System

Abstract

An object of the system according to the embodiment is to simulate and propose a specific life or economic plan after marriage in advance.SOLUTION: A system includes a profile creation part, a simulation part, and a proposal part. The profile creation unit creates a detailed profile of the user. The simulation unit simulates marriage based on the detailed profile created by the profile creation unit. The proposal unit proposes a specific lifestyle and an economic plan based on the marital life simulated by the simulation unit.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] With conventional technology, it was difficult to simulate specific lifestyle and financial plans after marriage in advance, which often led to anxiety.

[0005] The system according to the embodiment aims to simulate and propose specific life and financial plans after marriage in advance. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile creation unit, a simulation unit, and a proposal unit. The profile creation unit creates a detailed profile of a user. The simulation unit simulates married life based on the detailed profile created by the profile creation unit. The proposal unit proposes specific lifestyles and financial plans based on the married life simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can simulate and propose specific life and financial plans after marriage in advance. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 Marriage Simulator system according to an embodiment of the present invention simulates married life based on a user's detailed profile and proposes specific lifestyle and financial plans. This allows the Marriage Simulator to dispel users' anxieties about life after marriage and concretely depict their ideal married life.

[0029] The marriage Sim system according to the embodiment includes a profile creation unit, a simulation unit, and a proposal unit. The profile creation unit creates a detailed profile for the user. For example, the user inputs information such as their hobbies, child-rearing philosophy, housing preferences, and financial situation. The simulation unit simulates married life based on the detailed profile created by the profile creation unit. For example, the generation AI specifically depicts the user's desired residential area, layout, child-rearing environment, and financial plan. The proposal unit proposes a specific lifestyle and financial plan based on the married life simulated by the simulation unit. For example, the generation AI performs a simulation that comprehensively considers the user's ideal lifestyle, child-rearing vision, ideal housing, financial plan, and other aspects, and makes specific proposals. As a result, the marriage Sim system according to the embodiment can alleviate the user's anxieties about life after marriage and specifically depict their ideal married life.

[0030] The profile creation unit can analyze a user's past SNS posts and blog articles, automatically extract hobbies and values, and reflect them in the profile. For example, the profile creation unit analyzes a user's past SNS posts and extracts frequently mentioned hobbies and interests. For example, if there are many posts about travel or cooking, this is reflected in the profile. The profile creation unit also analyzes the content of blog articles to understand the user's values ​​and lifestyle. For example, if a user is highly interested in ecology, this information is added to the profile. The profile creation unit also analyzes the content of SNS photos and videos to visually grasp the user's activities and interests. For example, if there are many photos of outdoor activities, this is reflected in the profile as a hobby. In this way, by analyzing a user's past SNS posts and blog articles, hobbies and values ​​can be automatically extracted and reflected in the profile.

[0031] The profile creation unit can import the user's health data and create a profile that takes into account the user's health condition and lifestyle habits. The profile creation unit, for example, analyzes data from a fitness tracker and reflects the user's exercise habits and activity level in the profile. For example, the health condition is evaluated based on the number of steps taken each day and the amount of exercise time. The profile creation unit also imports sleep data from a smartwatch and adds the user's sleep patterns and quality to the profile. For example, the average sleep time and the percentage of deep sleep are taken into account. The profile creation unit also analyzes data from a health app and reflects the user's eating habits and calorie intake in the profile. For example, nutritional balance is evaluated based on daily meal records. In this way, by importing the user's health data, a profile that takes into account the user's health condition and lifestyle habits can be created.

[0032] The simulation unit collects user feedback on the simulation results, and the generation AI learns to improve the accuracy of the simulation. For example, the simulation unit collects user feedback on the simulation results, and the generation AI learns based on that data. For example, it prioritizes learning from scenarios that satisfy users. The simulation unit also analyzes the feedback data and identifies patterns for improving the accuracy of the simulation. For example, it analyzes user reactions under specific conditions. The simulation unit also builds a system that adjusts the simulation results in real time based on user feedback. For example, it regenerates scenarios that reflect user opinions. In this way, the generation AI can collect user feedback and learn, thereby improving the accuracy of the simulation.

