Based on this, the user can start an activity or join a community.

[0450] (Example 1)

[0451] 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."

[0452] For seniors after retirement, finding suitable activities and learning opportunities to lead a fulfilling second life is often difficult. There is a lack of automated methods to provide personalized suggestions based on individual hobbies, interests, and professional experience, resulting in users being unable to efficiently explore activities that best suit them.

[0453] 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.

[0454] In this invention, the server includes an information input means for collecting attribute information from a user, an information processing means having a generative model for analyzing the attribute information obtained from the information input means and generating the user's potential interests and activities, and a presentation means for displaying and making selectable the activities generated by the information processing means to the user. This makes it possible to provide optimal activity suggestions and related information based on the user's individual preferences.

[0455] "User" refers to an individual who inputs attribute information using the system and is eligible to receive activity suggestions.

[0456] "Attribute information" refers to data related to individual interests and backgrounds, such as users' preferences, work experience, and areas of interest.

[0457] "Information input means" refers to methods or devices for collecting attribute information from users, and specifically includes keyboards, touch panels, and the like.

[0458] A "generative model" refers to a model that uses machine learning algorithms to analyze user attribute information and identify and generate potential interests and activities.

[0459] "Information processing means" refers to technologies and devices that analyze input attribute information and use generative models to propose appropriate activities.

[0460] "Presentation means" refers to a method or device that displays and allows users to select activities generated by information processing means.

[0461] "Support measures" refer to methods or devices for searching for and providing relevant information based on the activities selected by the user.

[0462] This invention is a system that proposes activities and learning opportunities optimized for an individual based on the user's attribute information. This system comprises an input device, an information processing device, a display device, and a support device.

[0463] Users access the system through a terminal and first input attribute information such as their preferences and work experience. Specifically, they use input devices such as keyboards and touchscreens. By collecting this information, it is possible to clearly understand the user's interests and concerns.

[0464] An information processing device installed within the server analyzes this attribute information using a generative AI model. The generative AI model utilizes machine learning algorithms to extract the user's potential interests and preferences. This process generates a list of activities tailored to the user. The activity list includes hobbies, sports, new skill acquisition, and travel plans based on the user's interests.

[0465] The generated activity list is presented to the user on a user-friendly interface via a display device. Users can browse this interface and select the activity that best matches their interests from the presented options. For example, a user interested in cooking and travel might be offered a cooking class or a travel plan to learn about cuisines from around the country.

[0466] When a user selects a specific activity, that information is retransmitted to the server via the terminal. Based on this information, the support device searches for and provides specific resources related to the selected activity. These include local activity groups, online communities, and event schedules. Opportunities to participate in learning programs and vocational training programs are also provided.

[0467] As a concrete example, if a user expresses a desire to enrich their life after retirement, the prompt "Please suggest suitable activities for retirement based on my hobbies and interests" is entered into the generating AI model. Based on this prompt, the system proposes a specific activity plan based on the user's attribute information.

[0468] This invention makes it possible for users to easily try new activities and gain direction for enjoying a fulfilling second life.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] Users log in to the system via their device and enter attribute information to clarify their preferences, work experience, and current interests. The entered data is collected in the form of text or multiple-choice options and temporarily stored on the device. Specifically, users enter information into on-screen forms using a keyboard or touch panel.

[0472] Step 2:

[0473] The terminal sends the entered attribute information to the server. Protocols are applied to ensure data accuracy and security during this process. The transmitted data is stored in a database on the server and prepared for analysis. Specifically, the terminal sends data to the server using encrypted communication and performs error checking.

[0474] Step 3:

[0475] The server begins data analysis using a generative AI model based on the received attribute information. Here, machine learning algorithms extract the user's latent preferences. The input attribute information is pattern-recognized within the model, and a list of activities best suited to the user is output. The generated list includes suggestions for hobbies and new skills. Specifically, the server executes an analysis job to generate the activity list.

[0476] Step 4:

[0477] The server sends the generated activity list to the terminal. The terminal displays the received list in a user-friendly interface. The user uses this interface to select the activity that interests them most from the provided activities. Specifically, the terminal renders the information as visual elements on the screen and waits for the user's selection.

[0478] Step 5:

[0479] When a user selects a specific activity, that information is sent back from the terminal to the server. Based on this selection, the server uses assistive devices to search for and collect additional relevant information. Specifically, local activity groups, online communities, and event schedules are searched for and prepared to be provided to the user. The process involves the server generating database queries based on the selection and collecting relevant information.

[0480] Step 6:

[0481] Based on information obtained from the support device, the server provides the user with the resources necessary to carry out detailed activities. This includes links to learning programs and vocational training programs. These suggestions are displayed sequentially to the user on the terminal, and can be accessed immediately through the relevant links. Specifically, the system provides the user with the ability to review detailed information and choose to proceed to the next step.

[0482] (Application Example 1)

[0483] 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."

[0484] In modern society, for seniors to enrich their lives after retirement, it is crucial to find activities that suit their preferences and health condition. However, with so much information available, making the optimal choice is not easy. Therefore, there is a need for support systems that enable seniors to efficiently find and participate in activities that match their interests.

[0485] 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.

[0486] In this invention, the server includes an information input means for inputting attribute information from the user, an information processing means equipped with a generation algorithm for analyzing the attribute information received from the information input means and extracting the user's potential preferences and activities, and an information display means for presenting the activities extracted by the information processing means and allowing the user to select one. This makes it possible for the user to intuitively select and participate in activities that suit their hobbies and health condition.

[0487] An "information input means" is an interface device that allows a user to input their own attribute information into the system.

[0488] An "information processing device" is a device that analyzes the received user attribute information and extracts the user's potential preferences and activities using a generation algorithm.

[0489] A "generative algorithm" is a computational method that uses machine learning to suggest the most suitable activities for a user based on attribute information provided by that user.

[0490] An "information display means" is a device that visually presents extracted activity information as a user interface, allowing the user to make a selection.

