System

Abstract

A system is provided.SOLUTION: Means for receiving personal information, preferences, and social experience input by a user, means for analyzing the received information and identifying preferences and interests of the user, means for listing candidate activities and tasks based on the identified preferences and interests, means for selecting an activity and a task most suitable for the user from the listed activities and tasks, and means for presenting the selected activity and task to the user, A system comprising: means for receiving a selection of a user; means for performing matching with a related facility or service based on the selection of the user; and means for providing a result of the matching to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In an aging society, many seniors do not know what to do after retirement, making it difficult for them to live a fulfilling second life. Seniors who have worked hard in particular have difficulty finding new hobbies or activities after retirement. The present invention aims to solve this problem by providing a method and system that allows retired seniors to find activities and jobs that suit them and live a fulfilling second life. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system including: means for receiving personal information, preferences, and social experiences input by a user; means for analyzing the received information and identifying the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for selecting the activity or job most suitable for the user from the listed activities and jobs; means for presenting the selected activity or job to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; and means for providing the user with the matching results.

[0006] By using the persona function to narrow down the activities and jobs listed to the most suitable ones, and by further including a means for recording feedback based on the user's selections in a database and reflecting this in future suggestions, it is possible to suggest activities and jobs that are more suited to the user's individual tastes and interests.

[0007] "User" refers to an individual who uses this system, and is particularly intended for retired seniors.

[0008] "Personal Information" refers to basic data provided by users, such as age, gender, work history, hobbies, and interests.

[0009] "Tastes" refer to a user's preferences and interests, including tendencies toward particular activities and hobbies.

[0010] "Social experience" refers to the achievements and experiences that the user has had up to now, such as work and volunteer work.

[0011] "Means for receiving" refers to the interface that allows users to input personal information, preferences, and social experiences into the system.

[0012] "Means of analyzing" refers to the process of using an AI model based on the received information to identify the user's preferences and interests.

[0013] "Listing method" refers to the process of generating suitable activities and job candidates based on analyzed preferences and interests.

[0014] "Selection" refers to the process of choosing from the listed activities or tasks the one that best suits the user.

[0015] "Presentation means" refers to an interface that visually shows the selected activities or tasks to the user and prompts them to make a selection.

[0016] "Means for matching" refers to the process of searching for and connecting with facilities and services related to the activity or job selected by the user.

[0017] "Means for providing" refers to an interface for presenting matching results and specific plans to users.

[0018] The "persona function" refers to a function that creates a virtual image of a person based on the user's characteristics and optimizes the content of proposals based on that image.

[0019] "Means for recording feedback" refers to the process of storing the user's choices and behavioral results in a database and reflecting them in future proposed algorithms. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

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

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] Overall Overview

[0042] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[0043] Overview of program processing

[0044] Entering initial data

[0045] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data, verifies it, and then sends it to the server.

[0046] Data reception and storage

[0047] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[0048] Analysis of tastes and interests

[0049] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[0050] Listing potential activities

[0051] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[0052] Persona-based optimization

[0053] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[0054] Matching and proposal implementation

[0055] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The device displays specific plans (e.g., golf lessons or community activities to be held on a specific day) to the user.

[0056] User Choice and Feedback

[0057] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[0058] Specific examples

[0059] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[0060] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[0061] The server then searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Mr. A, such as "golf practice every Tuesday and Saturday" or "senior travel tour." Finally, if Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future proposal algorithms.

[0062] In this way, the system suggests activities and jobs that are suitable for seniors to live a fulfilling second life.

[0063] The processing flow will be explained below.

[0064] Step 1: Enter initial data

[0065] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[0066] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[0067] Step 2: Receiving and storing data

[0068] The server receives the user data transmitted from the terminal.

[0069] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[0070] Step 3: Analyze tastes and interests

[0071] The server transmits the stored user data to the AI ​​analysis module.

[0072] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[0073] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[0074] Step 4: List potential activities

[0075] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0076] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[0077] Step 5: Optimize with personas

[0078] The server further optimizes the generated candidate list using a persona function.

[0079] The server narrows down the candidates based on other users' success stories and past data.

[0080] Step 6: Matching and proposal formulation

[0081] The server searches for relevant facilities and services to carry out the selected activity.

[0082] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[0083] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[0084] Step 7: User Choices and Feedback

[0085] Users review the specific activities and facilities proposed and select those that interest them.

[0086] The terminal transmits the user's selection information to the server.

[0087] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[0088] In this way, the system makes suggestions based on the user's tastes and interests to support a fulfilling second life after retirement, allowing seniors to find the activities and jobs that are best suited to them.

[0089] Example 1

[0090] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0091] For today's seniors, finding activities and work that suit their tastes and interests is important for them to enjoy a fulfilling second life after retirement. However, it is not easy to accurately analyze individual tastes and interests and suggest optimal activities and work. Conventional methods lack efficient and accurate systems for making appropriate suggestions to seniors. There is a need for a system that can solve this problem and support seniors in living a satisfying second life.

[0092] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0093] In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; analysis means using a generative AI model to analyze the received information and identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the activities and jobs that are most suitable for the user from the listed activities and jobs using a persona function; means for presenting the optimized activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; means for providing the matching results to the user; and means for recording feedback based on the user's selection in a database and reflecting it in future suggestions. This enables highly accurate activity and job suggestions to be made to each user.

[0094] "User" refers to a person who uses the system to input their personal information, preferences, and social experiences and receive suggestions.

[0095] "Personal Information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[0096] "Taste" refers to a particular preference or thing that a user has a preference for.

[0097] "Interests" refer to areas or activities that interest a user.

[0098] "Receiving means" refers to the means for transmitting personal information, preferences, and social experiences entered by the user to the server.

[0099] "Analysis means" refers to the means for identifying a user's preferences and interests using a generative AI model based on the received information.

[0100] A "generative AI model" refers to an artificial intelligence algorithm that analyzes a user's tastes and interests based on the data it receives.

[0101] "Listing means" refers to a means for generating candidate activities or jobs based on analyzed tastes and interests.

[0102] The "persona function" refers to a means of narrowing down the activities and tasks that are best suited to individual users based on a large amount of user data and success stories.

[0103] "Optimization measures" refer to measures that use the persona function to narrow down candidate activities and tasks and select the most suitable ones for the user.

[0104] "Matching means" refers to a means for searching for facilities and services related to the activity or work selected by the user and making the optimal match.

[0105] "Providing means" refers to a means for presenting the matching results to the user.

[0106] "Feedback means" refers to a means for recording user selections in a database and reflecting them in future suggestions.

[0107] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. This system uses a generative AI model to analyze the user's interests based on personal information, preferences, social experiences, etc. entered by the user, and suggests optimal activities and jobs.

[0108] First, the user uses a dedicated web portal or mobile app to enter basic information such as age, gender, work history, hobbies, and interests. The device receives this information, validates it, and then sends it to the server.

[0109] The server stores the received user data in a database and verifies its consistency and completeness. If the data is OK, the server sends it to a data analysis module, which uses a generative AI model to identify preferences and interests. This generative AI model uses machine learning algorithms to analyze preferences and interests based on the received data. The analysis results are stored back in the database and assigned relevant category tags.

[0110] The server then generates a list of activities and jobs based on the analyzed preferences and interests, including a number of categories such as sports, hobbies, reskilling, volunteering, etc. It then uses a persona function to optimize the generated list and narrow down the activities and jobs that are most suitable for the user.

[0111] The server then searches for and matches related facilities and services to realize the selected activities or tasks. The matching results are presented to the user via their device as specific plans. For example, specific activities such as "golf practice every Tuesday and Saturday" or "travel tours for seniors" may be presented.

[0112] The user selects the plans they are interested in from the ones presented, and the device sends the selection data to the server, which stores the selection information in a database and reflects it in future proposals.

[0113] As a concrete example, consider the case where a user with 40 years of experience in sales uses this system. The user accesses a dedicated app and enters their age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies the user's tastes and interests as "golf" and "travel." Based on this, it generates a list of golf lesson and travel plan candidates and further optimizes the candidates using the persona function. The server then searches local golf clubs and travel agencies to find the best match. The device presents the user with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tour." If the user selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in the proposal algorithm.

[0114] An example of a prompt sentence is, "Please give us an overview of a system that suggests the best activities for a retired man in his 60s who enjoys golf." Such a system can help seniors live a fulfilling second life.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1: Enter initial data

[0117] The user enters basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data and validates whether it is in a valid format. After validation is complete, the device sends the data to the server. For example, a man in his 60s enters "65 years old, male, 40 years in sales, hobby is golf, interest is travel."

[0118] Input: User's age, gender, work history, hobbies, interests, etc.

[0119] Output: Validated user data

[0120] Step 2: Receiving and storing data

[0121] The server receives the user data sent from the terminal. It verifies the consistency and completeness of the received data, and if there are no problems, it stores it in the database. If there is a defect in the data, the server returns an error message to the terminal. Specifically, the server inserts the information "65 years old, male, 40 years in sales, hobby is golf, interest is travel" into the database.

[0122] Input: Validated user data

[0123] Output: User data stored in the database, error messages (if required)

[0124] Step 3: Analyze tastes and interests

[0125] The server sends the stored user data to an analytics module where a generative AI model runs to identify the user's preferences and interests. The generative AI model uses machine learning algorithms to analyze the user data and identify preferences and interests. The results of this analysis are stored in a database and assigned category tags. For example, the analysis might generate tags for "golf" and "travel."

[0126] Input: User data

[0127] Output: Analyzed hobbies and interests (e.g. golf, travel) and their tags

[0128] Step 4: List potential activities

[0129] The server generates a list of suitable activities and jobs based on the analyzed tastes and interests. It searches the database for matching activities and creates a list of candidates. Specifically, the server extracts activities related to "golf" and "travel" from the database and creates a list of candidates such as "golf lessons" and "travel tours for seniors."

[0130] Input: Parsed tastes and interest tags

[0131] Output: A list of suggested activities and tasks

[0132] Step 5: Optimize with personas

[0133] The server further optimizes the generated candidate list using the persona function. This function refers to the success stories and feedback of other users and prioritizes the most suitable candidates. Specifically, the persona function prioritizes the inclusion in the list of "golf lesson programs that have been popular with users with similar interests in the past."

[0134] Input: List of suggested activities and jobs

[0135] Output: Optimized candidate list

[0136] Step 6: Matching and proposal formulation

[0137] The server searches for related facilities and services to realize the selected activity and finds the best match. The resulting matching information is presented to the user via their device as a specific plan. For example, "Golf practice at a local golf club every Tuesday and Saturday."

[0138] Input: Optimized candidate list

[0139] Output: Specific action plan

[0140] Step 7: User Choices and Feedback

[0141] The user selects the specific activity plan they are interested in from the presented plan. The device receives this selection data and sends it to the server. The server stores the selection information in a database and reflects it in future suggestions.

[0142] Input: User selected data

[0143] Output: Selection information stored in a database, feedback reflected in future proposed algorithms

[0144] (Application example 1)

[0145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] Conventional Second Life support systems have had difficulty accurately analyzing users' tastes and interests and suggesting optimal activities and jobs. They also lacked the means to match users with local physical stores and services in real time and present specific activity suggestions. This meant that users could not receive appropriate guidance to lead a fulfilling Second Life. Furthermore, feedback based on user choices was not reflected in real time, which led to a problem of poor quality in the suggestions provided.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0148] In this invention, the server includes means for receiving personal information, preferences, and social experiences input by a user, means for analyzing the received information and identifying the user's preferences and interests, means for listing candidate activities and jobs based on the identified preferences and interests, means for selecting the activity or job most suitable for the user from the listed activities and jobs, means for presenting the selected activities and jobs to the user and accepting the user's selection, means for matching with related facilities and services based on the user's selection, means for the user to receive suggestions in real time via a smart device, and means for providing the matching results to the user. This allows the user to find the optimal activity or job that suits their preferences and interests, match with local services provided in real time, and quickly receive a specific plan for living a fulfilling second life.

[0149] A "user" is a person who uses this system and inputs personal information, preferences, and interests.

[0150] "Personal information" refers to basic information such as a user's age, gender, and work history.

[0151] "Tastes" refer to activities or interests that a user particularly likes.

[0152] "Interests" are areas or topics that interest a user.

[0153] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[0154] The "analysis means" refers to a function for analyzing and identifying the user's tastes and interests using the received information.

[0155] "Means for listing" refers to a function that lists candidate activities or tasks based on the results of the analysis.

[0156] "Means of selection" refers to the function of selecting the most suitable activity or task for the user from the list of activities or tasks.

[0157] "Means for presentation" refers to the function of displaying selected activities or tasks to the user and prompting them to make a selection.

[0158] The "means for receiving" refers to a function that allows the system to receive a selection from the user.

[0159] "Means for matching" refers to a function that connects with related facilities and services based on the user's selection and makes appropriate suggestions.

[0160] A "smart device" is a terminal such as a smartphone or smart glasses that a user uses to input information and receive suggestions.

[0161] "Means of receiving in real time" refers to a function that allows users to instantly view the content of the proposal.

[0162] "Means for providing" refers to a function for providing the matching results to the user.

[0163] "Feedback" refers to information that the system uses to make future suggestions based on the user's selections.

[0164] Overall Overview

[0165] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[0166] Overview of program processing

[0167] Entering initial data

[0168] A user uses a smart device (such as a smartphone or smart glasses) to input their basic information (such as age, gender, work history, hobbies, and interests). The device receives this input data and sends it to the server.

[0169] Data reception and storage

[0170] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[0171] Analysis of tastes and interests

[0172] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[0173] Listing potential activities

[0174] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[0175] Persona-based optimization

[0176] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[0177] Matching and proposal implementation

[0178] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The terminal displays specific plans (e.g., golf lessons every Tuesday and Saturday, or travel tours for seniors) to the user.

[0179] User Choice and Feedback

[0180] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[0181] Processing hardware and software

[0182] The following hardware and software are used to implement this system.

[0183] Hardware: Raspberry Pi, smartphone, smart glasses

[0184] Software: OpenCV, TensorFlow, JSON format database, AI analysis module

[0185] Specific examples

[0186] For example, if Mr. A, a former salesperson with 40 years of experience, were to use this system, he would use smart glasses to enter his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies Mr. A's tastes and interests as "golf" and "travel." Based on this analysis, a list of options such as golf lessons and travel planning is generated. Next, the persona function is used to optimize the options based on other users' success stories and Mr. A's data. Finally, the server matches Mr. A with local golf clubs and travel agencies and presents him with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tours." If Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in future proposal algorithms.

[0187] Prompt Sentence Examples

[0188] "Choose the activity that best suits you: 1. Golf lessons (Tuesdays and Saturdays) 2. Domestic travel tours (for seniors)"

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] The device receives basic information such as personal information, preferences, interests, and social experiences entered by the user. This input data is entered by the user using a smart device (smartphone or smart glasses). The input data includes age, gender, work history, hobbies, and interests. The received data is verified and then sent from the device to the server.

[0192] Input: User's personal information, preferences, and interests

[0193] Output: Send data to the server

[0194] Step 2:

[0195] The server stores the received user data in a database. When the data is stored, it is verified for consistency and completeness, and if a problem occurs, an error message is returned to the terminal. The data is stored in JSON format.

[0196] Input: User data sent from the terminal

[0197] Output: Data saved to database, error messages (if necessary)

[0198] Step 3:

[0199] The server sends the user data stored in the database to the AI ​​analysis module, which uses a generative AI model to identify preferences and interests. The generative AI model analyzes the input data and identifies the user's preferences and interests as category tags. This clarifies the user's hobbies and interests. The analysis results are then stored back in the database.

[0200] Input: Saved user data

[0201] Output: Identified preferences and interests, stored in a database

[0202] Step 4:

[0203] The server generates a list of suitable activities and jobs based on the identified preferences and interests, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering. The list is generated based on the user's interests, such as "golf lessons" or "trip planning."

[0204] Input: Identified tastes and interests

[0205] Output: A list of suggested activities and tasks

[0206] Step 5:

[0207] The server further optimizes the generated candidate list using the persona function, which narrows down the most suitable activities and tasks based on the success stories of other users and the current user's data. The optimized list is then stored in the database.

[0208] Input: List of suggested activities and jobs

[0209] Output: Optimized activity and task lists

[0210] Step 6:

[0211] The server searches for and matches related facilities and services to realize the selected activity. For example, it searches for local golf clubs and travel agencies, and then identifies the most suitable partners and proposes specific content. The matching results are provided to the user.

[0212] Input: Optimized activity and task lists

[0213] Output: Matching results

[0214] Step 7:

[0215] The terminal provides the user with specific plans and accepts the user's selection. Examples of plans include "Golf lessons every Tuesday and Saturday" and "Domestic travel tours for seniors." The user's selection data is acquired and sent from the terminal to the server.

[0216] Input: Matching results

[0217] Output: User selection, selection data sent to server

[0218] Step 8:

[0219] The server stores the user's selection data in a database and incorporates this feedback information into the suggestion algorithm for future visits, allowing the system to make more accurate suggestions based on the user's preferences.

[0220] Input: User selected data

[0221] Output: Save to database, reflect in algorithm

[0222] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0223] Overall Overview

[0224] This invention relates to a system that suggests activities and jobs for seniors after retirement to help them enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and concerns, and suggests optimal activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[0225] Overview of program processing

[0226] Entering initial data

[0227] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device temporarily stores this input data and sends it to the server when the confirmation button is pressed.

[0228] Data reception and storage

[0229] The server receives the user data sent from the terminal. The server verifies the integrity and consistency of the received data and stores it in a database. If there is a problem, it creates an error message and returns it to the terminal.

[0230] Analysis of tastes and interests

[0231] The server sends the stored user data to an AI analysis module. The server uses a generative AI model to analyze the input data and identify the user's preferences and interests. The server stores the analysis results (e.g., "golf" or "travel") in a database and adds category tags.

[0232] Listing potential activities

[0233] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, listing specific suggestions from categories such as sports, hobbies, reskilling, and volunteer work.

[0234] Persona-based optimization

[0235] The server further optimizes the generated candidate list using the persona function, narrowing down the candidates based on the success stories of other users and past data.

[0236] Emotion recognition by emotion engine

[0237] The server collects emotional data from the user's inputs and selections, recognizes the user's emotional state using an emotion engine, and stores this emotional data in a database to understand the user's emotional state.

[0238] Adjusting the proposal

[0239] The server then tailors its suggestions based on the user's perceived emotional state, for example, suggesting relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[0240] Matching and proposal implementation

[0241] The server searches for and matches related facilities and services to realize the selected activity. It matches the most suitable facilities and services (e.g., local golf clubs, travel agencies). The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tours for seniors) to the user.