[0033] The simulation unit can draw a life scenario that includes future career changes, taking into account the user's occupation and career path. The simulation unit, for example, reflects the user's future career path in the simulation based on the user's occupation data. For example, it draws a life scenario that takes into account the possibility of promotion or job change. The simulation unit also performs a simulation that includes career changes, and predicts changes in future income and lifestyle. For example, it reflects increases in income due to career advancement. The simulation unit also takes into account market trends and economic conditions related to the user's occupation, and draws a realistic career scenario. For example, it performs a simulation based on industry growth forecasts. This makes it possible to draw a life scenario that includes future career changes, taking into account the user's occupation and career path.

[0034] The simulation unit can incorporate lifestyles from different cultures and countries to depict married life from an international perspective. For example, the simulation unit incorporates lifestyles from different cultures and countries into the simulation to depict married life from an international perspective. For example, it generates a life scenario abroad. The simulation unit also takes into account the user's cultural background and nationality and performs a simulation that incorporates elements of different cultures. For example, it simulates married life between different cultures. The simulation unit also simulates married life from an international perspective and compares lifestyles from different cultures and countries. For example, it presents life scenarios in multiple countries. This makes it possible to depict married life from an international perspective by incorporating lifestyles from different cultures and countries.

[0035] The simulation unit allows the user to experience the simulation results in VR, allowing the user to feel a more realistic sense of future life. The simulation unit, for example, allows the user to experience the simulation results in VR, allowing the user to feel a more realistic sense of future married life. For example, the simulation unit may experience a home or living environment in virtual reality. The simulation unit may also use VR technology to visually reproduce the simulation results, allowing the user to experience future life. For example, the simulation unit may simulate daily life with family in virtual reality. The simulation unit may also allow the user to more concretely imagine future married life by experiencing the simulation results in VR. For example, the simulation unit may experience a child-rearing environment in virtual reality. In this way, the user may feel a more realistic sense of future life by experiencing the simulation results in VR.

[0036] The suggestion unit can analyze the user's past consumption history and purchasing data and reflect it in lifestyle suggestions. The suggestion unit, for example, analyzes the user's past consumption history and reflects it in lifestyle suggestions. For example, it makes suggestions based on frequently purchased products and services. The suggestion unit also understands the user's consumption patterns and preferences based on the purchasing data and reflects them in lifestyle suggestions. For example, it makes suggestions to users who prefer products of a specific brand or category. The suggestion unit also analyzes the user's consumption history and builds a system that reflects it in lifestyle suggestions. For example, it suggests an optimal lifestyle based on the user's past purchase history. In this way, the analysis of the user's past consumption history and purchasing data can be reflected in lifestyle suggestions.

[0037] The suggestion unit can suggest a lifestyle that takes into consideration detailed home environment such as the user's family composition and whether or not they have pets. The suggestion unit, for example, suggests an optimal lifestyle based on the user's family composition. For example, it suggests environments and activities that are suitable for raising children for families with children. The suggestion unit also considers whether or not they have pets and suggests a pet-friendly lifestyle. For example, it provides options for outdoor activities and housing that can be enjoyed with pets. The suggestion unit also builds a system that suggests an optimal lifestyle to the user based on detailed information about the home environment. For example, it suggests ways to spend the weekend that the whole family can enjoy. This makes it possible to suggest a lifestyle that takes into consideration detailed home environment such as the user's family composition and whether or not they have pets.

[0038] The suggestion unit can simulate the proposed lifestyle under different seasons and climatic conditions to suggest changes in lifestyle throughout the year. For example, the suggestion unit simulates the proposed lifestyle under different seasons to suggest changes in lifestyle throughout the year. For example, it simulates differences in summer and winter activities. The suggestion unit also performs a lifestyle simulation that takes climatic conditions into account to suggest an optimal lifestyle for the user. For example, it makes lifestyle suggestions suitable for the rainy season and the dry season. The suggestion unit also simulates changes in lifestyle for each season to build a system that suggests lifestyles throughout the year. For example, it suggests events and activities for each season. In this way, it is possible to suggest changes in lifestyle throughout the year by simulating the proposed lifestyle under different seasons and climatic conditions.

