System

Abstract

An object of the system according to the embodiment is to provide personalized advice based on the lifestyle, goal, and risk tolerance of the user.SOLUTION: A system according to an embodiment includes a lifestyle analysis unit, a goal analysis unit, a risk evaluation unit, and a personalization unit. The lifestyle analysis unit analyzes lifestyle data of a user. The goal analysis unit analyzes a goal of the user on the basis of the data analyzed by the lifestyle analysis unit. The risk evaluator evaluates a risk tolerance of the user based on the data analyzed by the goal analyzer. The personalizer provides personalized advice based on the data analyzed by the lifestyle analyzer, the goal analyzer, and the risk evaluator.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 technologies have had the problem of making it difficult to provide personalized advice based on a user's lifestyle, goals, and risk tolerance.

[0005] The system according to the embodiment aims to provide personalized advice based on a user's lifestyle, goals, and risk tolerance. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle analysis unit, a goal analysis unit, a risk assessment unit, and a personalization unit. The lifestyle analysis unit analyzes lifestyle data of a user. The goal analysis unit analyzes the user's goals based on the data analyzed by the lifestyle analysis unit. The risk assessment unit evaluates the user's risk tolerance based on the data analyzed by the goal analysis unit. The personalization unit provides personalized advice based on the data analyzed by the lifestyle analysis unit, the goal analysis unit, and the risk assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized advice based on a user's lifestyle, goals, and risk tolerance. [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) A financial planning platform according to an embodiment of the present invention is a system that assists users in selecting the most suitable insurance and plan based on their lifestyle, goals, and risk tolerance, thereby providing users with personalized advice and ushering in a new era of financial planning.

[0029] A financial planning platform according to an embodiment includes a lifestyle analysis unit, a goal analysis unit, a risk assessment unit, and a personalization unit. The lifestyle analysis unit analyzes a user's lifestyle data. For example, the lifestyle analysis unit collects and analyzes information such as the user's income, expenses, hobbies, and family structure. The lifestyle analysis unit also analyzes data related to the user's lifestyle using a generation AI. For example, the generation AI analyzes the user's income and expense patterns to identify lifestyle trends. The goal analysis unit analyzes the user's goals based on the data analyzed by the lifestyle analysis unit. For example, the goal analysis unit sets and analyzes the user's short-term and long-term goals. The goal analysis unit also analyzes data related to the user's goals using a generation AI. For example, the generation AI analyzes the user's goals, such as purchasing a home, children's education expenses, and retirement living expenses, and proposes an optimal plan. The risk assessment unit evaluates the user's risk tolerance based on the data analyzed by the goal analysis unit. For example, the risk assessment unit has the user answer questions about risk to assess the level of risk the user can tolerate. The risk assessment unit also uses the generation AI to assess the user's risk tolerance. For example, the generation AI analyzes the user's responses regarding risk and assesses the risk tolerance. The personalization unit provides personalized advice based on the data analyzed by the lifestyle analysis unit, goal analysis unit, and risk assessment unit. For example, the personalization unit analyzes data such as the user's income, expenses, goals, and risk tolerance, and proposes optimal insurance and investment plans based on the data. The personalization unit also uses the generation AI to provide personalized advice to the user. For example, the generation AI analyzes the user's data and proposes optimal insurance and investment plans. As a result, the financial planning platform according to the embodiment can provide personalized advice to users and usher in a new era of financial planning.

[0030] The lifestyle analysis unit can estimate the user's health condition and stress level and suggest health insurance and stress reduction plans based on that. In the lifestyle analysis unit, for example, the generation AI analyzes the user's lifestyle data and estimates the user's health condition. For example, the generation AI evaluates health risks based on the user's diet, exercise, and sleep patterns. In addition, the lifestyle analysis unit suggests an appropriate health insurance plan based on the estimated health condition. For example, the generation AI selects an insurance plan that corresponds to a specific health risk. In addition, the lifestyle analysis unit analyzes the user's stress level and suggests a stress reduction plan. For example, the generation AI suggests relaxation methods and activities for stress management. This makes it possible to suggest an appropriate plan based on the user's health condition and stress level.

[0031] The lifestyle analysis unit can perform a more detailed lifestyle analysis by taking into account the user's social media activity and online shopping history. In the lifestyle analysis unit, for example, the generation AI analyzes the user's social media activity to understand lifestyle trends. For example, the generation AI identifies the user's hobbies and interests based on the content of the user's posts and trends in likes. The lifestyle analysis unit also analyzes the user's online shopping history to understand the user's consumption patterns. For example, the generation AI evaluates the user's lifestyle based on the user's purchase frequency and product categories. The lifestyle analysis unit also integrates the social media activity and online shopping history to perform a more detailed lifestyle analysis. For example, the generation AI provides personalized advice based on the user's hobbies and interests. This enables a detailed lifestyle analysis that takes into account the user's social media activity and online shopping history.

[0032] The lifestyle analysis unit collects data on the user's pet ownership and hobbies and can propose pet insurance and hobby-related plans. In the lifestyle analysis unit, for example, the generation AI analyzes the user's pet ownership and proposes appropriate pet insurance. For example, the generation AI selects an insurance plan based on the pet's type and health condition. The lifestyle analysis unit also collects data on the user's hobbies and proposes plans related to the hobbies. For example, the generation AI proposes outdoor insurance and related services to a user who enjoys outdoor activities. The lifestyle analysis unit also integrates data on the pet ownership and hobbies and proposes plans that are optimal for the user's lifestyle. For example, the generation AI proposes activities and services that can be enjoyed with pets. This makes it possible to propose appropriate plans based on the user's pet ownership and hobbies.