[0491] A "support device" is a device that searches for relevant activity information based on the user's selection and provides it to the user.

[0492] A "visual display means" is a device that visually displays information so that users can intuitively and easily understand and select activities.

[0493] The "analysis means" is a device that uses machine learning models to identify local communities and events based on user attribute information and to make recommendations.

[0494] In implementing this invention, first, the user inputs attribute information such as their preferences and health status using a terminal. In response, the server receives this attribute information through an information input means. Next, an information processing means installed on the server executes a generation algorithm to analyze the received attribute information. This algorithm is built using a common machine learning library (e.g., TensorFlow or PyTorch) and is used to reveal the user's latent preferences.

[0495] The analyzed information is optimized on the server and extracted as an activity list to be presented to the user. Next, the information display means visually presents this activity list, representing it on an interface that allows the user to intuitively interact with it. For example, by referencing Google Material Design or Apple's Human Interface Guidelines, an environment that is easy for the user to see and operate is provided.

[0496] When a user selects a specific activity, that selection information is sent back to the server. Based on the user's selection, the server's support system searches the internet for various relevant information, such as local communities, online events, and educational programs, and provides it to the user. This system makes it easy for users to find activities that interest them and obtain resources to participate.

[0497] For example, if a male user in his 70s operates a device and inputs that he is interested in "travel" and "DIY," the platform will suggest opportunities to participate in local travel communities and DIY workshops. An example of a related prompt would be: "A man in his 70s, interested in travel and DIY, looking for nearby events." This prompt allows the generative AI model to function properly and provide the user with valuable information.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user uses a terminal to input attribute information about their preferences and health status. This input is sent to the server via the terminal's information input device. The input data is sent to the server in a well-formed format (such as JSON or XML) through the terminal's input device.

[0501] Step 2:

[0502] The server's information processing mechanism uses a generative AI model to analyze the received attribute information. This model is trained using machine learning algorithms (such as TensorFlow or PyTorch) and extracts the user's latent preferences based on the input data. This generates a user profile and outputs indicators showing which activities are suitable.

[0503] Step 3:

[0504] The server creates an optimal activity list based on the analysis results. This activity list is transmitted to the terminal using an information display device. Here, the generated data is converted into a human-readable format and organized so that it can be displayed in a format that is easy for the user to understand intuitively.

[0505] Step 4:

[0506] The user reviews the activity list displayed on their device and selects the activities they are interested in. The selection results are sent back from the device to the server, and the selection information is saved as a log.

[0507] Step 5:

[0508] The server uses support tools to collect additional information related to the user's selection. Specifically, it searches the internet for local communities, online events, and relevant learning materials. The search results are organized and provided to the user's device in an easily accessible format.

[0509] Step 6:

[0510] Based on the information provided on the device, users prepare to participate in their chosen activity. At this stage, support information (e.g., links for registration and contact information) is displayed on the device as needed, allowing users to take action.

[0511] 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.

[0512] This invention relates to a system that recognizes a user's attribute information and emotions and makes optimal activity suggestions based on that information. The system comprises an input device, a processing device, a display device, a support device, and an emotion engine, and provides useful guidelines for enriching the user's second life.

[0513] Users first use a device to input attribute information such as their preferences and work experience. In addition, the emotion engine acquires emotional data from facial expressions, tone of voice, and input patterns to analyze the user's emotional state during input and activity selection. This emotional data is key to evaluating the user's motivation and level of interest.

[0514] The processing unit on the server uses a generative model to comprehensively analyze user attribute information and emotional data. This process generates a list of activities that take into account the user's current emotional state and past psychological tendencies. For example, if a user expresses interest in "travel" and "cooking," but their current emotional state indicates they are seeking a relaxing experience, a quiet cooking-related workshop will be suggested.

[0515] The suggested list of activities is presented to the user via a display device. The user selects activities of interest from the provided options, and their selection history and associated sentiment data are stored in the system to inform future suggestions.

[0516] Based on user selections, the assistive device searches for relevant activity information and provides specific actionable information. The device displays details such as stores, groups, and learning programs related to the activity, helping the user to smoothly begin the activity. Furthermore, the emotion engine continuously collects user emotional feedback and measures activity satisfaction, which helps to provide more accurate suggestions.

[0517] Thus, the present invention provides a comprehensive system that combines user emotions and attribute data, supporting users in enriching their second lives emotionally.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users input attribute information about their preferences and work experience through their device. The input data includes specific information such as "I like traveling" or "I worked as an engineer for 30 years."

[0521] Step 2:

[0522] The terminal formats the input attribute information and simultaneously collects the user's emotional state using an emotion engine. The emotion engine detects emotions from the user's facial expressions and tone of voice and sends the analyzed data to the server.

[0523] Step 3:

[0524] Based on the received attribute information and sentiment data, the server uses a generative model to extract the user's potential preferences and suitable activities. The generative model also takes sentiment data into consideration to create activity suggestions that match the user's current mood.

[0525] Step 4:

[0526] The server sends the generated activity list to the terminal. The activity list includes multiple suggestions that match the user's interests and are appropriate to their current emotional state.

[0527] Step 5:

[0528] The device displays the received activity list on the user interface for the user to review. For example, relaxing activities related to cooking or travel might be presented.

[0529] Step 6:

[0530] The user selects an activity of interest from the provided suggestions. During the activity selection process, the user's emotional state is also detected.

[0531] Step 7:

[0532] The device then sends the user's selected activity and their emotional data back to the server.

[0533] Step 8:

[0534] Based on the user's selection, the server uses assistive devices to search for relevant activity information. This includes detailed information for carrying out the selected activity, such as participation procedures, schedules, and relevant community information.

[0535] Step 9:

[0536] The server sends activity information to the terminal and provides it to the user.

[0537] Step 10:

[0538] The device displays received information to the user, supporting a smooth start to activities. The emotion engine continuously records the user's emotional feedback and uses it to improve the accuracy of future suggestions.

[0539] (Example 2)

[0540] 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."