[0039] The suggestion unit can simulate the proposed lifestyle according to different life stages and make suggestions from a long-term perspective. For example, the suggestion unit simulates the proposed lifestyle at different life stages and makes suggestions from a long-term perspective. For example, it simulates changes in lifestyle as children grow up. The suggestion unit also simulates a lifestyle that takes into account life in old age and makes optimal long-term suggestions to the user. For example, it simulates a lifestyle after retirement. The suggestion unit also simulates changes in lifestyle for each life stage and builds a system that makes lifestyle suggestions from a long-term perspective. For example, it makes suggestions according to changes in family composition. In this way, by simulating the proposed lifestyle according to different life stages, it is possible to make suggestions from a long-term perspective.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The MarriageSim system can also suggest joint activities after marriage based on the user's hobbies and interests. For example, if the user likes outdoor activities, it can suggest plans for weekend hiking and camping. If the user is interested in cooking, it can suggest taking a cooking class together. Furthermore, if the user enjoys music, it can suggest attending a concert or music festival. By suggesting joint activities based on the user's hobbies and interests, it can make life after marriage more fulfilling.

[0042] The MarriageSim system can also analyze a user's past travel history and suggest future travel plans. For example, it can suggest similar travel destinations based on data on places and accommodations the user has visited in the past. If the user is interested in a particular region or culture, it can also suggest travel plans related to that region. It can also make suggestions based on the user's travel style (e.g., backpacking or luxury travel). This allows the system to utilize the user's past travel history to suggest future travel plans, making post-wedding travel even more enjoyable.

[0043] The Marriage Simulator system can also suggest healthy lifestyles based on the user's health data. For example, it can analyze the user's exercise habits and dietary data to suggest appropriate exercise plans and meal menus. It can also provide advice on how to promote good quality sleep based on the user's sleep data. It can also monitor the user's stress level and suggest relaxation methods and stress management. In this way, by utilizing the user's health data to suggest healthy lifestyles, the couple can live a healthier life after marriage.

[0044] The Marriage Sim system can also provide career advancement advice based on the user's occupational data. For example, it can analyze the user's current occupation and skill set and suggest training or qualifications for career advancement. It can also provide information on industry trends and job openings related to the user's occupation. It can also propose long-term career plans tailored to the user's career path. This allows the system to utilize the user's occupational data to provide advice for career advancement, further enhancing the careers of those married.

[0045] The MarriageSim system can also suggest activities that the whole family can enjoy based on the user's family composition data. For example, for families with children, it can suggest outdoor activities and events that the whole family can enjoy. For families with pets, it can also suggest activities that can be enjoyed with the pets. It can also suggest ways to spend the weekend and holiday plans based on the user's family composition. In this way, by utilizing the user's family composition data to suggest activities that the whole family can enjoy, life after marriage can be made more fulfilling.

[0046] The Marriage Simulator system can also provide financial advice based on the user's consumption data. For example, it can analyze the user's past consumption patterns and provide specific advice on saving. It can also propose an optimal savings plan by taking into account the balance between the user's income and expenses. It can also propose plans to prepare for large future expenses (such as purchasing a home or children's education expenses) based on the user's consumption data. In this way, by utilizing the user's consumption data to provide financial advice, it can make post-marriage financial planning more stable.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The profile creation unit creates a detailed profile of the user. For example, the user enters their hobbies, child-rearing philosophy, housing preferences, financial situation, etc. Step 2: The simulation unit simulates married life based on the detailed profile created by the profile creation unit. For example, the generation AI specifically depicts the user's desired residential area, floor plan, child-rearing environment, financial plan, etc. Step 3: The proposal unit proposes specific lifestyle and economic plans based on the married life simulated by the simulation unit. For example, the generation AI performs a simulation that considers the user's ideal lifestyle, child-rearing vision, ideal home, economic plan, etc. in an integrated manner, and makes specific proposals.

[0049] (Example 2) The Marriage Simulator system according to an embodiment of the present invention simulates married life based on a user's detailed profile and proposes specific lifestyle and financial plans. This allows the Marriage Simulator to dispel users' anxieties about life after marriage and concretely depict their ideal married life.

[0050] The marriage Sim system according to the embodiment includes a profile creation unit, a simulation unit, and a proposal unit. The profile creation unit creates a detailed profile for the user. For example, the user inputs information such as their hobbies, child-rearing philosophy, housing preferences, and financial situation. The simulation unit simulates married life based on the detailed profile created by the profile creation unit. For example, the generation AI specifically depicts the user's desired residential area, layout, child-rearing environment, and financial plan. The proposal unit proposes a specific lifestyle and financial plan based on the married life simulated by the simulation unit. For example, the generation AI performs a simulation that comprehensively considers the user's ideal lifestyle, child-rearing vision, ideal housing, financial plan, and other aspects, and makes specific proposals. As a result, the marriage Sim system according to the embodiment can alleviate the user's anxieties about life after marriage and specifically depict their ideal married life.