[0033] The lifestyle analysis unit can propose region-specific insurance and plans based on the user's living environment. In the lifestyle analysis unit, for example, the generation AI analyzes the user's living environment and proposes region-specific insurance plans. For example, the generation AI proposes insurance that addresses risks specific to cities to a user living in an urban area. The lifestyle analysis unit also proposes region-specific plans to users living in suburban or rural areas. For example, the generation AI proposes agricultural insurance and natural disaster prevention plans in rural areas. The lifestyle analysis unit also provides personalized advice tailored to the characteristics of the region based on the living environment data. For example, the generation AI proposes health plans based on local medical facilities and services. This makes it possible to propose region-specific insurance and plans based on the user's living environment.

[0034] The goal analysis unit analyzes the user's past goal achievement history, identifies success patterns, and can reflect them in new goal setting. In the goal analysis unit, for example, the generation AI analyzes the user's past goal achievement history and identifies success patterns. For example, the generation AI extracts commonalities and success factors between goals achieved in the past. The goal analysis unit also reflects these in new goal setting based on the success patterns. For example, the generation AI proposes a goal achievement plan that takes into account past success factors. The goal analysis unit also analyzes the user's goal achievement history over the long term and identifies changes in success patterns. This allows the generation AI to provide advice according to the user's growth and changes. This allows new goals to be set based on the user's past success patterns.

[0035] The goal analysis unit can propose a career-specific goal achievement plan by taking into account the user's occupation and career path. For example, the generation AI analyzes the user's occupation data and proposes a career-specific goal achievement plan. For example, the generation AI provides a plan that takes into account the skills and qualifications required for a specific occupation. The goal analysis unit also analyzes the user's career path and proposes a goal achievement plan according to career progress. For example, the generation AI suggests specific steps for a user aiming for promotion or changing jobs. The goal analysis unit also integrates the occupation data and career path to provide a career-specific goal achievement plan. For example, the generation AI provides advice that takes into account the characteristics of the occupation and industry trends. This makes it possible to propose a goal achievement plan based on the user's occupation and career path.

[0036] The goal analysis unit takes into account the goals of the entire family and can propose a plan that can be shared by all family members. For example, the generation AI analyzes the user's family data and proposes a plan that takes into account the goals of the entire family. For example, the generation AI provides a savings plan or travel plan that can be shared by the entire family. The goal analysis unit also proposes a personalized plan based on the goals of the entire family. For example, the generation AI provides a plan that suits the life stage and needs of the family. The goal analysis unit also collects family data over the long term and monitors the progress of the entire family in achieving their goals. This allows the generation AI to provide advice that responds to changes in the family. This makes it possible to propose a plan that takes into account the goals of the entire family.

[0037] The goal analysis unit can propose a goal achievement plan based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobby data and proposes a goal achievement plan based on the hobbies and interests. For example, the generation AI provides a skill improvement plan related to a specific hobby. The goal analysis unit also takes the user's interests into consideration and the generation AI proposes a personalized goal achievement plan. For example, the generation AI suggests side jobs or projects that utilize the user's hobbies. The goal analysis unit also collects hobby data over the long term and provides a goal achievement plan that corresponds to changes in the user's interests. This allows the generation AI to provide advice that corresponds to the user's growth and changes. This makes it possible to propose a goal achievement plan based on the user's hobbies and interests.

[0038] The risk assessment unit analyzes the user's past investment history and risk preference patterns to perform more accurate risk assessments. In the risk assessment unit, for example, the generation AI analyzes the user's past investment history and identifies the risk preference pattern. For example, the generation AI performs risk assessment based on past investment successes and failures. Furthermore, the risk assessment unit provides a more accurate risk assessment based on the risk preference pattern. For example, the generation AI performs risk assessment taking into account the user's past investment behavior. Furthermore, the risk assessment unit analyzes the user's investment history over the long term to identify changes in the risk preference pattern. This allows the generation AI to provide advice according to changes in the user's risk tolerance. This enables a more accurate risk assessment based on the user's past investment history and risk preference pattern.

[0039] The risk assessment unit can propose an insurance plan based on the user's health risk, taking into account the user's health condition and lifestyle habits. In the risk assessment unit, for example, the generation AI analyzes the user's health condition and evaluates risk tolerance. For example, the generation AI performs risk assessment based on the user's health checkup results and lifestyle habits. In addition, the risk assessment unit proposes an appropriate insurance plan based on the health risk. For example, the generation AI selects an insurance plan that corresponds to a specific health risk. In addition, the risk assessment unit collects the user's health data over the long term and provides a risk assessment according to changes in the health condition. This allows the generation AI to provide advice based on the user's health risk. This makes it possible to propose an insurance plan based on the user's health condition and lifestyle habits.