[0242] User Choice and Feedback

[0243] The user reviews the specific activities and facilities presented and selects the ones they are interested in. The device then sends this selection data to the server, which stores the selected information and associated emotion data in a database and uses them as feedback for future proposed algorithms.

[0244] Specific examples

[0245] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[0246] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[0247] The server then uses an emotion engine to collect emotion data from A and recognize the user's emotional state. For example, if A has been feeling stressed recently, the server will make suggestions to help A relax (e.g., a trip to a quiet mountain area).

[0248] The server searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Person A, such as "golf practice every Tuesday and Saturday" or "a trip to a quiet mountain area." Finally, if Person A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future suggestion algorithms.

[0249] In this way, by combining emotion engines, it is possible to realize flexible and highly accurate suggestions that correspond to the user's emotional state, allowing seniors to further enrich and enjoy activities that are best suited to them.

[0250] The processing flow will be explained below.

[0251] Step 1: Enter initial data

[0252] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[0253] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[0254] Step 2: Receiving and storing data

[0255] The server receives the user data transmitted from the terminal.

[0256] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[0257] Step 3: Analyze tastes and interests

[0258] The server transmits the stored user data to the AI ​​analysis module.

[0259] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[0260] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[0261] Step 4: List potential activities

[0262] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0263] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[0264] Step 5: Optimize with personas

[0265] The server further optimizes the generated candidate list using a persona function.

[0266] The server narrows down the candidates based on other users' success stories and past data.

[0267] Step 6: Emotion Recognition with the Emotion Engine

[0268] The server collects emotional data associated with the user's inputs and selections, and recognizes the user's emotional state using an emotion engine.

[0269] The server stores this emotional data in a database to understand the user's emotional state.

[0270] Step 7: Adjust your proposal

[0271] The server adjusts the suggestions based on the perceived emotional state.

[0272] The server suggests relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[0273] Step 8: Matching and proposal formulation

[0274] The server searches for and matches related facilities and services to realize the selected activity.

[0275] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[0276] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[0277] Step 9: User Choices and Feedback

[0278] The user selects the specific activities and facilities that interest them from the presented options.

[0279] The terminal transmits the user's selection information to the server.

[0280] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[0281] In this way, by combining the emotion engine, this system makes flexible and accurate suggestions based on the user's emotional state, allowing seniors to enjoy activities that are best suited to them.

[0282] Example 2

[0283] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0284] In the past, retired seniors had to find suitable activities and jobs on their own to enjoy a fulfilling second life, which required a lot of time and effort. Furthermore, the quality of activity suggestions based on tastes and interests was not high, resulting in low satisfaction among seniors. In addition, flexible suggestions that took into account the user's emotional state were not made, making it difficult to provide suggestions that met individual needs.

[0285] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; means for temporarily storing the received information and transmitting the data upon confirmation; means for receiving the transmitted data, verifying its completeness and consistency, and storing it in a database; means for analyzing the stored user data using a generative AI model to identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the listed candidates using a persona function; means for collecting emotional data associated with the user data and input and recognizing the user's emotional state using an emotion engine; means for adjusting the listed and optimized activities and jobs based on the recognized emotional state; means for presenting the adjusted activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; and means for providing the matching results to the user. This makes it easier for seniors to find the activities and jobs that are best suited to them, enabling them to live a fulfilling and satisfying second life.

[0286] "User" refers to a person who uses the system to input personal information, preferences, and social experiences and receive suggestions.

[0287] "Personal information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[0288] "Interests" refer to areas or activities in which a user is particularly interested.

[0289] "Social experience" refers to the user's past work history and social activity experience.

[0290] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[0291] The "means for temporarily saving" refers to a function for temporarily storing information input by a user in a storage device.

[0292] The "confirmation operation" refers to an operation performed by the user to confirm the input contents.

[0293] "Means for transmitting data" refers to a function for sending input data to a server.

[0294] "Means for verifying integrity and consistency" refers to the ability to verify that the data received is accurate and consistent.

[0295] "Database" refers to a storage device for storing and managing user data and analysis results.

[0296] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and identify user preferences and interests.

[0297] "Means for identifying tastes and interests" refers to the function of analyzing and clarifying a user's areas of interest using a generative AI model.

[0298] "Listing means" refers to the ability to list suitable activities or job opportunities based on identified preferences or interests.

[0299] The "persona function" refers to the ability to optimize the list of candidates by utilizing the success stories and past history of other users.

[0300] "Emotion Engine" refers to algorithms and functions for analyzing and recognizing a user's emotional state.

[0301] "Emotional state" refers to the psychological state a user exhibits when making an input or selection.

[0302] "Regulatory measures" refer to the ability to change the listed activities or tasks based on the perceived emotional state.

[0303] "Presenting means" refers to a function that displays optimized suggestions to the user.

[0304] The "means for accepting a selection" refers to a function by which the system receives a selection when the user makes a selection from the presented suggestions.

[0305] "Means for matching" refers to the function of searching for and matching related facilities and services for the activity or job selected by the user.

[0306] "Means for providing matching results" refers to the function of notifying users of matching results with optimal facilities and services.

[0307] This invention relates to a system that suggests activities and jobs that will help seniors live a fulfilling second life after retirement. A specific method for implementing this system will be described below.

[0308] System Overview

[0309] Based on the user's personal information, preferences, and social experiences, the system uses a generative AI model to analyze the user's interests and suggests the most suitable activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[0310] Hardware and software used

[0311] Server: Receives, stores, analyzes, and generates proposals on data. Specific server software that can be used is a web server such as Apache or Nginx, or a database server such as MySQL or PostgreSQL.

[0312] Terminal: A device on which a user inputs data and displays the proposed results. Specifically, this can be a smartphone, tablet, or PC.

[0313] Generative AI model: An AI algorithm used to analyze user preferences and interests. Specifically, it uses a large-scale language model such as GPT-4.

[0314] Emotion engine: Software for analyzing a user's emotional state, specifically using emotion recognition algorithms.

[0315] Specific examples of programs

[0316] For example, consider the case where Mr. A, a former sales professional with 40 years of experience, uses this system.

[0317] 1. User Input

[0318] Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device temporarily stores this data and sends it to the server when the confirmation button is pressed.

[0319] 2. Data Receipt and Storage

[0320] The server receives the user data sent from the terminal, checks the integrity and consistency of the received data, and stores it in a database.

[0321] 3. Analysis of tastes and interests

[0322] The server sends the saved user data to an AI analysis module, which uses a generative AI model to analyze and identify Person A's hobbies and interests, such as "golf" and "travel." The analysis results are stored in a database and category tags are added.

[0323] 4. List potential activities

[0324] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, for example, specific activities such as "golf lessons" and "trip planning."

[0325] 5. Persona-based optimization

[0326] The server further optimizes the generated candidate list using the persona function. Specifically, it narrows down the candidates based on the success stories of other users and data on Person A, and evaluates the success rate and user satisfaction.

[0327] 6. Emotion Recognition by Emotion Engine

[0328] The server collects emotional data from user A's inputs and selections, and uses an emotion engine to recognize user A's emotional state. For example, if user A has been feeling stressed recently, that information is stored in the database.

[0329] 7. Adjusting the proposal

[0330] The server adjusts its suggestions based on the perceived emotional state. For example, if A is feeling stressed, it suggests a relaxing trip to a quiet mountain area.

[0331] 8. Matching and proposal formulation

[0332] The server searches for related facilities and services to realize suitable activities and work, and performs optimal matching. For example, it searches for local golf clubs and travel agencies and presents specific activity plans to Mr. A (e.g., golf practice every Tuesday and Saturday, travel tours for seniors).

[0333] Prompt Sentence Examples

[0334] 1. Example of what a user enters into a dedicated app:

[0335] "I'm a 65-year-old man who used to work in sales. My hobbies are golf and I'm also interested in traveling."

[0336] 2. Example of user reviewing the suggestions:

[0337] "Your ideal activities include golf practice and a quiet mountain getaway every Tuesday and Saturday."

[0338] As described above, this system uses a generative AI model and an emotion engine to make specific suggestions to help seniors live a fulfilling second life.

[0339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0340] Step 1:

[0341] Entering User Data

[0342] Users access a dedicated web portal or mobile app and enter their personal information (age, gender, work history, hobbies, interests, etc.).

[0343] Input: Personal information, preferences, and social experiences you enter.

[0344] The terminal temporarily stores the input information in its memory.

[0345] Specific operation: When a user enters information into a form and presses "Confirm," the data is stored in the device's memory.

[0346] Step 2:

[0347] Data transmission and temporary storage

[0348] When the user presses the "Confirm" button, the device encrypts the temporarily stored data and sends it to the server.

[0349] Input: Temporary data when confirmation operation is received.

[0350] Output: The encrypted data being sent.

[0351] Specific operation: The terminal encrypts the data and sends it to the server via the network.

[0352] Step 3:

[0353] Receiving, verifying and storing data

[0354] The server receives the data sent from the terminal.

[0355] Input: The encrypted data sent.

[0356] The server calculates a checksum and verifies the consistency to ensure the integrity of the data.

[0357] Output: Validated data.

[0358] The server stores the verified data in a database, and if there is a problem, generates an error message and returns it to the terminal.

[0359] Specific operations: Calculates the checksum of the received data, checks its consistency, and stores it in the database.

[0360] Step 4:

[0361] Analysis of tastes and interests

[0362] The server transmits the stored user data to the AI ​​analysis module.

[0363] Input: User data stored in the database.

[0364] The server uses a generative AI model to analyze and identify the user's tastes and interests.

[0365] Output: Analysis results (e.g. "golf" or "travel").

[0366] How it works: The generative AI model analyzes user data, identifies preferences and interests, and generates category tags, which are then stored in a database.

[0367] Step 5:

[0368] Listing potential activities

[0369] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0370] Input: Category tags for tastes and interests.

[0371] Output: A list of suggested activities and tasks.

[0372] Specific operation: The server retrieves candidate data from the database and lists them.

[0373] Step 6:

[0374] Persona-based optimization

[0375] The server optimizes the generated candidate list using a persona function.

[0376] Inputs: candidate lists, success stories of other users, historical data.

[0377] Output: Optimized candidate list.

[0378] Specific operation: Compare past databases, evaluate success rates and user satisfaction, and create rankings.

[0379] Step 7:

[0380] Emotion recognition by emotion engine

[0381] The server collects emotional data associated with the user's inputs and selections, and uses an emotion engine to recognize the user's emotional state.

[0382] Input: User input data, selection operations.

[0383] Output: Perceived emotional state.

[0384] Specific operation: The emotion engine analyzes the collected data and stores the emotional state in a database.

[0385] Step 8:

[0386] Adjusting the proposal

[0387] The server adjusts the suggestions based on the perceived emotional state.

[0388] Input: A perceived emotional state, a list of possible activities and tasks.

[0389] Output: Adjusted proposal.

[0390] Specific actions: The server considers the emotional state, selects an appropriate activity, and updates the suggestions.

[0391] Step 9:

[0392] Matching and proposal implementation

[0393] The server searches for and matches related facilities and services to realize the selected activity.

[0394] Input: Adjusted proposal.

[0395] Output: Specific action plans and matching results.

[0396] Specific operation: Search a database of related facilities and services, select the most suitable candidates, and display them to the user.

[0397] Step 10:

[0398] User Choice and Feedback

[0399] The user reviews the specific activities and facilities presented and selects those that interest them.

[0400] Input: User's choice.

[0401] The terminal transmits this selection data to the server.

[0402] The server stores the selected information and related emotional data in a database and reflects this in future proposed algorithms.

[0403] Output: The updated database.

[0404] Specific operations: Receives and stores user selection information, and reflects the data as feedback.

[0405] (Application example 2)

[0406] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0407] Currently, it is not easy for seniors to find activities and jobs that will allow them to enjoy a fulfilling second life after retirement. In particular, there is a lack of means to suggest optimal activities and jobs that take into account the user's emotional state. Therefore, there is a need for a system that can suggest more accurate activities and jobs based on the user's emotional state. Furthermore, there is a need for flexible and effective automated work support to support senior workers.

[0408] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0409] In this invention, the server includes a means for receiving personal information, preferences, and social experiences input by the user, a means for optimizing activity candidates based on the analyzed emotional state, and a means for presenting the optimized activity candidates to the user and accepting the user's selection, thereby enabling flexible and effective work support that takes the user's emotional state into consideration.

[0410] "Means for receiving personal information, preferences, and social experiences entered by users" refers to a function that allows users to enter information such as age, gender, work history, hobbies, and interests, and collects and transmits that data to a processing system.

[0411] The "means for analyzing the received information and identifying the user's preferences and interests" refers to a function that analyzes collected data using algorithms and generative AI models to identify the user's preferences and interests.

[0412] "Means for listing candidate activities and jobs based on the identified preferences and interests" is a function that generates and lists candidate activities and jobs suitable for the user based on the analysis results.

[0413] The "means for selecting the activity or job that is most suitable for the user from the listed activities or jobs" is a function for narrowing down the listed candidates to the activity or job that best suits the user.

[0414] The "means for presenting the selected activities or tasks to the user and accepting the user's selection" is a function for displaying the selected activities or tasks to the user and accepting the user's operation to select from among them.

[0415] "Means for matching with related facilities and services based on the user's selection" is a function that searches for facilities and services related to the activity or work selected by the user and matches them with the user's requests.

[0416] The "means for providing the user with the matching results" is a function for notifying and providing the user with information about matched facilities and services.

[0417] The "means for analyzing the user's emotional state and suggesting appropriate activities and work" is a function for analyzing the user's emotional data and suggesting appropriate activities and work according to the user's emotional state.

[0418] The "means for optimizing activity candidates based on the analyzed emotional state" is a function that further narrows down and optimizes the suggested activity and job candidates by taking into account the user's emotional state.

[0419] This invention is a system that helps seniors live a fulfilling second life after retirement. This system receives personal information, preferences, and social experiences entered by users, analyzes that data, and suggests activities and jobs that are optimal for the user. In particular, it is characterized by analyzing the user's emotional state to improve the quality of the suggestions.

[0420] System Program and Processing Overview

[0421] The system includes the following main features:

[0422] 1. Data Receipt and Storage:

[0423] Users enter data such as personal information, preferences, and social experiences through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[0424] 2. Taste and Interest Analysis:

[0425] The server uses a generative AI model to analyze the received user data, which identifies preferences and interests based on the user's input data and stores the results in a database.

[0426] 3. Use the Emotion Engine:

[0427] The server uses an emotion engine to recognize the user's emotional state. The emotion engine collects emotional data associated with the user's inputs and selections and grasps the user's emotional state.

[0428] 4. List and optimize potential activities:

[0429] The server generates and optimizes activities and tasks suitable for the user based on the analysis results of the AI ​​model and the emotional state recognition by the emotion engine. Specifically, it tags the user's emotional state to suggest activities that are more relaxing or tasks that are slightly more demanding but give a sense of accomplishment.

[0430] 5. Matching and Proposal:

[0431] Based on the optimized candidates, the server searches for related facilities and services and suggests them to the user, for example, by searching for information on local golf clubs or travel agencies, etc.

[0432] 6. User Choices and Feedback:

[0433] The user selects the activities and services they are interested in from the presented options, and the device sends this selection data to the server, which stores it in a database to be used in future proposal algorithms.

[0434] Hardware and software used

[0435] Hardware:

[0436] Mobile terminal: A device through which a user enters data.

[0437] Server: A central computer system that analyzes and stores user data and makes optimal recommendations

[0438] software:

[0439] Web portal / mobile app: Serves as the user interface and is used to input data and display the proposed results

[0440] Generative AI models: Built using programming languages ​​such as Python, they contain algorithms that analyze user preferences and interests.

[0441] Emotion Engine: A software module for analyzing a user's emotional state

[0442] Examples and prompts

[0443] Examples:

[0444] If a 65-year-old male worker, Mr. B, uses this system to find activities and work that suit him after retirement, the system will make the following suggestions: When Mr. B enters his hobbies and interests into a dedicated app, the system uses a generative AI model to analyze his interests and identify preferences such as "golf" and "travel." The emotion engine then grasps Mr. B's emotional state and suggests a relaxing travel plan if he is feeling stressed.

[0445] Prompt statement:

[0446] "Based on user input data, analyze the user's preferences and emotional state and suggest the most suitable activities and tasks from a list of candidates."

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] Users enter personal information and preferences such as age, gender, work history, hobbies, interests, etc. through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[0450] - Input: Personal information, preferences, and social experiences entered by the user

[0451] - Output: Data packets sent to the server

[0452] Step 2:

[0453] The server receives the user data sent from the terminal and verifies the integrity and consistency of the data. If there is a problem with the data, it generates an error message and returns it to the terminal. If there is no problem with the data, it stores it in the database.

[0454] - Input: User data sent to the server

[0455] - Output: Validated data and error messages (if necessary)

[0456] Step 3:

[0457] The server sends the user data stored in the database to the generative AI model for analysis. This analysis identifies the user's tastes and interests. The analysis results are then stored back in the database and tagged.

[0458] - Input: User data in the database

[0459] - Output: tagged data about preferences and interests

[0460] Step 4:

[0461] The server generates a list of potential activities and tasks based on the tagged data, linking the generative AI model with the database to create a list of activities suitable for the user.

[0462] - Input: tagged user preferences and interests data

[0463] - Output: List of potential activities and tasks

[0464] Step 5:

[0465] The server uses an emotion engine to analyze the user's emotional state. It collects emotional data from the user's inputs and selections to understand the user's emotional state. This emotional data is also stored in the database.

[0466] - Input: User emotion data

[0467] - Output: Parsed emotional state data

[0468] Step 6:

[0469] The server optimizes the list of potential activities and tasks based on the analyzed emotional state data, selecting activities that are relaxing or low-impact, depending on the user's emotional state.

[0470] - Input: Analyzed emotional state data, candidate activity list

[0471] - Output: Optimized list of activities and tasks

[0472] Step 7:

[0473] The server presents a list of optimized activities and tasks to the user and accepts the user's selection. The user selects the activities and tasks that interest them from the displayed list. The device then transmits this selection data to the server.

[0474] - Input: Optimized activity candidate list

[0475] - Output: User selection data

[0476] Step 8:

[0477] The server searches for and matches relevant facilities and services based on the user's selection data, and provides the user with the matched results, such as information about local golf clubs or travel agencies.

[0478] - Input: User selected data

[0479] - Output: Matching results and notification information

[0480] Step 9:

[0481] The server stores the data on the activities and services selected by the user in a database and reflects it in future recommendation algorithms, allowing the system to make recommendations that better meet the user's needs.

[0482] - Input: User selected data

[0483] - Output: Feedback data stored in a database

[0484] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0486] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0487] [Second embodiment]

[0488] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0489] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0490] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0491] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0492] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0493] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0494] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0495] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0496] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0497] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0498] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0499] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0500] Overall Overview

[0501] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[0502] Overview of program processing

[0503] Entering initial data

[0504] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data, verifies it, and then sends it to the server.

