System

Abstract

An object of a system according to an embodiment is to centrally manage a variety of problems in marriage and provide appropriate advice.SOLUTION: A system according to an embodiment includes a financial planning unit, a house selection unit, a family relation unit, a lifestyle adjustment unit, a child raising unit, and a communication unit. The Financial Planner automates the management of income, expenses, and savings. The dwelling selection part recommends an optimum property on the basis of a desired condition. The family relation unit performs schedule sharing and event planning between family members. The lifestyle adjusting unit adjusts a schedule of a meal plan, health management, and housework sharing. The child care unit provides information and advice related to pregnancy, childbirth, and child care. The communication unit supports communication between husband and wife.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] Conventional techniques have had the problem of making it difficult to centrally manage the wide range of issues that arise in married life and provide appropriate advice.

[0005] The system according to the embodiment aims to centrally manage a wide range of issues in married life and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment comprises a financial planning unit, a residence selection unit, a family relationship unit, a lifestyle adjustment unit, a childcare unit, and a communication unit. The financial planning unit automates the management of income, expenses, and savings. The residence selection unit recommends optimal properties based on desired conditions. The family relationship unit shares schedules and plans events among family members. The lifestyle adjustment unit adjusts schedules for meal planning, health management, and household chores. The childcare unit provides information and advice on pregnancy, childbirth, and childcare. The communication unit supports communication between spouses. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage a wide range of issues in married life and provide appropriate advice. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The marital life support system according to the embodiment of the present invention is a system that uses a generative AI to provide planning and advice tailored to the parties involved in order to alleviate anxiety about unknown issues in married life. As a result, the marital life support system can alleviate anxiety about unknown issues in married life and support a smooth married life.

[0029] A marriage support system according to an embodiment includes a financial planning unit, a residence selection unit, a family relationship unit, a lifestyle adjustment unit, a childcare unit, and a communication unit. The financial planning unit automates management of income, expenses, and savings. For example, the generation AI analyzes a user's income and expense data and assists in estimating wedding expenses and setting long-term savings goals. For example, when a user inputs a prompt such as, "Please enter your monthly income and expenses," the generation AI proposes an optimal financial plan based on the data. The residence selection unit recommends optimal properties based on desired conditions. For example, the generation AI analyzes a user's desired conditions and recommends properties that meet the user's requirements when the user inputs a prompt such as, "Please find a property within a certain price range and within a 10-minute walk from the station." The family relationship unit shares schedules and plans events among family members. For example, the generation AI analyzes data related to family schedules and values, and proposes an optimal plan when the user inputs a prompt such as, "Please share everyone's schedules and plan the next family event." The lifestyle adjustment unit adjusts schedules for meals, health management, and household chores. For example, the generation AI analyzes data related to a user's lifestyle habits and suggests an optimal meal plan when a user inputs a prompt such as "Make a daily meal plan." The childcare unit provides information and advice related to pregnancy, childbirth, and childcare. For example, the generation AI analyzes data related to pregnancy, childbirth, and childcare and suggests optimal advice when a user inputs a prompt such as "Please give me some advice on health management during pregnancy." The communication unit supports communication between couples. For example, the generation AI analyzes data related to communication between couples and suggests optimal methods when a user inputs a prompt such as "Please tell me how to improve communication between couples." As a result, the marriage support system according to the embodiment can eliminate anxiety about unknown challenges in married life and support a smooth married life. For example, financial planning can reduce financial anxiety, and efficient housing selection can save time. Furthermore, smoother relationships with family members and smoother lifestyle adjustments can improve the quality of married life.Furthermore, by providing information and advice on whether or not couples have children and on child-rearing, anxiety about child-rearing can be alleviated, and by supporting communication between couples, mutual understanding can be deepened.

[0030] The financial planning unit analyzes the user's past consumption patterns and predicts expenditures, allowing it to propose more accurate financial plans. For example, the generation AI analyzes the user's consumption patterns over the past year and identifies monthly spending trends. For example, it classifies expenditures by category, such as food, transportation, and entertainment, and predicts future expenditures. This allows it to propose more accurate financial plans by analyzing the user's past consumption patterns and predicting future expenditures.

