System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting questions or worries related with a house, a means for allowing a generation system AI to provide advice to the questions or worries, a means for referring to a database including the skill of a senior mentor living in the neighborhood and user evaluation, and for matching the optimal mentor, a means for adjusting both schedules, and a means for arranging business trip support by the senior mentor.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] Balancing work and family life is a significant burden for dual-income and single-parent families. Furthermore, in today's society, where nuclear families are becoming more prevalent, there are fewer people to rely on for advice on housework and child-rearing, as well as issues such as a lack of trustworthy advice, a weakening of local communication, and a lack of social roles for senior citizens. These issues increase stress within the home and ultimately reduce happiness within families. This invention aims to efficiently resolve family concerns while strengthening ties to the local community and creating new roles for senior citizens. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting questions and concerns about family life, a means for a generative AI to provide advice on the questions and concerns, a means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living in the neighborhood, a means for coordinating schedules for both parties, and a means for arranging on-site support by the senior mentor. Specifically, when a user inputs questions and concerns about family life into the system, the generative AI provides expert advice. The system then references a database of nearby mentors to select and match the most suitable mentor and automatically adjust the schedule. This enables senior mentors to provide actual support in response to a wide range of family concerns, including education, childcare, nursing care, and housework. It also has the effect of expanding opportunities for senior citizens to play an active role and strengthening ties in the local community.

[0006] "Questions and concerns about the home" refers to inquiries and consultations from users about problems and difficulties that arise in running a home.

[0007] "Generative AI" refers to an artificial intelligence system that generates appropriate advice and answers based on input questions or concerns in natural language.

[0008] "Advice" refers to information that provides solutions or recommended actions to users' questions or concerns.

[0009] "Senior mentors living nearby" refers to elderly people who live within the community and have a wealth of knowledge and experience.

[0010] "Skills" refers to abilities based on specialized knowledge and practical experience in a particular field.

[0011] "User evaluation" refers to the evaluation made by system users of the services and mentors provided.

[0012] A "database" is a collection of information managed by the system, which stores data such as mentor skills and user ratings.

[0013] "Matching" refers to the process of selecting and introducing a mentor who is suitable for the user's questions and concerns.

[0014] "Adjusting schedules" refers to the time management process of determining a date and time that is convenient for both the user and the mentor.

[0015] "On-site support" refers to a selected mentor visiting the user's place of residence to provide the necessary support and services. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that uses generative AI to provide accurate advice in response to questions and concerns about home life, and also matches people with nearby senior mentors to provide on-site support. The operation of this system is explained in detail below.

[0038] 1. Enter your questions and concerns

[0039] User

[0040] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0041] Terminal

[0042] The terminal receives input from the user and transmits the information to the server.

[0043] 2. Initial advice provided by AI

[0044] server

[0045] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[0046] Terminal

[0047] The server sends the generated advice to the device and displays it to the user. For example, the advice might be, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[0048] 3. Matching potential mentors

[0049] server

[0050] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[0051] Terminal

[0052] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[0053] 4. Selecting the best mentor

[0054] User

[0055] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[0056] Terminal

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

[0058] 5. Schedule adjustments

[0059] server

[0060] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0061] Terminal

[0062] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0063] 6. On-site support

[0064] Mentor

[0065] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0066] Specific system flow example

[0067] Input Phase

[0068] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[0069] Advice Generation Phase

[0070] The server's natural language processing module analyzes the text. The AI ​​model generates "time management methods for efficient housework" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[0071] Mentor Matching Phase

[0072] The server searches a database of nearby mentors based on the question. It selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the user's device and displays it.

[0073] Mentor selection phase

[0074] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[0075] Schedule adjustment phase

[0076] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[0077] Implementation Phase

[0078] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[0079] In this way, the system provides a concrete means to efficiently resolve household concerns and strengthen community ties.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] User

[0083] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0084] Step 2:

[0085] Terminal

[0086] The terminal receives input from the user and transmits the information to the server.

[0087] Step 3:

[0088] server

[0089] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[0090] Step 4:

[0091] Terminal

[0092] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively" is presented.

[0093] Step 5:

[0094] server

[0095] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[0096] Step 6:

[0097] Terminal

[0098] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[0099] Step 7:

[0100] User

[0101] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[0102] Step 8:

[0103] Terminal

[0104] The terminal transmits the user's selection to the server.

[0105] Step 9:

[0106] server

[0107] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0108] Step 10:

[0109] Terminal

[0110] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0111] Step 11:

[0112] Mentor

[0113] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0114] Example 1

[0115] 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."

[0116] In recent years, the number of dual-income households and single-parent households has increased, resulting in many worries and problems within the home regarding education, childcare, nursing care, housework, etc. To efficiently solve these problems, there is a need for a system that allows users to easily receive professional advice. There is also a need for a system that allows users to quickly and appropriately find available supporters and receive that support in the form of a visit.

[0117] 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.

[0118] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice on the questions and concerns using a natural language processing module, means for displaying the advice on the user's terminal, means for matching the user with the most suitable support person by referencing a database containing the skills and user ratings of supporters living in the neighborhood, means for coordinating schedules between the user and the support person, and means for arranging for on-site support by the supporter. This allows the user to receive prompt and accurate advice on problems related to the home, and also enables the user to receive actual on-site support from a supporter in the neighborhood.

[0119] "Questions and worries about the home" refer to problems and difficulties that users face at home, such as education, childcare, nursing care, and housework.

[0120] A "natural language processing module" is a software component that analyzes input text and understands its content.

[0121] "Generative AI" is artificial intelligence that generates appropriate advice and information based on input text data.

[0122] A "supporter" is an expert or experienced person who gives advice and support to users to solve problems related to their home.

[0123] A "database" is a data structure that manages information such as supporter skills and user ratings, and can be referenced as needed.

[0124] "Matching" is the process of selecting and connecting with the supporter who is best suited to the user's concerns or problems.

[0125] "Schedule adjustment" refers to the process of adjusting and confirming a mutually convenient date and time between the user and the supporter.

[0126] "Home visit support" refers to support activities in which a supporter visits the user's home in person.

[0127] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches nearby supporters to provide on-site support. The operation of this system is explained in detail below. The hardware and software used include a server, terminal, natural language processing module, generative AI, database, and schedule management system.

[0128] 1. Enter your questions and concerns

[0129] User

[0130] Users input their worries and questions about their household into the terminal. For example, a user might input a worry such as, "We're both working and busy, so we don't have time to help our children with their homework." The terminal then sends this information to the server.

[0131] 2. Initial advice provided by AI

[0132] server

[0133] The server passes the received text data of questions and concerns to a natural language processing module (e.g., Google Cloud Natural Language API) for analysis. Based on the analysis results, the server creates a prompt for a generative AI (e.g., OpenAI's GPT-3) and requests it to generate advice.

[0134] 3. Displaying Advice

[0135] Terminal

[0136] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively."

[0137] 4. Matching potential mentors

[0138] server

[0139] Based on the question and advice, the server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters. The server then creates a list of the most suitable supporters and sends it to the device. The user can then select the supporter they wish to request from the list.

[0140] 5. Schedule adjustments

[0141] server

[0142] The server receives the user's selection information, accesses the supporter's schedule database (e.g., Google Calendar API), and compares it with the user's desired date and time. The optimal schedule is then adjusted and confirmed. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[0143] 6. Arranging on-site support

[0144] server

[0145] The server sends the confirmed schedule to the supporter's device and the user's device and arranges for the visit. The supporter visits the user's home at the notified date and time and provides the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0146] Example prompt

[0147] An example of a household problem entered by a user on a device: "It's difficult to balance housework and work. Cleaning in particular keeps getting put off."

[0148] Example prompt for generative AI: "I'm having trouble balancing work and housework. Please tell me some time management methods to do my housework efficiently."

[0149] This system provides a concrete means to efficiently resolve household problems and strengthen community ties.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Step 1:

[0152] User

[0153] Users input their worries and questions about their home life into the terminal. For example, they input the text "We are both working and busy, so we don't have time to help our children with their homework."

[0154] Input: Text data of questions and concerns about the home

[0155] Output: Sending text data of questions and concerns from the device to the server

[0156] Step 2:

[0157] Terminal

[0158] The device receives text data of questions and concerns entered by the user, converts it into JSON format, and sends it to the server.

[0159] Input: Text data entered by the user

[0160] Output: Text data converted to JSON format

[0161] Step 3:

[0162] server

[0163] The server parses the received JSON data, extracts the text of the question or concern, and passes this text to a natural language processing module (e.g., Google Cloud Natural Language API) to identify keywords and important phrases.

[0164] Input: JSON format text data

[0165] Output: Keywords and important phrases analyzed by natural language processing

[0166] Step 4:

[0167] server

[0168] The server generates a prompt based on the analysis results and asks a generative AI (e.g., OpenAI's GPT-3) to generate advice. For example, the generated advice might be, "If you don't have time to help with your child's homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[0169] Input: Parsed keywords and key phrases

[0170] Output: Advice generated by generative AI

[0171] Step 5:

[0172] server

[0173] The server transmits the generated advice to the terminal.

[0174] Input: Advice generated by generative AI

[0175] Output: Advice sent to the user's terminal

[0176] Step 6:

[0177] Terminal

[0178] The terminal displays the advice received from the server to the user through a user interface, and the user confirms the presented advice.

[0179] Input: Advice sent by the server

[0180] Output: Advice displayed to the user

[0181] Step 7:

[0182] server

[0183] The server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters based on the question and advice, and uses an SQL query to create a list of the most suitable supporters and send it to the device.

[0184] Input: Question and advice

[0185] Output: A list of eligible donors

[0186] Step 8:

[0187] Terminal

[0188] The device displays a list of potential supporters to the user. The user can then select the most suitable supporter from the displayed list. For example, "Supporter A, a former teacher" or "Supporter B, a former childcare teacher" may be displayed.

[0189] Input: List of potential supporters

[0190] Output: A list of donors displayed to the user

[0191] Step 9:

[0192] User

[0193] The user selects the supporter they wish to request from the displayed supporter candidates, and then inputs the selection information into the terminal.

[0194] Input: User-selected supporter

[0195] Output: Text data containing the selection information

[0196] Step 10:

[0197] Terminal

[0198] The device converts the user's selection information into JSON format and sends it to the server.

[0199] Input: Supporter information selected by the user

[0200] Output: Supporter information sent to the server in JSON format

[0201] Step 11:

[0202] server

[0203] The server accesses the schedule database of the selected supporters (e.g., Google Calendar API) and compares it with the user's desired date and time. It then adjusts and confirms the optimal schedule. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[0204] Input: Supporter information selected by the user and desired date and time

[0205] Output: Confirmed schedule

[0206] Step 12:

[0207] server

[0208] The server transmits the finalized schedule to the terminal and notifies the user and the supporter.

[0209] Input: Confirmed schedule information

[0210] Output: Schedule notifications sent to users and donors

[0211] Step 13:

[0212] Users and Supporters

[0213] The user and the supporter will follow the notified schedule, and the supporter will visit the user's home at the specified date and time and provide the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0214] Input: Notified schedule

[0215] Output: Actual visit support

[0216] (Application example 1)

[0217] 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."

[0218] In modern society, many worries and problems arise in daily life, especially in dual-income households and those busy with childcare, nursing care, and housework. Specific concerns include dietary issues and not having time to prepare healthy meals. There is also a need for a method to efficiently resolve these issues by utilizing the expertise and experience of the elderly. Therefore, a system is needed that provides accurate advice for questions and worries within the home, and also matches nearby experts to provide on-site support.

[0219] 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.

[0220] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of elderly mentors living nearby, means for coordinating schedules between the two parties, means for arranging on-site support by the elderly mentor, and means for quickly generating appropriate advice online in response to inquiries about meals at home, and further means for matching with nearby cooking experts and nutritionists to provide on-site cooking support, thereby enabling effective and quick support for meals at home and other daily life issues.

[0221] "A means of inputting questions and concerns about the home" refers to an interface that allows users to input various problems and questions that arise within the home into a terminal.

[0222] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate answers and advice in response to questions or concerns entered by users.

[0223] An "elderly mentor" is an elderly person who has a wealth of life experience and specialized knowledge and is able to provide various types of support to the user's home.

[0224] "Database including the skills and user ratings of elderly mentors living in the neighborhood" refers to a database that stores information on mentor skills and ratings from other users in order to select an appropriate elderly mentor for a user.

[0225] "Means of matching the most suitable mentor" refers to a method of utilizing information in the database to select and suggest a mentor who is most suitable for the user's concerns.

[0226] "Means to coordinate schedules between both parties" refers to the function that coordinates the schedules of the user and mentor and identifies a date and time that is convenient for both parties.

[0227] "Means for arranging on-site support by the elderly mentor" refers to a method of arranging for a selected mentor to visit the user's home at a specified date and time and provide specific support.

[0228] "Generating appropriate advice online quickly in response to dietary concerns at home" means that when a user enters their dietary concerns online, the generative AI will provide specific advice in a short amount of time.

[0229] "Means of matching nearby cooking experts and nutritionists and providing on-site cooking support" refers to a method in which cooking experts and nutritionists are selected for users who have concerns about their diet, and they actually visit their homes to provide support.

[0230] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches users with nearby senior mentors and cooking experts to provide on-site support. This system is designed to provide prompt and appropriate support according to each user's concerns. The operation of the system is explained in detail below using specific examples.

[0231] 1. Enter your questions and concerns

[0232] User

[0233] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "I don't always have time, so it's difficult for me to cook healthy meals."

[0234] Terminal

[0235] The device receives input from the user and sends that information to a backend server, using an interface such as a smartphone or tablet, and the software used is a front-end application (e.g., React Native).

[0236] 2. Initial advice provided by AI

[0237] server

[0238] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI (e.g., GPT model) generates advice and sends it back to the server. The software used is a backend server (e.g., Node.js) and a natural language processing module (e.g., SpaCy, GPT model).

[0239] Terminal

[0240] The server sends the generated advice to the device and displays it to the user. Specifically, it presents, for example, "healthy dinner recipes that can be made in under 30 minutes."

[0241] 3. Matching potential mentors

[0242] server

[0243] The server searches a database of nearby senior mentors and cooking experts based on the question and advice. It selects the most suitable mentor, taking into account the mentor's skills and ratings. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python).

[0244] Terminal

[0245] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Former cooking class instructor A" or "Experienced nutritionist B" may be displayed.

[0246] 4. Selecting the best mentor

[0247] User

[0248] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Former Cooking Class Instructor A."

[0249] Terminal

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

[0251] 5. Schedule adjustments

[0252] server

[0253] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed date. The software used is a calendar API (e.g., Google Calendar API).

[0254] Terminal

[0255] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0256] 6. On-site support

[0257] Mentor

[0258] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Instructor A will visit the user's home every Saturday at 5:00 PM and provide instruction on healthy cooking that can be made in a short amount of time."

[0259] Specific system flow example

[0260] Input Phase

[0261] The user inputs a concern such as "I'm busy at work and don't have time to make a simple and healthy dinner" into the device, and the device sends this information to the server.

[0262] Advice Generation Phase

[0263] The server's natural language processing module analyzes the text. The AI ​​model generates "healthy dinner recipes that can be made in under 30 minutes" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[0264] Mentor Matching Phase

[0265] The server searches a database of nearby mentors based on the question and advice. It selects "Former Cooking Class Instructor A" and "Experienced Nutritionist B" as candidates. The server then sends the candidate list to the user's device and displays it.

[0266] Mentor selection phase

[0267] The user selects "Former cooking class instructor A." The device sends the selection information to the server.

[0268] Schedule adjustment phase

[0269] The server checks the schedules of the user and instructor A and suggests "every Saturday at 5:00 PM." The device presents the adjusted schedule to the user and notifies the mentor.

[0270] Implementation Phase

[0271] Instructor A visits users' homes every Saturday at 5:00 p.m. to help them prepare healthy meals that can be prepared in a short amount of time.

[0272] Prompt Sentence Examples

[0273] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[0274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0275] Step 1:

[0276] The user inputs household concerns or questions into the device. The input information is specific text, such as "I'm always short on time and it's difficult to cook healthy meals." The input is the question or concern in text format, and the output is the information sent from the device to the server. The specific operation is that the user inputs the concerns as text using an application installed on a smartphone or tablet.

[0277] Step 2:

[0278] The terminal receives input from the user and sends the information to the backend server. The input is the text data entered by the user into the terminal, and the output is the JSON format data sent to the server. Specifically, the terminal's API captures the user's input and sends the data to the server using an HTTP request.

[0279] Step 3:

[0280] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. The software used here is a natural language processing module (e.g., SpaCy, GPT model). The input is the question or concern data in JSON format sent from the device, and the output is the analysis results, which are analyzed data such as keywords and sentence structure. Specifically, the server passes the received data to the analysis module, which then analyzes the text data.

[0281] Step 4:

[0282] The generative AI generates advice based on the analysis results and sends it back to the server. The generative AI model used here is a GPT model. The input is the keywords and sentence structure data obtained as a result of the analysis, and the output is specific advice text. The specific operation is that the generative AI model generates advice content based on the analysis results within the server and sends it back to the server.

[0283] Step 5:

[0284] The server sends the generated advice to the terminal and displays it to the user. The input is the text data of the advice returned from the generative AI, and the output is the specific advice displayed on the user's terminal. The specific operation is that the server sends the advice data to the terminal, and the terminal displays the data to the user.

[0285] Step 6:

[0286] The server searches a database of nearby elderly mentors and cooking experts based on the question and advice. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python). The input is text data containing the advice, and the output is a list of candidate mentors and experts. Specifically, the server queries the database based on the advice and lists appropriate mentors and experts.

[0287] Step 7:

[0288] The server sends the selected mentor candidates to the terminal and displays them to the user. The input is the candidate list data, and the output is the information of the mentor candidates displayed on the user's terminal. The specific operation is that the server sends the candidate list to the terminal, and the terminal displays it to the user.

[0289] Step 8:

[0290] The user selects the mentor they wish to request from the presented mentor candidates. The input is the displayed list of mentor candidates, and the output is information about the selected mentor. The specific operation is for the user to select from the candidates using the terminal interface.

[0291] Step 9:

[0292] The terminal sends the user's selection to the server. The input is the information of the mentor selected by the user, and the output is the selection information sent to the server. The specific operation is that the terminal captures the selection data and sends it to the server via an HTTP request.

[0293] Step 10:

[0294] The server accesses the mentor's schedule database and compares it with the user's desired date and time. The software used is a calendar API (e.g., Google Calendar API). The input is the user's and mentor's desired date and time data, and the output is the adjusted schedule data. Specifically, the server compares the schedules of both parties and determines the optimal date and time.

[0295] Step 11:

[0296] The server sends the adjusted schedule to the terminal and notifies the user. It also notifies the mentor's terminal of the schedule. The input is the adjusted schedule data, and the output is a schedule notification that is displayed on the user's and mentor's terminals. Specifically, the server sends the schedule data to each terminal, and the terminal displays the contents.