[0040] The risk assessment unit can propose an optimal plan for the entire family, taking into account the user's family structure and the family's risk tolerance. For example, the generation AI analyzes the user's family structure and proposes a plan that takes into account the risk tolerance of the entire family. For example, the generation AI provides insurance plans and investment plans that can be shared by the entire family. The risk assessment unit also proposes personalized plans based on the risk tolerance of the entire family. For example, the generation AI provides plans that suit the family's life stage and needs. The risk assessment unit also collects family data over the long term and monitors changes in the family's overall risk tolerance. This allows the generation AI to provide advice that reflects changes in the family. This makes it possible to propose plans that take into account the risk tolerance of the entire family.

[0041] The risk assessment unit can perform risk assessment based on the user's hobbies and interests and suggest insurance plans related to the hobbies. In the risk assessment unit, for example, the generation AI analyzes the user's hobby data and performs risk assessment based on the hobbies and interests. For example, the generation AI evaluates the risks associated with a specific hobby. Furthermore, the risk assessment unit proposes an appropriate insurance plan based on the risk assessment related to the hobby. For example, the generation AI suggests outdoor insurance to a user who likes outdoor activities. Furthermore, the risk assessment unit collects hobby data over the long term and provides risk assessment according to changes in the user's interests. This allows the generation AI to provide advice according to the user's growth and changes. This makes it possible to perform risk assessment and suggest insurance plans based on the user's hobbies and interests.

[0042] The personalization unit can analyze the user's past advice history, identify successful patterns, and reflect them in new advice. In the personalization unit, for example, the generation AI analyzes the user's past advice history and identifies successful patterns. For example, the generation AI extracts commonalities and success factors of advice that was successful in the past. The personalization unit also reflects these in new advice based on the successful patterns. For example, the generation AI provides advice that takes into account past success factors. The personalization unit also analyzes the user's advice history over the long term and identifies changes in successful patterns. This allows the generation AI to provide advice that corresponds to the user's growth and changes. This allows new advice to be provided based on the user's past successful patterns.

[0043] The personalization unit can provide occupation-specific personalized advice by taking into account the user's occupation and career path. In the personalization unit, for example, the generation AI analyzes the user's occupation data and provides occupation-specific personalized advice. For example, the generation AI provides advice that takes into account the skills and qualifications required for a specific occupation. The personalization unit also analyzes the user's career path and provides personalized advice according to career progress. For example, the generation AI suggests specific steps for a user who is aiming for promotion or changing jobs. The personalization unit also integrates the occupation data and career path to provide advice specific to the user's occupation. For example, the generation AI provides advice that takes into account the characteristics of the occupation and industry trends. This makes it possible to provide personalized advice based on the user's occupation and career path.

[0044] The personalization unit takes into account data for the entire family and can provide personalized advice that can be shared by all family members. For example, the generation AI analyzes the user's family data and provides personalized advice that takes into account data for the entire family. For example, the generation AI provides savings plans and travel plans that can be shared by all family members. The personalization unit also provides personalized advice based on data for the entire family. For example, the generation AI provides advice according to the life stage and needs of the family. The personalization unit also collects family data over the long term and monitors changes in the data for the entire family. This allows the generation AI to provide advice that responds to changes in the family. This makes it possible to provide personalized advice that takes into account data for the entire family.

[0045] The personalization unit can provide personalized advice based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobby data and provides personalized advice based on the hobbies and interests. For example, the generation AI provides a skill improvement plan related to a specific hobby. The personalization unit also takes the user's interests into consideration and provides personalized advice. For example, the generation AI suggests side jobs or projects that utilize the user's hobbies. The personalization unit also collects hobby data over the long term and provides personalized advice in response to changes in the user's interests. This allows the generation AI to provide advice in response to the user's growth and changes. This makes it possible to provide personalized advice based on the user's hobbies and interests.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The financial planning platform can further analyze a user's travel plans and provide advice on travel insurance and activities at the travel destination. For example, the travel plan analysis unit collects and analyzes data such as the user's travel destination, travel duration, and budget. The generation AI suggests risks at the travel destination and necessary insurance. The travel plan analysis unit also provides information on activities at the travel destination and suggests activities based on the user's interests. For example, for a user who likes outdoor activities, it provides information on hiking and camping. This makes it possible to provide personalized advice based on the user's travel plans.

[0048] The financial planning platform can also estimate the user's health condition and stress level and recommend health insurance and stress reduction plans based on that. For example, the health condition analysis unit assesses health risks based on the user's diet, exercise, and sleep patterns. The generation AI selects insurance plans that address specific health risks. The stress level analysis unit analyzes the user's stress level and suggests relaxation methods and activities for stress management. This allows the platform to recommend appropriate plans based on the user's health condition and stress level.

[0049] The financial planning platform can also take into account a user's social media activity and online shopping history to conduct more detailed lifestyle analysis. For example, the social media analysis unit identifies a user's hobbies and interests based on their postings and likes. The generative AI analyzes their online shopping history to understand their spending patterns. This allows it to provide personalized advice based on the user's hobbies and interests.

[0050] The financial planning platform can also collect data on users' pet ownership and hobbies to suggest pet insurance and plans related to their hobbies. For example, the pet analysis unit selects insurance plans based on the pet's type and health condition. The generation AI can suggest outdoor insurance and related services to users who enjoy outdoor activities. This allows the platform to suggest appropriate plans based on the user's pet ownership and hobbies.