[0031] The financial planning unit can predict the user's life events and provide a long-term financial plan based on them. For example, the generative AI predicts future life events based on data such as the user's age, occupation, and family structure, and proposes a long-term financial plan based on them. For example, it predicts expenses associated with marriage and childbirth and sets savings goals. This makes it possible to predict the user's life events and provide a long-term financial plan based on them.

[0032] The residence selection unit can propose the optimal residential area by taking into consideration the user's lifestyle and commuting time. For example, the generation AI analyzes the user's lifestyle (e.g., hobbies and daily activities) and proposes the optimal residential area based on that. For example, for a user who likes the outdoors, it proposes an area with a rich natural environment. This makes it possible to propose the optimal residential area by taking into consideration the user's lifestyle and commuting time.

[0033] The housing selection unit can analyze past real estate market data and recommend properties that are expected to increase in value in the future. For example, the generation AI analyzes past real estate market data and identifies areas where future increases in asset value are expected. For example, it recommends areas where asset value is expected to increase based on information on urban development plans and infrastructure development. This makes it possible to recommend properties that are expected to increase in value in the future.

[0034] The family relationship unit can analyze communication history between family members and provide specific advice to improve relationships. For example, the generative AI analyzes messages and conversation history between family members to understand communication patterns. For example, it can identify the causes of frequent misunderstandings and conflicts and provide advice based on that. This makes it possible to analyze communication history between family members and provide specific advice to improve relationships.

[0035] The family relationship unit can deepen family bonds by analyzing family values ​​and interests and suggesting common hobbies and activities. For example, the generative AI can analyze family values ​​and interests and suggest common hobbies and activities. For example, it can suggest sports and leisure activities that the whole family can enjoy. This allows family bonds to be deepened by analyzing family values ​​and interests and suggesting common hobbies and activities.

[0036] The lifestyle adjustment unit can analyze the user's dietary history and propose a nutritionally balanced meal plan. For example, the generation AI analyzes the user's past dietary history and proposes a nutritionally balanced meal plan. For example, it proposes a meal plan that supplements necessary nutrients based on the dietary content of the past week. This allows the user's dietary history to be analyzed and a nutritionally balanced meal plan to be proposed.

[0037] The lifestyle adjustment unit can analyze the user's exercise history and provide an optimal exercise plan. For example, the generation AI analyzes the user's past exercise history and provides an optimal exercise plan. For example, it proposes an effective exercise plan based on the exercise content of the past month. This allows the user's exercise history to be analyzed and an optimal exercise plan to be provided.

[0038] The childcare unit can analyze the user's health data and provide an optimal health management plan during pregnancy. For example, the generation AI analyzes the user's health data and provides an optimal health management plan during pregnancy. For example, it can suggest a nutritionally balanced meal plan and an appropriate exercise plan. This allows the user's health data to be analyzed and an optimal health management plan during pregnancy to be provided.

[0039] The communication section analyzes the communication history between couples and can provide specific advice to avoid misunderstandings. For example, the generative AI analyzes messages and conversation history between couples to understand communication patterns. For example, it identifies the causes of frequent misunderstandings and conflicts and provides advice based on that. This allows the communication history between couples to be analyzed and specific advice to avoid misunderstandings to be provided.

[0040] The communication section can facilitate communication by analyzing the values ​​and interests of a couple and suggesting common topics and activities. For example, the generative AI can analyze the values ​​and interests of a couple and suggest common topics and activities. For example, it can suggest event or travel plans based on common hobbies and interests. This allows the communication section to facilitate communication by analyzing the values ​​and interests of a couple and suggesting common topics and activities.

[0041] The communication unit can analyze the couple's health data, set common health goals, and promote communication. For example, the generative AI can analyze the couple's health data and set common health goals. For example, it can suggest common diet goals and exercise plans. This allows the couple's health data to be analyzed, set common health goals, and promote communication.