[0297] Step 12:

[0298] The mentor visits the user's home at the notified date and time and provides the requested support. The input is the schedule notification received from the server before the visit, and the output is the specific support activities at the visit location. The specific operation is for the mentor to visit the user's home at the specified date and time and provide support such as cooking assistance.

[0299] These steps will enable us to solve household problems and provide efficient support. Furthermore, the following example prompts will enable the generative AI model to generate effective advice.

[0300] Prompt Sentence Examples

[0301] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[0302] 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.

[0303] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[0304] 1. Enter your questions and concerns

[0305] User

[0306] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0307] Terminal

[0308] The terminal receives input from the user and transmits the information to the server.

[0309] 2. Emotion Recognition by Emotion Engine

[0310] server

[0311] The server sends the received user input to the emotion engine for analysis. The emotion engine analyzes the text and, if necessary, recognizes the user's face and voice tone to determine their emotion. For example, if the user is feeling stressed, the emotion is recognized as "stress."

[0312] 3. Initial advice provided by AI

[0313] server

[0314] The server provides the emotion analysis results obtained from the emotion engine to the generative AI, which then generates advice that takes the user's emotional state into account. For example, if the user is feeling stressed, advice on relaxation methods and time management will be emphasized.

[0315] Terminal

[0316] The server sends the generated advice to the device and displays it to the user. For example, it might say, "It seems you're feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively."

[0317] 4. Matching potential mentors

[0318] server

[0319] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The server then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, if advice on stress reduction is needed, mentors with expertise in stress management will be prioritized.

[0320] Terminal

[0321] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[0322] 5. Selecting the best mentor

[0323] User

[0324] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[0325] Terminal

[0326] The terminal transmits the user's selection to the server.

[0327] 6. Schedule Adjustment

[0328] server

[0329] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0330] Terminal

[0331] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0332] 7. On-site support

[0333] Mentor

[0334] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0335] Specific system flow example

[0336] Input Phase

[0337] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[0338] Emotion Recognition Phase

[0339] The server sends the input to the emotion engine, which recognizes the user's emotion as "stress."

[0340] Advice Generation Phase

[0341] The server's generative AI generates advice based on the results of emotion analysis, suggesting ways for users to relax and how to manage their time for housework and work.

[0342] Mentor Matching Phase

[0343] The server searches a database of nearby mentors based on the results of the emotion analysis and the question. It selects two candidates: "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D." The server then sends the candidate list to the user's device and presents it to them.

[0344] Mentor selection phase

[0345] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[0346] Schedule adjustment phase

[0347] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[0348] Implementation Phase

[0349] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[0350] In this way, by combining the emotion engine, the system provides personalized advice and assistance that takes into account the user's emotional state, allowing for more efficient resolution of household problems.

[0351] The processing flow will be explained below.

[0352] Step 1:

[0353] User

[0354] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0355] Step 2:

[0356] Terminal

[0357] The terminal receives input from the user and transmits the information to the server.

[0358] Step 3:

[0359] server

[0360] The server sends the received questions and concerns to the emotion engine, which analyzes the input text and identifies the user's emotions, such as stress, fatigue, and anxiety.

[0361] Step 4:

[0362] Emotion Engine

[0363] The emotion engine sends the analysis results back to the server. For example, it is analyzed that the user is feeling stressed.

[0364] Step 5:

[0365] server

[0366] The server queries the generative AI based on the emotion analysis results and generates advice. Specific advice is generated that takes into account the user's emotional state. For example, if the user is feeling stressed, advice on "how to relax" and "how to manage time" will be emphasized.

[0367] Step 6:

[0368] Terminal

[0369] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "It seems you are feeling stressed, so try some relaxation techniques. Also, here are some time management techniques to help you do your homework more effectively" may be displayed.

[0370] Step 7:

[0371] server

[0372] The server searches a database of nearby senior mentors based on the question, advice, and sentiment analysis results. It then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, it prioritizes mentors with stress reduction skills.

[0373] Step 8:

[0374] Terminal

[0375] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, who is good at stress management" or "Mentor B, who is good at education" are displayed.

[0376] Step 9:

[0377] User

[0378] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, who is skilled in stress management."

[0379] Step 10:

[0380] Terminal

[0381] The terminal transmits the user's selection to the server.

[0382] Step 11:

[0383] server

[0384] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0385] Step 12:

[0386] Terminal

[0387] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0388] Step 13:

[0389] Mentor

[0390] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0391] Example 2

[0392] 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."

[0393] There is a need for a system that can quickly provide users with appropriate and personalized advice for various worries and problems at home. Solving problems also requires physical support personnel who can provide specialized knowledge and assistance, but current systems have difficulty effectively matching these people and adjusting their schedules. This can lead to stress for users and prolong the problems.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0395] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for performing an emotional analysis of the questions and concerns using an emotion engine that recognizes the user's emotions, means for presenting advice generated based on the emotion analysis results, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, means for coordinating schedules for both parties, and means for arranging on-site support by the senior mentor. This allows the user to receive personalized advice based on their emotions while also quickly arranging physical support, making it possible to efficiently solve home problems.

[0396] The "means for inputting questions and concerns about the home" is a means for users to input problems and annoyances about housework, childcare, nursing care, and other home-related issues as text data.

[0397] "Generative AI" is an artificial intelligence system that uses natural language processing and machine learning algorithms to generate optimal advice and solutions based on input data.

[0398] An "emotion engine" is software or hardware that analyzes text data, voice data, facial image data, etc., to identify the user's emotional state.

[0399] "Sentiment analysis" is the process of extracting emotional states from user input data and tagging and scoring them.

[0400] "Means for presenting advice" refers to the means for displaying solutions and advice generated by generative AI to the user.

[0401] A "senior mentor" refers to an elderly person who has knowledge and experience that is useful in resolving family problems and concerns.

[0402] The "database" is a digital storage system that systematically stores information such as senior mentor skills and user ratings, and allows for search and reference.

[0403] The "matching method" is the process of selecting the most suitable senior mentor based on the user's problem, questions, and sentiment analysis results, and then introducing the two parties together.

[0404] The "means for adjusting schedules" is a process for adjusting the available time of both the user and the senior mentor and setting an appropriate date and time for support.

[0405] The "means for arranging on-site support" is a means for arranging for a selected senior mentor to visit the user's home and actually provide support.

[0406] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[0407] Enter your questions and concerns

[0408] Users input their household concerns and questions into a terminal. The terminal can be a digital device such as a smartphone, tablet, or PC. The user might input a concern such as, "We're both working and busy, so we don't have time to help my child with their homework." The terminal then sends this information to the server.

[0409] Emotion recognition by emotion engine

[0410] The server sends the received user input to the emotion engine for analysis. The emotion engine combines text analysis, facial recognition, and voice tone analysis to analyze the user's emotions. For example, a Python-based natural language processing library is used for text analysis, OpenCV for facial recognition, and a voice tone analysis program is typically used for voice analysis. If the user is feeling stressed, the emotion is recognized as "stress."

[0411] AI-powered initial advice

[0412] The server provides the emotion analysis results obtained from the emotion engine to a generative AI model. For example, a natural language processing model such as OpenAI's GPT-3 can be used. The generative AI takes into account the user's emotional state and generates appropriate advice. For example, if the user is feeling stressed, it will emphasize "ways to relax" and "time management advice." The generated advice is sent from the server to the device and displayed to the user. For example, advice such as "It seems you are feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively" may be presented.

[0413] Matching mentor candidates

[0414] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The database includes the skills of senior mentors and user ratings, and the most suitable mentor is extracted. For example, if advice on stress reduction is needed, a mentor who is skilled in stress management will be given priority. This information is sent from the server to the terminal, and the user is presented with candidates such as "Mentor A, a former teacher" and "Mentor B, a former childcare teacher."

[0415] Selection of the best mentor

[0416] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former faculty member." The selection information is sent from the terminal to the server.

[0417] Schedule adjustment

[0418] The server accesses the selected mentor's schedule database and compares it with the user's desired date and time. It adjusts the date and time to suit both parties and finalizes the adjusted schedule. For example, a schedule such as "every Wednesday from 5:00 PM to 6:00 PM" is finalized. This information is sent from the server to the terminal and notified to the user and mentor.

[0419] On-site support

[0420] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0421] Specific examples

[0422] The user inputs their concern on their device, saying, "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device sends this information to the server. The server sends the input to an emotion engine, which recognizes the user's emotion as "stress." A generative AI generates advice based on the emotion analysis results, suggesting ways for the user to relax and manage their time for housework and work. The server searches a database of nearby mentors based on the emotion analysis results and the question, and selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the device and presents it. The user selects "Former Housekeeper Mentor C." The device sends the selection information to the server. The server checks the user's and Mentor C's schedules and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor. Mentor C will visit the user's home every Tuesday at 10:00 to provide cleaning assistance.

[0423] In this way, the combination of an emotion engine and generative AI can provide personalized advice and assistance that takes into account the user's emotional state, making it possible to solve household problems more efficiently.

[0424] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0425] Step 1:

[0426] Handling User Input

[0427] The user uses the device to input questions or concerns about their household. For example, they might input, "We're both working and busy, so we don't have time to help our child with their homework." The device receives this information as text data and converts it into JSON format. The input data looks like this:

[0428] Input data:

[0429] "Problem": "We both work and are busy, so we don't have time to help our children with their homework."

[0430] The terminal sends this data to the server as an HTTP request.

[0431] Step 2:

[0432] Sentiment analysis processing

[0433] The server sends the received text data to the emotion engine, which performs text analysis, facial recognition, and speech tone analysis to analyze the user's emotional state, using libraries such as the Natural Language Toolkit (NLTK), OpenCV, and speech analysis.

[0434] Input text:

[0435] "We both work and are busy, so we don't have time to help our children with their homework."

[0436] The sentiment engine parses this text and produces output like this:

[0437] Output data:

[0438] "Emotion": "Stress"

[0439] This output data is returned to the server.

[0440] Step 3:

[0441] AI-powered initial advice

[0442] The server sends the sentiment analysis results as prompts to a generative AI model, for example, OpenAI's GPT-3.

[0443] Input prompt:

[0444] "Users are stressed and need relaxation and time management advice."

[0445] The generative AI model generates advice based on the prompts, producing an output similar to the following:

[0446] Output advice:

[0447] "Try yoga or meditation as a way to relax, and create a to-do list to help you manage your time."

[0448] The server transmits this generated advice to the terminal.

[0449] Step 4:

[0450] Advice display and mentor matching

[0451] The terminal will display the advice received from the server to the user, for example, in the following format:

[0452] "It sounds like you're stressed, so I'd like to recommend some yoga and meditation techniques. I'd also recommend creating a to-do list and managing your time."

[0453] At the same time, the server searches a database of senior mentors living nearby and selects the most suitable mentor.

[0454] Input data:

[0455] "Question content", "Advice content", "Emotion analysis results"

[0456] The server generates a list of mentor candidates as follows and sends it to the device:

[0457] Output data:

[0458] "Mentor A, a former teacher" and "Mentor B, a former childcare teacher"

[0459] The terminal also displays this mentor candidate to the user.

[0460] Step 5:

[0461] Selection of the best mentor

[0462] The user selects from the presented mentor candidates. For example, the user selects "Mentor A, a former teacher."

[0463] The terminal transmits this selection to the server.

[0464] Input data:

[0465] "Mentor A, a former teacher"

[0466] Step 6:

[0467] Schedule adjustment

[0468] The server accesses the schedule database of the user and the selected mentor and adjusts the available time for both parties. For example, it determines that "every Wednesday from 5:00 PM to 6:00 PM" is optimal.

[0469] Input data:

[0470] "Desired date and time", "Mentor schedule"

[0471] The server determines the adjusted schedule as follows and sends it to the device:

[0472] Output data:

[0473] Every Wednesday from 5:00 PM to 6:00 PM

[0474] The terminal displays this schedule to the user and also notifies the mentor.

[0475] Step 7:

[0476] On-site support

[0477] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "I will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0478] Specifically, the mentor visits the user's home at a specified time and provides support according to the previously agreed upon details.

[0479] (Application example 2)

[0480] 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."

[0481] Conventional home support systems often provide general advice and support without considering the user's emotional state, resulting in a lack of personalized support tailored to individual needs. Furthermore, while there is a demand for services that take emotional states into account in other lifestyle services, including food delivery, this has not yet been realized. Therefore, a system that can provide appropriate advice and services by empathizing with the user's emotions is needed.

[0482] 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.

[0483] In this invention, the server includes a means for inputting questions and concerns about the home, a means for a generative AI to provide advice in response to the questions and concerns, an emotion analysis means for analyzing the user's emotions, a means for the generative AI to provide personalized advice based on the user's emotional state, a means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, a means for coordinating schedules for both parties, and a means for arranging on-site support by the senior mentor. This allows the server to provide personalized advice and support taking into account the user's emotional state, thereby improving satisfaction with home concerns and food delivery.

[0484] "Means for inputting questions and concerns about the home" refers to an interface that allows users to input problems and questions that arise within the home in the form of text or voice.

[0485] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate solutions and advice in response to questions or concerns entered by users.

[0486] "Emotion analysis means" is a technology that analyzes the emotional state from text or voice input by the user and identifies emotions such as stress, fatigue, and joy.

[0487] The "means for providing personalized advice" is an artificial intelligence system that generates optimal solutions and advice based on the emotional state of each individual user, based on the results of emotion analysis.

[0488] The "database including the skills and user ratings of senior mentors living nearby" is a database that accumulates senior mentors' specialized knowledge, service experience, and user ratings.

[0489] "Means for matching the most suitable mentor" refers to algorithms and systems for selecting the most suitable senior mentor based on the user's questions, concerns, and emotional analysis results.

[0490] The "means of adjusting schedules" is a system that compares the schedules of both the user and the mentor and arranges a date and time that is convenient for both parties.

[0491] The "means for arranging on-site support" is a system that coordinates schedules and arranges visits by selected mentors to the user's home to provide the necessary support.

[0492] This invention is a system that uses generative AI to provide personalized advice in response to questions and concerns about the home. The system uses emotion analysis to recognize the user's emotions and provide optimal advice, as well as matching with nearby senior mentors and providing on-site support.

[0493] Specific system configuration

[0494] 1. Enter your questions and concerns

[0495] User

[0496] Users input questions or concerns about their home into a smartphone application. At this stage, users often write sentences that touch on their emotions. They input their content in response to prompts such as, "How are you feeling today?"

[0497] Example input

[0498] I'm very tired today and I don't know what to eat.

[0499] Terminal

[0500] The terminal receives input from the user and transmits the information to the server.

[0501] 2. Emotion analysis

[0502] server

[0503] The server analyzes the received user input using a sentiment analysis engine, which analyzes the text to identify emotions such as stress, fatigue, and joy.

[0504] Specific examples

[0505] The input "I'm very tired today and I don't know what to eat" is recognized as "fatigue."

[0506] 3. Generating Advice

[0507] server

[0508] The server uses generative AI to provide personalized advice based on the results of sentiment analysis. For example, if the user is tired, it will suggest healthy and easy-to-eat meals.

[0509] Specific examples

[0510] "You seem tired, so I recommend some relaxing herbal tea and a nutritious salad."

[0511] Terminal

[0512] The server transmits the generated advice to the terminal and displays it to the user.

[0513] 4. Mentor matching

[0514] server

[0515] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis to identify the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[0516] Specific examples

[0517] A "nutritionist mentor" who is knowledgeable about fatigue recovery will be selected and presented to the user.

[0518] Terminal

[0519] The user is prompted to select a mentor from the displayed candidates.

[0520] 5. Selecting the best mentor and adjusting the schedule

[0521] User

[0522] The user selects the mentor they wish to request from the presented mentor candidates. For example, they can select "Mentor A, a nutritionist."

[0523] Terminal

[0524] The terminal transmits the user's selection to the server.

[0525] server

[0526] The server checks the mentor's schedule, adjusts it to the prepared date and time, and notifies the user and mentor of the adjusted schedule.

[0527] 6. On-site support

[0528] Mentor

[0529] The mentor will visit the user's home at the notified date and time and provide the requested support.

[0530] Hardware and software used

[0531] Smartphone: User Interface

[0532] Sentiment analysis engine: Emotion-Analysis-Service

[0533] Generative AI: Generated-Advice-Service using natural language processing

[0534] Food recommendation engine: Food-Recommendation-Service

[0535] Prompt Sentence Examples

[0536] "What dish would you recommend for someone who is feeling tired these days?"

[0537] This makes it possible to provide personalized advice that reflects the user's emotional state, improving satisfaction with household concerns and food delivery.

[0538] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0539] Step 1:

[0540] The user inputs questions and concerns about the home.

[0541] An example of input would be "I'm very tired today and I don't know what to eat."

[0542] This input data is sent to the terminal in text format.

[0543] Step 2:

[0544] The terminal receives input data from the user and sends the information to the server for analysis.

[0545] The input is text data, which is transferred to the server as is.

[0546] Step 3:

[0547] The server transmits the received text data to the emotion analysis means.

[0548] The emotion analysis means uses natural language processing and machine learning algorithms to analyze emotional states from text data.

[0549] Data processing includes part-of-speech analysis of text and sentiment analysis.

[0550] The output is emotion tags such as "fatigue" and "stress."

[0551] Step 4:

[0552] The server sends the emotion analysis results to the generative AI.

[0553] Here, appropriate advice is generated based on input data, which is the result of emotion analysis.

[0554] Generative AI takes into account the user's emotional state and generates optimal solutions based on specific prompts.

[0555] As a data calculation, specific advice is generated by referencing a knowledge base and past data.

[0556] The output is specific advice such as relaxation methods and dietary suggestions.

[0557] Step 5:

[0558] The server transmits the generated advice to the terminal.

[0559] The input data is text-based advice created by generative AI.

[0560] The device will then display this advice to the user, such as "We recommend a relaxing herbal tea and a nutritious salad."

[0561] Step 6:

[0562] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis.

[0563] The input data is the emotion analysis results and the question content.

[0564] The server selects the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[0565] As an output, a list of suitable mentors is generated.

[0566] Step 7:

[0567] The server transmits the selected mentor candidates to the terminal and displays them to the user.

[0568] The input data is a list of selected mentors, and the output shows the candidates to the user, who can select "Dietitian A Mentor."

[0569] Step 8:

[0570] The user selects the mentor he or she wishes to request from among the presented mentor candidates.

[0571] The input data is the mentor selected by the user.

[0572] The terminal transmits its selection to the server.

[0573] Step 9:

[0574] The server checks the mentor's schedule and adjusts it to the user's desired date and time.

[0575] The input data is the schedule information of the user and the mentor.

[0576] As an output, a firm schedule is produced.