[0541] In modern society, there is a demand for users to select activities that are appropriate to their individual lifestyles and interests. However, it is difficult for users to efficiently select the activities they desire from a variety of options, and it is particularly challenging to respond quickly to changes in their emotions and interests. Conventional systems fail to make suggestions that adequately consider the user's emotional state, resulting in decreased user satisfaction. Therefore, the challenge is to provide a system that automatically suggests the optimal activity based on the user's attribute information and emotional data.

[0542] 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.

[0543] In this invention, the server includes data input means for acquiring attribute information and emotional data from the user, information processing means for analyzing the attribute information and emotional data obtained using a generative model, and information display means for presenting and allowing the user to select activities based on the analysis results. This makes it possible to suggest activities that reflect changes in the user's attributes and emotions.

[0544] "Data input means" refers to a device or method for obtaining attribute information and sentiment data from a user.

[0545] "Information processing means" refers to a device or method for analyzing acquired attribute information and sentiment data, and for making activity suggestions to the user using a generative model.

[0546] A "generative model" is an algorithm or program that automatically generates optimal activity suggestions using a user's past selection history, attribute information, and sentiment data.

[0547] "Information display means" refers to a device or method that visually presents activities to the user based on the analysis results of information processing means.

[0548] "Guidance means" refers to a device or method for searching for and providing detailed information related to a selected activity based on the user's choice.

[0549] "Data collection means" refers to a device or method for collecting users' emotional feedback, evaluating their satisfaction with the activity, and improving future suggestions.

[0550] This invention relates to a system that provides optimal activity suggestions based on user attribute information and sentiment data. The system includes data input means, information processing means, information display means, guidance means, and data collection means.

[0551] The user first uses a device to input attribute information such as their hobbies and work experience. This device is equipped with a camera and microphone, and emotional data is acquired through the user's facial expressions and tone of voice. Through this process, the user's current emotional state is evaluated.

[0552] The server receives attribute information and sentiment data transmitted from the terminal and analyzes it through information processing tools. This analysis uses a generative AI model, which automatically generates activity suggestions that reflect the user's past selection history and emotional state. For example, if a user's interests are "travel" and "cooking" and they desire a relaxing experience, the server might suggest quiet activities such as cooking classes.

[0553] The generated activity suggestions are presented to the user through an information display device. The user can select an activity that interests them from this list. Furthermore, the terminal sends a request to the server based on the selected activity, and the server, via a guidance device, searches for and provides the user with detailed information about the relevant activity. This allows the user to obtain specific steps to begin the activity.

[0554] Furthermore, the system records users' emotional feedback using data collection methods and evaluates their satisfaction with the activity. This information is used to make more effective suggestions for future activities.

[0555] An example of a prompt message is as follows: "User attributes: Hobbies include traveling, professional experience is teaching, current emotional state is desiring a relaxing experience." Based on such prompts, the generative AI model will suggest appropriate activities.

[0556] The hardware requires user terminals (PCs or smartphones) and a central server, while the software consists of an emotion engine for sentiment analysis and a generative AI model for suggesting activities.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The user inputs their attribute information through the terminal. The terminal captures information such as hobbies and work experience provided by the user using a data entry mechanism. As a result of the data entry, the user's attribute information is obtained.

[0560] Step 2:

[0561] The device uses its camera and microphone to collect emotional data from the user's facial expressions and tone of voice. An emotion engine analyzes this information to identify the user's current emotional state. Based on the emotional data analysis of the input, the user's emotional state is output.

[0562] Step 3:

[0563] The terminal sends collected attribute information and sentiment data to the server. The server uses information processing tools to input this data into a generative AI model and generate prompt sentences. Based on these prompt sentences, the generative AI model performs data calculations to suggest activities appropriate for the user. The resulting output is a list of activity suggestions for the user.

[0564] Step 4:

[0565] The server sends a list of generated activity suggestions to the terminal. The terminal uses an information display device to present this list to the user. The user selects activities of interest from the displayed suggestions. The user's selection is used in the next step.

[0566] Step 5:

[0567] Based on the user's selection, the terminal sends a request to the server, which uses guidance tools to search for and provide detailed information about the relevant activity. This may include, for example, information about the location and time of the selected activity. The retrieved information is sent to the terminal and presented to the user.

[0568] Step 6:

[0569] After a user participates in an activity, emotional feedback is collected through the device and sent to the server. The server uses data collection tools to analyze this feedback and evaluate user satisfaction. This feedback data is used to improve future activity suggestions.

[0570] (Application Example 2)

[0571] 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."

[0572] In modern society, there is a demand for systems that propose personalized activities based on each individual's emotional state and attribute data, thereby enriching their lifestyles. However, existing systems are insufficient in making suggestions based on emotional state, making it difficult to increase user emotional satisfaction. Therefore, a new method is needed to effectively utilize emotional data and propose activities that are optimal for the user.

[0573] 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.

[0574] In this invention, the server includes an input means for inputting attribute data and emotional state from the user; a processing means equipped with a generative model for analyzing the attribute data and emotional state received from the input means and extracting the user's potential preferences and activities; and a display means for optimizing the activities extracted by the processing means based on the user's emotional state and allowing the user to select them. This enables personalized activity suggestions that take the user's emotional state into consideration.

[0575] 1. "Attribute data" refers to information that indicates individual characteristics and backgrounds, such as user preferences, work experience, and past activity history.

[0576] 2. "Emotional state" refers to the user's psychological or emotional condition, including emotions such as joy, sadness, excitement, and stress.

[0577] 3. "Input means" refers to an interface for receiving attribute data and emotional states from the user, and typically consists of sensors or user interface devices.

[0578] 4. "Processing means" refers to a computer or the core computing device of a system that analyzes received attribute data and emotional states and uses a generative model to recommend the most suitable activity to the user.

[0579] 5. A "generative model" is a data analysis algorithm used to suggest personalized activities based on user attribute data and emotional states.