[0042] The communication department can optimize the couple's schedules and make suggestions to increase the amount of time they spend together. For example, the generative AI analyzes the couple's schedules and makes suggestions to increase the amount of time they spend together. For example, it can suggest event or travel plans that take into account common holidays and vacations. This allows the communication department to optimize the couple's schedules and make suggestions to increase the amount of time they spend together.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The marriage support system also includes a health management unit. The health management unit can analyze the user's health data and provide advice for maintaining health in married life. For example, the generative AI can analyze the user's diet, exercise, and sleep data and suggest healthy lifestyle habits. Specifically, it can suggest nutritionally balanced recipes based on the user's daily diet, or detect lack of exercise and provide an appropriate exercise plan. It can also analyze sleep data and provide advice on how to get quality sleep. This allows users to maintain a healthy lifestyle and make their married life more fulfilling.

[0045] The marriage support system also includes a hobby suggestion unit. The hobby suggestion unit analyzes the user's interests and past activity data to suggest new hobbies and activities. For example, the generation AI analyzes the user's past hobbies and activity history and introduces communities and events with common hobbies. Specifically, for a user who enjoys outdoor activities, it can suggest nearby hiking groups and camping events. Furthermore, for a user who is interested in art and music, it can provide information on local art classes and concerts. This allows users to discover new hobbies and enrich their married life.

[0046] The married life support system also includes a travel planning unit, which can propose optimal travel plans based on the user's preferences and budget. For example, the generation AI can analyze the user's past travel history and preferences to suggest optimal travel destinations and activities. Specifically, for a user who likes beach resorts, it can suggest resorts that can be enjoyed within a budget, and for a user who prefers cultural experiences, it can introduce historical tourist sites and museums. It can also support planning of schedules and activities during the trip. This allows users to enjoy their trip without stress and create new memories in their married life.

[0047] The marriage support system also includes a pet care unit. This unit can analyze information about the user's pet and provide an optimal care plan. For example, the generative AI can analyze the pet's health data and behavioral patterns to suggest an appropriate diet and exercise plan. Specifically, it can suggest the optimal amount of food based on the pet's age and weight, and detect lack of exercise and provide appropriate exercise methods. It can also monitor the pet's health and issue an early alert if an abnormality is detected. This allows users to maintain their pet's health and enjoy more fulfilling time with their pet during their married life.

[0048] The marriage support system also includes an education support unit. The education support unit can analyze information about the user's children's education and provide optimal education plans. For example, the generation AI can analyze the child's learning history and interests and suggest appropriate learning methods and learning materials. Specifically, it can identify the child's strong and weak subjects and provide an individually customized learning plan. It can also introduce appropriate extracurricular activities and club activities based on the child's interests. This allows users to effectively support their children's education and reduce their anxiety about education in their married life.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The financial planning section automates the management of income, expenses, and savings. For example, the generative AI analyzes the user's income and expense data to help them estimate wedding expenses and set long-term savings goals. For example, when the user enters a prompt such as "Please enter your monthly income and expenses," the generative AI will propose an optimal financial plan based on that data. Step 2: The housing selection unit recommends the optimal property based on the desired conditions. For example, the generation AI analyzes the user's desired conditions and, when prompted with a prompt such as "Please find a property within a certain price range, within a 10-minute walk from the station," will recommend properties that meet those conditions. Step 3: The Family Relationships Department shares schedules and plans events among family members. For example, the generative AI analyzes data on family schedules and values, and when prompted with a prompt such as "Share everyone's schedules and plan the next family event," it suggests the best plan. Step 4: The lifestyle adjustment unit adjusts the schedule for meal planning, health management, and household chores. For example, the generative AI analyzes data on the user's lifestyle habits and suggests an optimal meal plan when prompted with a prompt such as "Make a daily meal plan." Step 5: The childcare department provides information and advice on pregnancy, childbirth, and childcare. For example, the generative AI analyzes data on pregnancy, childbirth, and childcare, and when a user inputs a prompt such as "Please give me some advice on health management during pregnancy," it provides the most appropriate advice. Step 6: The communication section supports communication between couples. For example, the generative AI analyzes data on communication between couples and suggests optimal methods when given a prompt such as, "Please tell me how to improve communication between couples."