[0051] The financial planning platform can also suggest region-specific insurance and plans based on the user's living environment. For example, the living environment analysis unit will suggest insurance that addresses risks specific to cities to users living in urban areas. The generation AI will suggest agricultural insurance and natural disaster countermeasure plans to users living in rural areas. This allows the platform to suggest region-specific insurance and plans based on the user's living environment.

[0052] The financial planning platform can also analyze the user's past goal achievement history, identify success patterns, and reflect them in new goal setting. For example, the goal achievement analysis unit extracts commonalities and success factors between previously achieved goals. The generation AI then proposes a goal achievement plan that takes past success factors into account. This allows new goal setting to be based on the user's past success patterns.

[0053] The financial planning platform can further consider a user's occupation and career path to propose occupation-specific goal achievement plans. For example, the occupation analysis unit provides plans that take into account the skills and qualifications required for a specific occupation. The generation AI suggests specific steps for users aiming for promotion or changing jobs. This allows it to propose goal achievement plans based on the user's occupation and career path.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The lifestyle analysis unit analyzes the user's lifestyle data. For example, information such as the user's income, expenses, hobbies, and family structure is collected and analyzed using the generation AI. This allows the user's lifestyle trends to be understood. Step 2: The goal analysis unit analyzes the user's goals based on the data analyzed by the lifestyle analysis unit. For example, the user's short-term and long-term goals are set and analyzed using the generation AI. This allows the system to understand the user's goals, such as purchasing a home, paying for their children's education, and living expenses in retirement, and propose the optimal plan. Step 3: The risk assessment unit evaluates the user's risk tolerance based on the data analyzed by the goal analysis unit. For example, the unit may have the user answer questions about risk and use a generation AI to evaluate the user's risk tolerance. This allows the user to understand how much risk they can tolerate. Step 4: The personalization unit provides personalized advice based on the data analyzed by the lifestyle analysis unit, goal analysis unit, and risk assessment unit. For example, it analyzes data such as the user's income, expenses, goals, and risk tolerance, and uses generative AI to suggest optimal insurance and investment plans.

[0056] (Example 2) A financial planning platform according to an embodiment of the present invention is a system that assists users in selecting the most suitable insurance and plan based on their lifestyle, goals, and risk tolerance, thereby providing users with personalized advice and ushering in a new era of financial planning.

[0057] A financial planning platform according to an embodiment includes a lifestyle analysis unit, a goal analysis unit, a risk assessment unit, and a personalization unit. The lifestyle analysis unit analyzes a user's lifestyle data. For example, the lifestyle analysis unit collects and analyzes information such as the user's income, expenses, hobbies, and family structure. The lifestyle analysis unit also analyzes data related to the user's lifestyle using a generation AI. For example, the generation AI analyzes the user's income and expense patterns to identify lifestyle trends. The goal analysis unit analyzes the user's goals based on the data analyzed by the lifestyle analysis unit. For example, the goal analysis unit sets and analyzes the user's short-term and long-term goals. The goal analysis unit also analyzes data related to the user's goals using a generation AI. For example, the generation AI analyzes the user's goals, such as purchasing a home, children's education expenses, and retirement living expenses, and proposes an optimal plan. The risk assessment unit evaluates the user's risk tolerance based on the data analyzed by the goal analysis unit. For example, the risk assessment unit has the user answer questions about risk to assess the level of risk the user can tolerate. The risk assessment unit also uses the generation AI to assess the user's risk tolerance. For example, the generation AI analyzes the user's responses regarding risk and assesses the risk tolerance. The personalization unit provides personalized advice based on the data analyzed by the lifestyle analysis unit, goal analysis unit, and risk assessment unit. For example, the personalization unit analyzes data such as the user's income, expenses, goals, and risk tolerance, and proposes optimal insurance and investment plans based on the data. The personalization unit also uses the generation AI to provide personalized advice to the user. For example, the generation AI analyzes the user's data and proposes optimal insurance and investment plans. As a result, the financial planning platform according to the embodiment can provide personalized advice to users and usher in a new era of financial planning.

[0058] The lifestyle analysis unit monitors the user's emotional state in real time and can adjust advice according to changes in that emotional state. For example, the lifestyle analysis unit monitors the user's emotional state in real time when the generation AI analyzes the user's lifestyle data. For example, the generation AI analyzes the user's heart rate and facial expressions to detect changes in stress and happiness. The lifestyle analysis unit also adjusts the advice provided by the generation AI according to changes in the emotional state. For example, if the user is feeling stressed, it suggests relaxation methods and stress reduction measures. The lifestyle analysis unit also collects the user's emotional data over the long term and analyzes emotional trends associated with changes in lifestyle. This allows the generation AI to understand the user's emotional patterns and provide more appropriate advice. This makes it possible to adjust advice according to the user's emotional state.

[0059] The lifestyle analysis unit can estimate the user's health condition and stress level and suggest health insurance and stress reduction plans based on that. In the lifestyle analysis unit, for example, the generation AI analyzes the user's lifestyle data and estimates the user's health condition. For example, the generation AI evaluates health risks based on the user's diet, exercise, and sleep patterns. In addition, the lifestyle analysis unit suggests an appropriate health insurance plan based on the estimated health condition. For example, the generation AI selects an insurance plan that corresponds to a specific health risk. In addition, the lifestyle analysis unit analyzes the user's stress level and suggests a stress reduction plan. For example, the generation AI suggests relaxation methods and activities for stress management. This makes it possible to suggest an appropriate plan based on the user's health condition and stress level.