[0577] Step 10:

[0578] The server transmits the adjusted schedule to the terminal and notifies the user.

[0579] Similarly, the mentor will be notified of the schedule.

[0580] Step 11:

[0581] Upon receiving the notification, the mentor visits the user's home at the confirmed date and time to provide the requested support.

[0582] A specific example of this would be "Mentor A, a nutritionist, visits and provides dietary advice."

[0583] 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.

[0584] 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.

[0585] 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.

[0586] [Second embodiment]

[0587] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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."

[0599] This invention is a system that uses generative AI to provide accurate advice in response to questions and concerns about home life, and also matches people with nearby senior mentors to provide on-site support. The operation of this system is explained in detail below.

[0600] 1. Enter your questions and concerns

[0601] User

[0602] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0603] Terminal

[0604] The terminal receives input from the user and transmits the information to the server.

[0605] 2. Initial advice provided by AI

[0606] server

[0607] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[0608] Terminal

[0609] The server sends the generated advice to the device and displays it to the user. For example, the advice might be, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[0610] 3. Matching potential mentors

[0611] server

[0612] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[0613] Terminal

[0614] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[0615] 4. Selecting the best mentor

[0616] User

[0617] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[0618] Terminal

[0619] The terminal transmits the user's selection to the server.

[0620] 5. Schedule adjustments

[0621] server

[0622] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0623] Terminal

[0624] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0625] 6. On-site support

[0626] Mentor

[0627] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0628] Specific system flow example

[0629] Input Phase

[0630] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[0631] Advice Generation Phase

[0632] The server's natural language processing module analyzes the text. The AI ​​model generates "time management methods for efficient housework" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[0633] Mentor Matching Phase

[0634] The server searches a database of nearby mentors based on the question. It selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the user's device and displays it.

[0635] Mentor selection phase

[0636] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[0637] Schedule adjustment phase

[0638] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[0639] Implementation Phase

[0640] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[0641] In this way, the system provides a concrete means to efficiently resolve household concerns and strengthen community ties.

[0642] The processing flow will be explained below.

[0643] Step 1:

[0644] User

[0645] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0646] Step 2:

[0647] Terminal

[0648] The terminal receives input from the user and transmits the information to the server.

[0649] Step 3:

[0650] server

[0651] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[0652] Step 4:

[0653] Terminal

[0654] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively" is presented.

[0655] Step 5:

[0656] server

[0657] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[0658] Step 6:

[0659] Terminal

[0660] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[0661] Step 7:

[0662] User

[0663] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[0664] Step 8:

[0665] Terminal

[0666] The terminal transmits the user's selection to the server.

[0667] Step 9:

[0668] server

[0669] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0670] Step 10:

[0671] Terminal

[0672] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0673] Step 11:

[0674] Mentor

[0675] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0676] Example 1

[0677] 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."

[0678] In recent years, the number of dual-income households and single-parent households has increased, resulting in many worries and problems within the home regarding education, childcare, nursing care, housework, etc. To efficiently solve these problems, there is a need for a system that allows users to easily receive professional advice. There is also a need for a system that allows users to quickly and appropriately find available supporters and receive that support in the form of a visit.

[0679] 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.

[0680] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice on the questions and concerns using a natural language processing module, means for displaying the advice on the user's terminal, means for matching the user with the most suitable support person by referencing a database containing the skills and user ratings of supporters living in the neighborhood, means for coordinating schedules between the user and the support person, and means for arranging for on-site support by the supporter. This allows the user to receive prompt and accurate advice on problems related to the home, and also enables the user to receive actual on-site support from a supporter in the neighborhood.

[0681] "Questions and worries about the home" refer to problems and difficulties that users face at home, such as education, childcare, nursing care, and housework.

[0682] A "natural language processing module" is a software component that analyzes input text and understands its content.

[0683] "Generative AI" is artificial intelligence that generates appropriate advice and information based on input text data.

[0684] A "supporter" is an expert or experienced person who gives advice and support to users to solve problems related to their home.

[0685] A "database" is a data structure that manages information such as supporter skills and user ratings, and can be referenced as needed.

[0686] "Matching" is the process of selecting and connecting with the supporter who is best suited to the user's concerns or problems.

[0687] "Schedule adjustment" refers to the process of adjusting and confirming a mutually convenient date and time between the user and the supporter.

[0688] "Home visit support" refers to support activities in which a supporter visits the user's home in person.

[0689] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches nearby supporters to provide on-site support. The operation of this system is explained in detail below. The hardware and software used include a server, terminal, natural language processing module, generative AI, database, and schedule management system.

[0690] 1. Enter your questions and concerns

[0691] User

[0692] Users input their worries and questions about their household into the terminal. For example, a user might input a worry such as, "We're both working and busy, so we don't have time to help our children with their homework." The terminal then sends this information to the server.

[0693] 2. Initial advice provided by AI

[0694] server

[0695] The server passes the received text data of questions and concerns to a natural language processing module (e.g., Google Cloud Natural Language API) for analysis. Based on the analysis results, the server creates a prompt for a generative AI (e.g., OpenAI's GPT-3) and requests it to generate advice.

[0696] 3. Displaying Advice

[0697] Terminal

[0698] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively."

[0699] 4. Matching potential mentors

[0700] server

[0701] Based on the question and advice, the server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters. The server then creates a list of the most suitable supporters and sends it to the device. The user can then select the supporter they wish to request from the list.

[0702] 5. Schedule adjustments

[0703] server

[0704] The server receives the user's selection information, accesses the supporter's schedule database (e.g., Google Calendar API), and compares it with the user's desired date and time. The optimal schedule is then adjusted and confirmed. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[0705] 6. Arranging on-site support

[0706] server

[0707] The server sends the confirmed schedule to the supporter's device and the user's device and arranges for the visit. The supporter visits the user's home at the notified date and time and provides the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0708] Example prompt

[0709] An example of a household problem entered by a user on a device: "It's difficult to balance housework and work. Cleaning in particular keeps getting put off."

[0710] Example prompt for generative AI: "I'm having trouble balancing work and housework. Please tell me some time management methods to do my housework efficiently."

[0711] This system provides a concrete means to efficiently resolve household problems and strengthen community ties.

[0712] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0713] Step 1:

[0714] User

[0715] Users input their worries and questions about their home life into the terminal. For example, they input the text "We are both working and busy, so we don't have time to help our children with their homework."

[0716] Input: Text data of questions and concerns about the home

[0717] Output: Sending text data of questions and concerns from the device to the server

[0718] Step 2:

[0719] Terminal

[0720] The device receives text data of questions and concerns entered by the user, converts it into JSON format, and sends it to the server.

[0721] Input: Text data entered by the user

[0722] Output: Text data converted to JSON format

[0723] Step 3:

[0724] server

[0725] The server parses the received JSON data, extracts the text of the question or concern, and passes this text to a natural language processing module (e.g., Google Cloud Natural Language API) to identify keywords and important phrases.

[0726] Input: JSON format text data

[0727] Output: Keywords and important phrases analyzed by natural language processing

[0728] Step 4:

[0729] server

[0730] The server generates a prompt based on the analysis results and asks a generative AI (e.g., OpenAI's GPT-3) to generate advice. For example, the generated advice might be, "If you don't have time to help with your child's homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[0731] Input: Parsed keywords and key phrases

[0732] Output: Advice generated by generative AI

[0733] Step 5:

[0734] server

[0735] The server transmits the generated advice to the terminal.

[0736] Input: Advice generated by generative AI

[0737] Output: Advice sent to the user's terminal

[0738] Step 6:

[0739] Terminal

[0740] The terminal displays the advice received from the server to the user through a user interface, and the user confirms the presented advice.

[0741] Input: Advice sent by the server

[0742] Output: Advice displayed to the user

[0743] Step 7:

[0744] server

[0745] The server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters based on the question and advice, and uses an SQL query to create a list of the most suitable supporters and send it to the device.

[0746] Input: Question and advice

[0747] Output: A list of eligible donors

[0748] Step 8:

[0749] Terminal

[0750] The device displays a list of potential supporters to the user. The user can then select the most suitable supporter from the displayed list. For example, "Supporter A, a former teacher" or "Supporter B, a former childcare teacher" may be displayed.

[0751] Input: List of potential supporters

[0752] Output: A list of donors displayed to the user

[0753] Step 9:

[0754] User

[0755] The user selects the supporter they wish to request from the displayed supporter candidates, and then inputs the selection information into the terminal.

[0756] Input: User-selected supporter

[0757] Output: Text data containing the selection information

[0758] Step 10:

[0759] Terminal

[0760] The device converts the user's selection information into JSON format and sends it to the server.

[0761] Input: Supporter information selected by the user

[0762] Output: Supporter information sent to the server in JSON format

[0763] Step 11:

[0764] server

[0765] The server accesses the schedule database of the selected supporters (e.g., Google Calendar API) and compares it with the user's desired date and time. It then adjusts and confirms the optimal schedule. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[0766] Input: Supporter information selected by the user and desired date and time

[0767] Output: Confirmed schedule

[0768] Step 12:

[0769] server

[0770] The server transmits the finalized schedule to the terminal and notifies the user and the supporter.

[0771] Input: Confirmed schedule information

[0772] Output: Schedule notifications sent to users and donors

[0773] Step 13:

[0774] Users and Supporters

[0775] The user and the supporter will follow the notified schedule, and the supporter will visit the user's home at the specified date and time and provide the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0776] Input: Notified schedule

[0777] Output: Actual visit support

[0778] (Application example 1)

[0779] 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."

[0780] In modern society, many worries and problems arise in daily life, especially in dual-income households and those busy with childcare, nursing care, and housework. Specific concerns include dietary issues and not having time to prepare healthy meals. There is also a need for a method to efficiently resolve these issues by utilizing the expertise and experience of the elderly. Therefore, a system is needed that provides accurate advice for questions and worries within the home, and also matches nearby experts to provide on-site support.

[0781] 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.

[0782] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of elderly mentors living nearby, means for coordinating schedules between the two parties, means for arranging on-site support by the elderly mentor, and means for quickly generating appropriate advice online in response to inquiries about meals at home, and further means for matching with nearby cooking experts and nutritionists to provide on-site cooking support, thereby enabling effective and quick support for meals at home and other daily life issues.

[0783] "A means of inputting questions and concerns about the home" refers to an interface that allows users to input various problems and questions that arise within the home into a terminal.

[0784] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate answers and advice in response to questions or concerns entered by users.

[0785] An "elderly mentor" is an elderly person who has a wealth of life experience and specialized knowledge and is able to provide various types of support to the user's home.

[0786] "Database including the skills and user ratings of elderly mentors living in the neighborhood" refers to a database that stores information on mentor skills and ratings from other users in order to select an appropriate elderly mentor for a user.

[0787] "Means of matching the most suitable mentor" refers to a method of utilizing information in the database to select and suggest a mentor who is most suitable for the user's concerns.

[0788] "Means to coordinate schedules between both parties" refers to the function that coordinates the schedules of the user and mentor and identifies a date and time that is convenient for both parties.

[0789] "Means for arranging on-site support by the elderly mentor" refers to a method of arranging for a selected mentor to visit the user's home at a specified date and time and provide specific support.

[0790] "Generating appropriate advice online quickly in response to dietary concerns at home" means that when a user enters their dietary concerns online, the generative AI will provide specific advice in a short amount of time.

[0791] "Means of matching nearby cooking experts and nutritionists and providing on-site cooking support" refers to a method in which cooking experts and nutritionists are selected for users who have concerns about their diet, and they actually visit their homes to provide support.

[0792] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches users with nearby senior mentors and cooking experts to provide on-site support. This system is designed to provide prompt and appropriate support according to each user's concerns. The operation of the system is explained in detail below using specific examples.

[0793] 1. Enter your questions and concerns

[0794] User

[0795] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "I don't always have time, so it's difficult for me to cook healthy meals."

[0796] Terminal

[0797] The device receives input from the user and sends that information to a backend server, using an interface such as a smartphone or tablet, and the software used is a front-end application (e.g., React Native).

[0798] 2. Initial advice provided by AI

[0799] server

[0800] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI (e.g., GPT model) generates advice and sends it back to the server. The software used is a backend server (e.g., Node.js) and a natural language processing module (e.g., SpaCy, GPT model).

[0801] Terminal

[0802] The server sends the generated advice to the device and displays it to the user. Specifically, it presents, for example, "healthy dinner recipes that can be made in under 30 minutes."

[0803] 3. Matching potential mentors

[0804] server

[0805] The server searches a database of nearby senior mentors and cooking experts based on the question and advice. It selects the most suitable mentor, taking into account the mentor's skills and ratings. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python).

[0806] Terminal

[0807] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Former cooking class instructor A" or "Experienced nutritionist B" may be displayed.

[0808] 4. Selecting the best mentor

[0809] User

[0810] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Former Cooking Class Instructor A."

[0811] Terminal

[0812] The terminal transmits the user's selection to the server.

[0813] 5. Schedule adjustments

[0814] server

[0815] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed date. The software used is a calendar API (e.g., Google Calendar API).

[0816] Terminal

[0817] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0818] 6. On-site support

[0819] Mentor

[0820] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Instructor A will visit the user's home every Saturday at 5:00 PM and provide instruction on healthy cooking that can be made in a short amount of time."

[0821] Specific system flow example

[0822] Input Phase

[0823] The user inputs a concern such as "I'm busy at work and don't have time to make a simple and healthy dinner" into the device, and the device sends this information to the server.

[0824] Advice Generation Phase

[0825] The server's natural language processing module analyzes the text. The AI ​​model generates "healthy dinner recipes that can be made in under 30 minutes" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[0826] Mentor Matching Phase

[0827] The server searches a database of nearby mentors based on the question and advice. It selects "Former Cooking Class Instructor A" and "Experienced Nutritionist B" as candidates. The server then sends the candidate list to the user's device and displays it.

[0828] Mentor selection phase

[0829] The user selects "Former cooking class instructor A." The device sends the selection information to the server.

[0830] Schedule adjustment phase

[0831] The server checks the schedules of the user and instructor A and suggests "every Saturday at 5:00 PM." The device presents the adjusted schedule to the user and notifies the mentor.

[0832] Implementation Phase

[0833] Instructor A visits users' homes every Saturday at 5:00 p.m. to help them prepare healthy meals that can be prepared in a short amount of time.

[0834] Prompt Sentence Examples

[0835] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[0836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0837] Step 1:

[0838] The user inputs household concerns or questions into the device. The input information is specific text, such as "I'm always short on time and it's difficult to cook healthy meals." The input is the question or concern in text format, and the output is the information sent from the device to the server. The specific operation is that the user inputs the concerns as text using an application installed on a smartphone or tablet.

[0839] Step 2:

[0840] The terminal receives input from the user and sends the information to the backend server. The input is the text data entered by the user into the terminal, and the output is the JSON format data sent to the server. Specifically, the terminal's API captures the user's input and sends the data to the server using an HTTP request.

[0841] Step 3:

[0842] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. The software used here is a natural language processing module (e.g., SpaCy, GPT model). The input is the question or concern data in JSON format sent from the device, and the output is the analysis results, which are analyzed data such as keywords and sentence structure. Specifically, the server passes the received data to the analysis module, which then analyzes the text data.

[0843] Step 4:

[0844] The generative AI generates advice based on the analysis results and sends it back to the server. The generative AI model used here is a GPT model. The input is the keywords and sentence structure data obtained as a result of the analysis, and the output is specific advice text. The specific operation is that the generative AI model generates advice content based on the analysis results within the server and sends it back to the server.

[0845] Step 5:

[0846] The server sends the generated advice to the terminal and displays it to the user. The input is the text data of the advice returned from the generative AI, and the output is the specific advice displayed on the user's terminal. The specific operation is that the server sends the advice data to the terminal, and the terminal displays the data to the user.

[0847] Step 6:

[0848] The server searches a database of nearby elderly mentors and cooking experts based on the question and advice. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python). The input is text data containing the advice, and the output is a list of candidate mentors and experts. Specifically, the server queries the database based on the advice and lists appropriate mentors and experts.

[0849] Step 7:

[0850] The server sends the selected mentor candidates to the terminal and displays them to the user. The input is the candidate list data, and the output is the information of the mentor candidates displayed on the user's terminal. The specific operation is that the server sends the candidate list to the terminal, and the terminal displays it to the user.

[0851] Step 8:

[0852] The user selects the mentor they wish to request from the presented mentor candidates. The input is the displayed list of mentor candidates, and the output is information about the selected mentor. The specific operation is for the user to select from the candidates using the terminal interface.

[0853] Step 9:

[0854] The terminal sends the user's selection to the server. The input is the information of the mentor selected by the user, and the output is the selection information sent to the server. The specific operation is that the terminal captures the selection data and sends it to the server via an HTTP request.

[0855] Step 10:

[0856] The server accesses the mentor's schedule database and compares it with the user's desired date and time. The software used is a calendar API (e.g., Google Calendar API). The input is the user's and mentor's desired date and time data, and the output is the adjusted schedule data. Specifically, the server compares the schedules of both parties and determines the optimal date and time.

[0857] Step 11:

[0858] The server sends the adjusted schedule to the terminal and notifies the user. It also notifies the mentor's terminal of the schedule. The input is the adjusted schedule data, and the output is a schedule notification that is displayed on the user's and mentor's terminals. Specifically, the server sends the schedule data to each terminal, and the terminal displays the contents.

[0859] Step 12:

[0860] The mentor visits the user's home at the notified date and time and provides the requested support. The input is the schedule notification received from the server before the visit, and the output is the specific support activities at the visit location. The specific operation is for the mentor to visit the user's home at the specified date and time and provide support such as cooking assistance.

[0861] These steps will enable us to solve household problems and provide efficient support. Furthermore, the following example prompts will enable the generative AI model to generate effective advice.

[0862] Prompt Sentence Examples

[0863] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[0864] 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.

[0865] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[0866] 1. Enter your questions and concerns

[0867] User

[0868] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0869] Terminal

[0870] The terminal receives input from the user and transmits the information to the server.

[0871] 2. Emotion Recognition by Emotion Engine

[0872] server

[0873] The server sends the received user input to the emotion engine for analysis. The emotion engine analyzes the text and, if necessary, recognizes the user's face and voice tone to determine their emotion. For example, if the user is feeling stressed, the emotion is recognized as "stress."

[0874] 3. Initial advice provided by AI

[0875] server

[0876] The server provides the emotion analysis results obtained from the emotion engine to the generative AI, which then generates advice that takes the user's emotional state into account. For example, if the user is feeling stressed, advice on relaxation methods and time management will be emphasized.

[0877] Terminal

[0878] The server sends the generated advice to the device and displays it to the user. For example, it might say, "It seems you're feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively."

[0879] 4. Matching potential mentors

[0880] server

[0881] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The server then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, if advice on stress reduction is needed, mentors with expertise in stress management will be prioritized.