[0051] (Example 2) The marital life support system according to the embodiment of the present invention is a system that uses a generative AI to provide planning and advice tailored to the parties involved in order to alleviate anxiety about unknown issues in married life. As a result, the marital life support system can alleviate anxiety about unknown issues in married life and support a smooth married life.

[0052] A marriage support system according to an embodiment includes a financial planning unit, a residence selection unit, a family relationship unit, a lifestyle adjustment unit, a childcare unit, and a communication unit. The financial planning unit automates management of income, expenses, and savings. For example, the generation AI analyzes a user's income and expense data and assists in estimating wedding expenses and setting long-term savings goals. For example, when a user inputs a prompt such as, "Please enter your monthly income and expenses," the generation AI proposes an optimal financial plan based on the data. The residence selection unit recommends optimal properties based on desired conditions. For example, the generation AI analyzes a user's desired conditions and recommends properties that meet the user's requirements when the user inputs a prompt such as, "Please find a property within a certain price range and within a 10-minute walk from the station." The family relationship unit shares schedules and plans events among family members. For example, the generation AI analyzes data related to family schedules and values, and proposes an optimal plan when the user inputs a prompt such as, "Please share everyone's schedules and plan the next family event." The lifestyle adjustment unit adjusts schedules for meals, health management, and household chores. For example, the generation AI analyzes data related to a user's lifestyle habits and suggests an optimal meal plan when a user inputs a prompt such as "Make a daily meal plan." The childcare unit provides information and advice related to pregnancy, childbirth, and childcare. For example, the generation AI analyzes data related to pregnancy, childbirth, and childcare and suggests optimal advice when a user inputs a prompt such as "Please give me some advice on health management during pregnancy." The communication unit supports communication between couples. For example, the generation AI analyzes data related to communication between couples and suggests optimal methods when a user inputs a prompt such as "Please tell me how to improve communication between couples." As a result, the marriage support system according to the embodiment can eliminate anxiety about unknown challenges in married life and support a smooth married life. For example, financial planning can reduce financial anxiety, and efficient housing selection can save time. Furthermore, smoother relationships with family members and smoother lifestyle adjustments can improve the quality of married life.Furthermore, by providing information and advice on whether or not couples have children and on child-rearing, anxiety about child-rearing can be alleviated, and by supporting communication between couples, mutual understanding can be deepened.

[0053] The financial planning unit analyzes the user's past consumption patterns and predicts expenditures, allowing it to propose more accurate financial plans. For example, the generation AI analyzes the user's consumption patterns over the past year and identifies monthly spending trends. For example, it classifies expenditures by category, such as food, transportation, and entertainment, and predicts future expenditures. This allows it to propose more accurate financial plans by analyzing the user's past consumption patterns and predicting future expenditures.

[0054] The financial planning unit can predict the user's life events and provide a long-term financial plan based on them. For example, the generative AI predicts future life events based on data such as the user's age, occupation, and family structure, and proposes a long-term financial plan based on them. For example, it predicts expenses associated with marriage and childbirth and sets savings goals. This makes it possible to predict the user's life events and provide a long-term financial plan based on them.

[0055] The financial planning unit can use the emotion estimation function to provide advice to reduce the user's financial anxiety and stress. For example, the financial planning unit can use the emotion estimation function to monitor in real time the anxiety and stress the user feels about finances and provide advice based on that data. For example, the financial planning unit can suggest relaxation methods when stress levels rise. This allows the financial planning unit to provide advice to reduce the user's financial anxiety and stress.

[0056] The residence selection unit can propose the optimal residential area by taking into consideration the user's lifestyle and commuting time. For example, the generation AI analyzes the user's lifestyle (e.g., hobbies and daily activities) and proposes the optimal residential area based on that. For example, for a user who likes the outdoors, it proposes an area with a rich natural environment. This makes it possible to propose the optimal residential area by taking into consideration the user's lifestyle and commuting time.

[0057] The housing selection unit can analyze past real estate market data and recommend properties that are expected to increase in value in the future. For example, the generation AI analyzes past real estate market data and identifies areas where future increases in asset value are expected. For example, it recommends areas where asset value is expected to increase based on information on urban development plans and infrastructure development. This makes it possible to recommend properties that are expected to increase in value in the future.