[0060] The lifestyle analysis unit can perform a more detailed lifestyle analysis by taking into account the user's social media activity and online shopping history. In the lifestyle analysis unit, for example, the generation AI analyzes the user's social media activity to understand lifestyle trends. For example, the generation AI identifies the user's hobbies and interests based on the content of the user's posts and trends in likes. The lifestyle analysis unit also analyzes the user's online shopping history to understand the user's consumption patterns. For example, the generation AI evaluates the user's lifestyle based on the user's purchase frequency and product categories. The lifestyle analysis unit also integrates the social media activity and online shopping history to perform a more detailed lifestyle analysis. For example, the generation AI provides personalized advice based on the user's hobbies and interests. This enables a detailed lifestyle analysis that takes into account the user's social media activity and online shopping history.

[0061] The lifestyle analysis unit collects data on the user's pet ownership and hobbies and can propose pet insurance and hobby-related plans. In the lifestyle analysis unit, for example, the generation AI analyzes the user's pet ownership and proposes appropriate pet insurance. For example, the generation AI selects an insurance plan based on the pet's type and health condition. The lifestyle analysis unit also collects data on the user's hobbies and proposes plans related to the hobbies. For example, the generation AI proposes outdoor insurance and related services to a user who enjoys outdoor activities. The lifestyle analysis unit also integrates data on the pet ownership and hobbies and proposes plans that are optimal for the user's lifestyle. For example, the generation AI proposes activities and services that can be enjoyed with pets. This makes it possible to propose appropriate plans based on the user's pet ownership and hobbies.

[0062] The lifestyle analysis unit can propose region-specific insurance and plans based on the user's living environment. In the lifestyle analysis unit, for example, the generation AI analyzes the user's living environment and proposes region-specific insurance plans. For example, the generation AI proposes insurance that addresses risks specific to cities to a user living in an urban area. The lifestyle analysis unit also proposes region-specific plans to users living in suburban or rural areas. For example, the generation AI proposes agricultural insurance and natural disaster prevention plans in rural areas. The lifestyle analysis unit also provides personalized advice tailored to the characteristics of the region based on the living environment data. For example, the generation AI proposes health plans based on local medical facilities and services. This makes it possible to propose region-specific insurance and plans based on the user's living environment.

[0063] The lifestyle analysis unit can use the emotion estimation function to analyze the emotions a user feels when entering lifestyle data and provide an interface that elicits positive emotions. The lifestyle analysis unit, for example, uses the emotion estimation function to analyze the emotions a user feels when entering lifestyle data in real time. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. The lifestyle analysis unit also provides an interface that elicits positive emotions based on the user's emotion data. For example, the generation AI displays encouraging messages and positive feedback. The lifestyle analysis unit also utilizes the emotion estimation data to design an interface that allows the user to enter data without feeling stressed. For example, the generation AI adjusts the design and color of the interface according to the user's emotional state. This makes it possible to elicit positive emotions when the user enters lifestyle data.

[0064] The goal analysis unit monitors the user's emotional state in real time and can adjust advice for achieving goals according to fluctuations in the emotional state. For example, the goal analysis unit monitors the user's emotional state in real time when the generation AI analyzes the user's goals. For example, the generation AI analyzes the user's heart rate and facial expressions to detect emotional fluctuations. The goal analysis unit also adjusts the advice for achieving goals provided by the generation AI according to fluctuations in the emotional state. For example, if the user is feeling stressed, it suggests relaxation methods to achieve the goal. The goal analysis unit also collects the user's emotional data over the long term and analyzes emotional trends during the goal achievement process. This allows the generation AI to understand the user's emotional patterns and provide more appropriate advice. This makes it possible to adjust advice for achieving goals according to the user's emotional state.

[0065] The goal analysis unit analyzes the user's past goal achievement history, identifies success patterns, and can reflect them in new goal setting. In the goal analysis unit, for example, the generation AI analyzes the user's past goal achievement history and identifies success patterns. For example, the generation AI extracts commonalities and success factors between goals achieved in the past. The goal analysis unit also reflects these in new goal setting based on the success patterns. For example, the generation AI proposes a goal achievement plan that takes into account past success factors. The goal analysis unit also analyzes the user's goal achievement history over the long term and identifies changes in success patterns. This allows the generation AI to provide advice according to the user's growth and changes. This allows new goals to be set based on the user's past success patterns.

[0066] The goal analysis unit can propose a career-specific goal achievement plan by taking into account the user's occupation and career path. For example, the generation AI analyzes the user's occupation data and proposes a career-specific goal achievement plan. For example, the generation AI provides a plan that takes into account the skills and qualifications required for a specific occupation. The goal analysis unit also analyzes the user's career path and proposes a goal achievement plan according to career progress. For example, the generation AI suggests specific steps for a user aiming for promotion or changing jobs. The goal analysis unit also integrates the occupation data and career path to provide a career-specific goal achievement plan. For example, the generation AI provides advice that takes into account the characteristics of the occupation and industry trends. This makes it possible to propose a goal achievement plan based on the user's occupation and career path.