[0882] Terminal

[0883] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[0884] 5. Selecting the best mentor

[0885] User

[0886] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[0887] Terminal

[0888] The terminal transmits the user's selection to the server.

[0889] 6. Schedule Adjustment

[0890] server

[0891] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0892] Terminal

[0893] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0894] 7. On-site support

[0895] Mentor

[0896] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0897] Specific system flow example

[0898] Input Phase

[0899] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[0900] Emotion Recognition Phase

[0901] The server sends the input to the emotion engine, which recognizes the user's emotion as "stress."

[0902] Advice Generation Phase

[0903] The server's generative AI generates advice based on the results of emotion analysis, suggesting ways for users to relax and how to manage their time for housework and work.

[0904] Mentor Matching Phase

[0905] The server searches a database of nearby mentors based on the results of the emotion analysis and the question. It selects two candidates: "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D." The server then sends the candidate list to the user's device and presents it to them.

[0906] Mentor selection phase

[0907] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[0908] Schedule adjustment phase

[0909] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[0910] Implementation Phase

[0911] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[0912] In this way, by combining the emotion engine, the system provides personalized advice and assistance that takes into account the user's emotional state, allowing for more efficient resolution of household problems.

[0913] The processing flow will be explained below.

[0914] Step 1:

[0915] User

[0916] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[0917] Step 2:

[0918] Terminal

[0919] The terminal receives input from the user and transmits the information to the server.

[0920] Step 3:

[0921] server

[0922] The server sends the received questions and concerns to the emotion engine, which analyzes the input text and identifies the user's emotions, such as stress, fatigue, and anxiety.

[0923] Step 4:

[0924] Emotion Engine

[0925] The emotion engine sends the analysis results back to the server. For example, it is analyzed that the user is feeling stressed.

[0926] Step 5:

[0927] server

[0928] The server queries the generative AI based on the emotion analysis results and generates advice. Specific advice is generated that takes into account the user's emotional state. For example, if the user is feeling stressed, advice on "how to relax" and "how to manage time" will be emphasized.

[0929] Step 6:

[0930] Terminal

[0931] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "It seems you are feeling stressed, so try some relaxation techniques. Also, here are some time management techniques to help you do your homework more effectively" may be displayed.

[0932] Step 7:

[0933] server

[0934] The server searches a database of nearby senior mentors based on the question, advice, and sentiment analysis results. It then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, it prioritizes mentors with stress reduction skills.

[0935] Step 8:

[0936] Terminal

[0937] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, who is good at stress management" or "Mentor B, who is good at education" are displayed.

[0938] Step 9:

[0939] User

[0940] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, who is skilled in stress management."

[0941] Step 10:

[0942] Terminal

[0943] The terminal transmits the user's selection to the server.

[0944] Step 11:

[0945] server

[0946] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[0947] Step 12:

[0948] Terminal

[0949] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[0950] Step 13:

[0951] Mentor

[0952] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0953] Example 2

[0954] 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."

[0955] There is a need for a system that can quickly provide users with appropriate and personalized advice for various worries and problems at home. Solving problems also requires physical support personnel who can provide specialized knowledge and assistance, but current systems have difficulty effectively matching these people and adjusting their schedules. This can lead to stress for users and prolong the problems.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0957] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for performing an emotional analysis of the questions and concerns using an emotion engine that recognizes the user's emotions, means for presenting advice generated based on the emotion analysis results, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, means for coordinating schedules for both parties, and means for arranging on-site support by the senior mentor. This allows the user to receive personalized advice based on their emotions while also quickly arranging physical support, making it possible to efficiently solve home problems.

[0958] The "means for inputting questions and concerns about the home" is a means for users to input problems and annoyances about housework, childcare, nursing care, and other home-related issues as text data.

[0959] "Generative AI" is an artificial intelligence system that uses natural language processing and machine learning algorithms to generate optimal advice and solutions based on input data.

[0960] An "emotion engine" is software or hardware that analyzes text data, voice data, facial image data, etc., to identify the user's emotional state.

[0961] "Sentiment analysis" is the process of extracting emotional states from user input data and tagging and scoring them.

[0962] "Means for presenting advice" refers to the means for displaying solutions and advice generated by generative AI to the user.

[0963] A "senior mentor" refers to an elderly person who has knowledge and experience that is useful in resolving family problems and concerns.

[0964] The "database" is a digital storage system that systematically stores information such as senior mentor skills and user ratings, and allows for search and reference.

[0965] The "matching method" is the process of selecting the most suitable senior mentor based on the user's problem, questions, and sentiment analysis results, and then introducing the two parties together.

[0966] The "means for adjusting schedules" is a process for adjusting the available time of both the user and the senior mentor and setting an appropriate date and time for support.

[0967] The "means for arranging on-site support" is a means for arranging for a selected senior mentor to visit the user's home and actually provide support.

[0968] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[0969] Enter your questions and concerns

[0970] Users input their household concerns and questions into a terminal. The terminal can be a digital device such as a smartphone, tablet, or PC. The user might input a concern such as, "We're both working and busy, so we don't have time to help my child with their homework." The terminal then sends this information to the server.

[0971] Emotion recognition by emotion engine

[0972] The server sends the received user input to the emotion engine for analysis. The emotion engine combines text analysis, facial recognition, and voice tone analysis to analyze the user's emotions. For example, a Python-based natural language processing library is used for text analysis, OpenCV for facial recognition, and a voice tone analysis program is typically used for voice analysis. If the user is feeling stressed, the emotion is recognized as "stress."

[0973] AI-powered initial advice

[0974] The server provides the emotion analysis results obtained from the emotion engine to a generative AI model. For example, a natural language processing model such as OpenAI's GPT-3 can be used. The generative AI takes into account the user's emotional state and generates appropriate advice. For example, if the user is feeling stressed, it will emphasize "ways to relax" and "time management advice." The generated advice is sent from the server to the device and displayed to the user. For example, advice such as "It seems you are feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively" may be presented.

[0975] Matching mentor candidates

[0976] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The database includes the skills of senior mentors and user ratings, and the most suitable mentor is extracted. For example, if advice on stress reduction is needed, a mentor who is skilled in stress management will be given priority. This information is sent from the server to the terminal, and the user is presented with candidates such as "Mentor A, a former teacher" and "Mentor B, a former childcare teacher."

[0977] Selection of the best mentor

[0978] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former faculty member." The selection information is sent from the terminal to the server.

[0979] Schedule adjustment

[0980] The server accesses the selected mentor's schedule database and compares it with the user's desired date and time. It adjusts the date and time to suit both parties and finalizes the adjusted schedule. For example, a schedule such as "every Wednesday from 5:00 PM to 6:00 PM" is finalized. This information is sent from the server to the terminal and notified to the user and mentor.

[0981] On-site support

[0982] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[0983] Specific examples

[0984] The user inputs their concern on their device, saying, "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device sends this information to the server. The server sends the input to an emotion engine, which recognizes the user's emotion as "stress." A generative AI generates advice based on the emotion analysis results, suggesting ways for the user to relax and manage their time for housework and work. The server searches a database of nearby mentors based on the emotion analysis results and the question, and selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the device and presents it. The user selects "Former Housekeeper Mentor C." The device sends the selection information to the server. The server checks the user's and Mentor C's schedules and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor. Mentor C will visit the user's home every Tuesday at 10:00 to provide cleaning assistance.

[0985] In this way, the combination of an emotion engine and generative AI can provide personalized advice and assistance that takes into account the user's emotional state, making it possible to solve household problems more efficiently.

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Step 1:

[0988] Handling User Input

[0989] The user uses the device to input questions or concerns about their household. For example, they might input, "We're both working and busy, so we don't have time to help our child with their homework." The device receives this information as text data and converts it into JSON format. The input data looks like this:

[0990] Input data:

[0991] "Problem": "We both work and are busy, so we don't have time to help our children with their homework."

[0992] The terminal sends this data to the server as an HTTP request.

[0993] Step 2:

[0994] Sentiment analysis processing

[0995] The server sends the received text data to the emotion engine, which performs text analysis, facial recognition, and speech tone analysis to analyze the user's emotional state, using libraries such as the Natural Language Toolkit (NLTK), OpenCV, and speech analysis.

[0996] Input text:

[0997] "We both work and are busy, so we don't have time to help our children with their homework."

[0998] The sentiment engine parses this text and produces output like this:

[0999] Output data:

[1000] "Emotion": "Stress"

[1001] This output data is returned to the server.

[1002] Step 3:

[1003] AI-powered initial advice

[1004] The server sends the sentiment analysis results as prompts to a generative AI model, for example, OpenAI's GPT-3.

[1005] Input prompt:

[1006] "Users are stressed and need relaxation and time management advice."

[1007] The generative AI model generates advice based on the prompts, producing an output similar to the following:

[1008] Output advice:

[1009] "Try yoga or meditation as a way to relax, and create a to-do list to help you manage your time."

[1010] The server transmits this generated advice to the terminal.

[1011] Step 4:

[1012] Advice display and mentor matching

[1013] The terminal will display the advice received from the server to the user, for example, in the following format:

[1014] "It sounds like you're stressed, so I'd like to recommend some yoga and meditation techniques. I'd also recommend creating a to-do list and managing your time."

[1015] At the same time, the server searches a database of senior mentors living nearby and selects the most suitable mentor.

[1016] Input data:

[1017] "Question content", "Advice content", "Emotion analysis results"

[1018] The server generates a list of mentor candidates as follows and sends it to the device:

[1019] Output data:

[1020] "Mentor A, a former teacher" and "Mentor B, a former childcare teacher"

[1021] The terminal also displays this mentor candidate to the user.

[1022] Step 5:

[1023] Selection of the best mentor

[1024] The user selects from the presented mentor candidates. For example, the user selects "Mentor A, a former teacher."

[1025] The terminal transmits this selection to the server.

[1026] Input data:

[1027] "Mentor A, a former teacher"

[1028] Step 6:

[1029] Schedule adjustment

[1030] The server accesses the schedule database of the user and the selected mentor and adjusts the available time for both parties. For example, it determines that "every Wednesday from 5:00 PM to 6:00 PM" is optimal.

[1031] Input data:

[1032] "Desired date and time", "Mentor schedule"

[1033] The server determines the adjusted schedule as follows and sends it to the device:

[1034] Output data:

[1035] Every Wednesday from 5:00 PM to 6:00 PM

[1036] The terminal displays this schedule to the user and also notifies the mentor.

[1037] Step 7:

[1038] On-site support

[1039] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "I will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1040] Specifically, the mentor visits the user's home at a specified time and provides support according to the previously agreed upon details.

[1041] (Application example 2)

[1042] 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."

[1043] Conventional home support systems often provide general advice and support without considering the user's emotional state, resulting in a lack of personalized support tailored to individual needs. Furthermore, while there is a demand for services that take emotional states into account in other lifestyle services, including food delivery, this has not yet been realized. Therefore, a system that can provide appropriate advice and services by empathizing with the user's emotions is needed.

[1044] 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.

[1045] In this invention, the server includes a means for inputting questions and concerns about the home, a means for a generative AI to provide advice in response to the questions and concerns, an emotion analysis means for analyzing the user's emotions, a means for the generative AI to provide personalized advice based on the user's emotional state, a means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, a means for coordinating schedules for both parties, and a means for arranging on-site support by the senior mentor. This allows the server to provide personalized advice and support taking into account the user's emotional state, thereby improving satisfaction with home concerns and food delivery.

[1046] "Means for inputting questions and concerns about the home" refers to an interface that allows users to input problems and questions that arise within the home in the form of text or voice.

[1047] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate solutions and advice in response to questions or concerns entered by users.

[1048] "Emotion analysis means" is a technology that analyzes the emotional state from text or voice input by the user and identifies emotions such as stress, fatigue, and joy.

[1049] The "means for providing personalized advice" is an artificial intelligence system that generates optimal solutions and advice based on the emotional state of each individual user, based on the results of emotion analysis.

[1050] The "database including the skills and user ratings of senior mentors living nearby" is a database that accumulates senior mentors' specialized knowledge, service experience, and user ratings.

[1051] "Means for matching the most suitable mentor" refers to algorithms and systems for selecting the most suitable senior mentor based on the user's questions, concerns, and emotional analysis results.

[1052] The "means of adjusting schedules" is a system that compares the schedules of both the user and the mentor and arranges a date and time that is convenient for both parties.

[1053] The "means for arranging on-site support" is a system that coordinates schedules and arranges visits by selected mentors to the user's home to provide the necessary support.

[1054] This invention is a system that uses generative AI to provide personalized advice in response to questions and concerns about the home. The system uses emotion analysis to recognize the user's emotions and provide optimal advice, as well as matching with nearby senior mentors and providing on-site support.

[1055] Specific system configuration

[1056] 1. Enter your questions and concerns

[1057] User

[1058] Users input questions or concerns about their home into a smartphone application. At this stage, users often write sentences that touch on their emotions. They input their content in response to prompts such as, "How are you feeling today?"

[1059] Example input

[1060] I'm very tired today and I don't know what to eat.

[1061] Terminal

[1062] The terminal receives input from the user and transmits the information to the server.

[1063] 2. Emotion analysis

[1064] server

[1065] The server analyzes the received user input using a sentiment analysis engine, which analyzes the text to identify emotions such as stress, fatigue, and joy.

[1066] Specific examples

[1067] The input "I'm very tired today and I don't know what to eat" is recognized as "fatigue."

[1068] 3. Generating Advice

[1069] server

[1070] The server uses generative AI to provide personalized advice based on the results of sentiment analysis. For example, if the user is tired, it will suggest healthy and easy-to-eat meals.

[1071] Specific examples

[1072] "You seem tired, so I recommend some relaxing herbal tea and a nutritious salad."

[1073] Terminal

[1074] The server transmits the generated advice to the terminal and displays it to the user.

[1075] 4. Mentor matching

[1076] server

[1077] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis to identify the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[1078] Specific examples

[1079] A "nutritionist mentor" who is knowledgeable about fatigue recovery will be selected and presented to the user.

[1080] Terminal

[1081] The user is prompted to select a mentor from the displayed candidates.

[1082] 5. Selecting the best mentor and adjusting the schedule

[1083] User

[1084] The user selects the mentor they wish to request from the presented mentor candidates. For example, they can select "Mentor A, a nutritionist."

[1085] Terminal

[1086] The terminal transmits the user's selection to the server.

[1087] server

[1088] The server checks the mentor's schedule, adjusts it to the prepared date and time, and notifies the user and mentor of the adjusted schedule.

[1089] 6. On-site support

[1090] Mentor

[1091] The mentor will visit the user's home at the notified date and time and provide the requested support.

[1092] Hardware and software used

[1093] Smartphone: User Interface

[1094] Sentiment analysis engine: Emotion-Analysis-Service

[1095] Generative AI: Generated-Advice-Service using natural language processing

[1096] Food recommendation engine: Food-Recommendation-Service

[1097] Prompt Sentence Examples

[1098] "What dish would you recommend for someone who is feeling tired these days?"

[1099] This makes it possible to provide personalized advice that reflects the user's emotional state, improving satisfaction with household concerns and food delivery.

[1100] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1101] Step 1:

[1102] The user inputs questions and concerns about the home.

[1103] An example of input would be "I'm very tired today and I don't know what to eat."

[1104] This input data is sent to the terminal in text format.

[1105] Step 2:

[1106] The terminal receives input data from the user and sends the information to the server for analysis.

[1107] The input is text data, which is transferred to the server as is.

[1108] Step 3:

[1109] The server transmits the received text data to the emotion analysis means.

[1110] The emotion analysis means uses natural language processing and machine learning algorithms to analyze emotional states from text data.

[1111] Data processing includes part-of-speech analysis of text and sentiment analysis.

[1112] The output is emotion tags such as "fatigue" and "stress."

[1113] Step 4:

[1114] The server sends the emotion analysis results to the generative AI.

[1115] Here, appropriate advice is generated based on input data, which is the result of emotion analysis.

[1116] Generative AI takes into account the user's emotional state and generates optimal solutions based on specific prompts.

[1117] As a data calculation, specific advice is generated by referencing a knowledge base and past data.

[1118] The output is specific advice such as relaxation methods and dietary suggestions.

[1119] Step 5:

[1120] The server transmits the generated advice to the terminal.

[1121] The input data is text-based advice created by generative AI.

[1122] The device will then display this advice to the user, such as "We recommend a relaxing herbal tea and a nutritious salad."

[1123] Step 6:

[1124] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis.

[1125] The input data is the emotion analysis results and the question content.

[1126] The server selects the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[1127] As an output, a list of suitable mentors is generated.

[1128] Step 7:

[1129] The server transmits the selected mentor candidates to the terminal and displays them to the user.

[1130] The input data is a list of selected mentors, and the output shows the candidates to the user, who can select "Dietitian A Mentor."

[1131] Step 8:

[1132] The user selects the mentor he or she wishes to request from among the presented mentor candidates.

[1133] The input data is the mentor selected by the user.

[1134] The terminal transmits its selection to the server.

[1135] Step 9:

[1136] The server checks the mentor's schedule and adjusts it to the user's desired date and time.

[1137] The input data is the schedule information of the user and the mentor.

[1138] As an output, a firm schedule is produced.

[1139] Step 10:

[1140] The server transmits the adjusted schedule to the terminal and notifies the user.

[1141] Similarly, the mentor will be notified of the schedule.

[1142] Step 11:

[1143] Upon receiving the notification, the mentor visits the user's home at the confirmed date and time to provide the requested support.

[1144] A specific example of this would be "Mentor A, a nutritionist, visits and provides dietary advice."

[1145] 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.

[1146] 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.

[1147] 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.

[1148] [Third embodiment]

[1149] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1150] 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.

[1151] 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).

[1152] 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.

[1153] 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.

[1154] 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).

[1155] 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.

[1156] 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.

[1157] 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.

[1158] 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.

[1159] 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.

[1160] 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."

[1161] This invention is a system that uses generative AI to provide accurate advice in response to questions and concerns about home life, and also matches people with nearby senior mentors to provide on-site support. The operation of this system is explained in detail below.

[1162] 1. Enter your questions and concerns

[1163] User

[1164] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1165] Terminal

[1166] The terminal receives input from the user and transmits the information to the server.

[1167] 2. Initial advice provided by AI

[1168] server

[1169] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[1170] Terminal

[1171] The server sends the generated advice to the device and displays it to the user. For example, the advice might be, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[1172] 3. Matching potential mentors

[1173] server

[1174] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[1175] Terminal

[1176] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[1177] 4. Selecting the best mentor

[1178] User

[1179] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[1180] Terminal

[1181] The terminal transmits the user's selection to the server.

[1182] 5. Schedule adjustments

[1183] server

[1184] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[1185] Terminal

[1186] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1187] 6. On-site support

[1188] Mentor

[1189] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1190] Specific system flow example

[1191] Input Phase

[1192] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[1193] Advice Generation Phase

[1194] The server's natural language processing module analyzes the text. The AI ​​model generates "time management methods for efficient housework" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[1195] Mentor Matching Phase

[1196] The server searches a database of nearby mentors based on the question. It selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the user's device and displays it.