[0058] The family relationship unit can analyze communication history between family members and provide specific advice to improve relationships. For example, the generative AI analyzes messages and conversation history between family members to understand communication patterns. For example, it can identify the causes of frequent misunderstandings and conflicts and provide advice based on that. This makes it possible to analyze communication history between family members and provide specific advice to improve relationships.

[0059] The family relationship unit can deepen family bonds by analyzing family values ​​and interests and suggesting common hobbies and activities. For example, the generative AI can analyze family values ​​and interests and suggest common hobbies and activities. For example, it can suggest sports and leisure activities that the whole family can enjoy. This allows family bonds to be deepened by analyzing family values ​​and interests and suggesting common hobbies and activities.

[0060] The family relationship unit can use the emotion estimation function to monitor changes in emotions between family members in real time and provide advice at the appropriate time. The family relationship unit can, for example, use the emotion estimation function to monitor changes in emotions between family members in real time and provide advice based on that data. For example, it can suggest relaxation methods when emotions become heightened. This makes it possible to monitor changes in emotions between family members in real time and provide advice at the appropriate time.

[0061] The lifestyle adjustment unit can analyze the user's dietary history and propose a nutritionally balanced meal plan. For example, the generation AI analyzes the user's past dietary history and proposes a nutritionally balanced meal plan. For example, it proposes a meal plan that supplements necessary nutrients based on the dietary content of the past week. This allows the user's dietary history to be analyzed and a nutritionally balanced meal plan to be proposed.

[0062] The lifestyle adjustment unit can analyze the user's exercise history and provide an optimal exercise plan. For example, the generation AI analyzes the user's past exercise history and provides an optimal exercise plan. For example, it proposes an effective exercise plan based on the exercise content of the past month. This allows the user's exercise history to be analyzed and an optimal exercise plan to be provided.

[0063] The lifestyle adjustment unit can monitor the user's stress level using the emotion estimation function and suggest relaxation methods. For example, the lifestyle adjustment unit can monitor the user's stress level in real time using the emotion estimation function and suggest relaxation methods based on that data. For example, it can suggest meditation or deep breathing when stress levels rise. In this way, the lifestyle adjustment unit can monitor the user's stress level and suggest relaxation methods.

[0064] The childcare unit can analyze the user's health data and provide an optimal health management plan during pregnancy. For example, the generation AI analyzes the user's health data and provides an optimal health management plan during pregnancy. For example, it can suggest a nutritionally balanced meal plan and an appropriate exercise plan. This allows the user's health data to be analyzed and an optimal health management plan during pregnancy to be provided.

[0065] The childcare department can use the emotion estimation function to provide counseling to reduce anxiety about childcare. For example, the childcare department can use the emotion estimation function to monitor anxiety about childcare in real time and provide counseling based on that data. For example, when anxiety increases, the department can suggest relaxation methods. This makes it possible to provide counseling to reduce anxiety about childcare.

[0066] The communication section analyzes the communication history between couples and can provide specific advice to avoid misunderstandings. For example, the generative AI analyzes messages and conversation history between couples to understand communication patterns. For example, it identifies the causes of frequent misunderstandings and conflicts and provides advice based on that. This allows the communication history between couples to be analyzed and specific advice to avoid misunderstandings to be provided.

[0067] The communication section can facilitate communication by analyzing the values ​​and interests of a couple and suggesting common topics and activities. For example, the generative AI can analyze the values ​​and interests of a couple and suggest common topics and activities. For example, it can suggest event or travel plans based on common hobbies and interests. This allows the communication section to facilitate communication by analyzing the values ​​and interests of a couple and suggesting common topics and activities.

[0068] The communication unit can use the emotion estimation function to monitor emotional changes between a couple in real time and provide advice at the appropriate time. The communication unit can, for example, use the emotion estimation function to monitor emotional changes between a couple in real time and provide advice based on that data. For example, it can suggest relaxation methods when emotions become heightened. This makes it possible to monitor emotional changes between a couple in real time and provide advice at the appropriate time.