[0580] 6. "Display means" refers to a device for visually or audibly presenting activity suggestions optimized by the processing means to the user.

[0581] The system for realizing this invention is accessible to users on a daily basis using devices such as smartphones and tablets. These devices are equipped with input devices such as cameras, microphones, and touchscreens, which are used to collect user attribute data and emotional states. Users provide information about their hobbies, work experience, and current mood to the device through these input devices.

[0582] The server features a processing program utilizing the Python language and TensorFlow to analyze input data. This processing program uses a generative model to analyze attribute data and emotional states obtained from the user. Data sent from the terminal is processed on the server, and personalized activity suggestions are generated for each user.

[0583] Furthermore, the generated activity suggestions are provided to the user through various display methods. For example, they can be visually presented on a smartphone screen or verbally guided through a voice assistant. The activity suggestions are tailored to the user's emotional state; for instance, based on information such as "You need a relaxing activity today," nearby relaxing events or locations can be suggested.

[0584] For example, if a busy business person is feeling stressed, the system might suggest relaxation classes or events in a park. This would involve a prompt message such as, "Create activity suggestions that can help users relax when they are feeling overwhelmed. Suggest events or locations that are easily accessible from their current location."

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The terminal accepts input from the user. The user uses the terminal's camera, microphone, and touchscreen to input attribute data (e.g., hobbies, work experience) and current emotional state (e.g., stress level, relaxation level). The input data is processed on the terminal and formatted for transmission to the server.

[0588] Step 2:

[0589] The server receives attribute data and emotional state transmitted from the terminal. The server runs a processing program to analyze this data, preprocesses it (e.g., denoising and normalizing), and then organizes it as input for a generative AI model.

[0590] Step 3:

[0591] The server uses a generative AI model to generate personalized activity suggestions based on the user's attribute data and emotional state. This process involves comparison with similar past datasets and analysis of user trends. The generative model uses prompts (e.g., "Please create activity suggestions that will help me relax") to extract the most suitable activity options.

[0592] Step 4:

[0593] The generated activity suggestions are sent from the server to the terminal. The terminal optimizes the content as screen displays and audio guidance to present these suggestions to the user. The user confirms and selects the suggested activities through visual or auditory means.

[0594] Step 5:

[0595] Based on the activity selected by the user, the device requests detailed information related to that activity from the server. The server uses support tools to search for the necessary information (e.g., the location and time of an event) and provides it to the device.

[0596] Step 6:

[0597] The information obtained on the client side is used to guide users to participate in activities. The terminal manages the user's scheduling and displays map and navigation information. User feedback at this stage (e.g., satisfaction with the activity) is also collected and sent to the server, where it is incorporated into future suggestions.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] This invention relates to an activity suggestion platform for senior citizens using a system comprising an input device, a processing device, a display device, and a support device. This system allows users to input their personal attribute information in order to enrich their second life after retirement, and then suggests appropriate activities and learning opportunities based on that information.

[0616] The user first inputs attribute information such as their preferences and professional experience through a terminal. This information clarifies the user's interests and forms the basis for providing optimal suggestions. The input device transmits this information to the processing unit, which then prepares it for analysis.

[0617] The processing unit installed within the server analyzes the received attribute information using a generative model. The generative model uses sophisticated algorithms to extract the user's potential preferences and generate a suitable activity list. This list includes various activities based on the user's interests, such as hobbies, sports, acquiring new skills, and travel plans.

[0618] The generated activity list is presented to the user via a display device on a user-friendly interface. The user can choose the activity that best suits their interests from the displayed options. For example, if the user is interested in cooking and travel, suggestions such as taking a cooking class or a travel plan to learn about local cuisines will be presented.

[0619] Once the selection is confirmed, the terminal resends the information to the server, and the support device then searches for information to assist in carrying out the activity. The support device provides specific resources related to the selected activity, such as local activity groups, online communities, and start dates. In addition, opportunities to participate in learning programs and vocational training programs are also presented to the user.

[0620] Through the suggestions and support provided by this platform, users can more easily try new activities and gain direction to realize the second life they envision. Furthermore, the incentive function provided by the system helps users to continue learning and engaging in activities, thereby building a fulfilling life.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The user uses a device to input attribute information about their preferences and professional experience. For example, the user might input information such as "travel," "cooking," or "30 years of engineering experience."

[0624] Step 2:

[0625] The terminal formats the entered attribute information and sends it to the server. This formatted information includes user preferences and work experience.

[0626] Step 3:

[0627] The server starts analysis using a generative model based on the received attribute information. The generative model uses machine learning algorithms to identify the user's potential preferences and extract appropriate activities and interests.

[0628] Step 4:

[0629] The server creates a list of activities extracted by the generative model and prepares the most suitable suggestions for the user. This list includes hobbies and ways to acquire new skills, selected based on the user's attribute information.

[0630] Step 5:

[0631] The server sends the created activity list to the terminal.

[0632] Step 6:

[0633] The terminal displays the received activity list on the user interface for the user to review. The user interface is designed to be intuitive and easy to use.

[0634] Step 7:

[0635] Users select activities that interest them from the displayed suggestions. For example, they can choose to "participate in a cooking class" or "join a hiking group."

[0636] Step 8:

[0637] The terminal sends the user's selection to the server.

[0638] Step 9:

[0639] Based on the user's selection, the server uses assistive devices to search for relevant activity information. The assistive devices find necessary resources, schedules, online forums, and other information for the selected activity.

[0640] Step 10:

[0641] The server sends the searched activity information to the terminal and provides it to the user. This includes specific instructions on how to participate and information on reskilling courses.

[0642] Step 11:

[0643] The device displays received activity information to the user and prompts interaction. Based on this, the user can start an activity or join a community.

[0644] (Example 1)

[0645] 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".

[0646] For seniors after retirement, finding suitable activities and learning opportunities to lead a fulfilling second life is often difficult. There is a lack of automated methods to provide personalized suggestions based on individual hobbies, interests, and professional experience, resulting in users being unable to efficiently explore activities that best suit them.