[1197] Mentor selection phase

[1198] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[1199] Schedule adjustment phase

[1200] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[1201] Implementation Phase

[1202] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[1203] In this way, the system provides a concrete means to efficiently resolve household concerns and strengthen community ties.

[1204] The processing flow will be explained below.

[1205] Step 1:

[1206] User

[1207] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1208] Step 2:

[1209] Terminal

[1210] The terminal receives input from the user and transmits the information to the server.

[1211] Step 3:

[1212] server

[1213] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[1214] Step 4:

[1215] Terminal

[1216] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively" is presented.

[1217] Step 5:

[1218] server

[1219] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[1220] Step 6:

[1221] Terminal

[1222] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[1223] Step 7:

[1224] User

[1225] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[1226] Step 8:

[1227] Terminal

[1228] The terminal transmits the user's selection to the server.

[1229] Step 9:

[1230] server

[1231] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[1232] Step 10:

[1233] Terminal

[1234] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1235] Step 11:

[1236] Mentor

[1237] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1238] Example 1

[1239] 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."

[1240] In recent years, the number of dual-income households and single-parent households has increased, resulting in many worries and problems within the home regarding education, childcare, nursing care, housework, etc. To efficiently solve these problems, there is a need for a system that allows users to easily receive professional advice. There is also a need for a system that allows users to quickly and appropriately find available supporters and receive that support in the form of a visit.

[1241] 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.

[1242] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice on the questions and concerns using a natural language processing module, means for displaying the advice on the user's terminal, means for matching the user with the most suitable support person by referencing a database containing the skills and user ratings of supporters living in the neighborhood, means for coordinating schedules between the user and the support person, and means for arranging for on-site support by the supporter. This allows the user to receive prompt and accurate advice on problems related to the home, and also enables the user to receive actual on-site support from a supporter in the neighborhood.

[1243] "Questions and worries about the home" refer to problems and difficulties that users face at home, such as education, childcare, nursing care, and housework.

[1244] A "natural language processing module" is a software component that analyzes input text and understands its content.

[1245] "Generative AI" is artificial intelligence that generates appropriate advice and information based on input text data.

[1246] A "supporter" is an expert or experienced person who gives advice and support to users to solve problems related to their home.

[1247] A "database" is a data structure that manages information such as supporter skills and user ratings, and can be referenced as needed.

[1248] "Matching" is the process of selecting and connecting with the supporter who is best suited to the user's concerns or problems.

[1249] "Schedule adjustment" refers to the process of adjusting and confirming a mutually convenient date and time between the user and the supporter.

[1250] "Home visit support" refers to support activities in which a supporter visits the user's home in person.

[1251] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches nearby supporters to provide on-site support. The operation of this system is explained in detail below. The hardware and software used include a server, terminal, natural language processing module, generative AI, database, and schedule management system.

[1252] 1. Enter your questions and concerns

[1253] User

[1254] Users input their worries and questions about their household into the terminal. For example, a user might input a worry such as, "We're both working and busy, so we don't have time to help our children with their homework." The terminal then sends this information to the server.

[1255] 2. Initial advice provided by AI

[1256] server

[1257] The server passes the received text data of questions and concerns to a natural language processing module (e.g., Google Cloud Natural Language API) for analysis. Based on the analysis results, the server creates a prompt for a generative AI (e.g., OpenAI's GPT-3) and requests it to generate advice.

[1258] 3. Displaying Advice

[1259] Terminal

[1260] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively."

[1261] 4. Matching potential mentors

[1262] server

[1263] Based on the question and advice, the server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters. The server then creates a list of the most suitable supporters and sends it to the device. The user can then select the supporter they wish to request from the list.

[1264] 5. Schedule adjustments

[1265] server

[1266] The server receives the user's selection information, accesses the supporter's schedule database (e.g., Google Calendar API), and compares it with the user's desired date and time. The optimal schedule is then adjusted and confirmed. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[1267] 6. Arranging on-site support

[1268] server

[1269] The server sends the confirmed schedule to the supporter's device and the user's device and arranges for the visit. The supporter visits the user's home at the notified date and time and provides the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1270] Example prompt

[1271] An example of a household problem entered by a user on a device: "It's difficult to balance housework and work. Cleaning in particular keeps getting put off."

[1272] Example prompt for generative AI: "I'm having trouble balancing work and housework. Please tell me some time management methods to do my housework efficiently."

[1273] This system provides a concrete means to efficiently resolve household problems and strengthen community ties.

[1274] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1275] Step 1:

[1276] User

[1277] Users input their worries and questions about their home life into the terminal. For example, they input the text "We are both working and busy, so we don't have time to help our children with their homework."

[1278] Input: Text data of questions and concerns about the home

[1279] Output: Sending text data of questions and concerns from the device to the server

[1280] Step 2:

[1281] Terminal

[1282] The device receives text data of questions and concerns entered by the user, converts it into JSON format, and sends it to the server.

[1283] Input: Text data entered by the user

[1284] Output: Text data converted to JSON format

[1285] Step 3:

[1286] server

[1287] The server parses the received JSON data, extracts the text of the question or concern, and passes this text to a natural language processing module (e.g., Google Cloud Natural Language API) to identify keywords and important phrases.

[1288] Input: JSON format text data

[1289] Output: Keywords and important phrases analyzed by natural language processing

[1290] Step 4:

[1291] server

[1292] The server generates a prompt based on the analysis results and asks a generative AI (e.g., OpenAI's GPT-3) to generate advice. For example, the generated advice might be, "If you don't have time to help with your child's homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[1293] Input: Parsed keywords and key phrases

[1294] Output: Advice generated by generative AI

[1295] Step 5:

[1296] server

[1297] The server transmits the generated advice to the terminal.

[1298] Input: Advice generated by generative AI

[1299] Output: Advice sent to the user's terminal

[1300] Step 6:

[1301] Terminal

[1302] The terminal displays the advice received from the server to the user through a user interface, and the user confirms the presented advice.

[1303] Input: Advice sent by the server

[1304] Output: Advice displayed to the user

[1305] Step 7:

[1306] server

[1307] The server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters based on the question and advice, and uses an SQL query to create a list of the most suitable supporters and send it to the device.

[1308] Input: Question and advice

[1309] Output: A list of eligible donors

[1310] Step 8:

[1311] Terminal

[1312] The device displays a list of potential supporters to the user. The user can then select the most suitable supporter from the displayed list. For example, "Supporter A, a former teacher" or "Supporter B, a former childcare teacher" may be displayed.

[1313] Input: List of potential supporters

[1314] Output: A list of donors displayed to the user

[1315] Step 9:

[1316] User

[1317] The user selects the supporter they wish to request from the displayed supporter candidates, and then inputs the selection information into the terminal.

[1318] Input: User-selected supporter

[1319] Output: Text data containing the selection information

[1320] Step 10:

[1321] Terminal

[1322] The device converts the user's selection information into JSON format and sends it to the server.

[1323] Input: Supporter information selected by the user

[1324] Output: Supporter information sent to the server in JSON format

[1325] Step 11:

[1326] server

[1327] The server accesses the schedule database of the selected supporters (e.g., Google Calendar API) and compares it with the user's desired date and time. It then adjusts and confirms the optimal schedule. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[1328] Input: Supporter information selected by the user and desired date and time

[1329] Output: Confirmed schedule

[1330] Step 12:

[1331] server

[1332] The server transmits the finalized schedule to the terminal and notifies the user and the supporter.

[1333] Input: Confirmed schedule information

[1334] Output: Schedule notifications sent to users and donors

[1335] Step 13:

[1336] Users and Supporters

[1337] The user and the supporter will follow the notified schedule, and the supporter will visit the user's home at the specified date and time and provide the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1338] Input: Notified schedule

[1339] Output: Actual visit support

[1340] (Application example 1)

[1341] 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."

[1342] In modern society, many worries and problems arise in daily life, especially in dual-income households and those busy with childcare, nursing care, and housework. Specific concerns include dietary issues and not having time to prepare healthy meals. There is also a need for a method to efficiently resolve these issues by utilizing the expertise and experience of the elderly. Therefore, a system is needed that provides accurate advice for questions and worries within the home, and also matches nearby experts to provide on-site support.

[1343] 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.

[1344] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of elderly mentors living nearby, means for coordinating schedules between the two parties, means for arranging on-site support by the elderly mentor, and means for quickly generating appropriate advice online in response to inquiries about meals at home, and further means for matching with nearby cooking experts and nutritionists to provide on-site cooking support, thereby enabling effective and quick support for meals at home and other daily life issues.

[1345] "A means of inputting questions and concerns about the home" refers to an interface that allows users to input various problems and questions that arise within the home into a terminal.

[1346] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate answers and advice in response to questions or concerns entered by users.

[1347] An "elderly mentor" is an elderly person who has a wealth of life experience and specialized knowledge and is able to provide various types of support to the user's home.

[1348] "Database including the skills and user ratings of elderly mentors living in the neighborhood" refers to a database that stores information on mentor skills and ratings from other users in order to select an appropriate elderly mentor for a user.

[1349] "Means of matching the most suitable mentor" refers to a method of utilizing information in the database to select and suggest a mentor who is most suitable for the user's concerns.

[1350] "Means to coordinate schedules between both parties" refers to the function that coordinates the schedules of the user and mentor and identifies a date and time that is convenient for both parties.

[1351] "Means for arranging on-site support by the elderly mentor" refers to a method of arranging for a selected mentor to visit the user's home at a specified date and time and provide specific support.

[1352] "Generating appropriate advice online quickly in response to dietary concerns at home" means that when a user enters their dietary concerns online, the generative AI will provide specific advice in a short amount of time.

[1353] "Means of matching nearby cooking experts and nutritionists and providing on-site cooking support" refers to a method in which cooking experts and nutritionists are selected for users who have concerns about their diet, and they actually visit their homes to provide support.

[1354] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches users with nearby senior mentors and cooking experts to provide on-site support. This system is designed to provide prompt and appropriate support according to each user's concerns. The operation of the system is explained in detail below using specific examples.

[1355] 1. Enter your questions and concerns

[1356] User

[1357] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "I don't always have time, so it's difficult for me to cook healthy meals."

[1358] Terminal

[1359] The device receives input from the user and sends that information to a backend server, using an interface such as a smartphone or tablet, and the software used is a front-end application (e.g., React Native).

[1360] 2. Initial advice provided by AI

[1361] server

[1362] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI (e.g., GPT model) generates advice and sends it back to the server. The software used is a backend server (e.g., Node.js) and a natural language processing module (e.g., SpaCy, GPT model).

[1363] Terminal

[1364] The server sends the generated advice to the device and displays it to the user. Specifically, it presents, for example, "healthy dinner recipes that can be made in under 30 minutes."

[1365] 3. Matching potential mentors

[1366] server

[1367] The server searches a database of nearby senior mentors and cooking experts based on the question and advice. It selects the most suitable mentor, taking into account the mentor's skills and ratings. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python).

[1368] Terminal

[1369] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Former cooking class instructor A" or "Experienced nutritionist B" may be displayed.

[1370] 4. Selecting the best mentor

[1371] User

[1372] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Former Cooking Class Instructor A."

[1373] Terminal

[1374] The terminal transmits the user's selection to the server.

[1375] 5. Schedule adjustments

[1376] server

[1377] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed date. The software used is a calendar API (e.g., Google Calendar API).

[1378] Terminal

[1379] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1380] 6. On-site support

[1381] Mentor

[1382] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Instructor A will visit the user's home every Saturday at 5:00 PM and provide instruction on healthy cooking that can be made in a short amount of time."

[1383] Specific system flow example

[1384] Input Phase

[1385] The user inputs a concern such as "I'm busy at work and don't have time to make a simple and healthy dinner" into the device, and the device sends this information to the server.

[1386] Advice Generation Phase

[1387] The server's natural language processing module analyzes the text. The AI ​​model generates "healthy dinner recipes that can be made in under 30 minutes" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[1388] Mentor Matching Phase

[1389] The server searches a database of nearby mentors based on the question and advice. It selects "Former Cooking Class Instructor A" and "Experienced Nutritionist B" as candidates. The server then sends the candidate list to the user's device and displays it.

[1390] Mentor selection phase

[1391] The user selects "Former cooking class instructor A." The device sends the selection information to the server.

[1392] Schedule adjustment phase

[1393] The server checks the schedules of the user and instructor A and suggests "every Saturday at 5:00 PM." The device presents the adjusted schedule to the user and notifies the mentor.

[1394] Implementation Phase

[1395] Instructor A visits users' homes every Saturday at 5:00 p.m. to help them prepare healthy meals that can be prepared in a short amount of time.

[1396] Prompt Sentence Examples

[1397] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[1398] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1399] Step 1:

[1400] The user inputs household concerns or questions into the device. The input information is specific text, such as "I'm always short on time and it's difficult to cook healthy meals." The input is the question or concern in text format, and the output is the information sent from the device to the server. The specific operation is that the user inputs the concerns as text using an application installed on a smartphone or tablet.

[1401] Step 2:

[1402] The terminal receives input from the user and sends the information to the backend server. The input is the text data entered by the user into the terminal, and the output is the JSON format data sent to the server. Specifically, the terminal's API captures the user's input and sends the data to the server using an HTTP request.

[1403] Step 3:

[1404] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. The software used here is a natural language processing module (e.g., SpaCy, GPT model). The input is the question or concern data in JSON format sent from the device, and the output is the analysis results, which are analyzed data such as keywords and sentence structure. Specifically, the server passes the received data to the analysis module, which then analyzes the text data.

[1405] Step 4:

[1406] The generative AI generates advice based on the analysis results and sends it back to the server. The generative AI model used here is a GPT model. The input is the keywords and sentence structure data obtained as a result of the analysis, and the output is specific advice text. The specific operation is that the generative AI model generates advice content based on the analysis results within the server and sends it back to the server.

[1407] Step 5:

[1408] The server sends the generated advice to the terminal and displays it to the user. The input is the text data of the advice returned from the generative AI, and the output is the specific advice displayed on the user's terminal. The specific operation is that the server sends the advice data to the terminal, and the terminal displays the data to the user.

[1409] Step 6:

[1410] The server searches a database of nearby elderly mentors and cooking experts based on the question and advice. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python). The input is text data containing the advice, and the output is a list of candidate mentors and experts. Specifically, the server queries the database based on the advice and lists appropriate mentors and experts.

[1411] Step 7:

[1412] The server sends the selected mentor candidates to the terminal and displays them to the user. The input is the candidate list data, and the output is the information of the mentor candidates displayed on the user's terminal. The specific operation is that the server sends the candidate list to the terminal, and the terminal displays it to the user.

[1413] Step 8:

[1414] The user selects the mentor they wish to request from the presented mentor candidates. The input is the displayed list of mentor candidates, and the output is information about the selected mentor. The specific operation is for the user to select from the candidates using the terminal interface.

[1415] Step 9:

[1416] The terminal sends the user's selection to the server. The input is the information of the mentor selected by the user, and the output is the selection information sent to the server. The specific operation is that the terminal captures the selection data and sends it to the server via an HTTP request.

[1417] Step 10:

[1418] The server accesses the mentor's schedule database and compares it with the user's desired date and time. The software used is a calendar API (e.g., Google Calendar API). The input is the user's and mentor's desired date and time data, and the output is the adjusted schedule data. Specifically, the server compares the schedules of both parties and determines the optimal date and time.

[1419] Step 11:

[1420] The server sends the adjusted schedule to the terminal and notifies the user. It also notifies the mentor's terminal of the schedule. The input is the adjusted schedule data, and the output is a schedule notification that is displayed on the user's and mentor's terminals. Specifically, the server sends the schedule data to each terminal, and the terminal displays the contents.

[1421] Step 12:

[1422] The mentor visits the user's home at the notified date and time and provides the requested support. The input is the schedule notification received from the server before the visit, and the output is the specific support activities at the visit location. The specific operation is for the mentor to visit the user's home at the specified date and time and provide support such as cooking assistance.

[1423] These steps will enable us to solve household problems and provide efficient support. Furthermore, the following example prompts will enable the generative AI model to generate effective advice.

[1424] Prompt Sentence Examples

[1425] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[1426] 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.

[1427] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[1428] 1. Enter your questions and concerns

[1429] User

[1430] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1431] Terminal

[1432] The terminal receives input from the user and transmits the information to the server.

[1433] 2. Emotion Recognition by Emotion Engine

[1434] server

[1435] The server sends the received user input to the emotion engine for analysis. The emotion engine analyzes the text and, if necessary, recognizes the user's face and voice tone to determine their emotion. For example, if the user is feeling stressed, the emotion is recognized as "stress."

[1436] 3. Initial advice provided by AI

[1437] server

[1438] The server provides the emotion analysis results obtained from the emotion engine to the generative AI, which then generates advice that takes the user's emotional state into account. For example, if the user is feeling stressed, advice on relaxation methods and time management will be emphasized.

[1439] Terminal

[1440] The server sends the generated advice to the device and displays it to the user. For example, it might say, "It seems you're feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively."

[1441] 4. Matching potential mentors

[1442] server

[1443] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The server then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, if advice on stress reduction is needed, mentors with expertise in stress management will be prioritized.

[1444] Terminal

[1445] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[1446] 5. Selecting the best mentor

[1447] User

[1448] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[1449] Terminal

[1450] The terminal transmits the user's selection to the server.

[1451] 6. Schedule Adjustment

[1452] server

[1453] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[1454] Terminal

[1455] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1456] 7. On-site support

[1457] Mentor

[1458] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1459] Specific system flow example

[1460] Input Phase

[1461] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[1462] Emotion Recognition Phase

[1463] The server sends the input to the emotion engine, which recognizes the user's emotion as "stress."

[1464] Advice Generation Phase

[1465] The server's generative AI generates advice based on the results of emotion analysis, suggesting ways for users to relax and how to manage their time for housework and work.

[1466] Mentor Matching Phase

[1467] The server searches a database of nearby mentors based on the results of the emotion analysis and the question. It selects two candidates: "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D." The server then sends the candidate list to the user's device and presents it to them.

[1468] Mentor selection phase

[1469] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[1470] Schedule adjustment phase

[1471] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[1472] Implementation Phase

[1473] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[1474] In this way, by combining the emotion engine, the system provides personalized advice and assistance that takes into account the user's emotional state, allowing for more efficient resolution of household problems.

[1475] The processing flow will be explained below.

[1476] Step 1:

[1477] User

[1478] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1479] Step 2:

[1480] Terminal

[1481] The terminal receives input from the user and transmits the information to the server.

[1482] Step 3:

[1483] server

[1484] The server sends the received questions and concerns to the emotion engine, which analyzes the input text and identifies the user's emotions, such as stress, fatigue, and anxiety.