[0051] The profile creation unit can analyze a user's past SNS posts and blog articles, automatically extract hobbies and values, and reflect them in the profile. For example, the profile creation unit analyzes a user's past SNS posts and extracts frequently mentioned hobbies and interests. For example, if there are many posts about travel or cooking, this is reflected in the profile. The profile creation unit also analyzes the content of blog articles to understand the user's values ​​and lifestyle. For example, if a user is highly interested in ecology, this information is added to the profile. The profile creation unit also analyzes the content of SNS photos and videos to visually grasp the user's activities and interests. For example, if there are many photos of outdoor activities, this is reflected in the profile as a hobby. In this way, by analyzing a user's past SNS posts and blog articles, hobbies and values ​​can be automatically extracted and reflected in the profile.

[0052] The profile creation unit can import the user's health data and create a profile that takes into account the user's health condition and lifestyle habits. The profile creation unit, for example, analyzes data from a fitness tracker and reflects the user's exercise habits and activity level in the profile. For example, the health condition is evaluated based on the number of steps taken each day and the amount of exercise time. The profile creation unit also imports sleep data from a smartwatch and adds the user's sleep patterns and quality to the profile. For example, the average sleep time and the percentage of deep sleep are taken into account. The profile creation unit also analyzes data from a health app and reflects the user's eating habits and calorie intake in the profile. For example, nutritional balance is evaluated based on daily meal records. In this way, by importing the user's health data, a profile that takes into account the user's health condition and lifestyle habits can be created.

[0053] The profile creation unit uses the emotion estimation function to analyze the emotions felt by the user when entering their profile, and can ask questions and make suggestions to elicit positive emotions. For example, the profile creation unit analyzes the user's facial expressions when entering their profile and estimates their emotions. For example, if the user smiles frequently, it continues to ask positive questions. The profile creation unit also uses voice analysis to estimate emotions from the tone and tempo of the user's voice. For example, if the user's voice sounds excited, it asks questions that delve deeper into related hobbies and interests. The profile creation unit also provides an interface based on the emotion estimation data that allows the user to enter their profile in a relaxed manner. For example, it employs a design that uses calming music and colors. This allows the emotion estimation function to analyze the emotions felt by the user when entering their profile, and can ask questions and make suggestions to elicit positive emotions.

[0054] The simulation unit collects user feedback on the simulation results, and the generation AI learns to improve the accuracy of the simulation. For example, the simulation unit collects user feedback on the simulation results, and the generation AI learns based on that data. For example, it prioritizes learning from scenarios that satisfy users. The simulation unit also analyzes the feedback data and identifies patterns for improving the accuracy of the simulation. For example, it analyzes user reactions under specific conditions. The simulation unit also builds a system that adjusts the simulation results in real time based on user feedback. For example, it regenerates scenarios that reflect user opinions. In this way, the generation AI can collect user feedback and learn, thereby improving the accuracy of the simulation.

[0055] The simulation unit can draw a life scenario that includes future career changes, taking into account the user's occupation and career path. The simulation unit, for example, reflects the user's future career path in the simulation based on the user's occupation data. For example, it draws a life scenario that takes into account the possibility of promotion or job change. The simulation unit also performs a simulation that includes career changes, and predicts changes in future income and lifestyle. For example, it reflects increases in income due to career advancement. The simulation unit also takes into account market trends and economic conditions related to the user's occupation, and draws a realistic career scenario. For example, it performs a simulation based on industry growth forecasts. This makes it possible to draw a life scenario that includes future career changes, taking into account the user's occupation and career path.

[0056] The simulation unit can use the emotion estimation function to analyze the user's emotional response to the simulation results and preferentially present scenarios that elicit positive emotions. The simulation unit, for example, analyzes the user's emotional response to the simulation results in real time and preferentially presents scenarios that elicit positive emotions. For example, it selects a scenario that makes the user feel happy. The simulation unit also identifies scenarios in which the user feels positive emotions based on the emotion estimation data and preferentially presents those scenarios. For example, it selects a scenario with a high emotion score. The simulation unit also analyzes the user's emotional response and builds a system that generates scenarios for eliciting positive emotions. For example, it automatically generates scenarios that match the user's preferences. In this way, by using the emotion estimation function, it is possible to analyze the user's emotional response to the simulation results and preferentially present scenarios that elicit positive emotions.