[0505] Data reception and storage

[0506] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[0507] Analysis of tastes and interests

[0508] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[0509] Listing potential activities

[0510] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[0511] Persona-based optimization

[0512] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[0513] Matching and proposal implementation

[0514] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The device displays specific plans (e.g., golf lessons or community activities to be held on a specific day) to the user.

[0515] User Choice and Feedback

[0516] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[0517] Specific examples

[0518] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[0519] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[0520] The server then searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Mr. A, such as "golf practice every Tuesday and Saturday" or "senior travel tour." Finally, if Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future proposal algorithms.

[0521] In this way, the system suggests activities and jobs that are suitable for seniors to live a fulfilling second life.

[0522] The processing flow will be explained below.

[0523] Step 1: Enter initial data

[0524] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[0525] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[0526] Step 2: Receiving and storing data

[0527] The server receives the user data transmitted from the terminal.

[0528] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[0529] Step 3: Analyze tastes and interests

[0530] The server transmits the stored user data to the AI ​​analysis module.

[0531] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[0532] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[0533] Step 4: List potential activities

[0534] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0535] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[0536] Step 5: Optimize with personas

[0537] The server further optimizes the generated candidate list using a persona function.

[0538] The server narrows down the candidates based on other users' success stories and past data.

[0539] Step 6: Matching and proposal formulation

[0540] The server searches for relevant facilities and services to carry out the selected activity.

[0541] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[0542] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[0543] Step 7: User Choices and Feedback

[0544] Users review the specific activities and facilities proposed and select those that interest them.

[0545] The terminal transmits the user's selection information to the server.

[0546] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[0547] In this way, the system makes suggestions based on the user's tastes and interests to support a fulfilling second life after retirement, allowing seniors to find the activities and jobs that best suit them.

[0548] Example 1

[0549] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0550] For today's seniors, finding activities and work that suit their tastes and interests is important for them to enjoy a fulfilling second life after retirement. However, it is not easy to accurately analyze individual tastes and interests and suggest optimal activities and work. Conventional methods lack efficient and accurate systems for making appropriate suggestions to seniors. There is a need for a system that can solve this problem and support seniors in living a satisfying second life.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0552] In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; analysis means using a generative AI model to analyze the received information and identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the activities and jobs that are most suitable for the user from the listed activities and jobs using a persona function; means for presenting the optimized activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; means for providing the matching results to the user; and means for recording feedback based on the user's selection in a database and reflecting it in future suggestions. This enables highly accurate activity and job suggestions to be made to each user.

[0553] "User" refers to a person who uses the system to input their personal information, preferences, and social experiences and receive suggestions.

[0554] "Personal Information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[0555] "Taste" refers to a particular preference or thing that a user has a preference for.

[0556] "Interests" refer to areas or activities that interest a user.

[0557] "Receiving means" refers to the means for transmitting personal information, preferences, and social experiences entered by the user to the server.

[0558] "Analysis means" refers to the means for identifying a user's preferences and interests using a generative AI model based on the received information.

[0559] A "generative AI model" refers to an artificial intelligence algorithm that analyzes a user's tastes and interests based on the data it receives.

[0560] "Listing means" refers to a means for generating candidate activities or jobs based on analyzed tastes and interests.

[0561] The "persona function" refers to a means of narrowing down the activities and tasks that are best suited to individual users based on a large amount of user data and success stories.

[0562] "Optimization measures" refer to measures that use the persona function to narrow down candidate activities and tasks and select the most suitable ones for the user.

[0563] "Matching means" refers to a means for searching for facilities and services related to the activity or work selected by the user and making the optimal match.

[0564] "Providing means" refers to a means for presenting the matching results to the user.

[0565] "Feedback means" refers to a means for recording user selections in a database and reflecting them in future suggestions.

[0566] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. This system uses a generative AI model to analyze the user's interests based on personal information, preferences, social experiences, etc. entered by the user, and suggests optimal activities and jobs.

[0567] First, the user uses a dedicated web portal or mobile app to enter basic information such as age, gender, work history, hobbies, and interests. The device receives this information, validates it, and then sends it to the server.

[0568] The server stores the received user data in a database and verifies its consistency and completeness. If the data is OK, the server sends it to a data analysis module, which uses a generative AI model to identify preferences and interests. This generative AI model uses machine learning algorithms to analyze preferences and interests based on the received data. The analysis results are stored back in the database and assigned relevant category tags.

[0569] The server then generates a list of activities and jobs based on the analyzed preferences and interests, including a number of categories such as sports, hobbies, reskilling, volunteering, etc. It then uses a persona function to optimize the generated list and narrow down the activities and jobs that are most suitable for the user.

[0570] The server then searches for and matches related facilities and services to realize the selected activities or tasks. The matching results are presented to the user via their device as specific plans. For example, specific activities such as "golf practice every Tuesday and Saturday" or "travel tours for seniors" may be presented.

[0571] The user selects the plans they are interested in from the ones presented, and the device sends the selection data to the server, which stores the selection information in a database and reflects it in future proposals.

[0572] As a concrete example, consider the case where a user with 40 years of experience in sales uses this system. The user accesses a dedicated app and enters their age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies the user's tastes and interests as "golf" and "travel." Based on this, it generates a list of golf lesson and travel plan candidates and further optimizes the candidates using the persona function. The server then searches local golf clubs and travel agencies to find the best match. The device presents the user with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tour." If the user selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in the proposal algorithm.

[0573] An example of a prompt sentence is, "Please give us an overview of a system that suggests the best activities for a retired man in his 60s who enjoys golf." Such a system can help seniors live a fulfilling second life.

[0574] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0575] Step 1: Enter initial data

[0576] The user enters basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data and validates whether it is in a valid format. After validation is complete, the device sends the data to the server. For example, a man in his 60s enters "65 years old, male, 40 years in sales, hobby is golf, interest is travel."

[0577] Input: User's age, gender, work history, hobbies, interests, etc.

[0578] Output: Validated user data

[0579] Step 2: Receiving and storing data

[0580] The server receives the user data sent from the terminal. It verifies the consistency and completeness of the received data, and if there are no problems, it stores it in the database. If there is a defect in the data, the server returns an error message to the terminal. Specifically, the server inserts the information "65 years old, male, 40 years in sales, hobby is golf, interest is travel" into the database.

[0581] Input: Validated user data

[0582] Output: User data stored in the database, error messages (if required)

[0583] Step 3: Analyze tastes and interests

[0584] The server sends the stored user data to an analytics module where a generative AI model runs to identify the user's preferences and interests. The generative AI model uses machine learning algorithms to analyze the user data and identify preferences and interests. The results of this analysis are stored in a database and assigned category tags. For example, the analysis might generate tags for "golf" and "travel."

[0585] Input: User data

[0586] Output: Analyzed hobbies and interests (e.g. golf, travel) and their tags

[0587] Step 4: List potential activities

[0588] The server generates a list of suitable activities and jobs based on the analyzed tastes and interests. It searches the database for matching activities and creates a list of candidates. Specifically, the server extracts activities related to "golf" and "travel" from the database and creates a list of candidates such as "golf lessons" and "travel tours for seniors."

[0589] Input: Parsed tastes and interest tags

[0590] Output: A list of suggested activities and tasks

[0591] Step 5: Optimize with personas

[0592] The server further optimizes the generated candidate list using the persona function. This function refers to the success stories and feedback of other users and prioritizes the most suitable candidates. Specifically, the persona function prioritizes the inclusion in the list of "golf lesson programs that have been popular with users with similar interests in the past."

[0593] Input: List of suggested activities and jobs

[0594] Output: Optimized candidate list

[0595] Step 6: Matching and proposal formulation

[0596] The server searches for related facilities and services to realize the selected activity and finds the best match. The resulting matching information is presented to the user via their device as a specific plan. For example, "Golf practice at a local golf club every Tuesday and Saturday."

[0597] Input: Optimized candidate list

[0598] Output: Specific action plan

[0599] Step 7: User Choices and Feedback

[0600] The user selects the specific activity plan they are interested in from the presented plan. The device receives this selection data and sends it to the server. The server stores the selection information in a database and reflects it in future suggestions.

[0601] Input: User selected data

[0602] Output: Selection information stored in a database, feedback reflected in future proposed algorithms

[0603] (Application example 1)

[0604] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0605] Conventional Second Life support systems have had difficulty accurately analyzing users' tastes and interests and suggesting optimal activities and jobs. They also lacked the means to match users with local physical stores and services in real time and present specific activity suggestions. This meant that users could not receive appropriate guidance to lead a fulfilling Second Life. Furthermore, feedback based on user choices was not reflected in real time, which led to a problem of poor quality in the suggestions provided.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0607] In this invention, the server includes means for receiving personal information, preferences, and social experiences input by a user, means for analyzing the received information and identifying the user's preferences and interests, means for listing candidate activities and jobs based on the identified preferences and interests, means for selecting the activity or job most suitable for the user from the listed activities and jobs, means for presenting the selected activities and jobs to the user and accepting the user's selection, means for matching with related facilities and services based on the user's selection, means for the user to receive suggestions in real time via a smart device, and means for providing the matching results to the user. This allows the user to find the optimal activity or job that suits their preferences and interests, match with local services provided in real time, and quickly receive a specific plan for living a fulfilling second life.

[0608] A "user" is a person who uses this system and inputs personal information, preferences, and interests.

[0609] "Personal information" refers to basic information such as a user's age, gender, and work history.

[0610] "Tastes" refer to activities or interests that a user particularly likes.

[0611] "Interests" are areas or topics that interest a user.

[0612] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[0613] The "analysis means" refers to a function for analyzing and identifying the user's tastes and interests using the received information.

[0614] "Means for listing" refers to a function that lists candidate activities or tasks based on the results of the analysis.

[0615] "Means of selection" refers to the function of selecting the most suitable activity or task for the user from the list of activities or tasks.

[0616] "Means for presentation" refers to the function of displaying selected activities or tasks to the user and prompting them to make a selection.

[0617] The "means for receiving" refers to a function that allows the system to receive a selection from the user.

[0618] "Means for matching" refers to a function that connects with related facilities and services based on the user's selection and makes appropriate suggestions.

[0619] A "smart device" is a terminal such as a smartphone or smart glasses that a user uses to input information and receive suggestions.

[0620] "Means of receiving in real time" refers to a function that allows users to instantly view the content of the proposal.

[0621] "Means for providing" refers to a function for providing the matching results to the user.

[0622] "Feedback" refers to information that the system uses to make future suggestions based on the user's selections.

[0623] Overall Overview

[0624] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[0625] Overview of program processing

[0626] Entering initial data

[0627] A user uses a smart device (such as a smartphone or smart glasses) to input their basic information (such as age, gender, work history, hobbies, and interests). The device receives this input data and sends it to the server.

[0628] Data reception and storage

[0629] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[0630] Analysis of tastes and interests

[0631] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[0632] Listing potential activities

[0633] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[0634] Persona-based optimization

[0635] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[0636] Matching and proposal implementation

[0637] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The terminal displays specific plans (e.g., golf lessons every Tuesday and Saturday, or travel tours for seniors) to the user.

[0638] User Choice and Feedback

[0639] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[0640] Processing hardware and software

[0641] The following hardware and software are used to implement this system.

[0642] Hardware: Raspberry Pi, smartphone, smart glasses

[0643] Software: OpenCV, TensorFlow, JSON format database, AI analysis module

[0644] Specific examples

[0645] For example, if Mr. A, a former salesperson with 40 years of experience, were to use this system, he would use smart glasses to enter his age (65), gender (male), work experience (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies Mr. A's tastes and interests as "golf" and "travel." Based on this analysis, a list of options such as golf lessons and travel planning is generated. Next, the persona function is used to optimize the options based on other users' success stories and Mr. A's data. Finally, the server matches Mr. A with local golf clubs and travel agencies and presents him with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tours." If Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in future proposal algorithms.

[0646] Prompt Sentence Examples

[0647] "Choose the activity that best suits you: 1. Golf lessons (Tuesdays and Saturdays) 2. Domestic travel tours (for seniors)"

[0648] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0649] Step 1:

[0650] The device receives basic information such as personal information, preferences, interests, and social experiences entered by the user. This input data is entered by the user using a smart device (smartphone or smart glasses). The input data includes age, gender, work history, hobbies, and interests. The received data is verified and then sent from the device to the server.

[0651] Input: User's personal information, preferences, and interests

[0652] Output: Send data to the server

[0653] Step 2:

[0654] The server stores the received user data in a database. When the data is stored, it is verified for consistency and completeness, and if a problem occurs, an error message is returned to the terminal. The data is stored in JSON format.

[0655] Input: User data sent from the terminal

[0656] Output: Data saved to database, error messages (if necessary)

[0657] Step 3:

[0658] The server sends the user data stored in the database to the AI ​​analysis module, which uses a generative AI model to identify preferences and interests. The generative AI model analyzes the input data and identifies the user's preferences and interests as category tags. This clarifies the user's hobbies and interests. The analysis results are then stored back in the database.

[0659] Input: Saved user data

[0660] Output: Identified preferences and interests, stored in a database

[0661] Step 4:

[0662] The server generates a list of suitable activities and jobs based on the identified preferences and interests, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering. The list is generated based on the user's interests, such as "golf lessons" or "trip planning."

[0663] Input: Identified tastes and interests

[0664] Output: A list of suggested activities and tasks

[0665] Step 5:

[0666] The server further optimizes the generated candidate list using the persona function, which narrows down the most suitable activities and tasks based on the success stories of other users and the current user's data. The optimized list is then stored in the database.

[0667] Input: List of suggested activities and jobs

[0668] Output: Optimized activity and task lists

[0669] Step 6:

[0670] The server searches for and matches related facilities and services to realize the selected activity. For example, it searches for local golf clubs and travel agencies, and then identifies the most suitable partners and proposes specific content. The matching results are provided to the user.

[0671] Input: Optimized activity and task lists

[0672] Output: Matching results

[0673] Step 7:

[0674] The terminal provides the user with specific plans and accepts the user's selection. Examples of plans include "Golf lessons every Tuesday and Saturday" and "Domestic travel tours for seniors." The user's selection data is acquired and sent from the terminal to the server.

[0675] Input: Matching results

[0676] Output: User selection, selection data sent to server

[0677] Step 8:

[0678] The server stores the user's selection data in a database and incorporates this feedback information into the suggestion algorithm for future visits, allowing the system to make more accurate suggestions based on the user's preferences.

[0679] Input: User selected data

[0680] Output: Save to database, reflect in algorithm

[0681] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0682] Overall Overview

[0683] This invention relates to a system that suggests activities and jobs for seniors after retirement to help them enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and concerns, and suggests optimal activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[0684] Overview of program processing

[0685] Entering initial data

[0686] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device temporarily stores this input data and sends it to the server when the confirmation button is pressed.

[0687] Data reception and storage

[0688] The server receives the user data sent from the terminal. The server verifies the integrity and consistency of the received data and stores it in a database. If there is a problem, it creates an error message and returns it to the terminal.

[0689] Analysis of tastes and interests

[0690] The server sends the stored user data to an AI analysis module. The server uses a generative AI model to analyze the input data and identify the user's preferences and interests. The server stores the analysis results (e.g., "golf" or "travel") in a database and adds category tags.

[0691] Listing potential activities

[0692] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, listing specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[0693] Persona-based optimization

[0694] The server further optimizes the generated candidate list using the persona function, narrowing down the candidates based on the success stories of other users and past data.

[0695] Emotion recognition by emotion engine

[0696] The server collects emotional data from the user's inputs and selections, recognizes the user's emotional state using an emotion engine, and stores this emotional data in a database to understand the user's emotional state.

[0697] Adjusting the proposal

[0698] The server then tailors its suggestions based on the user's perceived emotional state, for example, suggesting relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[0699] Matching and proposal implementation

[0700] The server searches for and matches related facilities and services to realize the selected activity. It matches the most suitable facilities and services (e.g., local golf clubs, travel agencies). The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tours for seniors) to the user.

[0701] User Choice and Feedback

[0702] The user reviews the specific activities and facilities presented and selects the ones they are interested in. The device then sends this selection data to the server, which stores the selected information and associated emotion data in a database and uses them as feedback for future proposed algorithms.

[0703] Specific examples

[0704] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[0705] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[0706] The server then uses an emotion engine to collect emotion data from A and recognize the user's emotional state. For example, if A has been feeling stressed recently, the server will make suggestions to help A relax (e.g., a trip to a quiet mountain area).

[0707] The server searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Person A, such as "golf practice every Tuesday and Saturday" or "a trip to a quiet mountain area." Finally, if Person A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future suggestion algorithms.

[0708] In this way, by combining emotion engines, it is possible to realize flexible and highly accurate suggestions that correspond to the user's emotional state, allowing seniors to further enrich and enjoy activities that are best suited to them.

[0709] The processing flow will be explained below.

[0710] Step 1: Enter initial data

[0711] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[0712] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[0713] Step 2: Receiving and storing data

[0714] The server receives the user data transmitted from the terminal.

[0715] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[0716] Step 3: Analyze tastes and interests

[0717] The server transmits the stored user data to the AI ​​analysis module.

[0718] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[0719] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[0720] Step 4: List potential activities

[0721] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0722] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[0723] Step 5: Optimize with personas

[0724] The server further optimizes the generated candidate list using a persona function.

[0725] The server narrows down the candidates based on other users' success stories and past data.

[0726] Step 6: Emotion Recognition with the Emotion Engine

[0727] The server collects emotional data associated with the user's inputs and selections, and recognizes the user's emotional state using an emotion engine.

[0728] The server stores this emotional data in a database to understand the user's emotional state.

[0729] Step 7: Adjust your proposal

[0730] The server adjusts the suggestions based on the perceived emotional state.

[0731] The server suggests relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[0732] Step 8: Matching and proposal formulation

[0733] The server searches for and matches related facilities and services to realize the selected activity.

[0734] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[0735] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[0736] Step 9: User Choices and Feedback

[0737] The user selects the specific activities and facilities that interest them from the presented options.

[0738] The terminal transmits the user's selection information to the server.

[0739] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[0740] In this way, by combining the emotion engine, this system makes flexible and accurate suggestions based on the user's emotional state, allowing seniors to enjoy activities that are best suited to them.

[0741] Example 2

[0742] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0743] In the past, retired seniors had to find suitable activities and jobs on their own to enjoy a fulfilling second life, which required a lot of time and effort. Furthermore, the quality of activity suggestions based on tastes and interests was not high, resulting in low satisfaction among seniors. In addition, flexible suggestions that took into account the user's emotional state were not made, making it difficult to provide suggestions that met individual needs.