[0067] The goal analysis unit takes into account the goals of the entire family and can propose a plan that can be shared by all family members. For example, the generation AI analyzes the user's family data and proposes a plan that takes into account the goals of the entire family. For example, the generation AI provides a savings plan or travel plan that can be shared by the entire family. The goal analysis unit also proposes a personalized plan based on the goals of the entire family. For example, the generation AI provides a plan that suits the life stage and needs of the family. The goal analysis unit also collects family data over the long term and monitors the progress of the entire family in achieving their goals. This allows the generation AI to provide advice that responds to changes in the family. This makes it possible to propose a plan that takes into account the goals of the entire family.

[0068] The goal analysis unit can propose a goal achievement plan based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobby data and proposes a goal achievement plan based on the hobbies and interests. For example, the generation AI provides a skill improvement plan related to a specific hobby. The goal analysis unit also takes the user's interests into consideration and the generation AI proposes a personalized goal achievement plan. For example, the generation AI suggests side jobs or projects that utilize the user's hobbies. The goal analysis unit also collects hobby data over the long term and provides a goal achievement plan that corresponds to changes in the user's interests. This allows the generation AI to provide advice that corresponds to the user's growth and changes. This makes it possible to propose a goal achievement plan based on the user's hobbies and interests.

[0069] The goal analysis unit can use the emotion estimation function to analyze the emotions a user feels when setting goals and provide a goal setting interface that elicits positive emotions. The goal analysis unit, for example, uses the emotion estimation function to analyze the emotions a user feels when setting goals in real time. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. The goal analysis unit also provides a goal setting interface that elicits positive emotions based on the user's emotion data. For example, the generation AI displays encouraging messages and positive feedback. The goal analysis unit also utilizes the emotion estimation data to design an interface that allows the user to set goals without feeling stressed. For example, the generation AI adjusts the design and color of the interface according to the user's emotional state. This makes it possible to elicit positive emotions when the user sets goals.

[0070] The risk assessment unit can monitor the user's emotional state in real time and adjust the risk assessment according to changes in the emotional state. For example, the risk assessment unit monitors the user's emotional state in real time when the generation AI evaluates the user's risk tolerance. For example, the generation AI analyzes the user's heart rate and facial expressions to detect emotional changes. The risk assessment unit also adjusts the risk assessment provided by the generation AI according to changes in the emotional state. For example, if the user is feeling stressed, it will suggest a low-risk investment plan. The risk assessment unit also collects the user's emotional data over the long term and analyzes emotional trends associated with changes in risk tolerance. This allows the generation AI to understand the user's emotional patterns and provide a more appropriate risk assessment. This makes it possible to adjust the risk assessment according to the user's emotional state.

[0071] The risk assessment unit analyzes the user's past investment history and risk preference patterns to perform more accurate risk assessments. In the risk assessment unit, for example, the generation AI analyzes the user's past investment history and identifies the risk preference pattern. For example, the generation AI performs risk assessment based on past investment successes and failures. Furthermore, the risk assessment unit provides a more accurate risk assessment based on the risk preference pattern. For example, the generation AI performs risk assessment taking into account the user's past investment behavior. Furthermore, the risk assessment unit analyzes the user's investment history over the long term to identify changes in the risk preference pattern. This allows the generation AI to provide advice according to changes in the user's risk tolerance. This enables a more accurate risk assessment based on the user's past investment history and risk preference pattern.

[0072] The risk assessment unit can propose an insurance plan based on the user's health risk, taking into account the user's health condition and lifestyle habits. In the risk assessment unit, for example, the generation AI analyzes the user's health condition and evaluates risk tolerance. For example, the generation AI performs risk assessment based on the user's health checkup results and lifestyle habits. In addition, the risk assessment unit proposes an appropriate insurance plan based on the health risk. For example, the generation AI selects an insurance plan that corresponds to a specific health risk. In addition, the risk assessment unit collects the user's health data over the long term and provides a risk assessment according to changes in the health condition. This allows the generation AI to provide advice based on the user's health risk. This makes it possible to propose an insurance plan based on the user's health condition and lifestyle habits.

[0073] The risk assessment unit can propose an optimal plan for the entire family, taking into account the user's family structure and the family's risk tolerance. For example, the generation AI analyzes the user's family structure and proposes a plan that takes into account the risk tolerance of the entire family. For example, the generation AI provides insurance plans and investment plans that can be shared by the entire family. The risk assessment unit also proposes personalized plans based on the risk tolerance of the entire family. For example, the generation AI provides plans that suit the family's life stage and needs. The risk assessment unit also collects family data over the long term and monitors changes in the family's overall risk tolerance. This allows the generation AI to provide advice that reflects changes in the family. This makes it possible to propose plans that take into account the risk tolerance of the entire family.

[0074] The risk assessment unit can perform risk assessment based on the user's hobbies and interests and suggest insurance plans related to the hobbies. In the risk assessment unit, for example, the generation AI analyzes the user's hobby data and performs risk assessment based on the hobbies and interests. For example, the generation AI evaluates the risks associated with a specific hobby. Furthermore, the risk assessment unit proposes an appropriate insurance plan based on the risk assessment related to the hobby. For example, the generation AI suggests outdoor insurance to a user who likes outdoor activities. Furthermore, the risk assessment unit collects hobby data over the long term and provides risk assessment according to changes in the user's interests. This allows the generation AI to provide advice according to the user's growth and changes. This makes it possible to perform risk assessment and suggest insurance plans based on the user's hobbies and interests.