[0069] The communication unit can analyze the couple's health data, set common health goals, and promote communication. For example, the generative AI can analyze the couple's health data and set common health goals. For example, it can suggest common diet goals and exercise plans. This allows the couple's health data to be analyzed, set common health goals, and promote communication.

[0070] The communication department can optimize the couple's schedules and make suggestions to increase the amount of time they spend together. For example, the generative AI analyzes the couple's schedules and makes suggestions to increase the amount of time they spend together. For example, it can suggest event or travel plans that take into account common holidays and vacations. This allows the communication department to optimize the couple's schedules and make suggestions to increase the amount of time they spend together.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The marriage support system also includes a health management unit. The health management unit can analyze the user's health data and provide advice for maintaining health in married life. For example, the generative AI can analyze the user's diet, exercise, and sleep data and suggest healthy lifestyle habits. Specifically, it can suggest nutritionally balanced recipes based on the user's daily diet, or detect lack of exercise and provide an appropriate exercise plan. It can also analyze sleep data and provide advice on how to get quality sleep. This allows users to maintain a healthy lifestyle and make their married life more fulfilling.

[0073] The marriage support system also includes a hobby suggestion unit. The hobby suggestion unit analyzes the user's interests and past activity data to suggest new hobbies and activities. For example, the generation AI analyzes the user's past hobbies and activity history and introduces communities and events with common hobbies. Specifically, for a user who enjoys outdoor activities, it can suggest nearby hiking groups and camping events. Furthermore, for a user who is interested in art and music, it can provide information on local art classes and concerts. This allows users to discover new hobbies and enrich their married life.

[0074] The married life support system also includes a travel planning unit, which can propose optimal travel plans based on the user's preferences and budget. For example, the generation AI can analyze the user's past travel history and preferences to suggest optimal travel destinations and activities. Specifically, for a user who likes beach resorts, it can suggest resorts that can be enjoyed within a budget, and for a user who prefers cultural experiences, it can introduce historical tourist sites and museums. It can also support planning of schedules and activities during the trip. This allows users to enjoy their trip without stress and create new memories in their married life.

[0075] The marriage support system also includes a pet care unit. This unit can analyze information about the user's pet and provide an optimal care plan. For example, the generative AI can analyze the pet's health data and behavioral patterns to suggest an appropriate diet and exercise plan. Specifically, it can suggest the optimal amount of food based on the pet's age and weight, and detect lack of exercise and provide appropriate exercise methods. It can also monitor the pet's health and issue an early alert if an abnormality is detected. This allows users to maintain their pet's health and enjoy more fulfilling time with their pet during their married life.

[0076] The marriage support system also includes an education support unit. The education support unit can analyze information about the user's children's education and provide optimal education plans. For example, the generation AI can analyze the child's learning history and interests and suggest appropriate learning methods and learning materials. Specifically, it can identify the child's strong and weak subjects and provide an individually customized learning plan. It can also introduce appropriate extracurricular activities and club activities based on the child's interests. This allows users to effectively support their children's education and reduce their anxiety about education in their married life.

[0077] The marriage support system can also use emotion estimation to monitor the user's stress level and suggest appropriate relaxation methods. For example, the generative AI can analyze the user's emotional data and suggest relaxation methods when stress levels rise. Specifically, it can suggest meditation or deep breathing techniques, or recommend relaxing music or aromatherapy. It can also identify the causes of stress and suggest countermeasures. This allows users to effectively manage stress and enjoy a more comfortable married life.

[0078] The marriage support system can also use an emotion estimation function to monitor emotional changes between couples and suggest appropriate communication methods. For example, the generative AI can analyze a couple's emotional data and suggest appropriate communication methods when emotions become heightened. Specifically, it can suggest ways to calmly discuss things when emotions become heightened, as well as relaxation methods. It can also predict emotional changes and take measures in advance. This can smooth communication between couples and maintain a better marriage.