[0744] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; means for temporarily storing the received information and transmitting the data upon confirmation; means for receiving the transmitted data, verifying its completeness and consistency, and storing it in a database; means for analyzing the stored user data using a generative AI model to identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the listed candidates using a persona function; means for collecting emotional data associated with the user data and input and recognizing the user's emotional state using an emotion engine; means for adjusting the listed and optimized activities and jobs based on the recognized emotional state; means for presenting the adjusted activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; and means for providing the matching results to the user. This makes it easier for seniors to find the activities and jobs that are best suited to them, enabling them to live a fulfilling and satisfying second life.

[0745] "User" refers to a person who uses the system to input personal information, preferences, and social experiences and receive suggestions.

[0746] "Personal information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[0747] "Interests" refer to areas or activities in which a user is particularly interested.

[0748] "Social experience" refers to the user's past work history and social activity experience.

[0749] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[0750] The "means for temporarily saving" refers to a function for temporarily storing information input by a user in a storage device.

[0751] The "confirmation operation" refers to an operation performed by the user to confirm the input contents.

[0752] "Means for transmitting data" refers to a function for sending input data to a server.

[0753] "Means for verifying integrity and consistency" refers to the ability to verify that the data received is accurate and consistent.

[0754] "Database" refers to a storage device for storing and managing user data and analysis results.

[0755] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and identify user preferences and interests.

[0756] "Means for identifying tastes and interests" refers to the function of analyzing and clarifying a user's areas of interest using a generative AI model.

[0757] "Listing means" refers to the ability to list suitable activities or job opportunities based on identified preferences or interests.

[0758] The "persona function" refers to the ability to optimize the list of candidates by utilizing the success stories and past history of other users.

[0759] "Emotion Engine" refers to algorithms and functions for analyzing and recognizing a user's emotional state.

[0760] "Emotional state" refers to the psychological state a user exhibits when making an input or selection.

[0761] "Regulatory measures" refer to the ability to change the listed activities or tasks based on the perceived emotional state.

[0762] "Means for presenting" refers to a function for displaying optimized suggestions to the user.

[0763] The "means for accepting a selection" refers to a function by which the system receives a selection when the user makes a selection from the presented suggestions.

[0764] "Means for matching" refers to the function of searching for and matching related facilities and services for the activity or job selected by the user.

[0765] "Means for providing matching results" refers to the function of notifying users of matching results with optimal facilities and services.

[0766] This invention relates to a system that suggests activities and jobs that will help seniors live a fulfilling second life after retirement. A specific method for implementing this system will be described below.

[0767] System Overview

[0768] Based on the user's personal information, preferences, and social experiences, the system uses a generative AI model to analyze the user's interests and suggests the most suitable activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[0769] Hardware and software used

[0770] Server: Receives, stores, analyzes, and generates proposals on data. Specific server software that can be used is a web server such as Apache or Nginx, or a database server such as MySQL or PostgreSQL.

[0771] Terminal: A device on which a user inputs data and displays the proposed results. Specifically, this can be a smartphone, tablet, or PC.

[0772] Generative AI model: An AI algorithm used to analyze user preferences and interests. Specifically, it uses a large-scale language model such as GPT-4.

[0773] Emotion engine: Software for analyzing a user's emotional state, specifically using emotion recognition algorithms.

[0774] Specific examples of programs

[0775] For example, consider the case where Mr. A, a former sales professional with 40 years of experience, uses this system.

[0776] 1. User Input

[0777] Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device temporarily stores this data and sends it to the server when the confirmation button is pressed.

[0778] 2. Data Receipt and Storage

[0779] The server receives the user data sent from the terminal, checks the integrity and consistency of the received data, and stores it in a database.

[0780] 3. Analysis of tastes and interests

[0781] The server sends the saved user data to an AI analysis module, which uses a generative AI model to analyze and identify Person A's hobbies and interests, such as "golf" and "travel." The analysis results are stored in a database and category tags are added.

[0782] 4. List potential activities

[0783] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, for example, specific activities such as "golf lessons" and "trip planning."

[0784] 5. Persona-based optimization

[0785] The server further optimizes the generated candidate list using the persona function. Specifically, it narrows down the candidates based on the success stories of other users and data on Person A, and evaluates the success rate and user satisfaction.

[0786] 6. Emotion Recognition by Emotion Engine

[0787] The server collects emotional data from user A's inputs and selections, and uses an emotion engine to recognize user A's emotional state. For example, if user A has been feeling stressed recently, that information is stored in the database.

[0788] 7. Adjusting the proposal

[0789] The server adjusts its suggestions based on the perceived emotional state. For example, if A is feeling stressed, it suggests a relaxing trip to a quiet mountain area.

[0790] 8. Matching and proposal formulation

[0791] The server searches for related facilities and services to realize suitable activities and work, and performs optimal matching. For example, it searches for local golf clubs and travel agencies and presents specific activity plans to Mr. A (e.g., golf practice every Tuesday and Saturday, travel tours for seniors).

[0792] Prompt Sentence Examples

[0793] 1. Example of what a user enters into a dedicated app:

[0794] "I'm a 65-year-old man who used to work in sales. My hobbies are golf and I'm also interested in traveling."

[0795] 2. Example of user reviewing the suggestions:

[0796] "Your ideal activities include golf practice and a quiet mountain getaway every Tuesday and Saturday."

[0797] As described above, this system uses a generative AI model and an emotion engine to make specific suggestions to help seniors live a fulfilling second life.

[0798] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0799] Step 1:

[0800] Entering User Data

[0801] Users access a dedicated web portal or mobile app and enter their personal information (age, gender, work history, hobbies, interests, etc.).

[0802] Input: Personal information, preferences, and social experiences you enter.

[0803] The terminal temporarily stores the input information in its memory.

[0804] Specific operation: When a user enters information into a form and presses "Confirm," the data is stored in the device's memory.

[0805] Step 2:

[0806] Data transmission and temporary storage

[0807] When the user presses the "Confirm" button, the device encrypts the temporarily stored data and sends it to the server.

[0808] Input: Temporary data when confirmation operation is received.

[0809] Output: The encrypted data being sent.

[0810] Specific operation: The terminal encrypts the data and sends it to the server via the network.

[0811] Step 3:

[0812] Receiving, verifying and storing data

[0813] The server receives the data sent from the terminal.

[0814] Input: The encrypted data sent.

[0815] The server calculates a checksum and verifies the consistency to ensure the integrity of the data.

[0816] Output: Validated data.

[0817] The server stores the verified data in a database, and if there is a problem, generates an error message and returns it to the terminal.

[0818] Specific operations: Calculates the checksum of the received data, checks its consistency, and stores it in the database.

[0819] Step 4:

[0820] Analysis of tastes and interests

[0821] The server transmits the stored user data to the AI ​​analysis module.

[0822] Input: User data stored in the database.

[0823] The server uses a generative AI model to analyze and identify the user's tastes and interests.

[0824] Output: Analysis results (e.g. "golf", "travel").

[0825] How it works: The generative AI model analyzes user data, identifies preferences and interests, and generates category tags, which are then stored in a database.

[0826] Step 5:

[0827] Listing potential activities

[0828] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0829] Input: Category tags for tastes and interests.

[0830] Output: A list of suggested activities and tasks.

[0831] Specific operation: The server retrieves candidate data from the database and lists them.

[0832] Step 6:

[0833] Persona-based optimization

[0834] The server optimizes the generated candidate list using a persona function.

[0835] Inputs: candidate lists, success stories of other users, historical data.

[0836] Output: Optimized candidate list.

[0837] Specific operation: Compare past databases, evaluate success rates and user satisfaction, and create rankings.

[0838] Step 7:

[0839] Emotion recognition by emotion engine

[0840] The server collects emotional data associated with the user's inputs and selections, and uses an emotion engine to recognize the user's emotional state.

[0841] Input: User input data, selection operations.

[0842] Output: Perceived emotional state.

[0843] Specific operation: The emotion engine analyzes the collected data and stores the emotional state in a database.

[0844] Step 8:

[0845] Adjusting the proposal

[0846] The server adjusts the suggestions based on the perceived emotional state.

[0847] Input: A perceived emotional state, a list of possible activities and tasks.

[0848] Output: Adjusted proposal.

[0849] Specific actions: The server considers the emotional state, selects an appropriate activity, and updates the suggestions.

[0850] Step 9:

[0851] Matching and proposal implementation

[0852] The server searches for and matches related facilities and services to realize the selected activity.

[0853] Input: Adjusted proposal.

[0854] Output: Specific action plans and matching results.

[0855] Specific operation: Search a database of related facilities and services, select the most suitable candidates, and display them to the user.

[0856] Step 10:

[0857] User Choice and Feedback

[0858] The user reviews the specific activities and facilities presented and selects those that interest them.

[0859] Input: User's choice.

[0860] The terminal transmits this selection data to the server.

[0861] The server stores the selected information and related emotional data in a database and reflects this in future proposed algorithms.

[0862] Output: The updated database.

[0863] Specific operations: Receives and stores user selection information, and reflects the data as feedback.

[0864] (Application example 2)

[0865] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0866] Currently, it is not easy for seniors to find activities and jobs that will allow them to enjoy a fulfilling second life after retirement. In particular, there is a lack of means to suggest optimal activities and jobs that take into account the user's emotional state. Therefore, there is a need for a system that can suggest more accurate activities and jobs based on the user's emotional state. Furthermore, there is a need for flexible and effective automated work support to support senior workers.

[0867] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0868] In this invention, the server includes a means for receiving personal information, preferences, and social experiences input by the user, a means for optimizing activity candidates based on the analyzed emotional state, and a means for presenting the optimized activity candidates to the user and accepting the user's selection, thereby enabling flexible and effective work support that takes the user's emotional state into consideration.

[0869] "Means for receiving personal information, preferences, and social experiences entered by users" refers to a function that allows users to enter information such as age, gender, work history, hobbies, and interests, and collects and transmits that data to a processing system.

[0870] The "means for analyzing the received information and identifying the user's preferences and interests" refers to a function that analyzes collected data using algorithms and generative AI models to identify the user's preferences and interests.

[0871] "Means for listing candidate activities and jobs based on the identified preferences and interests" is a function that generates and lists candidate activities and jobs suitable for the user based on the analysis results.

[0872] The "means for selecting the activity or job that is most suitable for the user from the listed activities or jobs" is a function for narrowing down the listed candidates to the activity or job that best suits the user.

[0873] The "means for presenting the selected activities or tasks to the user and accepting the user's selection" is a function for displaying the selected activities or tasks to the user and accepting the user's operation to select from among them.

[0874] "Means for matching with related facilities and services based on the user's selection" is a function that searches for facilities and services related to the activity or work selected by the user and matches them with the user's requests.

[0875] The "means for providing the user with the matching results" is a function for notifying and providing the user with information about matched facilities and services.

[0876] The "means for analyzing the user's emotional state and suggesting appropriate activities and work" is a function for analyzing the user's emotional data and suggesting appropriate activities and work according to the user's emotional state.

[0877] The "means for optimizing activity candidates based on the analyzed emotional state" is a function that further narrows down and optimizes the suggested activity and job candidates by taking into account the user's emotional state.

[0878] This invention is a system that helps seniors live a fulfilling second life after retirement. This system receives personal information, preferences, and social experiences entered by users, analyzes that data, and suggests activities and jobs that are optimal for the user. In particular, it is characterized by analyzing the user's emotional state to improve the quality of the suggestions.

[0879] System Program and Processing Overview

[0880] The system includes the following main features:

[0881] 1. Data Receipt and Storage:

[0882] Users enter data such as personal information, preferences, and social experiences through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[0883] 2. Taste and Interest Analysis:

[0884] The server uses a generative AI model to analyze the received user data, which identifies preferences and interests based on the user's input data and stores the results in a database.

[0885] 3. Use the Emotion Engine:

[0886] The server uses an emotion engine to recognize the user's emotional state. The emotion engine collects emotional data associated with the user's inputs and selections and grasps the user's emotional state.

[0887] 4. List and optimize potential activities:

[0888] The server generates and optimizes activities and tasks suitable for the user based on the analysis results of the AI ​​model and the emotional state recognition by the emotion engine. Specifically, it tags the user's emotional state to suggest activities that are more relaxing or tasks that are slightly more demanding but give a sense of accomplishment.

[0889] 5. Matching and Proposal:

[0890] Based on the optimized candidates, the server searches for related facilities and services and suggests them to the user, for example, by searching for information on local golf clubs or travel agencies, etc.

[0891] 6. User Choices and Feedback:

[0892] The user selects the activities and services they are interested in from the presented options, and the device sends this selection data to the server, which stores it in a database to be used in future proposal algorithms.

[0893] Hardware and software used

[0894] Hardware:

[0895] Mobile terminal: A device through which a user enters data.

[0896] Server: A central computer system that analyzes and stores user data and makes optimal recommendations

[0897] software:

[0898] Web portal / mobile app: Serves as the user interface and is used to input data and display the proposed results

[0899] Generative AI models: Built using programming languages ​​such as Python, they contain algorithms that analyze user preferences and interests.

[0900] Emotion Engine: A software module for analyzing a user's emotional state

[0901] Examples and prompts

[0902] Examples:

[0903] If a 65-year-old male worker, Mr. B, uses this system to find activities and work that suit him after retirement, the system will make the following suggestions: When Mr. B enters his hobbies and interests into a dedicated app, the system uses a generative AI model to analyze his interests and identify preferences such as "golf" and "travel." The emotion engine then grasps Mr. B's emotional state and suggests a relaxing travel plan if he is feeling stressed.

[0904] Prompt statement:

[0905] "Based on user input data, analyze the user's preferences and emotional state and suggest the most suitable activities and tasks from a list of candidates."

[0906] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0907] Step 1:

[0908] Users enter personal information and preferences such as age, gender, work history, hobbies, interests, etc. through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[0909] - Input: Personal information, preferences, and social experiences entered by the user

[0910] - Output: Data packets sent to the server

[0911] Step 2:

[0912] The server receives the user data sent from the terminal and verifies the integrity and consistency of the data. If there is a problem with the data, it generates an error message and returns it to the terminal. If there is no problem with the data, it stores it in the database.

[0913] - Input: User data sent to the server

[0914] - Output: Validated data and error messages (if necessary)

[0915] Step 3:

[0916] The server sends the user data stored in the database to the generative AI model for analysis. This analysis identifies the user's tastes and interests. The analysis results are then stored back in the database and tagged.

[0917] - Input: User data in the database

[0918] - Output: tagged data about preferences and interests

[0919] Step 4:

[0920] The server generates a list of potential activities and tasks based on the tagged data, linking the generative AI model with the database to create a list of activities suitable for the user.

[0921] - Input: tagged user preferences and interests data

[0922] - Output: List of potential activities and tasks

[0923] Step 5:

[0924] The server uses an emotion engine to analyze the user's emotional state. It collects emotional data from the user's inputs and selections to understand the user's emotional state. This emotional data is also stored in the database.

[0925] - Input: User emotion data

[0926] - Output: Parsed emotional state data

[0927] Step 6:

[0928] The server optimizes the list of potential activities and tasks based on the analyzed emotional state data, selecting activities that are relaxing or low-impact, depending on the user's emotional state.

[0929] - Input: Analyzed emotional state data, candidate activity list

[0930] - Output: Optimized list of activities and tasks

[0931] Step 7:

[0932] The server presents a list of optimized activities and tasks to the user and accepts the user's selection. The user selects the activities and tasks that interest them from the displayed list. The device then transmits this selection data to the server.

[0933] - Input: Optimized activity candidate list

[0934] - Output: User selection data

[0935] Step 8:

[0936] The server searches for and matches relevant facilities and services based on the user's selection data, and provides the user with the matched results, such as information about local golf clubs or travel agencies.

[0937] - Input: User selected data

[0938] - Output: Matching results and notification information

[0939] Step 9:

[0940] The server stores the data on the activities and services selected by the user in a database and reflects it in future recommendation algorithms, allowing the system to make recommendations that better meet the user's needs.

[0941] - Input: User selected data

[0942] - Output: Feedback data stored in a database

[0943] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0944] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0945] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0946] [Third embodiment]

[0947] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0948] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0949] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0950] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0951] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0952] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0953] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0954] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0955] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0956] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0957] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0958] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0959] Overall Overview

[0960] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[0961] Overview of program processing

[0962] Entering initial data

[0963] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data, verifies it, and then sends it to the server.

[0964] Data reception and storage

[0965] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[0966] Analysis of tastes and interests

[0967] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[0968] Listing potential activities

[0969] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[0970] Persona-based optimization

[0971] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[0972] Matching and proposal implementation

[0973] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The device displays specific plans (e.g., golf lessons or community activities to be held on a specific day) to the user.

[0974] User Choice and Feedback

[0975] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[0976] Specific examples

[0977] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[0978] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[0979] The server then searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Mr. A, such as "golf practice every Tuesday and Saturday" or "senior travel tour." Finally, if Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future proposal algorithms.

[0980] In this way, the system suggests activities and jobs that are suitable for seniors to live a fulfilling second life.

[0981] The processing flow will be explained below.

[0982] Step 1: Enter initial data

[0983] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[0984] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[0985] Step 2: Receiving and storing data

[0986] The server receives the user data transmitted from the terminal.

[0987] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[0988] Step 3: Analyze tastes and interests

[0989] The server transmits the stored user data to the AI ​​analysis module.

[0990] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[0991] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[0992] Step 4: List potential activities

[0993] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[0994] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[0995] Step 5: Optimize with personas

[0996] The server further optimizes the generated candidate list using a persona function.

[0997] The server narrows down the candidates based on other users' success stories and past data.

[0998] Step 6: Matching and proposal formulation

[0999] The server searches for relevant facilities and services to carry out the selected activity.

[1000] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[1001] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[1002] Step 7: User Choices and Feedback

[1003] Users review the specific activities and facilities proposed and select those that interest them.

[1004] The terminal transmits the user's selection information to the server.

[1005] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[1006] In this way, the system makes suggestions based on the user's tastes and interests to support a fulfilling second life after retirement, allowing seniors to find the activities and jobs that best suit them.

[1007] Example 1

[1008] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1009] For today's seniors, finding activities and work that suit their tastes and interests is important for them to enjoy a fulfilling second life after retirement. However, it is not easy to accurately analyze individual tastes and interests and suggest optimal activities and work. Conventional methods lack efficient and accurate systems for making appropriate suggestions to seniors. There is a need for a system that can solve this problem and support seniors in living a satisfying second life.

[1010] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1011] In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; analysis means using a generative AI model to analyze the received information and identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the activities and jobs that are most suitable for the user from the listed activities and jobs using a persona function; means for presenting the optimized activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; means for providing the matching results to the user; and means for recording feedback based on the user's selection in a database and reflecting it in future suggestions. This enables highly accurate activity and job suggestions to be made to each user.