[0057] The simulation unit can incorporate lifestyles from different cultures and countries to depict married life from an international perspective. For example, the simulation unit incorporates lifestyles from different cultures and countries into the simulation to depict married life from an international perspective. For example, it generates a life scenario abroad. The simulation unit also takes into account the user's cultural background and nationality and performs a simulation that incorporates elements of different cultures. For example, it simulates married life between different cultures. The simulation unit also simulates married life from an international perspective and compares lifestyles from different cultures and countries. For example, it presents life scenarios in multiple countries. This makes it possible to depict married life from an international perspective by incorporating lifestyles from different cultures and countries.

[0058] The simulation unit allows the user to experience the simulation results in VR, allowing the user to feel a more realistic sense of future life. The simulation unit, for example, allows the user to experience the simulation results in VR, allowing the user to feel a more realistic sense of future married life. For example, the simulation unit may experience a home or living environment in virtual reality. The simulation unit may also use VR technology to visually reproduce the simulation results, allowing the user to experience future life. For example, the simulation unit may simulate daily life with family in virtual reality. The simulation unit may also allow the user to more concretely imagine future married life by experiencing the simulation results in VR. For example, the simulation unit may experience a child-rearing environment in virtual reality. In this way, the user may feel a more realistic sense of future life by experiencing the simulation results in VR.

[0059] The simulation unit can use the emotion estimation function to monitor the emotions felt by the user during the simulation in real time and dynamically adjust the scenario. For example, the simulation unit monitors the user's emotions in real time during the simulation and dynamically adjusts the scenario based on the emotion estimation data. For example, if negative emotions are strong, the scenario is changed. The simulation unit also uses the emotion estimation function to analyze the user's emotional reactions during the simulation and preferentially present scenarios that elicit positive emotions. For example, it selects scenarios with high emotion scores. The simulation unit also constructs a system that provides feedback on the user's emotional reactions in real time and dynamically adjusts the scenario. For example, it automatically generates scenarios tailored to the user's preferences. In this way, by using the emotion estimation function, the emotions felt by the user during the simulation can be monitored in real time and the scenario can be dynamically adjusted.

[0060] The suggestion unit can analyze the user's past consumption history and purchasing data and reflect it in lifestyle suggestions. The suggestion unit, for example, analyzes the user's past consumption history and reflects it in lifestyle suggestions. For example, it makes suggestions based on frequently purchased products and services. The suggestion unit also understands the user's consumption patterns and preferences based on the purchasing data and reflects them in lifestyle suggestions. For example, it makes suggestions to users who prefer products of a specific brand or category. The suggestion unit also analyzes the user's consumption history and builds a system that reflects it in lifestyle suggestions. For example, it suggests an optimal lifestyle based on the user's past purchase history. In this way, the analysis of the user's past consumption history and purchasing data can be reflected in lifestyle suggestions.

[0061] The suggestion unit can suggest a lifestyle that takes into consideration detailed home environment such as the user's family composition and whether or not they have pets. The suggestion unit, for example, suggests an optimal lifestyle based on the user's family composition. For example, it suggests environments and activities that are suitable for raising children for families with children. The suggestion unit also considers whether or not they have pets and suggests a pet-friendly lifestyle. For example, it provides options for outdoor activities and housing that can be enjoyed with pets. The suggestion unit also builds a system that suggests an optimal lifestyle to the user based on detailed information about the home environment. For example, it suggests ways to spend the weekend that the whole family can enjoy. This makes it possible to suggest a lifestyle that takes into consideration detailed home environment such as the user's family composition and whether or not they have pets.

[0062] The suggestion unit can use the emotion estimation function to analyze the user's emotional response to the proposed lifestyle and prioritize suggestions that elicit positive emotions. The suggestion unit, for example, analyzes the user's emotional response to the proposed lifestyle in real time and prioritizes suggestions that elicit positive emotions. For example, it selects suggestions that make the user feel happy. The suggestion unit also identifies lifestyles that the user feels positive about based on the emotion estimation data and preferentially presents those proposals. For example, it selects proposals with high emotion scores. The suggestion unit also analyzes the user's emotional response and builds a system that generates lifestyle proposals that elicit positive emotions. For example, it automatically generates proposals tailored to the user's preferences. In this way, the emotion estimation function can be used to analyze the user's emotional response to the proposed lifestyle and prioritize suggestions that elicit positive emotions.