[0647] 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.

[0648] In this invention, the server includes an information input means for collecting attribute information from a user, an information processing means having a generative model for analyzing the attribute information obtained from the information input means and generating the user's potential interests and activities, and a presentation means for displaying and making selectable the activities generated by the information processing means to the user. This makes it possible to provide optimal activity suggestions and related information based on the user's individual preferences.

[0649] "User" refers to an individual who inputs attribute information using the system and is eligible to receive activity suggestions.

[0650] "Attribute information" refers to data related to individual interests and backgrounds, such as users' preferences, work experience, and areas of interest.

[0651] "Information input means" refers to methods or devices for collecting attribute information from users, and specifically includes keyboards, touch panels, and the like.

[0652] A "generative model" refers to a model that uses machine learning algorithms to analyze user attribute information and identify and generate potential interests and activities.

[0653] "Information processing means" refers to technologies and devices that analyze input attribute information and use generative models to propose appropriate activities.

[0654] "Presentation means" refers to a method or device that displays and allows users to select activities generated by information processing means.

[0655] "Support measures" refer to methods or devices for searching for and providing relevant information based on the activities selected by the user.

[0656] This invention is a system that proposes activities and learning opportunities optimized for an individual based on the user's attribute information. This system comprises an input device, an information processing device, a display device, and a support device.

[0657] Users access the system through a terminal and first input attribute information such as their preferences and work experience. Specifically, they use input devices such as keyboards and touchscreens. By collecting this information, it is possible to clearly understand the user's interests and concerns.

[0658] An information processing device installed within the server analyzes this attribute information using a generative AI model. The generative AI model utilizes machine learning algorithms to extract the user's potential interests and preferences. This process generates a list of activities tailored to the user. The activity list includes hobbies, sports, new skill acquisition, and travel plans based on the user's interests.

[0659] The generated activity list is presented to the user on a user-friendly interface via a display device. Users can browse this interface and select the activity that best matches their interests from the presented options. For example, a user interested in cooking and travel might be offered a cooking class or a travel plan to learn about cuisines from around the country.

[0660] When a user selects a specific activity, that information is retransmitted to the server via the terminal. Based on this information, the support device searches for and provides specific resources related to the selected activity. These include local activity groups, online communities, and event schedules. Opportunities to participate in learning programs and vocational training programs are also provided.

[0661] As a concrete example, if a user expresses a desire to enrich their life after retirement, the prompt "Please suggest suitable activities for retirement based on my hobbies and interests" is entered into the generating AI model. Based on this prompt, the system proposes a specific activity plan based on the user's attribute information.

[0662] This invention makes it possible for users to easily try new activities and gain direction for enjoying a fulfilling second life.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] Users log in to the system via their device and enter attribute information to clarify their preferences, work experience, and current interests. The entered data is collected in the form of text or multiple-choice options and temporarily stored on the device. Specifically, users enter information into on-screen forms using a keyboard or touch panel.

[0666] Step 2:

[0667] The terminal sends the entered attribute information to the server. Protocols are applied to ensure data accuracy and security during this process. The transmitted data is stored in a database on the server and prepared for analysis. Specifically, the terminal sends data to the server using encrypted communication and performs error checking.

[0668] Step 3:

[0669] The server begins data analysis using a generative AI model based on the received attribute information. Here, machine learning algorithms extract the user's latent preferences. The input attribute information is pattern-recognized within the model, and a list of activities best suited to the user is output. The generated list includes suggestions for hobbies and new skills. Specifically, the server executes an analysis job to generate the activity list.

[0670] Step 4:

[0671] The server sends the generated activity list to the terminal. The terminal displays the received list in a user-friendly interface. The user uses this interface to select the activity that interests them most from the provided activities. Specifically, the terminal renders the information as visual elements on the screen and waits for the user's selection.

[0672] Step 5:

[0673] When a user selects a specific activity, that information is sent back from the terminal to the server. Based on this selection, the server uses assistive devices to search for and collect additional relevant information. Specifically, local activity groups, online communities, and event schedules are searched for and prepared to be provided to the user. The process involves the server generating database queries based on the selection and collecting relevant information.

[0674] Step 6:

[0675] Based on information obtained from the support device, the server provides the user with the resources necessary to carry out detailed activities. This includes links to learning programs and vocational training programs. These suggestions are displayed sequentially to the user on the terminal, and can be accessed immediately through the relevant links. Specifically, the system provides the user with the ability to review detailed information and choose to proceed to the next step.

[0676] (Application Example 1)

[0677] 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".

[0678] In modern society, for seniors to enrich their lives after retirement, it is crucial to find activities that suit their preferences and health condition. However, with so much information available, making the optimal choice is not easy. Therefore, there is a need for support systems that enable seniors to efficiently find and participate in activities that match their interests.

[0679] 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.

[0680] In this invention, the server includes an information input means for inputting attribute information from the user, an information processing means equipped with a generation algorithm for analyzing the attribute information received from the information input means and extracting the user's potential preferences and activities, and an information display means for presenting the activities extracted by the information processing means and allowing the user to select one. This makes it possible for the user to intuitively select and participate in activities that suit their hobbies and health condition.

[0681] An "information input means" is an interface device that allows a user to input their own attribute information into the system.

[0682] An "information processing device" is a device that analyzes the received user attribute information and extracts the user's potential preferences and activities using a generation algorithm.

[0683] A "generative algorithm" is a computational method that uses machine learning to suggest the most suitable activities for a user based on attribute information provided by that user.

[0684] An "information display means" is a device that visually presents extracted activity information as a user interface, allowing the user to make a selection.

[0685] A "support device" is a device that searches for relevant activity information based on the user's selection and provides it to the user.

[0686] A "visual display means" is a device that visually displays information so that users can intuitively and easily understand and select activities.

[0687] The "analysis means" is a device that uses machine learning models to identify local communities and events based on user attribute information and to make recommendations.