[1012] "User" refers to a person who uses the system to input their personal information, preferences, and social experiences and receive suggestions.

[1013] "Personal Information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[1014] "Taste" refers to a particular preference or thing that a user has a preference for.

[1015] "Interests" refer to areas or activities that interest a user.

[1016] "Receiving means" refers to the means for transmitting personal information, preferences, and social experiences entered by the user to the server.

[1017] "Analysis means" refers to the means for identifying a user's preferences and interests using a generative AI model based on the received information.

[1018] A "generative AI model" refers to an artificial intelligence algorithm that analyzes a user's tastes and interests based on the data it receives.

[1019] "Listing means" refers to a means for generating candidate activities or jobs based on analyzed tastes and interests.

[1020] The "persona function" refers to a means of narrowing down the activities and tasks that are best suited to individual users based on a large amount of user data and success stories.

[1021] "Optimization measures" refer to measures that use the persona function to narrow down candidate activities and tasks and select the most suitable ones for the user.

[1022] "Matching means" refers to a means for searching for facilities and services related to the activity or work selected by the user and making the optimal match.

[1023] "Providing means" refers to a means for presenting the matching results to the user.

[1024] "Feedback means" refers to a means for recording user selections in a database and reflecting them in future suggestions.

[1025] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. This system uses a generative AI model to analyze the user's interests based on personal information, preferences, social experiences, etc. entered by the user, and suggests optimal activities and jobs.

[1026] First, the user uses a dedicated web portal or mobile app to enter basic information such as age, gender, work history, hobbies, and interests. The device receives this information, validates it, and then sends it to the server.

[1027] The server stores the received user data in a database and verifies its consistency and completeness. If the data is OK, the server sends it to a data analysis module, which uses a generative AI model to identify preferences and interests. This generative AI model uses machine learning algorithms to analyze preferences and interests based on the received data. The analysis results are stored back in the database and assigned relevant category tags.

[1028] The server then generates a list of activities and jobs based on the analyzed preferences and interests, including a number of categories such as sports, hobbies, reskilling, volunteering, etc. It then uses a persona function to optimize the generated list and narrow down the activities and jobs that are most suitable for the user.

[1029] The server then searches for and matches related facilities and services to realize the selected activities or tasks. The matching results are presented to the user via their device as specific plans. For example, specific activities such as "golf practice every Tuesday and Saturday" or "travel tours for seniors" may be presented.

[1030] The user selects the plans they are interested in from the ones presented, and the device sends the selection data to the server, which stores the selection information in a database and reflects it in future proposals.

[1031] As a concrete example, consider the case where a user with 40 years of experience in sales uses this system. The user accesses a dedicated app and enters their age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies the user's tastes and interests as "golf" and "travel." Based on this, it generates a list of golf lesson and travel plan candidates and further optimizes the candidates using the persona function. The server then searches local golf clubs and travel agencies to find the best match. The device presents the user with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tour." If the user selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in the proposal algorithm.

[1032] An example of a prompt sentence is, "Please give us an overview of a system that suggests the best activities for a retired man in his 60s who enjoys golf." Such a system can help seniors live a fulfilling second life.

[1033] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1034] Step 1: Enter initial data

[1035] The user enters basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data and validates whether it is in a valid format. After validation is complete, the device sends the data to the server. For example, a man in his 60s enters "65 years old, male, 40 years in sales, hobby is golf, interest is travel."

[1036] Input: User's age, gender, work history, hobbies, interests, etc.

[1037] Output: Validated user data

[1038] Step 2: Receiving and storing data

[1039] The server receives the user data sent from the terminal. It verifies the consistency and completeness of the received data, and if there are no problems, it stores it in the database. If there is a defect in the data, the server returns an error message to the terminal. Specifically, the server inserts the information "65 years old, male, 40 years in sales, hobby is golf, interest is travel" into the database.

[1040] Input: Validated user data

[1041] Output: User data stored in the database, error messages (if required)

[1042] Step 3: Analyze tastes and interests

[1043] The server sends the stored user data to an analytics module where a generative AI model runs to identify the user's preferences and interests. The generative AI model uses machine learning algorithms to analyze the user data and identify preferences and interests. The results of this analysis are stored in a database and assigned category tags. For example, the analysis might generate tags for "golf" and "travel."

[1044] Input: User data

[1045] Output: Analyzed hobbies and interests (e.g. golf, travel) and their tags

[1046] Step 4: List potential activities

[1047] The server generates a list of suitable activities and jobs based on the analyzed tastes and interests. It searches the database for matching activities and creates a list of candidates. Specifically, the server extracts activities related to "golf" and "travel" from the database and creates a list of candidates such as "golf lessons" and "travel tours for seniors."

[1048] Input: Parsed tastes and interest tags

[1049] Output: A list of suggested activities and tasks

[1050] Step 5: Optimize with personas

[1051] The server further optimizes the generated candidate list using the persona function. This function refers to the success stories and feedback of other users and prioritizes the most suitable candidates. Specifically, the persona function prioritizes the inclusion in the list of "golf lesson programs that have been popular with users with similar interests in the past."

[1052] Input: List of suggested activities and jobs

[1053] Output: Optimized candidate list

[1054] Step 6: Matching and proposal formulation

[1055] The server searches for related facilities and services to realize the selected activity and finds the best match. The resulting matching information is presented to the user via their device as a specific plan. For example, "Golf practice at a local golf club every Tuesday and Saturday."

[1056] Input: Optimized candidate list

[1057] Output: Specific action plan

[1058] Step 7: User Choices and Feedback

[1059] The user selects the specific activity plan they are interested in from the presented plan. The device receives this selection data and sends it to the server. The server stores the selection information in a database and reflects it in future suggestions.

[1060] Input: User selected data

[1061] Output: Selection information stored in a database, feedback reflected in future proposed algorithms

[1062] (Application example 1)

[1063] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1064] Conventional Second Life support systems have had difficulty accurately analyzing users' tastes and interests and suggesting optimal activities and jobs. They also lacked the means to match users with local physical stores and services in real time and present specific activity suggestions. This meant that users could not receive appropriate guidance to lead a fulfilling Second Life. Furthermore, feedback based on user choices was not reflected in real time, which led to a problem of poor quality in the suggestions provided.

[1065] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1066] In this invention, the server includes means for receiving personal information, preferences, and social experiences input by a user, means for analyzing the received information and identifying the user's preferences and interests, means for listing candidate activities and jobs based on the identified preferences and interests, means for selecting the activity or job most suitable for the user from the listed activities and jobs, means for presenting the selected activities and jobs to the user and accepting the user's selection, means for matching with related facilities and services based on the user's selection, means for the user to receive suggestions in real time via a smart device, and means for providing the matching results to the user. This allows the user to find the optimal activity or job that suits their preferences and interests, match with local services provided in real time, and quickly receive a specific plan for living a fulfilling second life.

[1067] A "user" is a person who uses this system and inputs personal information, preferences, and interests.

[1068] "Personal information" refers to basic information such as a user's age, gender, and work history.

[1069] "Tastes" refer to activities or interests that a user particularly likes.

[1070] "Interests" are areas or topics that interest a user.

[1071] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[1072] The "analysis means" refers to a function for analyzing and identifying the user's tastes and interests using the received information.

[1073] "Means for listing" refers to a function that lists candidate activities or tasks based on the results of the analysis.

[1074] "Means of selection" refers to the function of selecting the most suitable activity or task for the user from the list of activities or tasks.

[1075] "Means for presentation" refers to the function of displaying selected activities or tasks to the user and prompting them to make a selection.

[1076] The "means for receiving" refers to a function that allows the system to receive a selection from the user.

[1077] "Means for matching" refers to a function that connects with related facilities and services based on the user's selection and makes appropriate suggestions.

[1078] A "smart device" is a terminal such as a smartphone or smart glasses that a user uses to input information and receive suggestions.

[1079] "Means of receiving in real time" refers to a function that allows users to instantly view the content of the proposal.

[1080] "Means for providing" refers to a function for providing the matching results to the user.

[1081] "Feedback" refers to information that the system uses to make future suggestions based on the user's selections.

[1082] Overall Overview

[1083] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[1084] Overview of program processing

[1085] Entering initial data

[1086] A user uses a smart device (such as a smartphone or smart glasses) to input their basic information (such as age, gender, work history, hobbies, and interests). The device receives this input data and sends it to the server.

[1087] Data reception and storage

[1088] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[1089] Analysis of tastes and interests

[1090] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[1091] Listing potential activities

[1092] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[1093] Persona-based optimization

[1094] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[1095] Matching and proposal implementation

[1096] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The terminal displays specific plans (e.g., golf lessons every Tuesday and Saturday, or travel tours for seniors) to the user.

[1097] User Choice and Feedback

[1098] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[1099] Processing hardware and software

[1100] The following hardware and software are used to implement this system.

[1101] Hardware: Raspberry Pi, smartphone, smart glasses

[1102] Software: OpenCV, TensorFlow, JSON format database, AI analysis module

[1103] Specific examples

[1104] For example, if Mr. A, a former salesperson with 40 years of experience, were to use this system, he would use smart glasses to enter his age (65), gender (male), work experience (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies Mr. A's tastes and interests as "golf" and "travel." Based on this analysis, a list of options such as golf lessons and travel planning is generated. Next, the persona function is used to optimize the options based on other users' success stories and Mr. A's data. Finally, the server matches Mr. A with local golf clubs and travel agencies and presents him with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tours." If Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in future proposal algorithms.

[1105] Prompt Sentence Examples

[1106] "Choose the activity that best suits you: 1. Golf lessons (Tuesdays and Saturdays) 2. Domestic travel tours (for seniors)"

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] The device receives basic information such as personal information, preferences, interests, and social experiences entered by the user. This input data is entered by the user using a smart device (smartphone or smart glasses). The input data includes age, gender, work history, hobbies, and interests. The received data is verified and then sent from the device to the server.

[1110] Input: User's personal information, preferences, and interests

[1111] Output: Send data to the server

[1112] Step 2:

[1113] The server stores the received user data in a database. When the data is stored, it is verified for consistency and completeness, and if a problem occurs, an error message is returned to the terminal. The data is stored in JSON format.

[1114] Input: User data sent from the terminal

[1115] Output: Data saved to database, error messages (if necessary)

[1116] Step 3:

[1117] The server sends the user data stored in the database to the AI ​​analysis module, which uses a generative AI model to identify preferences and interests. The generative AI model analyzes the input data and identifies the user's preferences and interests as category tags. This clarifies the user's hobbies and interests. The analysis results are then stored back in the database.

[1118] Input: Saved user data

[1119] Output: Identified preferences and interests, stored in a database

[1120] Step 4:

[1121] The server generates a list of suitable activities and jobs based on the identified preferences and interests, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering. The list is generated based on the user's interests, such as "golf lessons" or "trip planning."

[1122] Input: Identified tastes and interests

[1123] Output: A list of suggested activities and tasks

[1124] Step 5:

[1125] The server further optimizes the generated candidate list using the persona function, which narrows down the most suitable activities and tasks based on the success stories of other users and the current user's data. The optimized list is then stored in the database.

[1126] Input: List of suggested activities and jobs

[1127] Output: Optimized activity and task lists

[1128] Step 6:

[1129] The server searches for and matches related facilities and services to realize the selected activity. For example, it searches for local golf clubs and travel agencies, and then identifies the most suitable partners and proposes specific content. The matching results are provided to the user.

[1130] Input: Optimized activity and task lists

[1131] Output: Matching results

[1132] Step 7:

[1133] The terminal provides the user with specific plans and accepts the user's selection. Examples of plans include "Golf lessons every Tuesday and Saturday" and "Domestic travel tours for seniors." The user's selection data is acquired and sent from the terminal to the server.

[1134] Input: Matching results

[1135] Output: User selection, selection data sent to server

[1136] Step 8:

[1137] The server stores the user's selection data in a database and incorporates this feedback information into the suggestion algorithm for future visits, allowing the system to make more accurate suggestions based on the user's preferences.

[1138] Input: User selected data

[1139] Output: Save to database, reflect in algorithm

[1140] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1141] Overall Overview

[1142] This invention relates to a system that suggests activities and jobs for seniors after retirement to help them enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and concerns, and suggests optimal activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[1143] Overview of program processing

[1144] Entering initial data

[1145] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device temporarily stores this input data and sends it to the server when the confirmation button is pressed.

[1146] Data reception and storage

[1147] The server receives the user data sent from the terminal. The server verifies the integrity and consistency of the received data and stores it in a database. If there is a problem, it creates an error message and returns it to the terminal.

[1148] Analysis of tastes and interests

[1149] The server sends the stored user data to an AI analysis module. The server uses a generative AI model to analyze the input data and identify the user's preferences and interests. The server stores the analysis results (e.g., "golf" or "travel") in a database and adds category tags.

[1150] Listing potential activities

[1151] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, listing specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[1152] Persona-based optimization

[1153] The server further optimizes the generated candidate list using the persona function, narrowing down the candidates based on the success stories of other users and past data.

[1154] Emotion recognition by emotion engine

[1155] The server collects emotional data from the user's inputs and selections, recognizes the user's emotional state using an emotion engine, and stores this emotional data in a database to understand the user's emotional state.

[1156] Adjusting the proposal

[1157] The server then tailors its suggestions based on the user's perceived emotional state, for example, suggesting relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[1158] Matching and proposal implementation

[1159] The server searches for and matches related facilities and services to realize the selected activity. It matches the most suitable facilities and services (e.g., local golf clubs, travel agencies). The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tours for seniors) to the user.

[1160] User Choice and Feedback

[1161] The user reviews the specific activities and facilities presented and selects the ones they are interested in. The device then sends this selection data to the server, which stores the selected information and associated emotion data in a database and uses them as feedback for future proposed algorithms.

[1162] Specific examples

[1163] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[1164] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[1165] The server then uses an emotion engine to collect emotion data from A and recognize the user's emotional state. For example, if A has been feeling stressed recently, the server will make suggestions to help A relax (e.g., a trip to a quiet mountain area).

[1166] The server searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Person A, such as "golf practice every Tuesday and Saturday" or "a trip to a quiet mountain area." Finally, if Person A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future suggestion algorithms.

[1167] In this way, by combining emotion engines, it is possible to realize flexible and highly accurate suggestions that correspond to the user's emotional state, allowing seniors to further enrich and enjoy activities that are best suited to them.

[1168] The processing flow will be explained below.

[1169] Step 1: Enter initial data

[1170] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[1171] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[1172] Step 2: Receiving and storing data

[1173] The server receives the user data transmitted from the terminal.

[1174] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[1175] Step 3: Analyze tastes and interests

[1176] The server transmits the stored user data to the AI ​​analysis module.

[1177] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[1178] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[1179] Step 4: List potential activities

[1180] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[1181] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[1182] Step 5: Optimize with personas

[1183] The server further optimizes the generated candidate list using a persona function.

[1184] The server narrows down the candidates based on other users' success stories and past data.

[1185] Step 6: Emotion Recognition with the Emotion Engine

[1186] The server collects emotional data associated with the user's inputs and selections, and recognizes the user's emotional state using an emotion engine.

[1187] The server stores this emotional data in a database to understand the user's emotional state.

[1188] Step 7: Adjust your proposal

[1189] The server adjusts the suggestions based on the perceived emotional state.

[1190] The server suggests relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[1191] Step 8: Matching and proposal formulation

[1192] The server searches for and matches related facilities and services to realize the selected activity.

[1193] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[1194] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[1195] Step 9: User Choices and Feedback

[1196] The user selects the specific activities and facilities that interest them from the presented options.

[1197] The terminal transmits the user's selection information to the server.

[1198] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[1199] In this way, by combining the emotion engine, this system makes flexible and accurate suggestions based on the user's emotional state, allowing seniors to enjoy activities that are best suited to them.

[1200] Example 2

[1201] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1202] In the past, retired seniors had to find suitable activities and jobs on their own to enjoy a fulfilling second life, which required a lot of time and effort. Furthermore, the quality of activity suggestions based on tastes and interests was not high, resulting in low satisfaction among seniors. In addition, flexible suggestions that took into account the user's emotional state were not made, making it difficult to provide suggestions that met individual needs.

[1203] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; means for temporarily storing the received information and transmitting the data upon confirmation; means for receiving the transmitted data, verifying its completeness and consistency, and storing it in a database; means for analyzing the stored user data using a generative AI model to identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the listed candidates using a persona function; means for collecting emotional data associated with the user data and input and recognizing the user's emotional state using an emotion engine; means for adjusting the listed and optimized activities and jobs based on the recognized emotional state; means for presenting the adjusted activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; and means for providing the matching results to the user. This makes it easier for seniors to find the activities and jobs that are best suited to them, enabling them to live a fulfilling and satisfying second life.

[1204] "User" refers to a person who uses the system to input personal information, preferences, and social experiences and receive suggestions.

[1205] "Personal information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[1206] "Interests" refer to areas or activities in which a user is particularly interested.

[1207] "Social experience" refers to the user's past work history and social activity experience.

[1208] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[1209] The "means for temporarily saving" refers to a function for temporarily storing information input by a user in a storage device.

[1210] The "confirmation operation" refers to an operation performed by the user to confirm the input contents.

[1211] "Means for transmitting data" refers to a function for sending input data to a server.

[1212] "Means for verifying integrity and consistency" refers to the ability to verify that the data received is accurate and consistent.

[1213] "Database" refers to a storage device for storing and managing user data and analysis results.

[1214] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and identify user preferences and interests.

[1215] "Means for identifying tastes and interests" refers to the function of analyzing and clarifying a user's areas of interest using a generative AI model.

[1216] "Listing means" refers to the ability to list suitable activities or job opportunities based on identified preferences or interests.

[1217] The "persona function" refers to the ability to optimize the list of candidates by utilizing the success stories and past history of other users.

[1218] "Emotion Engine" refers to algorithms and functions for analyzing and recognizing a user's emotional state.

[1219] "Emotional state" refers to the psychological state a user exhibits when making an input or selection.

[1220] "Regulatory measures" refer to the ability to change the listed activities or tasks based on the perceived emotional state.

[1221] "Means for presenting" refers to a function for displaying optimized suggestions to the user.

[1222] The "means for accepting a selection" refers to a function by which the system receives a selection when the user makes a selection from the presented suggestions.

[1223] "Means for matching" refers to the function of searching for and matching related facilities and services for the activity or job selected by the user.

[1224] "Means for providing matching results" refers to the function of notifying users of matching results with optimal facilities and services.

[1225] This invention relates to a system that suggests activities and jobs that will help seniors live a fulfilling second life after retirement. A specific method for implementing this system will be described below.