[0079] The marriage support system can also use emotion estimation functionality to monitor emotional changes between family members and provide advice at the appropriate time. For example, the generative AI can analyze family members' emotional data and suggest relaxation methods when emotions become heightened. Specifically, it can suggest meditation or deep breathing techniques, or recommend relaxing music or aromatherapy when emotions become heightened. It can also predict emotional changes and take measures in advance. This allows the system to monitor emotional changes between family members in real time and provide advice at the appropriate time.

[0080] The marriage support system can also use its emotion estimation function to provide counseling to reduce anxiety about child-rearing. For example, the generative AI can analyze emotional data about child-rearing and suggest relaxation methods when anxiety increases. Specifically, it can suggest meditation or deep breathing techniques, or introduce relaxing music or aromatherapy. It can also provide specific advice and support regarding child-rearing. This can reduce anxiety about child-rearing and make married life more comfortable.

[0081] The marriage support system can also use emotion estimation functions to monitor changes in the user's emotions and provide advice at the appropriate time. For example, the generative AI can analyze the user's emotional data and suggest relaxation methods when emotions become heightened. Specifically, it can suggest meditation or deep breathing techniques, or recommend relaxing music or aromatherapy. It can also predict changes in emotions and take measures in advance. This allows the system to monitor changes in the user's emotions in real time and provide advice at the appropriate time.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The financial planning section automates the management of income, expenses, and savings. For example, the generative AI analyzes the user's income and expense data to help them estimate wedding expenses and set long-term savings goals. For example, when the user enters a prompt such as "Please enter your monthly income and expenses," the generative AI will propose an optimal financial plan based on that data. Step 2: The housing selection unit recommends the optimal property based on the desired conditions. For example, the generation AI analyzes the user's desired conditions and, when prompted with a prompt such as "Please find a property within a certain price range, within a 10-minute walk from the station," will recommend properties that meet those conditions. Step 3: The Family Relationships Department shares schedules and plans events among family members. For example, the generative AI analyzes data on family schedules and values, and when prompted with a prompt such as "Share everyone's schedules and plan the next family event," it suggests the best plan. Step 4: The lifestyle adjustment unit adjusts the schedule for meal planning, health management, and household chores. For example, the generative AI analyzes data on the user's lifestyle habits and suggests an optimal meal plan when prompted with a prompt such as "Make a daily meal plan." Step 5: The childcare department provides information and advice on pregnancy, childbirth, and childcare. For example, the generative AI analyzes data on pregnancy, childbirth, and childcare, and when a user inputs a prompt such as "Please give me some advice on health management during pregnancy," it provides the most appropriate advice. Step 6: The communication section supports communication between couples. For example, the generative AI analyzes data on communication between couples and suggests optimal methods when given a prompt such as, "Please tell me how to improve communication between couples."

[0084] 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.

[0085] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0125] 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.

[0126] 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.

[0127] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] 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.

[0134] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0135] 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.

[0136] 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).

[0137] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A financial planning section that automates the management of income, expenses, and savings; A housing selection department that recommends the best properties based on desired conditions; The Family Relations Department, which shares schedules and plans events among family members, A lifestyle coordination department that coordinates meal plans, health management, and household chore sharing schedules; The childcare department provides information and advice on pregnancy, childbirth, and childcare, A communication section that supports communication between spouses. A system characterized by:

2. The financial planning department By analyzing users' past consumption patterns and making expenditure predictions, the system proposes more accurate financial plans.

2. The system of claim 1.

3. The residence selection unit Suggesting optimal residential areas based on the user's lifestyle and commute time 2. The system of claim 1.

4. The Family Relations Department: Analyzing the communication history between the family members and providing specific advice for improving the relationship 2. The system of claim 1.

5. The lifestyle adjustment unit Analyze the user's dietary history and propose a nutritionally balanced diet plan.

2. The system of claim 1.

6. The childcare department: Analyzes the user's health data and provides optimal health management plans during pregnancy 2. The system of claim 1.

7. The communication unit Analyzing the communication history between the couple and providing specific advice to avoid misunderstandings 2. The system of claim 1.

8. The financial planning department Using emotion estimation, the company provides users with advice to reduce financial anxiety and stress.

2. The system of claim 1.

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