[0688] In implementing this invention, first, the user inputs attribute information such as their preferences and health status using a terminal. In response, the server receives this attribute information through an information input means. Next, an information processing means installed on the server executes a generation algorithm to analyze the received attribute information. This algorithm is built using a common machine learning library (e.g., TensorFlow or PyTorch) and is used to reveal the user's latent preferences.

[0689] The analyzed information is optimized on the server and extracted as an activity list to be presented to the user. Next, the information display means visually presents this activity list, representing it on an interface that allows the user to intuitively interact with it. For example, by referencing Google Material Design or Apple's Human Interface Guidelines, an environment that is easy for the user to see and operate is provided.

[0690] When a user selects a specific activity, that selection information is sent back to the server. Based on the user's selection, the server's support system searches the internet for various relevant information, such as local communities, online events, and educational programs, and provides it to the user. This system makes it easy for users to find activities that interest them and obtain resources to participate.

[0691] For example, if a male user in his 70s operates a device and inputs that he is interested in "travel" and "DIY," the platform will suggest opportunities to participate in local travel communities and DIY workshops. An example of a related prompt would be: "A man in his 70s, interested in travel and DIY, looking for nearby events." This prompt allows the generative AI model to function properly and provide the user with valuable information.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The user uses a terminal to input attribute information about their preferences and health status. This input is sent to the server via the terminal's information input device. The input data is sent to the server in a well-formed format (such as JSON or XML) through the terminal's input device.

[0695] Step 2:

[0696] The server's information processing mechanism uses a generative AI model to analyze the received attribute information. This model is trained using machine learning algorithms (such as TensorFlow or PyTorch) and extracts the user's latent preferences based on the input data. This generates a user profile and outputs indicators showing which activities are suitable.

[0697] Step 3:

[0698] The server creates an optimal activity list based on the analysis results. This activity list is transmitted to the terminal using an information display device. Here, the generated data is converted into a human-readable format and organized so that it can be displayed in a format that is easy for the user to understand intuitively.

[0699] Step 4:

[0700] The user reviews the activity list displayed on their device and selects the activities they are interested in. The selection results are sent back from the device to the server, and the selection information is saved as a log.

[0701] Step 5:

[0702] The server uses support tools to collect additional information related to the user's selection. Specifically, it searches the internet for local communities, online events, and relevant learning materials. The search results are organized and provided to the user's device in an easily accessible format.

[0703] Step 6:

[0704] Based on the information provided on the device, users prepare to participate in their chosen activity. At this stage, support information (e.g., links for registration and contact information) is displayed on the device as needed, allowing users to take action.

[0705] 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.

[0706] This invention relates to a system that recognizes a user's attribute information and emotions and makes optimal activity suggestions based on that information. The system comprises an input device, a processing device, a display device, a support device, and an emotion engine, and provides useful guidelines for enriching the user's second life.

[0707] Users first use a device to input attribute information such as their preferences and work experience. In addition, the emotion engine acquires emotional data from facial expressions, tone of voice, and input patterns to analyze the user's emotional state during input and activity selection. This emotional data is key to evaluating the user's motivation and level of interest.

[0708] The processing unit on the server uses a generative model to comprehensively analyze user attribute information and emotional data. This process generates a list of activities that take into account the user's current emotional state and past psychological tendencies. For example, if a user expresses interest in "travel" and "cooking," but their current emotional state indicates they are seeking a relaxing experience, a quiet cooking-related workshop will be suggested.

[0709] The suggested list of activities is presented to the user via a display device. The user selects activities of interest from the provided options, and their selection history and associated sentiment data are stored in the system to inform future suggestions.

[0710] Based on user selections, the assistive device searches for relevant activity information and provides specific actionable information. The device displays details such as stores, groups, and learning programs related to the activity, helping the user to smoothly begin the activity. Furthermore, the emotion engine continuously collects user emotional feedback and measures activity satisfaction, which helps to provide more accurate suggestions.

[0711] Thus, the present invention provides a comprehensive system that combines user emotions and attribute data, supporting users in enriching their second lives emotionally.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] Users input attribute information about their preferences and work experience through their device. The input data includes specific information such as "I like traveling" or "I worked as an engineer for 30 years."

[0715] Step 2:

[0716] The terminal formats the input attribute information and simultaneously collects the user's emotional state using an emotion engine. The emotion engine detects emotions from the user's facial expressions and tone of voice and sends the analyzed data to the server.

[0717] Step 3:

[0718] Based on the received attribute information and sentiment data, the server uses a generative model to extract the user's potential preferences and suitable activities. The generative model also takes sentiment data into consideration to create activity suggestions that match the user's current mood.

[0719] Step 4:

[0720] The server sends the generated activity list to the terminal. The activity list includes multiple suggestions that match the user's interests and are appropriate to their current emotional state.

[0721] Step 5:

[0722] The device displays the received activity list on the user interface for the user to review. For example, relaxing activities related to cooking or travel might be presented.

[0723] Step 6:

[0724] The user selects an activity of interest from the provided suggestions. During the activity selection process, the user's emotional state is also detected.

[0725] Step 7:

[0726] The device then sends the user's selected activity and their emotional data back to the server.

[0727] Step 8:

[0728] Based on the user's selection, the server uses assistive devices to search for relevant activity information. This includes detailed information for carrying out the selected activity, such as participation procedures, schedules, and relevant community information.

[0729] Step 9:

[0730] The server sends activity information to the terminal and provides it to the user.

[0731] Step 10:

[0732] The device displays received information to the user, supporting a smooth start to activities. The emotion engine continuously records the user's emotional feedback and uses it to improve the accuracy of future suggestions.

[0733] (Example 2)

[0734] 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".

[0735] In modern society, there is a demand for users to select activities that are appropriate to their individual lifestyles and interests. However, it is difficult for users to efficiently select the activities they desire from a variety of options, and it is particularly challenging to respond quickly to changes in their emotions and interests. Conventional systems fail to make suggestions that adequately consider the user's emotional state, resulting in decreased user satisfaction. Therefore, the challenge is to provide a system that automatically suggests the optimal activity based on the user's attribute information and emotional data.