[1226] System Overview

[1227] Based on the user's personal information, preferences, and social experiences, the system uses a generative AI model to analyze the user's interests and suggests the most suitable activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[1228] Hardware and software used

[1229] Server: Receives, stores, analyzes, and generates proposals on data. Specific server software that can be used is a web server such as Apache or Nginx, or a database server such as MySQL or PostgreSQL.

[1230] Terminal: A device on which a user inputs data and displays the proposed results. Specifically, this can be a smartphone, tablet, or PC.

[1231] Generative AI model: An AI algorithm used to analyze user preferences and interests. Specifically, it uses a large-scale language model such as GPT-4.

[1232] Emotion engine: Software for analyzing a user's emotional state, specifically using emotion recognition algorithms.

[1233] Specific examples of programs

[1234] For example, consider the case where Mr. A, a former sales professional with 40 years of experience, uses this system.

[1235] 1. User Input

[1236] Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device temporarily stores this data and sends it to the server when the confirmation button is pressed.

[1237] 2. Data Receipt and Storage

[1238] The server receives the user data sent from the terminal, checks the integrity and consistency of the received data, and stores it in a database.

[1239] 3. Analysis of tastes and interests

[1240] The server sends the saved user data to an AI analysis module, which uses a generative AI model to analyze and identify Person A's hobbies and interests, such as "golf" and "travel." The analysis results are stored in a database and category tags are added.

[1241] 4. List potential activities

[1242] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, for example, specific activities such as "golf lessons" and "trip planning."

[1243] 5. Persona-based optimization

[1244] The server further optimizes the generated candidate list using the persona function. Specifically, it narrows down the candidates based on the success stories of other users and data on Person A, and evaluates the success rate and user satisfaction.

[1245] 6. Emotion Recognition by Emotion Engine

[1246] The server collects emotional data from user A's inputs and selections, and uses an emotion engine to recognize user A's emotional state. For example, if user A has been feeling stressed recently, that information is stored in the database.

[1247] 7. Adjusting the proposal

[1248] The server adjusts its suggestions based on the perceived emotional state. For example, if A is feeling stressed, it suggests a relaxing trip to a quiet mountain area.

[1249] 8. Matching and proposal formulation

[1250] The server searches for related facilities and services to realize suitable activities and work, and performs optimal matching. For example, it searches for local golf clubs and travel agencies and presents specific activity plans to Mr. A (e.g., golf practice every Tuesday and Saturday, travel tours for seniors).

[1251] Prompt Sentence Examples

[1252] 1. Example of what a user enters into a dedicated app:

[1253] "I'm a 65-year-old man who used to work in sales. My hobbies are golf and I'm also interested in traveling."

[1254] 2. Example of user reviewing the suggestions:

[1255] "Your ideal activities include golf practice and a quiet mountain getaway every Tuesday and Saturday."

[1256] As described above, this system uses a generative AI model and an emotion engine to make specific suggestions to help seniors live a fulfilling second life.

[1257] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1258] Step 1:

[1259] Entering User Data

[1260] Users access a dedicated web portal or mobile app and enter their personal information (age, gender, work history, hobbies, interests, etc.).

[1261] Input: Personal information, preferences, and social experiences you enter.

[1262] The terminal temporarily stores the input information in its memory.

[1263] Specific operation: When a user enters information into a form and presses "Confirm," the data is stored in the device's memory.

[1264] Step 2:

[1265] Data transmission and temporary storage

[1266] When the user presses the "Confirm" button, the device encrypts the temporarily stored data and sends it to the server.

[1267] Input: Temporary data when confirmation operation is received.

[1268] Output: The encrypted data being sent.

[1269] Specific operation: The terminal encrypts the data and sends it to the server via the network.

[1270] Step 3:

[1271] Receiving, verifying and storing data

[1272] The server receives the data sent from the terminal.

[1273] Input: The encrypted data sent.

[1274] The server calculates a checksum and verifies the consistency to ensure the integrity of the data.

[1275] Output: Validated data.

[1276] The server stores the verified data in a database, and if there is a problem, generates an error message and returns it to the terminal.

[1277] Specific operations: Calculates the checksum of the received data, checks its consistency, and stores it in the database.

[1278] Step 4:

[1279] Analysis of tastes and interests

[1280] The server transmits the stored user data to the AI ​​analysis module.

[1281] Input: User data stored in the database.

[1282] The server uses a generative AI model to analyze and identify the user's tastes and interests.

[1283] Output: Analysis results (e.g. "golf", "travel").

[1284] How it works: The generative AI model analyzes user data, identifies preferences and interests, and generates category tags, which are then stored in a database.

[1285] Step 5:

[1286] Listing potential activities

[1287] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[1288] Input: Category tags for tastes and interests.

[1289] Output: A list of suggested activities and tasks.

[1290] Specific operation: The server retrieves candidate data from the database and lists them.

[1291] Step 6:

[1292] Persona-based optimization

[1293] The server optimizes the generated candidate list using a persona function.

[1294] Inputs: candidate lists, success stories of other users, historical data.

[1295] Output: Optimized candidate list.

[1296] Specific operation: Compare past databases, evaluate success rates and user satisfaction, and create rankings.

[1297] Step 7:

[1298] Emotion recognition by emotion engine

[1299] The server collects emotional data associated with the user's inputs and selections, and uses an emotion engine to recognize the user's emotional state.

[1300] Input: User input data, selection operations.

[1301] Output: Perceived emotional state.

[1302] Specific operation: The emotion engine analyzes the collected data and stores the emotional state in a database.

[1303] Step 8:

[1304] Adjusting the proposal

[1305] The server adjusts the suggestions based on the perceived emotional state.

[1306] Input: A perceived emotional state, a list of possible activities and tasks.

[1307] Output: Adjusted proposal.

[1308] Specific actions: The server considers the emotional state, selects an appropriate activity, and updates the suggestions.

[1309] Step 9:

[1310] Matching and proposal implementation

[1311] The server searches for and matches related facilities and services to realize the selected activity.

[1312] Input: Adjusted proposal.

[1313] Output: Specific action plans and matching results.

[1314] Specific operation: Search a database of related facilities and services, select the most suitable candidates, and display them to the user.

[1315] Step 10:

[1316] User Choice and Feedback

[1317] The user reviews the specific activities and facilities presented and selects those that interest them.

[1318] Input: User's choice.

[1319] The terminal transmits this selection data to the server.

[1320] The server stores the selected information and related emotional data in a database and reflects this in future proposed algorithms.

[1321] Output: The updated database.

[1322] Specific operations: Receives and stores user selection information, and reflects the data as feedback.

[1323] (Application example 2)

[1324] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1325] Currently, it is not easy for seniors to find activities and jobs that will allow them to enjoy a fulfilling second life after retirement. In particular, there is a lack of means to suggest optimal activities and jobs that take into account the user's emotional state. Therefore, there is a need for a system that can suggest more accurate activities and jobs based on the user's emotional state. Furthermore, there is a need for flexible and effective automated work support to support senior workers.

[1326] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1327] In this invention, the server includes a means for receiving personal information, preferences, and social experiences input by the user, a means for optimizing activity candidates based on the analyzed emotional state, and a means for presenting the optimized activity candidates to the user and accepting the user's selection, thereby enabling flexible and effective work support that takes the user's emotional state into consideration.

[1328] "Means for receiving personal information, preferences, and social experiences entered by users" refers to a function that allows users to enter information such as age, gender, work history, hobbies, and interests, and collects and transmits that data to a processing system.

[1329] The "means for analyzing the received information and identifying the user's preferences and interests" refers to a function that analyzes collected data using algorithms and generative AI models to identify the user's preferences and interests.

[1330] "Means for listing candidate activities and jobs based on the identified preferences and interests" is a function that generates and lists candidate activities and jobs suitable for the user based on the analysis results.

[1331] The "means for selecting the activity or job that best suits the user from the listed activities or jobs" is a function for narrowing down the listed candidates to the activity or job that best suits the user.

[1332] The "means for presenting the selected activities or tasks to the user and accepting the user's selection" is a function for displaying the selected activities or tasks to the user and accepting the user's operation to select from among them.

[1333] "Means for matching related facilities and services based on the user's selection" is a function that searches for facilities and services related to the activity or work selected by the user and matches them with the user's requests.

[1334] The "means for providing the user with the matching results" is a function for notifying and providing the user with information about matched facilities and services.

[1335] The "means for analyzing the user's emotional state and suggesting appropriate activities and work" is a function for analyzing the user's emotional data and suggesting appropriate activities and work according to the user's emotional state.

[1336] The "means for optimizing activity candidates based on the analyzed emotional state" is a function that further narrows down and optimizes the suggested activity and job candidates by taking into account the user's emotional state.

[1337] This invention is a system that helps seniors live a fulfilling second life after retirement. This system receives personal information, preferences, and social experiences entered by users, analyzes that data, and suggests activities and jobs that are optimal for the user. In particular, it is characterized by analyzing the user's emotional state to improve the quality of the suggestions.

[1338] System Program and Processing Overview

[1339] The system includes the following main features:

[1340] 1. Data Receipt and Storage:

[1341] Users enter data such as personal information, preferences, and social experiences through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[1342] 2. Taste and Interest Analysis:

[1343] The server uses a generative AI model to analyze the received user data, which identifies preferences and interests based on the user's input data and stores the results in a database.

[1344] 3. Use the Emotion Engine:

[1345] The server uses an emotion engine to recognize the user's emotional state. The emotion engine collects emotional data associated with the user's inputs and selections and grasps the user's emotional state.

[1346] 4. List and optimize potential activities:

[1347] The server generates and optimizes activities and tasks suitable for the user based on the analysis results of the AI ​​model and the emotional state recognition by the emotion engine. Specifically, it tags the user's emotional state to suggest activities that are more relaxing or tasks that are slightly more demanding but give a sense of accomplishment.

[1348] 5. Matching and Proposal:

[1349] Based on the optimized candidates, the server searches for related facilities and services and suggests them to the user, for example, by searching for information on local golf clubs or travel agencies, etc.

[1350] 6. User Choices and Feedback:

[1351] The user selects the activities and services they are interested in from the presented options, and the device sends this selection data to the server, which stores it in a database to be used in future proposal algorithms.

[1352] Hardware and software used

[1353] Hardware:

[1354] Mobile terminal: A device through which a user enters data.

[1355] Server: A central computer system that analyzes and stores user data and makes optimal recommendations

[1356] software:

[1357] Web portal / mobile app: Serves as the user interface and is used to input data and display the proposed results

[1358] Generative AI models: Built using programming languages ​​such as Python, they contain algorithms that analyze user preferences and interests.

[1359] Emotion Engine: A software module for analyzing a user's emotional state

[1360] Examples and prompts

[1361] Examples:

[1362] If a 65-year-old male worker, Mr. B, uses this system to find activities and work that suit him after retirement, the system will make the following suggestions: When Mr. B enters his hobbies and interests into a dedicated app, the system uses a generative AI model to analyze his interests and identify preferences such as "golf" and "travel." The emotion engine then grasps Mr. B's emotional state and suggests a relaxing travel plan if he is feeling stressed.

[1363] Prompt statement:

[1364] "Based on user-entered data, analyze the user's preferences and emotional state and suggest the most suitable activities or tasks from a list of candidates."

[1365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1366] Step 1:

[1367] Users enter personal information and preferences such as age, gender, work history, hobbies, interests, etc. through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[1368] - Input: Personal information, preferences, and social experiences entered by the user

[1369] - Output: Data packets sent to the server

[1370] Step 2:

[1371] The server receives the user data sent from the terminal and verifies the integrity and consistency of the data. If there is a problem with the data, it generates an error message and returns it to the terminal. If there is no problem with the data, it stores it in the database.

[1372] - Input: User data sent to the server

[1373] - Output: Validated data and error messages (if necessary)

[1374] Step 3:

[1375] The server sends the user data stored in the database to the generative AI model for analysis. This analysis identifies the user's tastes and interests. The analysis results are then stored back in the database and tagged.

[1376] - Input: User data in the database

[1377] - Output: tagged data about preferences and interests

[1378] Step 4:

[1379] The server generates a list of potential activities and tasks based on the tagged data, linking the generative AI model with the database to create a list of activities suitable for the user.

[1380] - Input: tagged user preferences and interests data

[1381] - Output: List of potential activities and tasks

[1382] Step 5:

[1383] The server uses an emotion engine to analyze the user's emotional state. It collects emotional data from the user's inputs and selections to understand the user's emotional state. This emotional data is also stored in a database.

[1384] - Input: User emotion data

[1385] - Output: Parsed emotional state data

[1386] Step 6:

[1387] The server optimizes the list of potential activities and tasks based on the analyzed emotional state data, selecting activities that are relaxing or low-impact, depending on the user's emotional state.

[1388] - Input: Analyzed emotional state data, candidate activity list

[1389] - Output: Optimized list of activities and tasks

[1390] Step 7:

[1391] The server presents a list of optimized activity candidates and tasks to the user and accepts the user's selection. The user selects the activities and tasks that interest them from the displayed list. The device then transmits this selection data to the server.

[1392] - Input: Optimized activity candidate list

[1393] - Output: User selection data

[1394] Step 8:

[1395] The server searches for and matches relevant facilities and services based on the user's selection data, and provides the user with the matched results, such as information about local golf clubs or travel agencies.

[1396] - Input: User selected data

[1397] - Output: Matching results and notification information

[1398] Step 9:

[1399] The server stores the data on the activities and services selected by the user in a database and reflects it in future recommendation algorithms, allowing the system to make recommendations that better meet the user's needs.

[1400] - Input: User selected data

[1401] - Output: Feedback data stored in a database

[1402] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1404] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1405] [Fourth embodiment]

[1406] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1407] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1408] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1409] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1410] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1413] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1414] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1415] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1416] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1417] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1418] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1419] Overall Overview

[1420] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[1421] Overview of program processing

[1422] Entering initial data

[1423] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data, verifies it, and then sends it to the server.

[1424] Data reception and storage

[1425] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[1426] Analysis of tastes and interests

[1427] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[1428] Listing potential activities

[1429] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[1430] Persona-based optimization

[1431] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[1432] Matching and proposal implementation

[1433] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The device displays specific plans (e.g., golf lessons or community activities to be held on a specific day) to the user.

[1434] User Choice and Feedback

[1435] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[1436] Specific examples

[1437] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[1438] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[1439] The server then searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Mr. A, such as "golf practice every Tuesday and Saturday" or "senior travel tour." Finally, if Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future proposal algorithms.

[1440] In this way, the system suggests activities and jobs that are suitable for seniors to live a fulfilling second life.

[1441] The processing flow will be explained below.

[1442] Step 1: Enter initial data

[1443] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[1444] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[1445] Step 2: Receiving and storing data

[1446] The server receives the user data transmitted from the terminal.

[1447] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[1448] Step 3: Analyze tastes and interests

[1449] The server transmits the stored user data to the AI ​​analysis module.

[1450] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[1451] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[1452] Step 4: List potential activities

[1453] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[1454] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[1455] Step 5: Optimize with personas

[1456] The server further optimizes the generated candidate list using a persona function.

[1457] The server narrows down the candidates based on other users' success stories and past data.

[1458] Step 6: Matching and proposal formulation

[1459] The server searches for relevant facilities and services to carry out the selected activity.

[1460] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[1461] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[1462] Step 7: User Choices and Feedback

[1463] Users review the specific activities and facilities proposed and select those that interest them.

[1464] The terminal transmits the user's selection information to the server.

[1465] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[1466] In this way, the system makes suggestions based on the user's tastes and interests to support a fulfilling second life after retirement, allowing seniors to find the activities and jobs that best suit them.

[1467] Example 1

[1468] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1469] For today's seniors, finding activities and work that suit their tastes and interests is important for them to enjoy a fulfilling second life after retirement. However, it is not easy to accurately analyze individual tastes and interests and suggest optimal activities and work. Conventional methods lack efficient and accurate systems for making appropriate suggestions to seniors. There is a need for a system that can solve this problem and support seniors in living a satisfying second life.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1471] In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; analysis means using a generative AI model to analyze the received information and identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the activities and jobs that are most suitable for the user from the listed activities and jobs using a persona function; means for presenting the optimized activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; means for providing the matching results to the user; and means for recording feedback based on the user's selection in a database and reflecting it in future suggestions. This enables highly accurate activity and job suggestions to be made to each user.

[1472] "User" refers to a person who uses the system to input their personal information, preferences, and social experiences and receive suggestions.

[1473] "Personal Information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[1474] "Taste" refers to a particular preference or thing that a user has a preference for.

[1475] "Interests" refer to areas or activities that interest a user.

[1476] "Receiving means" refers to the means for transmitting personal information, preferences, and social experiences entered by the user to the server.

[1477] "Analysis means" refers to the means for identifying a user's preferences and interests using a generative AI model based on the received information.

[1478] A "generative AI model" refers to an artificial intelligence algorithm that analyzes a user's tastes and interests based on the data it receives.

[1479] "Listing means" refers to a means for generating candidate activities or jobs based on analyzed tastes and interests.

[1480] The "persona function" refers to a means of narrowing down the activities and tasks that are best suited to individual users based on a large amount of user data and success stories.

[1481] "Optimization measures" refer to measures that use the persona function to narrow down candidate activities and tasks and select the most suitable ones for the user.

[1482] "Matching means" refers to a means for searching for facilities and services related to the activity or work selected by the user and making the optimal match.

[1483] "Providing means" refers to a means for presenting the matching results to the user.

[1484] "Feedback means" refers to a means for recording user selections in a database and reflecting them in future suggestions.

[1485] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life after retirement. This system uses a generative AI model to analyze the user's interests based on personal information, preferences, social experiences, etc. entered by the user, and suggests optimal activities and jobs.

[1486] First, the user uses a dedicated web portal or mobile app to enter basic information such as age, gender, work history, hobbies, and interests. The device receives this information, validates it, and then sends it to the server.

[1487] The server stores the received user data in a database and verifies its consistency and completeness. If the data is OK, the server sends it to a data analysis module, which uses a generative AI model to identify preferences and interests. This generative AI model uses machine learning algorithms to analyze preferences and interests based on the received data. The analysis results are stored back in the database and assigned relevant category tags.

[1488] The server then generates a list of activities and jobs based on the analyzed preferences and interests, including a number of categories such as sports, hobbies, reskilling, volunteering, etc. It then uses a persona function to optimize the generated list and narrow down the activities and jobs that are most suitable for the user.

[1489] The server then searches for and matches related facilities and services to realize the selected activities or tasks. The matching results are presented to the user via their device as specific plans. For example, specific activities such as "golf practice every Tuesday and Saturday" or "travel tours for seniors" may be presented.

[1490] The user selects the plans they are interested in from the ones presented, and the device sends the selection data to the server, which stores the selection information in a database and reflects it in future proposals.