[0075] The risk assessment unit can use the emotion estimation function to analyze the emotions expressed by the user when answering questions about risk tolerance and provide a question interface that elicits positive emotions. The risk assessment unit, for example, uses the emotion estimation function to analyze the emotions expressed by the user when answering questions about risk tolerance in real time. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. The risk assessment unit also provides a question interface that elicits positive emotions based on the user's emotion data. For example, the generation AI displays encouraging messages and positive feedback. The risk assessment unit also utilizes the emotion estimation data to design an interface that allows the user to answer questions without feeling stressed. For example, the generation AI adjusts the design and color of the interface according to the user's emotional state. This makes it possible to elicit positive emotions when the user answers questions about risk tolerance.

[0076] The personalization unit can monitor the user's emotional state in real time and adjust personalized advice according to fluctuations in the emotional state. For example, the personalization unit monitors the user's emotional state in real time when the generation AI analyzes the user's data. For example, the generation AI analyzes the user's heart rate and facial expressions to detect emotional fluctuations. The personalization unit also adjusts the personalized advice provided by the generation AI according to fluctuations in the emotional state. For example, if the user is feeling stressed, the personalization unit suggests relaxation methods and stress reduction measures. The personalization unit also collects the user's emotional data over the long term and analyzes emotional trends associated with changes in the data. This allows the generation AI to understand the user's emotional patterns and provide more appropriate advice. This makes it possible to adjust personalized advice according to the user's emotional state.

[0077] The personalization unit can analyze the user's past advice history, identify successful patterns, and reflect them in new advice. In the personalization unit, for example, the generation AI analyzes the user's past advice history and identifies successful patterns. For example, the generation AI extracts commonalities and success factors of advice that was successful in the past. The personalization unit also reflects these in new advice based on the successful patterns. For example, the generation AI provides advice that takes into account past success factors. The personalization unit also analyzes the user's advice history over the long term and identifies changes in successful patterns. This allows the generation AI to provide advice that corresponds to the user's growth and changes. This allows new advice to be provided based on the user's past successful patterns.

[0078] The personalization unit can provide occupation-specific personalized advice by taking into account the user's occupation and career path. In the personalization unit, for example, the generation AI analyzes the user's occupation data and provides occupation-specific personalized advice. For example, the generation AI provides advice that takes into account the skills and qualifications required for a specific occupation. The personalization unit also analyzes the user's career path and provides personalized advice according to career progress. For example, the generation AI suggests specific steps for a user who is aiming for promotion or changing jobs. The personalization unit also integrates the occupation data and career path to provide advice specific to the user's occupation. For example, the generation AI provides advice that takes into account the characteristics of the occupation and industry trends. This makes it possible to provide personalized advice based on the user's occupation and career path.

[0079] The personalization unit takes into account data for the entire family and can provide personalized advice that can be shared by all family members. For example, the generation AI analyzes the user's family data and provides personalized advice that takes into account data for the entire family. For example, the generation AI provides savings plans and travel plans that can be shared by all family members. The personalization unit also provides personalized advice based on data for the entire family. For example, the generation AI provides advice according to the life stage and needs of the family. The personalization unit also collects family data over the long term and monitors changes in the data for the entire family. This allows the generation AI to provide advice that responds to changes in the family. This makes it possible to provide personalized advice that takes into account data for the entire family.

[0080] The personalization unit can provide personalized advice based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobby data and provides personalized advice based on the hobbies and interests. For example, the generation AI provides a skill improvement plan related to a specific hobby. The personalization unit also takes the user's interests into consideration and provides personalized advice. For example, the generation AI suggests side jobs or projects that utilize the user's hobbies. The personalization unit also collects hobby data over the long term and provides personalized advice in response to changes in the user's interests. This allows the generation AI to provide advice in response to the user's growth and changes. This makes it possible to provide personalized advice based on the user's hobbies and interests.

[0081] The personalization unit can use the emotion estimation function to analyze the emotions of a user when entering data and provide a data entry interface that elicits positive emotions. The personalization unit, for example, uses the emotion estimation function to analyze the emotions of a user when entering data in real time. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. The personalization unit also provides a data entry interface that elicits positive emotions based on the user's emotion data. For example, the generation AI displays encouraging messages and positive feedback. The personalization unit also utilizes the emotion estimation data to design an interface that allows the user to enter data without feeling stressed. For example, the generation AI adjusts the design and color of the interface according to the user's emotional state. This makes it possible to elicit positive emotions when the user enters data.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The financial planning platform can further analyze a user's travel plans and provide advice on travel insurance and activities at the travel destination. For example, the travel plan analysis unit collects and analyzes data such as the user's travel destination, travel duration, and budget. The generation AI suggests risks at the travel destination and necessary insurance. The travel plan analysis unit also provides information on activities at the travel destination and suggests activities based on the user's interests. For example, for a user who likes outdoor activities, it provides information on hiking and camping. This makes it possible to provide personalized advice based on the user's travel plans.