[0736] 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.

[0737] In this invention, the server includes data input means for acquiring attribute information and emotional data from the user, information processing means for analyzing the attribute information and emotional data obtained using a generative model, and information display means for presenting and allowing the user to select activities based on the analysis results. This makes it possible to suggest activities that reflect changes in the user's attributes and emotions.

[0738] "Data input means" refers to a device or method for obtaining attribute information and sentiment data from a user.

[0739] "Information processing means" refers to a device or method for analyzing acquired attribute information and sentiment data, and for making activity suggestions to the user using a generative model.

[0740] A "generative model" is an algorithm or program that automatically generates optimal activity suggestions using a user's past selection history, attribute information, and sentiment data.

[0741] "Information display means" refers to a device or method that visually presents activities to the user based on the analysis results of information processing means.

[0742] "Guidance means" refers to a device or method for searching for and providing detailed information related to a selected activity based on the user's choice.

[0743] "Data collection means" refers to a device or method for collecting users' emotional feedback, evaluating their satisfaction with the activity, and improving future suggestions.

[0744] This invention relates to a system that provides optimal activity suggestions based on user attribute information and sentiment data. The system includes data input means, information processing means, information display means, guidance means, and data collection means.

[0745] The user first uses a device to input attribute information such as their hobbies and work experience. This device is equipped with a camera and microphone, and emotional data is acquired through the user's facial expressions and tone of voice. Through this process, the user's current emotional state is evaluated.

[0746] The server receives attribute information and sentiment data transmitted from the terminal and analyzes it through information processing tools. This analysis uses a generative AI model, which automatically generates activity suggestions that reflect the user's past selection history and emotional state. For example, if a user's interests are "travel" and "cooking" and they desire a relaxing experience, the server might suggest quiet activities such as cooking classes.

[0747] The generated activity suggestions are presented to the user through an information display device. The user can select an activity that interests them from this list. Furthermore, the terminal sends a request to the server based on the selected activity, and the server, via a guidance device, searches for and provides the user with detailed information about the relevant activity. This allows the user to obtain specific steps to begin the activity.

[0748] Furthermore, the system records users' emotional feedback using data collection methods and evaluates their satisfaction with the activity. This information is used to make more effective suggestions for future activities.

[0749] An example of a prompt message is as follows: "User attributes: Hobbies include traveling, professional experience is teaching, current emotional state is desiring a relaxing experience." Based on such prompts, the generative AI model will suggest appropriate activities.

[0750] The hardware requires user terminals (PCs or smartphones) and a central server, while the software consists of an emotion engine for sentiment analysis and a generative AI model for suggesting activities.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The user inputs their attribute information through the terminal. The terminal captures information such as hobbies and work experience provided by the user using a data entry mechanism. As a result of the data entry, the user's attribute information is obtained.

[0754] Step 2:

[0755] The device uses its camera and microphone to collect emotional data from the user's facial expressions and tone of voice. An emotion engine analyzes this information to identify the user's current emotional state. Based on the emotional data analysis of the input, the user's emotional state is output.

[0756] Step 3:

[0757] The terminal sends collected attribute information and sentiment data to the server. The server uses information processing tools to input this data into a generative AI model and generate prompt sentences. Based on these prompt sentences, the generative AI model performs data calculations to suggest activities appropriate for the user. The resulting output is a list of activity suggestions for the user.

[0758] Step 4:

[0759] The server sends a list of generated activity suggestions to the terminal. The terminal uses an information display device to present this list to the user. The user selects activities of interest from the displayed suggestions. The user's selection is used in the next step.

[0760] Step 5:

[0761] Based on the user's selection, the terminal sends a request to the server, which uses guidance tools to search for and provide detailed information about the relevant activity. This may include, for example, information about the location and time of the selected activity. The retrieved information is sent to the terminal and presented to the user.

[0762] Step 6:

[0763] After a user participates in an activity, emotional feedback is collected through the device and sent to the server. The server uses data collection tools to analyze this feedback and evaluate user satisfaction. This feedback data is used to improve future activity suggestions.

[0764] (Application Example 2)

[0765] 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".

[0766] In modern society, there is a demand for systems that propose personalized activities based on each individual's emotional state and attribute data, thereby enriching their lifestyles. However, existing systems are insufficient in making suggestions based on emotional state, making it difficult to increase user emotional satisfaction. Therefore, a new method is needed to effectively utilize emotional data and propose activities that are optimal for the user.

[0767] 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.

[0768] In this invention, the server includes an input means for inputting attribute data and emotional state from the user; a processing means equipped with a generative model for analyzing the attribute data and emotional state received from the input means and extracting the user's potential preferences and activities; and a display means for optimizing the activities extracted by the processing means based on the user's emotional state and allowing the user to select them. This enables personalized activity suggestions that take the user's emotional state into consideration.

[0769] 1. "Attribute data" refers to information that indicates individual characteristics and backgrounds, such as user preferences, work experience, and past activity history.

[0770] 2. "Emotional state" refers to the user's psychological or emotional condition, including emotions such as joy, sadness, excitement, and stress.

[0771] 3. "Input means" refers to an interface for receiving attribute data and emotional states from the user, and typically consists of sensors or user interface devices.

[0772] 4. "Processing means" refers to a computer or the core computing device of a system that analyzes received attribute data and emotional states and uses a generative model to recommend the most suitable activity to the user.

[0773] 5. A "generative model" is a data analysis algorithm used to suggest personalized activities based on user attribute data and emotional states.

[0774] 6. "Display means" refers to a device for visually or audibly presenting activity suggestions optimized by the processing means to the user.

[0775] The system for realizing this invention is accessible to users on a daily basis using devices such as smartphones and tablets. These devices are equipped with input devices such as cameras, microphones, and touchscreens, which are used to collect user attribute data and emotional states. Users provide information about their hobbies, work experience, and current mood to the device through these input devices.