[1485] Step 4:

[1486] Emotion Engine

[1487] The emotion engine sends the analysis results back to the server. For example, it is analyzed that the user is feeling stressed.

[1488] Step 5:

[1489] server

[1490] The server queries the generative AI based on the emotion analysis results and generates advice. Specific advice is generated that takes into account the user's emotional state. For example, if the user is feeling stressed, advice on "how to relax" and "how to manage time" will be emphasized.

[1491] Step 6:

[1492] Terminal

[1493] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "It seems you are feeling stressed, so try some relaxation techniques. Also, here are some time management techniques to help you do your homework more effectively" may be displayed.

[1494] Step 7:

[1495] server

[1496] The server searches a database of nearby senior mentors based on the question, advice, and sentiment analysis results. It then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, it prioritizes mentors with stress reduction skills.

[1497] Step 8:

[1498] Terminal

[1499] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, who is good at stress management" or "Mentor B, who is good at education" are displayed.

[1500] Step 9:

[1501] User

[1502] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, who is skilled in stress management."

[1503] Step 10:

[1504] Terminal

[1505] The terminal transmits the user's selection to the server.

[1506] Step 11:

[1507] server

[1508] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[1509] Step 12:

[1510] Terminal

[1511] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1512] Step 13:

[1513] Mentor

[1514] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1515] Example 2

[1516] 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."

[1517] There is a need for a system that can quickly provide users with appropriate and personalized advice for various worries and problems at home. Solving problems also requires physical support personnel who can provide specialized knowledge and assistance, but current systems have difficulty effectively matching these people and adjusting their schedules. This can lead to stress for users and prolong the problems.

[1518] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1519] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for performing an emotional analysis of the questions and concerns using an emotion engine that recognizes the user's emotions, means for presenting advice generated based on the emotion analysis results, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, means for coordinating schedules for both parties, and means for arranging on-site support by the senior mentor. This allows the user to receive personalized advice based on their emotions while also quickly arranging physical support, making it possible to efficiently solve home problems.

[1520] The "means for inputting questions and concerns about the home" is a means for users to input problems and annoyances about housework, childcare, nursing care, and other home-related issues as text data.

[1521] "Generative AI" is an artificial intelligence system that uses natural language processing and machine learning algorithms to generate optimal advice and solutions based on input data.

[1522] An "emotion engine" is software or hardware that analyzes text data, voice data, facial image data, etc., to identify the user's emotional state.

[1523] "Sentiment analysis" is the process of extracting emotional states from user input data and tagging and scoring them.

[1524] "Means for presenting advice" refers to the means for displaying solutions and advice generated by generative AI to the user.

[1525] A "senior mentor" refers to an elderly person who has knowledge and experience that is useful in resolving family problems and concerns.

[1526] The "database" is a digital storage system that systematically stores information such as senior mentor skills and user ratings, and allows for search and reference.

[1527] The "matching method" is the process of selecting the most suitable senior mentor based on the user's problem, questions, and sentiment analysis results, and then introducing the two parties together.

[1528] The "means for adjusting schedules" is a process for adjusting the available time of both the user and the senior mentor and setting an appropriate date and time for support.

[1529] The "means for arranging on-site support" is a means for arranging for a selected senior mentor to visit the user's home and actually provide support.

[1530] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[1531] Enter your questions and concerns

[1532] Users input their household concerns and questions into a terminal. The terminal can be a digital device such as a smartphone, tablet, or PC. The user might input a concern such as, "We're both working and busy, so we don't have time to help my child with their homework." The terminal then sends this information to the server.

[1533] Emotion recognition by emotion engine

[1534] The server sends the received user input to the emotion engine for analysis. The emotion engine combines text analysis, facial recognition, and voice tone analysis to analyze the user's emotions. For example, a Python-based natural language processing library is used for text analysis, OpenCV for facial recognition, and a voice tone analysis program is typically used for voice analysis. If the user is feeling stressed, the emotion is recognized as "stress."

[1535] AI-powered initial advice

[1536] The server provides the emotion analysis results obtained from the emotion engine to a generative AI model. For example, a natural language processing model such as OpenAI's GPT-3 can be used. The generative AI takes into account the user's emotional state and generates appropriate advice. For example, if the user is feeling stressed, it will emphasize "ways to relax" and "time management advice." The generated advice is sent from the server to the device and displayed to the user. For example, advice such as "It seems you are feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively" may be presented.

[1537] Matching mentor candidates

[1538] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The database includes the skills of senior mentors and user ratings, and the most suitable mentor is extracted. For example, if advice on stress reduction is needed, a mentor who is skilled in stress management will be given priority. This information is sent from the server to the terminal, and the user is presented with candidates such as "Mentor A, a former teacher" and "Mentor B, a former childcare teacher."

[1539] Selection of the best mentor

[1540] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former faculty member." The selection information is sent from the terminal to the server.

[1541] Schedule adjustment

[1542] The server accesses the selected mentor's schedule database and compares it with the user's desired date and time. It adjusts the date and time to suit both parties and finalizes the adjusted schedule. For example, a schedule such as "every Wednesday from 5:00 PM to 6:00 PM" is finalized. This information is sent from the server to the terminal and notified to the user and mentor.

[1543] On-site support

[1544] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1545] Specific examples

[1546] The user inputs their concern on their device, saying, "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device sends this information to the server. The server sends the input to an emotion engine, which recognizes the user's emotion as "stress." A generative AI generates advice based on the emotion analysis results, suggesting ways for the user to relax and manage their time for housework and work. The server searches a database of nearby mentors based on the emotion analysis results and the question, and selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the device and presents it. The user selects "Former Housekeeper Mentor C." The device sends the selection information to the server. The server checks the user's and Mentor C's schedules and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor. Mentor C will visit the user's home every Tuesday at 10:00 to provide cleaning assistance.

[1547] In this way, the combination of an emotion engine and generative AI can provide personalized advice and assistance that takes into account the user's emotional state, making it possible to solve household problems more efficiently.

[1548] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1549] Step 1:

[1550] Handling User Input

[1551] The user uses the device to input questions or concerns about their household. For example, they might input, "We're both working and busy, so we don't have time to help our child with their homework." The device receives this information as text data and converts it into JSON format. The input data looks like this:

[1552] Input data:

[1553] "Problem": "We both work and are busy, so we don't have time to help our children with their homework."

[1554] The terminal sends this data to the server as an HTTP request.

[1555] Step 2:

[1556] Sentiment analysis processing

[1557] The server sends the received text data to the emotion engine, which performs text analysis, facial recognition, and speech tone analysis to analyze the user's emotional state, using libraries such as the Natural Language Toolkit (NLTK), OpenCV, and speech analysis.

[1558] Input text:

[1559] "We both work and are busy, so we don't have time to help our children with their homework."

[1560] The sentiment engine parses this text and produces output like this:

[1561] Output data:

[1562] "Emotion": "Stress"

[1563] This output data is returned to the server.

[1564] Step 3:

[1565] AI-powered initial advice

[1566] The server sends the sentiment analysis results as prompts to a generative AI model, for example, OpenAI's GPT-3.

[1567] Input prompt:

[1568] "Users are stressed and need relaxation and time management advice."

[1569] The generative AI model generates advice based on the prompts, producing an output similar to the following:

[1570] Output advice:

[1571] "Try yoga or meditation as a way to relax, and create a to-do list to help you manage your time."

[1572] The server transmits this generated advice to the terminal.

[1573] Step 4:

[1574] Advice display and mentor matching

[1575] The terminal will display the advice received from the server to the user, for example, in the following format:

[1576] "It sounds like you're stressed, so I'd like to recommend some yoga and meditation techniques. I'd also recommend creating a to-do list and managing your time."

[1577] At the same time, the server searches a database of senior mentors living nearby and selects the most suitable mentor.

[1578] Input data:

[1579] "Question content", "Advice content", "Emotion analysis results"

[1580] The server generates a list of mentor candidates as follows and sends it to the device:

[1581] Output data:

[1582] "Mentor A, a former teacher" and "Mentor B, a former childcare teacher"

[1583] The terminal also displays this mentor candidate to the user.

[1584] Step 5:

[1585] Selection of the best mentor

[1586] The user selects from the presented mentor candidates. For example, the user selects "Mentor A, a former teacher."

[1587] The terminal transmits this selection to the server.

[1588] Input data:

[1589] "Mentor A, a former teacher"

[1590] Step 6:

[1591] Schedule adjustment

[1592] The server accesses the schedule database of the user and the selected mentor and adjusts the available time for both parties. For example, it determines that "every Wednesday from 5:00 PM to 6:00 PM" is optimal.

[1593] Input data:

[1594] "Desired date and time", "Mentor schedule"

[1595] The server determines the adjusted schedule as follows and sends it to the device:

[1596] Output data:

[1597] Every Wednesday from 5:00 PM to 6:00 PM

[1598] The terminal displays this schedule to the user and also notifies the mentor.

[1599] Step 7:

[1600] On-site support

[1601] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "I will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1602] Specifically, the mentor visits the user's home at a specified time and provides support according to the previously agreed upon details.

[1603] (Application example 2)

[1604] 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."

[1605] Conventional home support systems often provide general advice and support without considering the user's emotional state, resulting in a lack of personalized support tailored to individual needs. Furthermore, while there is a demand for services that take emotional states into account in other lifestyle services, including food delivery, this has not yet been realized. Therefore, a system that can provide appropriate advice and services by empathizing with the user's emotions is needed.

[1606] 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.

[1607] In this invention, the server includes a means for inputting questions and concerns about the home, a means for a generative AI to provide advice in response to the questions and concerns, an emotion analysis means for analyzing the user's emotions, a means for the generative AI to provide personalized advice based on the user's emotional state, a means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, a means for coordinating schedules for both parties, and a means for arranging on-site support by the senior mentor. This allows the server to provide personalized advice and support taking into account the user's emotional state, thereby improving satisfaction with home concerns and food delivery.

[1608] "Means for inputting questions and concerns about the home" refers to an interface that allows users to input problems and questions that arise within the home in the form of text or voice.

[1609] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate solutions and advice in response to questions or concerns entered by users.

[1610] "Emotion analysis means" is a technology that analyzes the emotional state from text or voice input by the user and identifies emotions such as stress, fatigue, and joy.

[1611] The "means for providing personalized advice" is an artificial intelligence system that generates optimal solutions and advice based on the emotional state of each individual user, based on the results of emotion analysis.

[1612] The "database including the skills and user ratings of senior mentors living nearby" is a database that accumulates senior mentors' specialized knowledge, service experience, and user ratings.

[1613] "Means for matching the most suitable mentor" refers to algorithms and systems for selecting the most suitable senior mentor based on the user's questions, concerns, and emotional analysis results.

[1614] The "means of adjusting schedules" is a system that compares the schedules of both the user and the mentor and arranges a date and time that is convenient for both parties.

[1615] The "means for arranging on-site support" is a system that coordinates schedules and arranges visits by selected mentors to the user's home to provide the necessary support.

[1616] This invention is a system that uses generative AI to provide personalized advice in response to questions and concerns about the home. The system uses emotion analysis to recognize the user's emotions and provide optimal advice, as well as matching with nearby senior mentors and providing on-site support.

[1617] Specific system configuration

[1618] 1. Enter your questions and concerns

[1619] User

[1620] Users input questions or concerns about their home into a smartphone application. At this stage, users often write sentences that touch on their emotions. They input their content in response to prompts such as, "How are you feeling today?"

[1621] Example input

[1622] I'm very tired today and I don't know what to eat.

[1623] Terminal

[1624] The terminal receives input from the user and transmits the information to the server.

[1625] 2. Emotion analysis

[1626] server

[1627] The server analyzes the received user input using a sentiment analysis engine, which analyzes the text to identify emotions such as stress, fatigue, and joy.

[1628] Specific examples

[1629] The input "I'm very tired today and I don't know what to eat" is recognized as "fatigue."

[1630] 3. Generating Advice

[1631] server

[1632] The server uses generative AI to provide personalized advice based on the results of sentiment analysis. For example, if the user is tired, it will suggest healthy and easy-to-eat meals.

[1633] Specific examples

[1634] "You seem tired, so I recommend some relaxing herbal tea and a nutritious salad."

[1635] Terminal

[1636] The server transmits the generated advice to the terminal and displays it to the user.

[1637] 4. Mentor matching

[1638] server

[1639] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis to identify the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[1640] Specific examples

[1641] A "nutritionist mentor" who is knowledgeable about fatigue recovery will be selected and presented to the user.

[1642] Terminal

[1643] The user is prompted to select a mentor from the displayed candidates.

[1644] 5. Selecting the best mentor and adjusting the schedule

[1645] User

[1646] The user selects the mentor they wish to request from the presented mentor candidates. For example, they can select "Mentor A, a nutritionist."

[1647] Terminal

[1648] The terminal transmits the user's selection to the server.

[1649] server

[1650] The server checks the mentor's schedule, adjusts it to the prepared date and time, and notifies the user and mentor of the adjusted schedule.

[1651] 6. On-site support

[1652] Mentor

[1653] The mentor will visit the user's home at the notified date and time and provide the requested support.

[1654] Hardware and software used

[1655] Smartphone: User Interface

[1656] Sentiment analysis engine: Emotion-Analysis-Service

[1657] Generative AI: Generated-Advice-Service using natural language processing

[1658] Food recommendation engine: Food-Recommendation-Service

[1659] Prompt Sentence Examples

[1660] "What dish would you recommend for someone who is feeling tired these days?"

[1661] This makes it possible to provide personalized advice that reflects the user's emotional state, improving satisfaction with household concerns and food delivery.

[1662] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1663] Step 1:

[1664] The user inputs questions and concerns about the home.

[1665] An example of input would be "I'm very tired today and I don't know what to eat."

[1666] This input data is sent to the terminal in text format.

[1667] Step 2:

[1668] The terminal receives input data from the user and sends the information to the server for analysis.

[1669] The input is text data, which is transferred to the server as is.

[1670] Step 3:

[1671] The server transmits the received text data to the emotion analysis means.

[1672] The emotion analysis means uses natural language processing and machine learning algorithms to analyze emotional states from text data.

[1673] Data processing includes part-of-speech analysis of text and sentiment analysis.

[1674] The output is emotion tags such as "fatigue" and "stress."

[1675] Step 4:

[1676] The server sends the emotion analysis results to the generative AI.

[1677] Here, appropriate advice is generated based on input data, which is the result of emotion analysis.

[1678] Generative AI takes into account the user's emotional state and generates optimal solutions based on specific prompts.

[1679] As a data calculation, specific advice is generated by referencing a knowledge base and past data.

[1680] The output is specific advice such as relaxation methods and dietary suggestions.

[1681] Step 5:

[1682] The server transmits the generated advice to the terminal.

[1683] The input data is text-based advice created by generative AI.

[1684] The device will then display this advice to the user, such as "We recommend a relaxing herbal tea and a nutritious salad."

[1685] Step 6:

[1686] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis.

[1687] The input data is the emotion analysis results and the question content.

[1688] The server selects the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[1689] As an output, a list of suitable mentors is generated.

[1690] Step 7:

[1691] The server transmits the selected mentor candidates to the terminal and displays them to the user.

[1692] The input data is a list of selected mentors, and the output shows the candidates to the user, who can select "Dietitian A Mentor."

[1693] Step 8:

[1694] The user selects the mentor he or she wishes to request from among the presented mentor candidates.

[1695] The input data is the mentor selected by the user.

[1696] The terminal transmits its selection to the server.

[1697] Step 9:

[1698] The server checks the mentor's schedule and adjusts it to the user's desired date and time.

[1699] The input data is the schedule information of the user and the mentor.

[1700] As an output, a firm schedule is produced.

[1701] Step 10:

[1702] The server transmits the adjusted schedule to the terminal and notifies the user.

[1703] Similarly, the mentor will be notified of the schedule.

[1704] Step 11:

[1705] Upon receiving the notification, the mentor visits the user's home at the confirmed date and time to provide the requested support.

[1706] A specific example of this would be "Mentor A, a nutritionist, visits and provides dietary advice."

[1707] 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.

[1708] 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.

[1709] 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.

[1710] [Fourth embodiment]

[1711] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1712] 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.

[1713] 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).

[1714] 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.

[1715] 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.

[1716] 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).

[1717] 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.

[1718] 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.

[1719] 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.

[1720] 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.

[1721] 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.

[1722] 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.

[1723] 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."

[1724] This invention is a system that uses generative AI to provide accurate advice in response to questions and concerns about home life, and also matches people with nearby senior mentors to provide on-site support. The operation of this system is explained in detail below.

[1725] 1. Enter your questions and concerns

[1726] User

[1727] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1728] Terminal

[1729] The terminal receives input from the user and transmits the information to the server.

[1730] 2. Initial advice provided by AI

[1731] server

[1732] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[1733] Terminal

[1734] The server sends the generated advice to the device and displays it to the user. For example, the advice might be, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[1735] 3. Matching potential mentors

[1736] server

[1737] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[1738] Terminal

[1739] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[1740] 4. Selecting the best mentor

[1741] User

[1742] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[1743] Terminal

[1744] The terminal transmits the user's selection to the server.

[1745] 5. Schedule adjustments

[1746] server

[1747] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[1748] Terminal

[1749] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1750] 6. On-site support

[1751] Mentor

[1752] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1753] Specific system flow example

[1754] Input Phase

[1755] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[1756] Advice Generation Phase

[1757] The server's natural language processing module analyzes the text. The AI ​​model generates "time management methods for efficient housework" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[1758] Mentor Matching Phase

[1759] The server searches a database of nearby mentors based on the question. It selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the user's device and displays it.

[1760] Mentor selection phase

[1761] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[1762] Schedule adjustment phase

[1763] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[1764] Implementation Phase

[1765] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[1766] In this way, the system provides a concrete means to efficiently resolve household concerns and strengthen community ties.

[1767] The processing flow will be explained below.

[1768] Step 1:

[1769] User

[1770] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1771] Step 2:

[1772] Terminal

[1773] The terminal receives input from the user and transmits the information to the server.

[1774] Step 3:

[1775] server

[1776] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI generates advice and sends it back to the server.

[1777] Step 4:

[1778] Terminal

[1779] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively" is presented.

[1780] Step 5:

[1781] server

[1782] The server searches a database of nearby senior mentors based on the question and advice provided, and selects the most suitable mentor based on the mentor's skills and ratings.

[1783] Step 6:

[1784] Terminal

[1785] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[1786] Step 7:

[1787] User

[1788] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[1789] Step 8:

[1790] Terminal

[1791] The terminal transmits the user's selection to the server.

[1792] Step 9:

[1793] server

[1794] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[1795] Step 10:

[1796] Terminal

[1797] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1798] Step 11:

[1799] Mentor

[1800] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1801] Example 1

[1802] 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."