[1491] As a concrete example, consider the case where a user with 40 years of experience in sales uses this system. The user accesses a dedicated app and enters their age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies the user's tastes and interests as "golf" and "travel." Based on this, it generates a list of golf lesson and travel plan candidates and further optimizes the candidates using the persona function. The server then searches local golf clubs and travel agencies to find the best match. The device presents the user with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tour." If the user selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in the proposal algorithm.

[1492] An example of a prompt sentence is, "Please give us an overview of a system that suggests the best activities for a retired man in his 60s who enjoys golf." Such a system can help seniors live a fulfilling second life.

[1493] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1494] Step 1: Enter initial data

[1495] The user enters basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device receives this input data and validates whether it is in a valid format. After validation is complete, the device sends the data to the server. For example, a man in his 60s enters "65 years old, male, 40 years in sales, hobby is golf, interest is travel."

[1496] Input: User's age, gender, work history, hobbies, interests, etc.

[1497] Output: Validated user data

[1498] Step 2: Receiving and storing data

[1499] The server receives the user data sent from the terminal. It verifies the consistency and completeness of the received data, and if there are no problems, it stores it in the database. If there is a defect in the data, the server returns an error message to the terminal. Specifically, the server inserts the information "65 years old, male, 40 years in sales, hobby is golf, interest is travel" into the database.

[1500] Input: Validated user data

[1501] Output: User data stored in the database, error messages (if required)

[1502] Step 3: Analyze tastes and interests

[1503] The server sends the stored user data to an analytics module where a generative AI model runs to identify the user's preferences and interests. The generative AI model uses machine learning algorithms to analyze the user data and identify preferences and interests. The results of this analysis are stored in a database and assigned category tags. For example, the analysis might generate tags for "golf" and "travel."

[1504] Input: User data

[1505] Output: Analyzed hobbies and interests (e.g. golf, travel) and their tags

[1506] Step 4: List potential activities

[1507] The server generates a list of suitable activities and jobs based on the analyzed tastes and interests. It searches the database for matching activities and creates a list of candidates. Specifically, the server extracts activities related to "golf" and "travel" from the database and creates a list of candidates such as "golf lessons" and "travel tours for seniors."

[1508] Input: Parsed tastes and interest tags

[1509] Output: A list of suggested activities and tasks

[1510] Step 5: Optimize with personas

[1511] The server further optimizes the generated candidate list using the persona function. This function refers to the success stories and feedback of other users and prioritizes the most suitable candidates. Specifically, the persona function prioritizes the inclusion in the list of "golf lesson programs that have been popular with users with similar interests in the past."

[1512] Input: List of suggested activities and jobs

[1513] Output: Optimized candidate list

[1514] Step 6: Matching and proposal formulation

[1515] The server searches for related facilities and services to realize the selected activity and finds the best match. The resulting matching information is presented to the user via their device as a specific plan. For example, "Golf practice at a local golf club every Tuesday and Saturday."

[1516] Input: Optimized candidate list

[1517] Output: Specific action plan

[1518] Step 7: User Choices and Feedback

[1519] The user selects the specific activity plan they are interested in from the presented plan. The device receives this selection data and sends it to the server. The server stores the selection information in a database and reflects it in future suggestions.

[1520] Input: User selected data

[1521] Output: Selection information stored in a database, feedback reflected in future proposed algorithms

[1522] (Application example 1)

[1523] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1524] Conventional Second Life support systems have had difficulty accurately analyzing users' tastes and interests and suggesting optimal activities and jobs. They also lacked the means to match users with local physical stores and services in real time and present specific activity suggestions. This meant that users could not receive appropriate guidance to lead a fulfilling Second Life. Furthermore, feedback based on user choices was not reflected in real time, which led to a problem of poor quality in the suggestions provided.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1526] In this invention, the server includes means for receiving personal information, preferences, and social experiences input by a user, means for analyzing the received information and identifying the user's preferences and interests, means for listing candidate activities and jobs based on the identified preferences and interests, means for selecting the activity or job most suitable for the user from the listed activities and jobs, means for presenting the selected activities and jobs to the user and accepting the user's selection, means for matching with related facilities and services based on the user's selection, means for the user to receive suggestions in real time via a smart device, and means for providing the matching results to the user. This allows the user to find the optimal activity or job that suits their preferences and interests, match with local services provided in real time, and quickly receive a specific plan for living a fulfilling second life.

[1527] A "user" is a person who uses this system and inputs personal information, preferences, and interests.

[1528] "Personal information" refers to basic information such as a user's age, gender, and work history.

[1529] "Tastes" refer to activities or interests that a user particularly likes.

[1530] "Interests" are areas or topics that interest a user.

[1531] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[1532] The "analysis means" refers to a function for analyzing and identifying the user's tastes and interests using the received information.

[1533] "Means for listing" refers to a function that lists candidate activities or tasks based on the results of the analysis.

[1534] "Means of selection" refers to the function of selecting the most suitable activity or task for the user from the list of activities or tasks.

[1535] "Means for presentation" refers to the function of displaying selected activities or tasks to the user and prompting them to make a selection.

[1536] The "means for receiving" refers to a function that allows the system to receive a selection from the user.

[1537] "Means for matching" refers to a function that connects with related facilities and services based on the user's selection and makes appropriate suggestions.

[1538] A "smart device" is a terminal such as a smartphone or smart glasses that a user uses to input information and receive suggestions.

[1539] "Means of receiving in real time" refers to a function that allows users to instantly view the content of the proposal.

[1540] "Means for providing" refers to a function for providing the matching results to the user.

[1541] "Feedback" refers to information that the system uses to make future suggestions based on the user's selections.

[1542] Overall Overview

[1543] This invention relates to a system that suggests activities and jobs for seniors to enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and suggests optimal activities and jobs.

[1544] Overview of program processing

[1545] Entering initial data

[1546] A user uses a smart device (such as a smartphone or smart glasses) to input their basic information (such as age, gender, work history, hobbies, and interests). The device receives this input data and sends it to the server.

[1547] Data reception and storage

[1548] The server stores the received user data in a database, verifies the consistency and completeness of the data retrieved, and returns an error message to the terminal if a problem occurs.

[1549] Analysis of tastes and interests

[1550] The server sends the stored user data to an AI analysis module, which uses a generative AI model to identify preferences and interests. The analysis results are stored in a database and tagged with category tags for the analyzed preferences and interests.

[1551] Listing potential activities

[1552] Based on the analyzed preferences and interests, the server generates a list of suitable activities and jobs, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[1553] Persona-based optimization

[1554] The server further optimizes the generated candidate list using the persona function, which narrows down the activities and tasks that are most suitable for the user.

[1555] Matching and proposal implementation

[1556] The server searches for and matches related facilities and services to realize the selected activity. The most suitable facilities and services are included as suggestions to the user. The terminal displays specific plans (e.g., golf lessons every Tuesday and Saturday, or travel tours for seniors) to the user.

[1557] User Choice and Feedback

[1558] The user selects the specific activities and facilities that interest them from the presented list. The device sends this selection data to the server, which stores the selection information in a database and uses it as feedback for future proposal algorithms.

[1559] Processing hardware and software

[1560] The following hardware and software are used to implement this system.

[1561] Hardware: Raspberry Pi, smartphone, smart glasses

[1562] Software: OpenCV, TensorFlow, JSON format database, AI analysis module

[1563] Specific examples

[1564] For example, if Mr. A, a former salesperson with 40 years of experience, were to use this system, he would use smart glasses to enter his age (65), gender (male), work experience (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server. The server analyzes the received data and identifies Mr. A's tastes and interests as "golf" and "travel." Based on this analysis, a list of options such as golf lessons and travel planning is generated. Next, the persona function is used to optimize the options based on other users' success stories and Mr. A's data. Finally, the server matches Mr. A with local golf clubs and travel agencies and presents him with specific plans such as "golf practice every Tuesday and Saturday" or "senior travel tours." If Mr. A selects golf practice, the device sends the selection data to the server, which stores it in a database and reflects it in future proposal algorithms.

[1565] Prompt Sentence Examples

[1566] "Choose the activity that best suits you: 1. Golf lessons (Tuesdays and Saturdays) 2. Domestic travel tours (for seniors)"

[1567] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1568] Step 1:

[1569] The device receives basic information such as personal information, preferences, interests, and social experiences entered by the user. This input data is entered by the user using a smart device (smartphone or smart glasses). The input data includes age, gender, work history, hobbies, and interests. The received data is verified and then sent from the device to the server.

[1570] Input: User's personal information, preferences, and interests

[1571] Output: Send data to the server

[1572] Step 2:

[1573] The server stores the received user data in a database. When the data is stored, it is verified for consistency and completeness, and if a problem occurs, an error message is returned to the terminal. The data is stored in JSON format.

[1574] Input: User data sent from the terminal

[1575] Output: Data saved to database, error messages (if necessary)

[1576] Step 3:

[1577] The server sends the user data stored in the database to the AI ​​analysis module, which uses a generative AI model to identify preferences and interests. The generative AI model analyzes the input data and identifies the user's preferences and interests as category tags. This clarifies the user's hobbies and interests. The analysis results are then stored back in the database.

[1578] Input: Saved user data

[1579] Output: Identified preferences and interests, stored in a database

[1580] Step 4:

[1581] The server generates a list of suitable activities and jobs based on the identified preferences and interests, including specific suggestions from categories such as sports, hobbies, reskilling, and volunteering. The list is generated based on the user's interests, such as "golf lessons" or "trip planning."

[1582] Input: Identified tastes and interests

[1583] Output: A list of suggested activities and tasks

[1584] Step 5:

[1585] The server further optimizes the generated candidate list using the persona function, which narrows down the most suitable activities and tasks based on the success stories of other users and the current user's data. The optimized list is then stored in the database.

[1586] Input: List of suggested activities and jobs

[1587] Output: Optimized activity and task lists

[1588] Step 6:

[1589] The server searches for and matches related facilities and services to realize the selected activity. For example, it searches for local golf clubs and travel agencies, and then identifies the most suitable partners and proposes specific content. The matching results are provided to the user.

[1590] Input: Optimized activity and task lists

[1591] Output: Matching results

[1592] Step 7:

[1593] The terminal provides the user with specific plans and accepts the user's selection. Examples of plans include "Golf lessons every Tuesday and Saturday" and "Domestic travel tours for seniors." The user's selection data is acquired and sent from the terminal to the server.

[1594] Input: Matching results

[1595] Output: User selection, selection data sent to server

[1596] Step 8:

[1597] The server stores the user's selection data in a database and incorporates this feedback information into the suggestion algorithm for future visits, allowing the system to make more accurate suggestions based on the user's preferences.

[1598] Input: User selected data

[1599] Output: Save to database, reflect in algorithm

[1600] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1601] Overall Overview

[1602] This invention relates to a system that suggests activities and jobs for seniors after retirement to help them enjoy a fulfilling second life. Based on personal information, preferences, social experiences, etc. entered by the user, the system uses a generative AI model to analyze the user's interests and concerns, and suggests optimal activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[1603] Overview of program processing

[1604] Entering initial data

[1605] Users enter basic information such as age, gender, work history, hobbies, and interests through a dedicated web portal or mobile app. The device temporarily stores this input data and sends it to the server when the confirmation button is pressed.

[1606] Data reception and storage

[1607] The server receives the user data sent from the terminal. The server verifies the integrity and consistency of the received data and stores it in a database. If there is a problem, it creates an error message and returns it to the terminal.

[1608] Analysis of tastes and interests

[1609] The server sends the stored user data to an AI analysis module. The server uses a generative AI model to analyze the input data and identify the user's preferences and interests. The server stores the analysis results (e.g., "golf" or "travel") in a database and adds category tags.

[1610] Listing potential activities

[1611] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, listing specific suggestions from categories such as sports, hobbies, reskilling, and volunteering.

[1612] Persona-based optimization

[1613] The server further optimizes the generated candidate list using the persona function, narrowing down the candidates based on the success stories of other users and past data.

[1614] Emotion recognition by emotion engine

[1615] The server collects emotional data from the user's inputs and selections, recognizes the user's emotional state using an emotion engine, and stores this emotional data in a database to understand the user's emotional state.

[1616] Adjusting the proposal

[1617] The server then tailors its suggestions based on the user's perceived emotional state, for example, suggesting relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[1618] Matching and proposal implementation

[1619] The server searches for and matches related facilities and services to realize the selected activity. It matches the most suitable facilities and services (e.g., local golf clubs, travel agencies). The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tours for seniors) to the user.

[1620] User Choice and Feedback

[1621] The user reviews the specific activities and facilities presented and selects the ones they are interested in. The device then sends this selection data to the server, which stores the selected information and associated emotion data in a database and uses them as feedback for future proposed algorithms.

[1622] Specific examples

[1623] For example, consider the case where Mr. A, a former salesperson with 40 years of experience, uses this system. First, Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device receives this data and sends it to the server.

[1624] The server analyzes the received data and identifies A's tastes and interests as "golf" and "travel." Based on this, the server generates a list of options for golf lessons, travel planning, etc. The server then uses the persona function to optimize the options based on other users' success stories and A's data.

[1625] The server then uses an emotion engine to collect emotion data from A and recognize the user's emotional state. For example, if A has been feeling stressed recently, the server will make suggestions to help A relax (e.g., a trip to a quiet mountain area).

[1626] The server searches for local golf clubs and travel agencies to find the best match. The device presents specific plans to Person A, such as "golf practice every Tuesday and Saturday" or "a trip to a quiet mountain area." Finally, if Person A selects golf practice, the device sends the selection data to the server, which stores it in a database and uses it in future suggestion algorithms.

[1627] In this way, by combining emotion engines, it is possible to realize flexible and highly accurate suggestions that correspond to the user's emotional state, allowing seniors to further enrich and enjoy activities that are best suited to them.

[1628] The processing flow will be explained below.

[1629] Step 1: Enter initial data

[1630] Users open a dedicated web portal or mobile app and enter their basic information (age, gender, work history, hobbies, interests, etc.).

[1631] The terminal temporarily stores this input data and transmits it to the server when the confirmation button is pressed.

[1632] Step 2: Receiving and storing data

[1633] The server receives the user data transmitted from the terminal.

[1634] The server verifies the integrity and consistency of the received data, stores it in a database, and if there is a problem, creates an error message and returns it to the terminal.

[1635] Step 3: Analyze tastes and interests

[1636] The server transmits the stored user data to the AI ​​analysis module.

[1637] The server uses a generative AI model to analyze the input data and identify the user's preferences and interests.

[1638] The server stores the analysis results (e.g., "golf" and "travel") in a database and adds category tags.

[1639] Step 4: List potential activities

[1640] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[1641] The server will list specific suggestions from categories such as sports, hobbies, reskilling and volunteering.

[1642] Step 5: Optimize with personas

[1643] The server further optimizes the generated candidate list using a persona function.

[1644] The server narrows down the candidates based on other users' success stories and past data.

[1645] Step 6: Emotion Recognition with the Emotion Engine

[1646] The server collects emotional data associated with the user's inputs and selections, and recognizes the user's emotional state using an emotion engine.

[1647] The server stores this emotional data in a database to understand the user's emotional state.

[1648] Step 7: Adjust your proposal

[1649] The server adjusts the suggestions based on the perceived emotional state.

[1650] The server suggests relaxing activities if the user is feeling stressed. Information from the emotion engine is also used to optimize the suggested activities.

[1651] Step 8: Matching and proposal formulation

[1652] The server searches for and matches related facilities and services to realize the selected activity.

[1653] The server matches users with the best facilities and services (e.g., local golf clubs, travel agencies).

[1654] The terminal displays specific activity plans (e.g., golf practice every Tuesday and Saturday, travel tour for seniors) to the user.

[1655] Step 9: User Choices and Feedback

[1656] The user selects the specific activities and facilities that interest them from the presented options.

[1657] The terminal transmits the user's selection information to the server.

[1658] The server stores the selected information in a database and uses it as feedback for future proposal algorithms.

[1659] In this way, by combining the emotion engine, this system makes flexible and accurate suggestions based on the user's emotional state, allowing seniors to enjoy activities that are best suited to them.

[1660] Example 2

[1661] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1662] In the past, retired seniors had to find suitable activities and jobs on their own to enjoy a fulfilling second life, which required a lot of time and effort. Furthermore, the quality of activity suggestions based on tastes and interests was not high, resulting in low satisfaction among seniors. In addition, flexible suggestions that took into account the user's emotional state were not made, making it difficult to provide suggestions that met individual needs.

[1663] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving personal information, preferences, and social experiences input by a user; means for temporarily storing the received information and transmitting the data upon confirmation; means for receiving the transmitted data, verifying its completeness and consistency, and storing it in a database; means for analyzing the stored user data using a generative AI model to identify the user's preferences and interests; means for listing candidate activities and jobs based on the identified preferences and interests; means for optimizing the listed candidates using a persona function; means for collecting emotional data associated with the user data and input and recognizing the user's emotional state using an emotion engine; means for adjusting the listed and optimized activities and jobs based on the recognized emotional state; means for presenting the adjusted activities and jobs to the user and accepting the user's selection; means for matching with related facilities and services based on the user's selection; and means for providing the matching results to the user. This makes it easier for seniors to find the activities and jobs that are best suited to them, enabling them to live a fulfilling and satisfying second life.

[1664] "User" refers to a person who uses the system to input personal information, preferences, and social experiences and receive suggestions.

[1665] "Personal information" refers to basic information about a user, such as age, gender, work history, hobbies, and interests.

[1666] "Interests" refer to areas or activities in which a user is particularly interested.

[1667] "Social experience" refers to the user's past work history and social activity experience.

[1668] "Means for receiving" refers to the function that allows the system to receive personal information, preferences, and social experiences entered by the user.

[1669] The "means for temporarily saving" refers to a function for temporarily storing information input by a user in a storage device.

[1670] The "confirmation operation" refers to an operation performed by the user to confirm the input contents.

[1671] "Means for transmitting data" refers to a function for sending input data to a server.

[1672] "Means for verifying integrity and consistency" refers to the ability to verify that the data received is accurate and consistent.

[1673] "Database" refers to a storage device for storing and managing user data and analysis results.

[1674] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and identify user preferences and interests.

[1675] "Means for identifying tastes and interests" refers to the function of analyzing and clarifying a user's areas of interest using a generative AI model.

[1676] "Listing means" refers to the ability to list suitable activities or job opportunities based on identified preferences or interests.

[1677] The "persona function" refers to the ability to optimize the list of candidates by utilizing the success stories and past history of other users.

[1678] "Emotion Engine" refers to algorithms and functions for analyzing and recognizing a user's emotional state.

[1679] "Emotional state" refers to the psychological state a user exhibits when making an input or selection.