[0084] The financial planning platform can also monitor the user's emotional state in real time and adjust advice based on fluctuations. For example, it uses emotion estimation to analyze the user's heart rate and facial expressions to detect fluctuations in stress and happiness. If the user is feeling stressed, it can suggest relaxation methods and stress reduction measures. It also collects emotional data over the long term and analyzes emotional trends associated with lifestyle changes. This allows it to understand the user's emotional patterns and provide more appropriate advice.

[0085] The financial planning platform can also estimate the user's health condition and stress level and recommend health insurance and stress reduction plans based on that. For example, the health condition analysis unit assesses health risks based on the user's diet, exercise, and sleep patterns. The generation AI selects insurance plans that address specific health risks. The stress level analysis unit analyzes the user's stress level and suggests relaxation methods and activities for stress management. This allows the platform to recommend appropriate plans based on the user's health condition and stress level.

[0086] The financial planning platform can also take into account a user's social media activity and online shopping history to conduct more detailed lifestyle analysis. For example, the social media analysis unit identifies a user's hobbies and interests based on their postings and likes. The generative AI analyzes their online shopping history to understand their spending patterns. This allows it to provide personalized advice based on the user's hobbies and interests.

[0087] The financial planning platform can also collect data on users' pet ownership and hobbies to suggest pet insurance and plans related to their hobbies. For example, the pet analysis unit selects insurance plans based on the pet's type and health condition. The generation AI can suggest outdoor insurance and related services to users who enjoy outdoor activities. This allows the platform to suggest appropriate plans based on the user's pet ownership and hobbies.

[0088] The financial planning platform can also suggest region-specific insurance and plans based on the user's living environment. For example, the living environment analysis unit will suggest insurance that addresses risks specific to cities to users living in urban areas. The generation AI will suggest agricultural insurance and natural disaster countermeasure plans to users living in rural areas. This allows the platform to suggest region-specific insurance and plans based on the user's living environment.

[0089] The financial planning platform can further use the emotion estimation function to analyze the emotions felt when a user enters lifestyle data and provide an interface to elicit positive emotions. For example, the emotion estimation function can be used to analyze the user's facial expressions and voice to calculate an emotion score. The generative AI can then display encouraging messages and positive feedback, thereby eliciting positive emotions when the user enters lifestyle data.

[0090] The financial planning platform can also monitor the user's emotional state in real time and adjust advice for achieving goals according to fluctuations in their emotional state. For example, it can use emotion estimation to analyze the user's heart rate and facial expressions to detect emotional fluctuations. If the user is feeling stressed, it can suggest relaxation methods. This makes it possible to adjust advice for achieving goals according to the user's emotional state.

[0091] The financial planning platform can also analyze the user's past goal achievement history, identify success patterns, and reflect them in new goal setting. For example, the goal achievement analysis unit extracts commonalities and success factors between previously achieved goals. The generation AI then proposes a goal achievement plan that takes past success factors into account. This allows new goal setting to be based on the user's past success patterns.

[0092] The financial planning platform can further consider a user's occupation and career path to propose occupation-specific goal achievement plans. For example, the occupation analysis unit provides plans that take into account the skills and qualifications required for a specific occupation. The generation AI suggests specific steps for users aiming for promotion or changing jobs. This allows it to propose goal achievement plans based on the user's occupation and career path.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The lifestyle analysis unit analyzes the user's lifestyle data. For example, information such as the user's income, expenses, hobbies, and family structure is collected and analyzed using the generation AI. This allows the user's lifestyle trends to be understood. Step 2: The goal analysis unit analyzes the user's goals based on the data analyzed by the lifestyle analysis unit. For example, the user's short-term and long-term goals are set and analyzed using the generation AI. This allows the system to understand the user's goals, such as purchasing a home, paying for their children's education, and living expenses in retirement, and propose the optimal plan. Step 3: The risk assessment unit evaluates the user's risk tolerance based on the data analyzed by the goal analysis unit. For example, the unit may have the user answer questions about risk and use a generation AI to evaluate the user's risk tolerance. This allows the user to understand how much risk they can tolerate. Step 4: The personalization unit provides personalized advice based on the data analyzed by the lifestyle analysis unit, goal analysis unit, and risk assessment unit. For example, it analyzes data such as the user's income, expenses, goals, and risk tolerance, and uses generative AI to suggest optimal insurance and investment plans.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[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 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.

[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 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.

[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 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 lifestyle analysis unit that analyzes lifestyle data of a user; a goal analysis unit that analyzes a user's goal based on the data analyzed by the lifestyle analysis unit; a risk assessment unit that assesses a user's risk tolerance based on the data analyzed by the goal analysis unit; a personalization unit that provides personalized advice based on the data analyzed by the lifestyle analysis unit, the goal analysis unit, and the risk assessment unit. A system characterized by:

2. The lifestyle analysis unit Monitoring the user's emotional state in real time and adjusting the advice in response to fluctuations in the emotional state.

2. The system of claim 1.

3. The lifestyle analysis unit Estimate the user's health condition and stress level, and propose health insurance and stress reduction plans based on that.

2. The system of claim 1.

4. The lifestyle analysis unit Conduct a more detailed lifestyle analysis by taking into account the user's social media activity and online shopping history.

2. The system of claim 1.

5. The lifestyle analysis unit Collect data on the user's pet ownership status and hobbies, and propose pet insurance and plans related to the hobbies.

2. The system of claim 1.

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