[1803] In recent years, the number of dual-income households and single-parent households has increased, resulting in many worries and problems within the home regarding education, childcare, nursing care, housework, etc. To efficiently solve these problems, there is a need for a system that allows users to easily receive professional advice. There is also a need for a system that allows users to quickly and appropriately find available supporters and receive that support in the form of a visit.

[1804] 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.

[1805] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice on the questions and concerns using a natural language processing module, means for displaying the advice on the user's terminal, means for matching the user with the most suitable support person by referencing a database containing the skills and user ratings of supporters living in the neighborhood, means for coordinating schedules between the user and the support person, and means for arranging for on-site support by the supporter. This allows the user to receive prompt and accurate advice on problems related to the home, and also enables the user to receive actual on-site support from a supporter in the neighborhood.

[1806] "Questions and worries about the home" refer to problems and difficulties that users face at home, such as education, childcare, nursing care, and housework.

[1807] A "natural language processing module" is a software component that analyzes input text and understands its content.

[1808] "Generative AI" is artificial intelligence that generates appropriate advice and information based on input text data.

[1809] A "supporter" is an expert or experienced person who gives advice and support to users to solve problems related to their home.

[1810] A "database" is a data structure that manages information such as supporter skills and user ratings, and can be referenced as needed.

[1811] "Matching" is the process of selecting and connecting with the supporter who is best suited to the user's concerns or problems.

[1812] "Schedule adjustment" refers to the process of adjusting and confirming a mutually convenient date and time between the user and the supporter.

[1813] "Home visit support" refers to support activities in which a supporter visits the user's home in person.

[1814] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches nearby supporters to provide on-site support. The operation of this system is explained in detail below. The hardware and software used include a server, terminal, natural language processing module, generative AI, database, and schedule management system.

[1815] 1. Enter your questions and concerns

[1816] User

[1817] Users input their worries and questions about their household into the terminal. For example, a user might input a worry such as, "We're both working and busy, so we don't have time to help our children with their homework." The terminal then sends this information to the server.

[1818] 2. Initial advice provided by AI

[1819] server

[1820] The server passes the received text data of questions and concerns to a natural language processing module (e.g., Google Cloud Natural Language API) for analysis. Based on the analysis results, the server creates a prompt for a generative AI (e.g., OpenAI's GPT-3) and requests it to generate advice.

[1821] 3. Displaying Advice

[1822] Terminal

[1823] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as, "If you don't have time to help your child with their homework, you can use an online monitoring tool. We will also teach you time management methods to help you work on your homework more effectively."

[1824] 4. Matching potential mentors

[1825] server

[1826] Based on the question and advice, the server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters. The server then creates a list of the most suitable supporters and sends it to the device. The user can then select the supporter they wish to request from the list.

[1827] 5. Schedule adjustments

[1828] server

[1829] The server receives the user's selection information, accesses the supporter's schedule database (e.g., Google Calendar API), and compares it with the user's desired date and time. The optimal schedule is then adjusted and confirmed. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[1830] 6. Arranging on-site support

[1831] server

[1832] The server sends the confirmed schedule to the supporter's device and the user's device and arranges for the visit. The supporter visits the user's home at the notified date and time and provides the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1833] Example prompt

[1834] An example of a household problem entered by a user on a device: "It's difficult to balance housework and work. Cleaning in particular keeps getting put off."

[1835] Example prompt for generative AI: "I'm having trouble balancing work and housework. Please tell me some time management methods to do my housework efficiently."

[1836] This system provides a concrete means to efficiently resolve household problems and strengthen community ties.

[1837] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1838] Step 1:

[1839] User

[1840] Users input their worries and questions about their home life into the terminal. For example, they input the text "We are both working and busy, so we don't have time to help our children with their homework."

[1841] Input: Text data of questions and concerns about the home

[1842] Output: Sending text data of questions and concerns from the device to the server

[1843] Step 2:

[1844] Terminal

[1845] The device receives text data of questions and concerns entered by the user, converts it into JSON format, and sends it to the server.

[1846] Input: Text data entered by the user

[1847] Output: Text data converted to JSON format

[1848] Step 3:

[1849] server

[1850] The server parses the received JSON data, extracts the text of the question or concern, and passes this text to a natural language processing module (e.g., Google Cloud Natural Language API) to identify keywords and important phrases.

[1851] Input: JSON format text data

[1852] Output: Keywords and important phrases analyzed by natural language processing

[1853] Step 4:

[1854] server

[1855] The server generates a prompt based on the analysis results and asks a generative AI (e.g., OpenAI's GPT-3) to generate advice. For example, the generated advice might be, "If you don't have time to help with your child's homework, you can use an online monitoring tool. We'll also teach you time management techniques to help you do your homework more effectively."

[1856] Input: Parsed keywords and key phrases

[1857] Output: Advice generated by generative AI

[1858] Step 5:

[1859] server

[1860] The server transmits the generated advice to the terminal.

[1861] Input: Advice generated by generative AI

[1862] Output: Advice sent to the user's terminal

[1863] Step 6:

[1864] Terminal

[1865] The terminal displays the advice received from the server to the user through a user interface, and the user confirms the presented advice.

[1866] Input: Advice sent by the server

[1867] Output: Advice displayed to the user

[1868] Step 7:

[1869] server

[1870] The server searches a database (e.g., MySQL database) containing the skills and user ratings of nearby supporters based on the question and advice, and uses an SQL query to create a list of the most suitable supporters and send it to the device.

[1871] Input: Question and advice

[1872] Output: A list of eligible donors

[1873] Step 8:

[1874] Terminal

[1875] The device displays a list of potential supporters to the user. The user can then select the most suitable supporter from the displayed list. For example, "Supporter A, a former teacher" or "Supporter B, a former childcare teacher" may be displayed.

[1876] Input: List of potential supporters

[1877] Output: A list of donors displayed to the user

[1878] Step 9:

[1879] User

[1880] The user selects the supporter they wish to request from the displayed supporter candidates, and then inputs the selection information into the terminal.

[1881] Input: User-selected supporter

[1882] Output: Text data containing the selection information

[1883] Step 10:

[1884] Terminal

[1885] The device converts the user's selection information into JSON format and sends it to the server.

[1886] Input: Supporter information selected by the user

[1887] Output: Supporter information sent to the server in JSON format

[1888] Step 11:

[1889] server

[1890] The server accesses the schedule database of the selected supporters (e.g., Google Calendar API) and compares it with the user's desired date and time. It then adjusts and confirms the optimal schedule. For example, "every Wednesday from 5:00 PM to 6:00 PM has been confirmed as the optimal schedule."

[1891] Input: Supporter information selected by the user and desired date and time

[1892] Output: Confirmed schedule

[1893] Step 12:

[1894] server

[1895] The server transmits the finalized schedule to the terminal and notifies the user and the supporter.

[1896] Input: Confirmed schedule information

[1897] Output: Schedule notifications sent to users and donors

[1898] Step 13:

[1899] Users and Supporters

[1900] The user and the supporter will follow the notified schedule, and the supporter will visit the user's home at the specified date and time and provide the requested support. Specifically, "Supporter A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[1901] Input: Notified schedule

[1902] Output: Actual visit support

[1903] (Application example 1)

[1904] 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."

[1905] In modern society, many worries and problems arise in daily life, especially in dual-income households and those busy with childcare, nursing care, and housework. Specific concerns include dietary issues and not having time to prepare healthy meals. There is also a need for a method to efficiently resolve these issues by utilizing the expertise and experience of the elderly. Therefore, a system is needed that provides accurate advice for questions and worries within the home, and also matches nearby experts to provide on-site support.

[1906] 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.

[1907] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of elderly mentors living nearby, means for coordinating schedules between the two parties, means for arranging on-site support by the elderly mentor, and means for quickly generating appropriate advice online in response to inquiries about meals at home, and further means for matching with nearby cooking experts and nutritionists to provide on-site cooking support, thereby enabling effective and quick support for meals at home and other daily life issues.

[1908] "A means of inputting questions and concerns about the home" refers to an interface that allows users to input various problems and questions that arise within the home into a terminal.

[1909] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate answers and advice in response to questions or concerns entered by users.

[1910] An "elderly mentor" is an elderly person who has a wealth of life experience and specialized knowledge and is able to provide various types of support to the user's home.

[1911] "Database including the skills and user ratings of elderly mentors living in the neighborhood" refers to a database that stores information on mentor skills and ratings from other users in order to select an appropriate elderly mentor for a user.

[1912] "Means of matching the most suitable mentor" refers to a method of utilizing information in the database to select and suggest a mentor who is most suitable for the user's concerns.

[1913] "Means to coordinate schedules between both parties" refers to the function that coordinates the schedules of the user and mentor and identifies a date and time that is convenient for both parties.

[1914] "Means for arranging on-site support by the elderly mentor" refers to a method of arranging for a selected mentor to visit the user's home at a specified date and time and provide specific support.

[1915] "Generating appropriate advice online quickly in response to dietary concerns at home" means that when a user enters their dietary concerns online, the generative AI will provide specific advice in a short amount of time.

[1916] "Means of matching nearby cooking experts and nutritionists and providing on-site cooking support" refers to a method in which cooking experts and nutritionists are selected for users who have concerns about their diet, and they actually visit their homes to provide support.

[1917] This invention is a system in which generative AI provides accurate advice in response to questions and concerns about the home, and also matches users with nearby senior mentors and cooking experts to provide on-site support. This system is designed to provide prompt and appropriate support according to each user's concerns. The operation of the system is explained in detail below using specific examples.

[1918] 1. Enter your questions and concerns

[1919] User

[1920] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "I don't always have time, so it's difficult for me to cook healthy meals."

[1921] Terminal

[1922] The device receives input from the user and sends that information to a backend server, using an interface such as a smartphone or tablet, and the software used is a front-end application (e.g., React Native).

[1923] 2. Initial advice provided by AI

[1924] server

[1925] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. Based on the analysis results, a generative AI (e.g., GPT model) generates advice and sends it back to the server. The software used is a backend server (e.g., Node.js) and a natural language processing module (e.g., SpaCy, GPT model).

[1926] Terminal

[1927] The server sends the generated advice to the device and displays it to the user. Specifically, it presents, for example, "healthy dinner recipes that can be made in under 30 minutes."

[1928] 3. Matching potential mentors

[1929] server

[1930] The server searches a database of nearby senior mentors and cooking experts based on the question and advice. It selects the most suitable mentor, taking into account the mentor's skills and ratings. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python).

[1931] Terminal

[1932] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Former cooking class instructor A" or "Experienced nutritionist B" may be displayed.

[1933] 4. Selecting the best mentor

[1934] User

[1935] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Former Cooking Class Instructor A."

[1936] Terminal

[1937] The terminal transmits the user's selection to the server.

[1938] 5. Schedule adjustments

[1939] server

[1940] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed date. The software used is a calendar API (e.g., Google Calendar API).

[1941] Terminal

[1942] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[1943] 6. On-site support

[1944] Mentor

[1945] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Instructor A will visit the user's home every Saturday at 5:00 PM and provide instruction on healthy cooking that can be made in a short amount of time."

[1946] Specific system flow example

[1947] Input Phase

[1948] The user inputs a concern such as "I'm busy at work and don't have time to make a simple and healthy dinner" into the device, and the device sends this information to the server.

[1949] Advice Generation Phase

[1950] The server's natural language processing module analyzes the text. The AI ​​model generates "healthy dinner recipes that can be made in under 30 minutes" and sends them back to the server. The server then sends the advice to the user's device and displays it.

[1951] Mentor Matching Phase

[1952] The server searches a database of nearby mentors based on the question and advice. It selects "Former Cooking Class Instructor A" and "Experienced Nutritionist B" as candidates. The server then sends the candidate list to the user's device and displays it.

[1953] Mentor selection phase

[1954] The user selects "Former cooking class instructor A." The device sends the selection information to the server.

[1955] Schedule adjustment phase

[1956] The server checks the schedules of the user and instructor A and suggests "every Saturday at 5:00 PM." The device presents the adjusted schedule to the user and notifies the mentor.

[1957] Implementation Phase

[1958] Instructor A visits users' homes every Saturday at 5:00 p.m. to help them prepare healthy meals that can be prepared in a short amount of time.

[1959] Prompt Sentence Examples

[1960] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[1961] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1962] Step 1:

[1963] The user inputs household concerns or questions into the device. The input information is specific text, such as "I'm always short on time and it's difficult to cook healthy meals." The input is the question or concern in text format, and the output is the information sent from the device to the server. The specific operation is that the user inputs the concerns as text using an application installed on a smartphone or tablet.

[1964] Step 2:

[1965] The terminal receives input from the user and sends the information to the backend server. The input is the text data entered by the user into the terminal, and the output is the JSON format data sent to the server. Specifically, the terminal's API captures the user's input and sends the data to the server using an HTTP request.

[1966] Step 3:

[1967] The server passes the received questions and concerns to a natural language processing module, which analyzes the text. The software used here is a natural language processing module (e.g., SpaCy, GPT model). The input is the question or concern data in JSON format sent from the device, and the output is the analysis results, which are analyzed data such as keywords and sentence structure. Specifically, the server passes the received data to the analysis module, which then analyzes the text data.

[1968] Step 4:

[1969] The generative AI generates advice based on the analysis results and sends it back to the server. The generative AI model used here is a GPT model. The input is the keywords and sentence structure data obtained as a result of the analysis, and the output is specific advice text. The specific operation is that the generative AI model generates advice content based on the analysis results within the server and sends it back to the server.

[1970] Step 5:

[1971] The server sends the generated advice to the terminal and displays it to the user. The input is the text data of the advice returned from the generative AI, and the output is the specific advice displayed on the user's terminal. The specific operation is that the server sends the advice data to the terminal, and the terminal displays the data to the user.

[1972] Step 6:

[1973] The server searches a database of nearby elderly mentors and cooking experts based on the question and advice. The software used is a database (e.g., PostgreSQL) and a matching algorithm (e.g., Python). The input is text data containing the advice, and the output is a list of candidate mentors and experts. Specifically, the server queries the database based on the advice and lists appropriate mentors and experts.

[1974] Step 7:

[1975] The server sends the selected mentor candidates to the terminal and displays them to the user. The input is the candidate list data, and the output is the information of the mentor candidates displayed on the user's terminal. The specific operation is that the server sends the candidate list to the terminal, and the terminal displays it to the user.

[1976] Step 8:

[1977] The user selects the mentor they wish to request from the presented mentor candidates. The input is the displayed list of mentor candidates, and the output is information about the selected mentor. The specific operation is for the user to select from the candidates using the terminal interface.

[1978] Step 9:

[1979] The terminal sends the user's selection to the server. The input is the information of the mentor selected by the user, and the output is the selection information sent to the server. The specific operation is that the terminal captures the selection data and sends it to the server via an HTTP request.

[1980] Step 10:

[1981] The server accesses the mentor's schedule database and compares it with the user's desired date and time. The software used is a calendar API (e.g., Google Calendar API). The input is the user's and mentor's desired date and time data, and the output is the adjusted schedule data. Specifically, the server compares the schedules of both parties and determines the optimal date and time.

[1982] Step 11:

[1983] The server sends the adjusted schedule to the terminal and notifies the user. It also notifies the mentor's terminal of the schedule. The input is the adjusted schedule data, and the output is a schedule notification that is displayed on the user's and mentor's terminals. Specifically, the server sends the schedule data to each terminal, and the terminal displays the contents.

[1984] Step 12:

[1985] The mentor visits the user's home at the notified date and time and provides the requested support. The input is the schedule notification received from the server before the visit, and the output is the specific support activities at the visit location. The specific operation is for the mentor to visit the user's home at the specified date and time and provide support such as cooking assistance.

[1986] These steps will enable us to solve household problems and provide efficient support. Furthermore, the following example prompts will enable the generative AI model to generate effective advice.

[1987] Prompt Sentence Examples

[1988] Prompt: "I'm busy at work and don't have time to make a quick and healthy dinner. Generate five healthy dinner recipes that can be made in 30 minutes or less."

[1989] 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.

[1990] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[1991] 1. Enter your questions and concerns

[1992] User

[1993] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[1994] Terminal

[1995] The terminal receives input from the user and transmits the information to the server.

[1996] 2. Emotion Recognition by Emotion Engine

[1997] server

[1998] The server sends the received user input to the emotion engine for analysis. The emotion engine analyzes the text and, if necessary, recognizes the user's face and voice tone to determine their emotion. For example, if the user is feeling stressed, the emotion is recognized as "stress."

[1999] 3. Initial advice provided by AI

[2000] server

[2001] The server provides the emotion analysis results obtained from the emotion engine to the generative AI, which then generates advice that takes the user's emotional state into account. For example, if the user is feeling stressed, advice on relaxation methods and time management will be emphasized.

[2002] Terminal

[2003] The server sends the generated advice to the device and displays it to the user. For example, it might say, "It seems you're feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively."

[2004] 4. Matching potential mentors

[2005] server

[2006] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The server then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, if advice on stress reduction is needed, mentors with expertise in stress management will be prioritized.

[2007] Terminal

[2008] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, former teacher" or "Mentor B, former childcare teacher" may be displayed.

[2009] 5. Selecting the best mentor

[2010] User

[2011] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former teacher."

[2012] Terminal

[2013] The terminal transmits the user's selection to the server.

[2014] 6. Schedule Adjustment

[2015] server

[2016] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[2017] Terminal

[2018] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[2019] 7. On-site support

[2020] Mentor

[2021] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[2022] Specific system flow example

[2023] Input Phase

[2024] The user inputs their concerns into the device, such as "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device then sends this information to the server.

[2025] Emotion Recognition Phase

[2026] The server sends the input to the emotion engine, which recognizes the user's emotion as "stress."

[2027] Advice Generation Phase

[2028] The server's generative AI generates advice based on the results of emotion analysis, suggesting ways for users to relax and how to manage their time for housework and work.

[2029] Mentor Matching Phase

[2030] The server searches a database of nearby mentors based on the results of the emotion analysis and the question. It selects two candidates: "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D." The server then sends the candidate list to the user's device and presents it to them.

[2031] Mentor selection phase

[2032] The user selects "Former Housekeeper C Mentor." The device sends the selection information to the server.

[2033] Schedule adjustment phase

[2034] The server checks the schedules of the user and C-mentor and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor.

[2035] Implementation Phase

[2036] C Mentors visit users' homes every Tuesday at 10:00 to provide cleaning assistance.

[2037] In this way, by combining the emotion engine, the system provides personalized advice and assistance that takes into account the user's emotional state, allowing for more efficient resolution of household problems.

[2038] The processing flow will be explained below.

[2039] Step 1:

[2040] User

[2041] Users input their household concerns and questions into the terminal. For example, they can input a concern such as, "We're both working and busy, so we don't have time to help our children with their homework."

[2042] Step 2:

[2043] Terminal

[2044] The terminal receives input from the user and transmits the information to the server.