[1680] "Regulatory measures" refer to the ability to change the listed activities or tasks based on the perceived emotional state.

[1681] "Means for presenting" refers to a function for displaying optimized suggestions to the user.

[1682] The "means for accepting a selection" refers to a function by which the system receives a selection when the user makes a selection from the presented suggestions.

[1683] "Means for matching" refers to the function of searching for and matching related facilities and services for the activity or job selected by the user.

[1684] "Means for providing matching results" refers to the function of notifying users of matching results with optimal facilities and services.

[1685] This invention relates to a system that suggests activities and jobs that will help seniors live a fulfilling second life after retirement. A specific method for implementing this system will be described below.

[1686] System Overview

[1687] Based on the user's personal information, preferences, and social experiences, the system uses a generative AI model to analyze the user's interests and suggests the most suitable activities and jobs. In addition, by combining it with an emotion engine, the system recognizes the user's emotional state and improves the quality of the suggestions.

[1688] Hardware and software used

[1689] Server: Receives, stores, analyzes, and generates proposals on data. Specific server software that can be used is a web server such as Apache or Nginx, or a database server such as MySQL or PostgreSQL.

[1690] Terminal: A device on which a user inputs data and displays the proposed results. Specifically, this can be a smartphone, tablet, or PC.

[1691] Generative AI model: An AI algorithm used to analyze user preferences and interests. Specifically, it uses a large-scale language model such as GPT-4.

[1692] Emotion engine: Software for analyzing a user's emotional state, specifically using emotion recognition algorithms.

[1693] Specific examples of programs

[1694] For example, consider the case where Mr. A, a former sales professional with 40 years of experience, uses this system.

[1695] 1. User Input

[1696] Mr. A accesses the dedicated app and enters his age (65), gender (male), work history (40 years in sales), hobbies (golf), and interests (travel). The device temporarily stores this data and sends it to the server when the confirmation button is pressed.

[1697] 2. Data Receipt and Storage

[1698] The server receives the user data sent from the terminal, checks the integrity and consistency of the received data, and stores it in a database.

[1699] 3. Analysis of tastes and interests

[1700] The server sends the saved user data to an AI analysis module, which uses a generative AI model to analyze and identify Person A's hobbies and interests, such as "golf" and "travel." The analysis results are stored in a database and category tags are added.

[1701] 4. List potential activities

[1702] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags, for example, specific activities such as "golf lessons" and "trip planning."

[1703] 5. Persona-based optimization

[1704] The server further optimizes the generated candidate list using the persona function. Specifically, it narrows down the candidates based on the success stories of other users and data on Person A, and evaluates the success rate and user satisfaction.

[1705] 6. Emotion Recognition by Emotion Engine

[1706] The server collects emotional data from user A's inputs and selections, and uses an emotion engine to recognize user A's emotional state. For example, if user A has been feeling stressed recently, that information is stored in the database.

[1707] 7. Adjusting the proposal

[1708] The server adjusts its suggestions based on the perceived emotional state. For example, if A is feeling stressed, it suggests a relaxing trip to a quiet mountain area.

[1709] 8. Matching and proposal formulation

[1710] The server searches for related facilities and services to realize suitable activities and work, and performs optimal matching. For example, it searches for local golf clubs and travel agencies and presents specific activity plans to Mr. A (e.g., golf practice every Tuesday and Saturday, travel tours for seniors).

[1711] Prompt Sentence Examples

[1712] 1. Example of what a user enters into a dedicated app:

[1713] "I'm a 65-year-old man who used to work in sales. My hobbies are golf and I'm also interested in traveling."

[1714] 2. Example of user reviewing the suggestions:

[1715] "Your ideal activities include golf practice and a quiet mountain getaway every Tuesday and Saturday."

[1716] As described above, this system uses a generative AI model and an emotion engine to make specific suggestions to help seniors live a fulfilling second life.

[1717] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1718] Step 1:

[1719] Entering User Data

[1720] Users access a dedicated web portal or mobile app and enter their personal information (age, gender, work history, hobbies, interests, etc.).

[1721] Input: Personal information, preferences, and social experiences you enter.

[1722] The terminal temporarily stores the input information in its memory.

[1723] Specific operation: When a user enters information into a form and presses "Confirm," the data is stored in the device's memory.

[1724] Step 2:

[1725] Data transmission and temporary storage

[1726] When the user presses the "Confirm" button, the device encrypts the temporarily stored data and sends it to the server.

[1727] Input: Temporary data when confirmation operation is received.

[1728] Output: The encrypted data being sent.

[1729] Specific operation: The terminal encrypts the data and sends it to the server via the network.

[1730] Step 3:

[1731] Receiving, verifying and storing data

[1732] The server receives the data sent from the terminal.

[1733] Input: The encrypted data sent.

[1734] The server calculates a checksum and verifies the consistency to ensure the integrity of the data.

[1735] Output: Validated data.

[1736] The server stores the verified data in a database, and if there is a problem, generates an error message and returns it to the terminal.

[1737] Specific operations: Calculates the checksum of the received data, checks its consistency, and stores it in the database.

[1738] Step 4:

[1739] Analysis of tastes and interests

[1740] The server transmits the stored user data to the AI ​​analysis module.

[1741] Input: User data stored in the database.

[1742] The server uses a generative AI model to analyze and identify the user's tastes and interests.

[1743] Output: Analysis results (e.g. "golf", "travel").

[1744] How it works: The generative AI model analyzes user data, identifies preferences and interests, and generates category tags, which are then stored in a database.

[1745] Step 5:

[1746] Listing potential activities

[1747] The server generates suitable activity and job suggestions based on the analyzed tastes and interest category tags.

[1748] Input: Category tags for tastes and interests.

[1749] Output: A list of suggested activities and tasks.

[1750] Specific operation: The server retrieves candidate data from the database and lists them.

[1751] Step 6:

[1752] Persona-based optimization

[1753] The server optimizes the generated candidate list using a persona function.

[1754] Inputs: candidate lists, success stories of other users, historical data.

[1755] Output: Optimized candidate list.

[1756] Specific operation: Compare past databases, evaluate success rates and user satisfaction, and create rankings.

[1757] Step 7:

[1758] Emotion recognition by emotion engine

[1759] The server collects emotional data associated with the user's inputs and selections, and uses an emotion engine to recognize the user's emotional state.

[1760] Input: User input data, selection operations.

[1761] Output: Perceived emotional state.

[1762] Specific operation: The emotion engine analyzes the collected data and stores the emotional state in a database.

[1763] Step 8:

[1764] Adjusting the proposal

[1765] The server adjusts the suggestions based on the perceived emotional state.

[1766] Input: A perceived emotional state, a list of possible activities and tasks.

[1767] Output: Adjusted proposal.

[1768] Specific actions: The server considers the emotional state, selects an appropriate activity, and updates the suggestions.

[1769] Step 9:

[1770] Matching and proposal implementation

[1771] The server searches for and matches related facilities and services to realize the selected activity.

[1772] Input: Adjusted proposal.

[1773] Output: Specific action plans and matching results.

[1774] Specific operation: Search a database of related facilities and services, select the most suitable candidates, and display them to the user.

[1775] Step 10:

[1776] User Choice and Feedback

[1777] The user reviews the specific activities and facilities presented and selects those that interest them.

[1778] Input: User's choice.

[1779] The terminal transmits this selection data to the server.

[1780] The server stores the selected information and related emotional data in a database and reflects this in future proposed algorithms.

[1781] Output: The updated database.

[1782] Specific operations: Receives and stores user selection information, and reflects the data as feedback.

[1783] (Application example 2)

[1784] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1785] Currently, it is not easy for seniors to find activities and jobs that will allow them to enjoy a fulfilling second life after retirement. In particular, there is a lack of means to suggest optimal activities and jobs that take into account the user's emotional state. Therefore, there is a need for a system that can suggest more accurate activities and jobs based on the user's emotional state. Furthermore, there is a need for flexible and effective automated work support to support senior workers.

[1786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1787] In this invention, the server includes a means for receiving personal information, preferences, and social experiences input by the user, a means for optimizing activity candidates based on the analyzed emotional state, and a means for presenting the optimized activity candidates to the user and accepting the user's selection, thereby enabling flexible and effective work support that takes the user's emotional state into consideration.

[1788] "Means for receiving personal information, preferences, and social experiences entered by users" refers to a function that allows users to enter information such as age, gender, work history, hobbies, and interests, and collects and transmits that data to a processing system.

[1789] The "means for analyzing the received information and identifying the user's preferences and interests" refers to a function that analyzes collected data using algorithms and generative AI models to identify the user's preferences and interests.

[1790] "Means for listing candidate activities and jobs based on the identified preferences and interests" is a function that generates and lists candidate activities and jobs suitable for the user based on the analysis results.

[1791] The "means for selecting the activity or job that best suits the user from the listed activities or jobs" is a function for narrowing down the listed candidates to the activity or job that best suits the user.

[1792] The "means for presenting the selected activities or tasks to the user and accepting the user's selection" is a function for displaying the selected activities or tasks to the user and accepting the user's operation to select from among them.

[1793] "Means for matching related facilities and services based on the user's selection" is a function that searches for facilities and services related to the activity or work selected by the user and matches them with the user's requests.

[1794] The "means for providing the user with the matching results" is a function for notifying and providing the user with information about matched facilities and services.

[1795] The "means for analyzing the user's emotional state and suggesting appropriate activities and work" is a function for analyzing the user's emotional data and suggesting appropriate activities and work according to the user's emotional state.

[1796] The "means for optimizing activity candidates based on the analyzed emotional state" is a function that further narrows down and optimizes the suggested activity and job candidates by taking into account the user's emotional state.

[1797] This invention is a system that helps seniors live a fulfilling second life after retirement. This system receives personal information, preferences, and social experiences entered by users, analyzes that data, and suggests activities and jobs that are optimal for the user. In particular, it is characterized by analyzing the user's emotional state to improve the quality of the suggestions.

[1798] System Program and Processing Overview

[1799] The system includes the following main features:

[1800] 1. Data Receipt and Storage:

[1801] Users enter data such as personal information, preferences, and social experiences through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[1802] 2. Taste and Interest Analysis:

[1803] The server uses a generative AI model to analyze the received user data, which identifies preferences and interests based on the user's input data and stores the results in a database.

[1804] 3. Use the Emotion Engine:

[1805] The server uses an emotion engine to recognize the user's emotional state. The emotion engine collects emotional data associated with the user's inputs and selections and grasps the user's emotional state.

[1806] 4. List and optimize potential activities:

[1807] The server generates and optimizes activities and tasks suitable for the user based on the analysis results of the AI ​​model and the emotional state recognition by the emotion engine. Specifically, it tags the user's emotional state to suggest activities that are more relaxing or tasks that are slightly more demanding but give a sense of accomplishment.

[1808] 5. Matching and Proposal:

[1809] Based on the optimized candidates, the server searches for related facilities and services and suggests them to the user, for example, by searching for information on local golf clubs or travel agencies, etc.

[1810] 6. User Choices and Feedback:

[1811] The user selects the activities and services they are interested in from the presented options, and the device sends this selection data to the server, which stores it in a database to be used in future proposal algorithms.

[1812] Hardware and software used

[1813] Hardware:

[1814] Mobile terminal: A device through which a user enters data.

[1815] Server: A central computer system that analyzes and stores user data and makes optimal recommendations

[1816] software:

[1817] Web portal / mobile app: Serves as the user interface and is used to input data and display the proposed results

[1818] Generative AI models: Built using programming languages ​​such as Python, they contain algorithms that analyze user preferences and interests.

[1819] Emotion Engine: A software module for analyzing a user's emotional state

[1820] Examples and prompts

[1821] Examples:

[1822] If a 65-year-old male worker, Mr. B, uses this system to find activities and work that suit him after retirement, the system will make the following suggestions: When Mr. B enters his hobbies and interests into a dedicated app, the system uses a generative AI model to analyze his interests and identify preferences such as "golf" and "travel." The emotion engine then grasps Mr. B's emotional state and suggests a relaxing travel plan if he is feeling stressed.

[1823] Prompt statement:

[1824] "Based on user-entered data, analyze the user's preferences and emotional state and suggest the most suitable activities or tasks from a list of candidates."

[1825] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1826] Step 1:

[1827] Users enter personal information and preferences such as age, gender, work history, hobbies, interests, etc. through a dedicated web portal or mobile app. The device temporarily stores this data and sends it to the server when a confirmation button is pressed.

[1828] - Input: Personal information, preferences, and social experiences entered by the user

[1829] - Output: Data packets sent to the server

[1830] Step 2:

[1831] The server receives the user data sent from the terminal and verifies the integrity and consistency of the data. If there is a problem with the data, it generates an error message and returns it to the terminal. If there is no problem with the data, it stores it in the database.

[1832] - Input: User data sent to the server

[1833] - Output: Validated data and error messages (if necessary)

[1834] Step 3:

[1835] The server sends the user data stored in the database to the generative AI model for analysis. This analysis identifies the user's tastes and interests. The analysis results are then stored back in the database and tagged.

[1836] - Input: User data in the database

[1837] - Output: tagged data about preferences and interests

[1838] Step 4:

[1839] The server generates a list of potential activities and tasks based on the tagged data, linking the generative AI model with the database to create a list of activities suitable for the user.

[1840] - Input: tagged user preferences and interests data

[1841] - Output: List of potential activities and tasks

[1842] Step 5:

[1843] The server uses an emotion engine to analyze the user's emotional state. It collects emotional data from the user's inputs and selections to understand the user's emotional state. This emotional data is also stored in a database.

[1844] - Input: User emotion data

[1845] - Output: Parsed emotional state data

[1846] Step 6:

[1847] The server optimizes the list of potential activities and tasks based on the analyzed emotional state data, selecting activities that are relaxing or low-impact, depending on the user's emotional state.

[1848] - Input: Analyzed emotional state data, candidate activity list

[1849] - Output: Optimized list of activities and tasks

[1850] Step 7:

[1851] The server presents a list of optimized activity candidates and tasks to the user and accepts the user's selection. The user selects the activities and tasks that interest them from the displayed list. The device then transmits this selection data to the server.

[1852] - Input: Optimized activity candidate list

[1853] - Output: User selection data

[1854] Step 8:

[1855] The server searches for and matches relevant facilities and services based on the user's selection data, and provides the user with the matched results, such as information about local golf clubs or travel agencies.

[1856] - Input: User selected data

[1857] - Output: Matching results and notification information

[1858] Step 9:

[1859] The server stores the data on the activities and services selected by the user in a database and reflects it in future recommendation algorithms, allowing the system to make recommendations that better meet the user's needs.

[1860] - Input: User selected data

[1861] - Output: Feedback data stored in a database

[1862] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1863] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1864] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1865] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1866] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1867] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1868] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1869] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1870] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1871] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1872] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1873] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1874] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1875] 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.

[1876] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1877] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1878] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1879] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1880] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1881] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1882] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1883] The following is further disclosed regarding the above embodiment.

[1884] (Claim 1)

[1885] a means for receiving personal information, preferences, and social experiences entered by a user;

[1886] means for analyzing the received information and identifying the user's preferences and interests;

[1887] means for generating a list of potential activities or jobs based on said identified preferences and interests;

[1888] A means for selecting an activity or task that is most suitable for the user from the listed activities or tasks;

[1889] means for presenting the selected activities or tasks to a user and accepting a user selection;

[1890] A means for matching related facilities and services based on the user's selection;

[1891] means for providing the matching result to a user;

[1892] A system including:

[1893] (Claim 2)

[1894] The system of claim 1, further comprising means for narrowing down the activities and tasks from the list to the most suitable ones using a persona function.

[1895] (Claim 3)

[1896] 10. The system of claim 1, further comprising means for recording feedback based on the user's selection in a database and incorporating it into future suggestions.

[1897] "Example 1"

[1898] (Claim 1)

[1899] a means for receiving personal information, preferences, and social experiences entered by a user;

[1900] an analysis means using a generative AI model to analyze the received information and identify the user's preferences and interests;

[1901] means for generating a list of potential activities or jobs based on said identified preferences and interests;

[1902] A means for optimizing the activities and tasks that are most suitable for the user from among the listed activities and tasks using a persona function;

[1903] means for presenting the optimized activities or tasks to a user and accepting a user selection;

[1904] A means for matching related facilities and services based on the user's selection;

[1905] means for providing the matching result to a user;

[1906] means for recording feedback based on said user's selection in a database and reflecting it in future suggestions;

[1907] A system including:

[1908] (Claim 2)

[1909] The system of claim 1, further comprising means for narrowing down the activities and tasks from the list to the most suitable ones using a persona function.

[1910] (Claim 3)

[1911] 10. The system of claim 1, further comprising means for recording feedback based on the user's selection in a database and incorporating it into future suggestions.

[1912] "Application Example 1"

[1913] (Claim 1)

[1914] a means for receiving personal information, preferences, and social experiences entered by a user;

[1915] means for analyzing the received information and identifying the user's preferences and interests;

[1916] means for generating a list of potential activities or jobs based on said identified preferences and interests;

[1917] A means for selecting an activity or task that is most suitable for the user from the listed activities or tasks;

[1918] means for presenting the selected activities or tasks to a user and accepting a user selection;

[1919] A means for matching related facilities and services based on the user's selection;

[1920] A means for users to receive suggestions in real time via their smart devices;

[1921] means for providing the matching result to a user;

[1922] A system including:

[1923] (Claim 2)

[1924] The system of claim 1, further comprising means for narrowing down the activities and tasks from the list to the most suitable ones using a persona function.

[1925] (Claim 3)

[1926] 10. The system of claim 1, further comprising means for recording feedback based on the user's selection in a database and incorporating it into future suggestions.

[1927] "Example 2: Combining Emotion Engines"

[1928] (Claim 1)

[1929] a means for receiving personal information, preferences, and social experiences entered by a user;

[1930] means for temporarily storing the received information and transmitting the data when a confirmation operation is received;

[1931] means for receiving the transmitted data, verifying its integrity and consistency, and storing it in a database;

[1932] A means of analyzing stored user data using a generative AI model to identify user preferences and interests;

[1933] means for generating a list of potential activities or jobs based on said identified pr...

Claims

1. a means for receiving personal information, preferences, and social experiences entered by a user; means for analyzing the received information and identifying the user's preferences and interests; means for generating a list of potential activities or jobs based on said identified preferences and interests; A means for selecting an activity or task that is most suitable for the user from the listed activities or tasks; means for presenting the selected activities or tasks to a user and accepting a user selection; A means for matching related facilities and services based on the user's selection; means for providing the matching result to a user; A system including:

2. The system according to claim 1 , further comprising means for narrowing down the activities and tasks listed to the most suitable ones using a persona function.

3. 10. The system of claim 1, further comprising means for recording feedback based on the user's selection in a database for use in future recommendations.

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