[0063] The suggestion unit can simulate the proposed lifestyle under different seasons and climatic conditions to suggest changes in lifestyle throughout the year. For example, the suggestion unit simulates the proposed lifestyle under different seasons to suggest changes in lifestyle throughout the year. For example, it simulates differences in summer and winter activities. The suggestion unit also performs a lifestyle simulation that takes climatic conditions into account to suggest an optimal lifestyle for the user. For example, it makes lifestyle suggestions suitable for the rainy season and the dry season. The suggestion unit also simulates changes in lifestyle for each season to build a system that suggests lifestyles throughout the year. For example, it suggests events and activities for each season. In this way, it is possible to suggest changes in lifestyle throughout the year by simulating the proposed lifestyle under different seasons and climatic conditions.

[0064] The suggestion unit can simulate the proposed lifestyle according to different life stages and make suggestions from a long-term perspective. For example, the suggestion unit simulates the proposed lifestyle at different life stages and makes suggestions from a long-term perspective. For example, it simulates changes in lifestyle as children grow up. The suggestion unit also simulates a lifestyle that takes into account life in old age and makes optimal long-term suggestions to the user. For example, it simulates a lifestyle after retirement. The suggestion unit also simulates changes in lifestyle for each life stage and builds a system that makes lifestyle suggestions from a long-term perspective. For example, it makes suggestions according to changes in family composition. In this way, by simulating the proposed lifestyle according to different life stages, it is possible to make suggestions from a long-term perspective.

[0065] The suggestion unit can use the emotion estimation function to collect the user's emotional reactions to the proposed lifestyle in real time and dynamically adjust the content of the proposal. For example, the suggestion unit collects the user's emotional reactions to the proposed lifestyle in real time and dynamically adjusts the content of the proposal based on the data. For example, if there are a lot of negative reactions, the content of the proposal is changed. The suggestion unit also uses the emotion estimation function to analyze the user's emotional reactions and preferentially present proposals that elicit positive emotions. For example, it selects proposals with high emotion scores. The suggestion unit also constructs a system that provides feedback on the user's emotional reactions in real time and dynamically adjusts the content of the proposal. For example, it automatically generates proposals tailored to the user's preferences. In this way, by using the emotion estimation function, the user's emotional reactions to the proposed lifestyle can be collected in real time and the content of the proposal can be dynamically adjusted.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The MarriageSim system can also suggest joint activities after marriage based on the user's hobbies and interests. For example, if the user likes outdoor activities, it can suggest plans for weekend hiking and camping. If the user is interested in cooking, it can suggest taking a cooking class together. Furthermore, if the user enjoys music, it can suggest attending a concert or music festival. By suggesting joint activities based on the user's hobbies and interests, it can make life after marriage more fulfilling.

[0068] The MarriageSim system can also analyze a user's past travel history and suggest future travel plans. For example, it can suggest similar travel destinations based on data on places and accommodations the user has visited in the past. If the user is interested in a particular region or culture, it can also suggest travel plans related to that region. It can also make suggestions based on the user's travel style (e.g., backpacking or luxury travel). This allows the system to utilize the user's past travel history to suggest future travel plans, making post-wedding travel even more enjoyable.

[0069] The Marriage Simulator system can also suggest healthy lifestyles based on the user's health data. For example, it can analyze the user's exercise habits and dietary data to suggest appropriate exercise plans and meal menus. It can also provide advice on how to promote good quality sleep based on the user's sleep data. It can also monitor the user's stress level and suggest relaxation methods and stress management. In this way, by utilizing the user's health data to suggest healthy lifestyles, the couple can live a healthier life after marriage.

[0070] The Marriage Sim system can also use the user's emotion estimation function to support stress management after marriage. For example, if a user is feeling stressed, the system can estimate their emotions and suggest relaxation methods and activities to relieve stress. Also, if the user is feeling positive, the system can suggest activities to maintain those emotions. Furthermore, based on the user's emotion data, it is possible to identify the cause of stress and suggest specific countermeasures. In this way, the emotion estimation function can support stress management after marriage and lead to a more comfortable life.

[0071] The Marriage Sim system can also provide career advancement advice based on the user's occupational data. For example, it can analyze the user's current occupation and skill set and suggest training or qualifications for career advancement. It can also provide information on industry trends and job openings related to the user's occupation. It can also propose long-term career plans tailored to the user's career path. This allows the system to utilize the user's occupational data to provide advice for career advancement, further enhancing the careers of those married.