[2045] Step 3:

[2046] server

[2047] The server sends the received questions and concerns to the emotion engine, which analyzes the input text and identifies the user's emotions, such as stress, fatigue, and anxiety.

[2048] Step 4:

[2049] Emotion Engine

[2050] The emotion engine sends the analysis results back to the server. For example, it is analyzed that the user is feeling stressed.

[2051] Step 5:

[2052] server

[2053] The server queries the generative AI based on the emotion analysis results and generates advice. Specific advice is generated that takes into account the user's emotional state. For example, if the user is feeling stressed, advice on "how to relax" and "how to manage time" will be emphasized.

[2054] Step 6:

[2055] Terminal

[2056] The server sends the generated advice to the terminal and displays it to the user. For example, advice such as "It seems you are feeling stressed, so try some relaxation techniques. Also, here are some time management techniques to help you do your homework more effectively" may be displayed.

[2057] Step 7:

[2058] server

[2059] The server searches a database of nearby senior mentors based on the question, advice, and sentiment analysis results. It then selects the most suitable mentor, taking into account the mentor's skills and ratings. For example, it prioritizes mentors with stress reduction skills.

[2060] Step 8:

[2061] Terminal

[2062] The server sends the selected mentor candidates to the terminal and displays them to the user. For example, "Mentor A, who is good at stress management" or "Mentor B, who is good at education" are displayed.

[2063] Step 9:

[2064] User

[2065] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, who is skilled in stress management."

[2066] Step 10:

[2067] Terminal

[2068] The terminal transmits the user's selection to the server.

[2069] Step 11:

[2070] server

[2071] The server accesses the mentor's schedule database and compares it with the user's desired date and time. It then adjusts the optimal schedule for both parties and generates a confirmed schedule. For example, "Every Wednesday from 5:00 PM to 6:00 PM is confirmed as the optimal schedule."

[2072] Step 12:

[2073] Terminal

[2074] The server sends the adjusted schedule to the terminal and notifies the user, and also notifies the mentor's terminal of the schedule.

[2075] Step 13:

[2076] Mentor

[2077] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[2078] Example 2

[2079] 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."

[2080] There is a need for a system that can quickly provide users with appropriate and personalized advice for various worries and problems at home. Solving problems also requires physical support personnel who can provide specialized knowledge and assistance, but current systems have difficulty effectively matching these people and adjusting their schedules. This can lead to stress for users and prolong the problems.

[2081] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2082] In this invention, the server includes means for inputting questions and concerns about the home, means for a generative AI to provide advice in response to the questions and concerns, means for performing an emotional analysis of the questions and concerns using an emotion engine that recognizes the user's emotions, means for presenting advice generated based on the emotion analysis results, means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, means for coordinating schedules for both parties, and means for arranging on-site support by the senior mentor. This allows the user to receive personalized advice based on their emotions while also quickly arranging physical support, making it possible to efficiently solve home problems.

[2083] The "means for inputting questions and concerns about the home" is a means for users to input problems and annoyances about housework, childcare, nursing care, and other home-related issues as text data.

[2084] "Generative AI" is an artificial intelligence system that uses natural language processing and machine learning algorithms to generate optimal advice and solutions based on input data.

[2085] An "emotion engine" is software or hardware that analyzes text data, voice data, facial image data, etc., to identify the user's emotional state.

[2086] "Sentiment analysis" is the process of extracting emotional states from user input data and tagging and scoring them.

[2087] "Means for presenting advice" refers to the means for displaying solutions and advice generated by generative AI to the user.

[2088] A "senior mentor" refers to an elderly person who has knowledge and experience that is useful in resolving family problems and concerns.

[2089] The "database" is a digital storage system that systematically stores information such as senior mentor skills and user ratings, and allows for search and reference.

[2090] The "matching method" is the process of selecting the most suitable senior mentor based on the user's problem, questions, and sentiment analysis results, and then introducing the two parties together.

[2091] The "means for adjusting schedules" is a process for adjusting the available time of both the user and the senior mentor and setting an appropriate date and time for support.

[2092] The "means for arranging on-site support" is a means for arranging for a selected senior mentor to visit the user's home and actually provide support.

[2093] This invention is a system that not only uses generative AI to provide accurate advice in response to questions and concerns about the home, but also combines it with an emotion engine that recognizes the user's emotions to provide more personalized support. It also matches users with nearby senior mentors and provides on-site support. The operation of this system is explained in detail below.

[2094] Enter your questions and concerns

[2095] Users input their household concerns and questions into a terminal. The terminal can be a digital device such as a smartphone, tablet, or PC. The user might input a concern such as, "We're both working and busy, so we don't have time to help my child with their homework." The terminal then sends this information to the server.

[2096] Emotion recognition by emotion engine

[2097] The server sends the received user input to the emotion engine for analysis. The emotion engine combines text analysis, facial recognition, and voice tone analysis to analyze the user's emotions. For example, a Python-based natural language processing library is used for text analysis, OpenCV for facial recognition, and a voice tone analysis program is typically used for voice analysis. If the user is feeling stressed, the emotion is recognized as "stress."

[2098] AI-powered initial advice

[2099] The server provides the emotion analysis results obtained from the emotion engine to a generative AI model. For example, a natural language processing model such as OpenAI's GPT-3 can be used. The generative AI takes into account the user's emotional state and generates appropriate advice. For example, if the user is feeling stressed, it will emphasize "ways to relax" and "time management advice." The generated advice is sent from the server to the device and displayed to the user. For example, advice such as "It seems you are feeling stressed, so I'd like to recommend some ways to relax after work. I'll also teach you time management techniques to help you do your homework more effectively" may be presented.

[2100] Matching mentor candidates

[2101] The server searches a database of nearby senior mentors based on the content of the question, the advice, and the results of sentiment analysis. The database includes the skills of senior mentors and user ratings, and the most suitable mentor is extracted. For example, if advice on stress reduction is needed, a mentor who is skilled in stress management will be given priority. This information is sent from the server to the terminal, and the user is presented with candidates such as "Mentor A, a former teacher" and "Mentor B, a former childcare teacher."

[2102] Selection of the best mentor

[2103] The user selects the mentor they wish to request from the presented mentor candidates. For example, the user may select "Mentor A, a former faculty member." The selection information is sent from the terminal to the server.

[2104] Schedule adjustment

[2105] The server accesses the selected mentor's schedule database and compares it with the user's desired date and time. It adjusts the date and time to suit both parties and finalizes the adjusted schedule. For example, a schedule such as "every Wednesday from 5:00 PM to 6:00 PM" is finalized. This information is sent from the server to the terminal and notified to the user and mentor.

[2106] On-site support

[2107] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "Mentor A will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[2108] Specific examples

[2109] The user inputs their concern on their device, saying, "It's difficult to balance housework and work. Cleaning in particular keeps getting put off." The device sends this information to the server. The server sends the input to an emotion engine, which recognizes the user's emotion as "stress." A generative AI generates advice based on the emotion analysis results, suggesting ways for the user to relax and manage their time for housework and work. The server searches a database of nearby mentors based on the emotion analysis results and the question, and selects "Former Housekeeper Mentor C" and "Former Housekeeper Mentor D" as candidates. The server sends the candidate list to the device and presents it. The user selects "Former Housekeeper Mentor C." The device sends the selection information to the server. The server checks the user's and Mentor C's schedules and suggests "every Tuesday at 10:00." The device presents the adjusted schedule to the user and notifies the mentor. Mentor C will visit the user's home every Tuesday at 10:00 to provide cleaning assistance.

[2110] In this way, the combination of an emotion engine and generative AI can provide personalized advice and assistance that takes into account the user's emotional state, making it possible to solve household problems more efficiently.

[2111] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2112] Step 1:

[2113] Handling User Input

[2114] The user uses the device to input questions or concerns about their household. For example, they might input, "We're both working and busy, so we don't have time to help our child with their homework." The device receives this information as text data and converts it into JSON format. The input data looks like this:

[2115] Input data:

[2116] "Problem": "We both work and are busy, so we don't have time to help our children with their homework."

[2117] The terminal sends this data to the server as an HTTP request.

[2118] Step 2:

[2119] Sentiment analysis processing

[2120] The server sends the received text data to the emotion engine, which performs text analysis, facial recognition, and speech tone analysis to analyze the user's emotional state, using libraries such as the Natural Language Toolkit (NLTK), OpenCV, and speech analysis.

[2121] Input text:

[2122] "We both work and are busy, so we don't have time to help our children with their homework."

[2123] The sentiment engine parses this text and produces output like this:

[2124] Output data:

[2125] "Emotion": "Stress"

[2126] This output data is returned to the server.

[2127] Step 3:

[2128] AI-powered initial advice

[2129] The server sends the sentiment analysis results as prompts to a generative AI model, for example, OpenAI's GPT-3.

[2130] Input prompt:

[2131] "Users are stressed and need relaxation and time management advice."

[2132] The generative AI model generates advice based on the prompts, producing an output similar to the following:

[2133] Output advice:

[2134] "Try yoga or meditation as a way to relax, and create a to-do list to help you manage your time."

[2135] The server transmits this generated advice to the terminal.

[2136] Step 4:

[2137] Advice display and mentor matching

[2138] The terminal will display the advice received from the server to the user, for example, in the following format:

[2139] "It sounds like you're stressed, so I'd like to recommend some yoga and meditation techniques. I'd also recommend creating a to-do list and managing your time."

[2140] At the same time, the server searches a database of senior mentors living nearby and selects the most suitable mentor.

[2141] Input data:

[2142] "Question content", "Advice content", "Emotion analysis results"

[2143] The server generates a list of mentor candidates as follows and sends it to the device:

[2144] Output data:

[2145] "Mentor A, a former teacher" and "Mentor B, a former childcare teacher"

[2146] The terminal also displays this mentor candidate to the user.

[2147] Step 5:

[2148] Selection of the best mentor

[2149] The user selects from the presented mentor candidates. For example, the user selects "Mentor A, a former teacher."

[2150] The terminal transmits this selection to the server.

[2151] Input data:

[2152] "Mentor A, a former teacher"

[2153] Step 6:

[2154] Schedule adjustment

[2155] The server accesses the schedule database of the user and the selected mentor and adjusts the available time for both parties. For example, it determines that "every Wednesday from 5:00 PM to 6:00 PM" is optimal.

[2156] Input data:

[2157] "Desired date and time", "Mentor schedule"

[2158] The server determines the adjusted schedule as follows and sends it to the device:

[2159] Output data:

[2160] Every Wednesday from 5:00 PM to 6:00 PM

[2161] The terminal displays this schedule to the user and also notifies the mentor.

[2162] Step 7:

[2163] On-site support

[2164] The mentor will visit the user's home at the notified date and time and provide the requested support. For example, "I will visit the user's home every Wednesday at 5:00 PM to help the child with their homework."

[2165] Specifically, the mentor visits the user's home at a specified time and provides support according to the previously agreed upon details.

[2166] (Application example 2)

[2167] 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."

[2168] Conventional home support systems often provide general advice and support without considering the user's emotional state, resulting in a lack of personalized support tailored to individual needs. Furthermore, while there is a demand for services that take emotional states into account in other lifestyle services, including food delivery, this has not yet been realized. Therefore, a system that can provide appropriate advice and services by empathizing with the user's emotions is needed.

[2169] 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.

[2170] In this invention, the server includes a means for inputting questions and concerns about the home, a means for a generative AI to provide advice in response to the questions and concerns, an emotion analysis means for analyzing the user's emotions, a means for the generative AI to provide personalized advice based on the user's emotional state, a means for matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby, a means for coordinating schedules for both parties, and a means for arranging on-site support by the senior mentor. This allows the server to provide personalized advice and support taking into account the user's emotional state, thereby improving satisfaction with home concerns and food delivery.

[2171] "Means for inputting questions and concerns about the home" refers to an interface that allows users to input problems and questions that arise within the home in the form of text or voice.

[2172] "Means for generative AI to provide advice" refers to an artificial intelligence system that uses natural language processing to generate appropriate solutions and advice in response to questions or concerns entered by users.

[2173] "Emotion analysis means" is a technology that analyzes the emotional state from text or voice input by the user and identifies emotions such as stress, fatigue, and joy.

[2174] The "means for providing personalized advice" is an artificial intelligence system that generates optimal solutions and advice based on the emotional state of each individual user, based on the results of emotion analysis.

[2175] The "database including the skills and user ratings of senior mentors living nearby" is a database that accumulates senior mentors' specialized knowledge, service experience, and user ratings.

[2176] "Means for matching the most suitable mentor" refers to algorithms and systems for selecting the most suitable senior mentor based on the user's questions, concerns, and emotional analysis results.

[2177] The "means of adjusting schedules" is a system that compares the schedules of both the user and the mentor and arranges a date and time that is convenient for both parties.

[2178] The "means for arranging on-site support" is a system that coordinates schedules and arranges visits by selected mentors to the user's home to provide the necessary support.

[2179] This invention is a system that uses generative AI to provide personalized advice in response to questions and concerns about the home. The system uses emotion analysis to recognize the user's emotions and provide optimal advice, as well as matching with nearby senior mentors and providing on-site support.

[2180] Specific system configuration

[2181] 1. Enter your questions and concerns

[2182] User

[2183] Users input questions or concerns about their home into a smartphone application. At this stage, users often write sentences that touch on their emotions. They input their content in response to prompts such as, "How are you feeling today?"

[2184] Example input

[2185] I'm very tired today and I don't know what to eat.

[2186] Terminal

[2187] The terminal receives input from the user and transmits the information to the server.

[2188] 2. Emotion analysis

[2189] server

[2190] The server analyzes the received user input using a sentiment analysis engine, which analyzes the text to identify emotions such as stress, fatigue, and joy.

[2191] Specific examples

[2192] The input "I'm very tired today and I don't know what to eat" is recognized as "fatigue."

[2193] 3. Generating Advice

[2194] server

[2195] The server uses generative AI to provide personalized advice based on the results of sentiment analysis. For example, if the user is tired, it will suggest healthy and easy-to-eat meals.

[2196] Specific examples

[2197] "You seem tired, so I recommend some relaxing herbal tea and a nutritious salad."

[2198] Terminal

[2199] The server transmits the generated advice to the terminal and displays it to the user.

[2200] 4. Mentor matching

[2201] server

[2202] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis to identify the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[2203] Specific examples

[2204] A "nutritionist mentor" who is knowledgeable about fatigue recovery will be selected and presented to the user.

[2205] Terminal

[2206] The user is prompted to select a mentor from the displayed candidates.

[2207] 5. Selecting the best mentor and adjusting the schedule

[2208] User

[2209] The user selects the mentor they wish to request from the presented mentor candidates. For example, they can select "Mentor A, a nutritionist."

[2210] Terminal

[2211] The terminal transmits the user's selection to the server.

[2212] server

[2213] The server checks the mentor's schedule, adjusts it to the prepared date and time, and notifies the user and mentor of the adjusted schedule.

[2214] 6. On-site support

[2215] Mentor

[2216] The mentor will visit the user's home at the notified date and time and provide the requested support.

[2217] Hardware and software used

[2218] Smartphone: User Interface

[2219] Sentiment analysis engine: Emotion-Analysis-Service

[2220] Generative AI: Generated-Advice-Service using natural language processing

[2221] Food recommendation engine: Food-Recommendation-Service

[2222] Prompt Sentence Examples

[2223] "What dish would you recommend for someone who is feeling tired these days?"

[2224] This makes it possible to provide personalized advice that reflects the user's emotional state, improving satisfaction with household concerns and food delivery.

[2225] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2226] Step 1:

[2227] The user inputs questions and concerns about the home.

[2228] An example of input would be "I'm very tired today and I don't know what to eat."

[2229] This input data is sent to the terminal in text format.

[2230] Step 2:

[2231] The terminal receives input data from the user and sends the information to the server for analysis.

[2232] The input is text data, which is transferred to the server as is.

[2233] Step 3:

[2234] The server transmits the received text data to the emotion analysis means.

[2235] The emotion analysis means uses natural language processing and machine learning algorithms to analyze emotional states from text data.

[2236] Data processing includes part-of-speech analysis of text and sentiment analysis.

[2237] The output is emotion tags such as "fatigue" and "stress."

[2238] Step 4:

[2239] The server sends the emotion analysis results to the generative AI.

[2240] Here, appropriate advice is generated based on input data, which is the result of emotion analysis.

[2241] Generative AI takes into account the user's emotional state and generates optimal solutions based on specific prompts.

[2242] As a data calculation, specific advice is generated by referencing a knowledge base and past data.

[2243] The output is specific advice such as relaxation methods and dietary suggestions.

[2244] Step 5:

[2245] The server transmits the generated advice to the terminal.

[2246] The input data is text-based advice created by generative AI.

[2247] The device will then display this advice to the user, such as "We recommend a relaxing herbal tea and a nutritious salad."

[2248] Step 6:

[2249] The server searches a database of nearby senior mentors based on the content of the question and the results of sentiment analysis.

[2250] The input data is the emotion analysis results and the question content.

[2251] The server selects the most suitable mentor, taking into consideration the mentor's skills and evaluation.

[2252] As an output, a list of suitable mentors is generated.

[2253] Step 7:

[2254] The server transmits the selected mentor candidates to the terminal and displays them to the user.

[2255] The input data is a list of selected mentors, and the output shows the candidates to the user, who can select "Dietitian A Mentor."

[2256] Step 8:

[2257] The user selects the mentor he or she wishes to request from among the presented mentor candidates.

[2258] The input data is the mentor selected by the user.

[2259] The terminal transmits its selection to the server.

[2260] Step 9:

[2261] The server checks the mentor's schedule and adjusts it to the user's desired date and time.

[2262] The input data is the schedule information of the user and the mentor.

[2263] As an output, a firm schedule is produced.

[2264] Step 10:

[2265] The server transmits the adjusted schedule to the terminal and notifies the user.

[2266] Similarly, the mentor will be notified of the schedule.

[2267] Step 11:

[2268] Upon receiving the notification, the mentor visits the user's home at the confirmed date and time to provide the requested support.

[2269] A specific example of this would be "Mentor A, a nutritionist, visits and provides dietary advice."

[2270] 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.

[2271] 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.

[2272] 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.

[2273] 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.

[2274] 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.

[2275] 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.

[2276] 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).

[2277] 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.

[2278] 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."

[2279] 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 s...

Claims

1. A way to input questions and concerns about the home, A means for generative AI to provide advice on the questions and concerns mentioned above; A means of matching the most suitable mentor by referencing a database containing the skills and user ratings of senior mentors living nearby; A means of coordinating both parties' schedules; The system includes a means for arranging on-site support by the senior mentor.

2. The system according to claim 1, which matches senior mentors who specialize in at least one field of home-related education, childcare, nursing care, and housework.

3. The system of claim 1 , wherein the generative AI provides advice using natural language processing.

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