[0776] The server features a processing program utilizing the Python language and TensorFlow to analyze input data. This processing program uses a generative model to analyze attribute data and emotional states obtained from the user. Data sent from the terminal is processed on the server, and personalized activity suggestions are generated for each user.

[0777] Furthermore, the generated activity suggestions are provided to the user through various display methods. For example, they can be visually presented on a smartphone screen or verbally guided through a voice assistant. The activity suggestions are tailored to the user's emotional state; for instance, based on information such as "You need a relaxing activity today," nearby relaxing events or locations can be suggested.

[0778] For example, if a busy business person is feeling stressed, the system might suggest relaxation classes or events in a park. This would involve a prompt message such as, "Create activity suggestions that can help users relax when they are feeling overwhelmed. Suggest events or locations that are easily accessible from their current location."

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The terminal accepts input from the user. The user uses the terminal's camera, microphone, and touchscreen to input attribute data (e.g., hobbies, work experience) and current emotional state (e.g., stress level, relaxation level). The input data is processed on the terminal and formatted for transmission to the server.

[0782] Step 2:

[0783] The server receives attribute data and emotional state transmitted from the terminal. The server runs a processing program to analyze this data, preprocesses it (e.g., denoising and normalizing), and then organizes it as input for a generative AI model.

[0784] Step 3:

[0785] The server uses a generative AI model to generate personalized activity suggestions based on the user's attribute data and emotional state. This process involves comparison with similar past datasets and analysis of user trends. The generative model uses prompts (e.g., "Please create activity suggestions that will help me relax") to extract the most suitable activity options.

[0786] Step 4:

[0787] The generated activity suggestions are sent from the server to the terminal. The terminal optimizes the content as screen displays and audio guidance to present these suggestions to the user. The user confirms and selects the suggested activities through visual or auditory means.

[0788] Step 5:

[0789] Based on the activity selected by the user, the device requests detailed information related to that activity from the server. The server uses support tools to search for the necessary information (e.g., the location and time of an event) and provides it to the device.

[0790] Step 6:

[0791] The information obtained on the client side is used to guide users to participate in activities. The terminal manages the user's scheduling and displays map and navigation information. User feedback at this stage (e.g., satisfaction with the activity) is also collected and sent to the server, where it is incorporated into future suggestions.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] An input device for inputting attribute information from the user,

[0816] A processing device equipped with a generative model for analyzing attribute information received from the input device and extracting the user's potential preferences and activities,

[0817] A display device that presents the activities extracted by the processing device and allows the user to select one,

[0818] A support device for searching for relevant activity information based on the user's selection and providing it to the user,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the generation model has a function of proposing activities based on the user's historical information.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the support device includes a learning program or a vocational training program among the information it provides based on the user's selection.

[0824] "Example 1"

[0825] (Claim 1)

[0826] An information input means for collecting attribute information from users,

[0827] An information processing means having a generative model for analyzing attribute information obtained from the information input means and generating the user's potential interests and activities,

[0828] A presentation means for displaying and making selectable the activities generated by the information processing means to the user,

[0829] A support mechanism for searching for and providing corresponding activity information to the user based on the user's selection,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the generation model has a function of proposing activities based on the user's history information.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the support means includes an educational program or a vocational training program among the information provided based on the user's choice.

[0835] "Application Example 1"

[0836] (Claim 1)

[0837] An information input means for inputting attribute information from the user,

[0838] Information processing means equipped with a generation algorithm for analyzing attribute information received from the information input means and extracting the user's potential preferences and activities,

[0839] Information display means that presents the activities extracted by the information processing means and allows the user to make a selection,

[0840] A support means for searching for relevant activity information based on the user's selection and providing it to the user,

[0841] A visual display means that allows users to intuitively display information through a user interface and select activities,

[0842] An analytical method for analyzing information using machine learning models to identify local communities and events,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the generation algorithm has the function of suggesting the optimal activity based on the user's past activity history.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the support means includes an educational program or a skills training program among the information provided based on the user's selection.

[0848] "Example 2 of combining an emotion engine"

[0849] (Claim 1)

[0850] A data input means for inputting attribute information from the user,

[0851] Information processing means having a generative model for analyzing attribute information and emotional data received from the data input means to extract the user's potential preferences, emotional state, and activities,

[0852] Information display means for displaying the activities extracted by the information processing means and allowing the user to select them,

[0853] A means of guiding the user to search for and provide details about related activities based on the user's selection,

[0854] A means of collecting data to gather user emotional feedback, evaluate satisfaction with activities, and use it to make suggestions for the next time,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the generative model has the function of suggesting activities based on the user's past selection history and associated sentiment data.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the guidance means includes an educational program or a skills training program among the information provided based on the user's selection.

[0860] "Application example 2 when combining with an emotional engine"

[0861] (Claim 1)

[0862] An input means for inputting attribute data and emotional state from the user,

[0863] A processing means comprising a generative model for analyzing attribute data and emotional states received from the input means and extracting the user's potential preferences and activities,

[0864] A display means that optimizes the activities extracted by the processing means based on the user's emotional state and allows the user to select them,

[0865] A support means for searching for relevant activity information based on the user's selection and providing it to the user,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the generative model has the function of suggesting activities based on the user's historical data and emotional state.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the support means includes an educational program or a vocational training program among the information provided based on the user's selection. [Explanation of Symbols]

[0871] 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. An input device for inputting attribute information from the user, A processing device equipped with a generative model for analyzing attribute information received from the input device and extracting the user's potential preferences and activities, A display device that presents the activities extracted by the processing device and allows the user to select one, A support device for searching for relevant activity information based on the user's selection and providing it to the user, A system that includes this.

2. The system according to claim 1, wherein the generation model has a function of proposing activities based on the user's historical information.

3. The system according to claim 1, wherein the support device includes a learning program or a vocational training program among the information it provides based on the user's selection.

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