[0072] The Marriage Sim system can also use the user's emotion estimation function to support communication after marriage. For example, if a user is emotionally unstable, the system can estimate their emotions and suggest appropriate communication methods. Also, if the user is feeling positive, the system can suggest activities for sharing those emotions. Furthermore, based on the user's emotion data, it can identify areas for improvement in communication and provide specific advice. In this way, the emotion estimation function can support communication after marriage and build a better relationship.

[0073] The MarriageSim system can also suggest activities that the whole family can enjoy based on the user's family composition data. For example, for families with children, it can suggest outdoor activities and events that the whole family can enjoy. For families with pets, it can also suggest activities that can be enjoyed with the pets. It can also suggest ways to spend the weekend and holiday plans based on the user's family composition. In this way, by utilizing the user's family composition data to suggest activities that the whole family can enjoy, life after marriage can be made more fulfilling.

[0074] The MarriageSim system can also use the user's emotion estimation function to support planning post-marriage life events. For example, when a user approaches an emotionally significant life event (such as a wedding anniversary or birthday), the system can estimate their emotions and suggest appropriate event plans. If the user is feeling positive, the system can also suggest events to further enhance those emotions. Furthermore, it is conceivable that the system can dynamically adjust life event plans based on the user's emotion data. This allows the emotion estimation function to support planning post-marriage life events and create even more special moments.

[0075] The Marriage Simulator system can also provide financial advice based on the user's consumption data. For example, it can analyze the user's past consumption patterns and provide specific advice on saving. It can also propose an optimal savings plan by taking into account the balance between the user's income and expenses. It can also propose plans to prepare for large future expenses (such as purchasing a home or children's education expenses) based on the user's consumption data. In this way, by utilizing the user's consumption data to provide financial advice, it can make post-marriage financial planning more stable.

[0076] The MarriageSim system can also use the user's emotion estimation function to suggest activities to deepen hobbies and interests after marriage. For example, if a user has positive feelings about a particular hobby, it can suggest activities to further develop that hobby. Also, if a user is interested in a new hobby, it can estimate their emotions and suggest related activities. Furthermore, it is possible to suggest communities and events that share hobbies and interests based on the user's emotion data. This allows the emotion estimation function to suggest activities to deepen hobbies and interests after marriage, leading to a more fulfilling life.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The profile creation unit creates a detailed profile of the user. For example, the user enters their hobbies, child-rearing philosophy, housing preferences, financial situation, etc. Step 2: The simulation unit simulates married life based on the detailed profile created by the profile creation unit. For example, the generation AI specifically depicts the user's desired residential area, floor plan, child-rearing environment, financial plan, etc. Step 3: The proposal unit proposes specific lifestyle and economic plans based on the married life simulated by the simulation unit. For example, the generation AI performs a simulation that considers the user's ideal lifestyle, child-rearing vision, ideal home, economic plan, etc. in an integrated manner, and makes specific proposals.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0092] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0093] 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.

[0094] 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.

[0095] 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 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 profile creation unit that creates a detailed profile of the user; a simulation unit that simulates married life based on the detailed profile created by the profile creation unit; a proposal unit that proposes specific lifestyles and economic plans based on the married life simulated by the simulation unit. A system characterized by:

2. The profile creation unit Analyze the user's past social media posts and blog posts, automatically extract their hobbies and values, and reflect them in their profile.

2. The system of claim 1.

3. The profile creation unit Import the user's health data and create a profile that takes into account their health condition and lifestyle habits 2. The system of claim 1.

4. The profile creation unit Analyze the emotions felt by the user when filling out their profile and ask questions and make suggestions to elicit positive emotions 2. The system of claim 1.

5. The simulation unit The user's feedback on the simulation results is collected, and the generation AI learns and improves the accuracy of the simulation.

2. The system of claim 1.

6. The simulation unit Considering the user's occupation and career path, draw a life scenario that includes future career changes 2. The system of claim 1.

7. The simulation unit Analyzing the user's emotional response to the simulation results and preferentially presenting scenarios that elicit positive emotions 2. The system of claim 1.

8. The simulation unit Incorporating lifestyles from different cultures and countries, depicting married life from an international perspective 2. The system of claim 1.

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