system

Abstract

The system according to this embodiment aims to provide personalized advice based on the user's personality and to assist with household finance management and health management. [Solution] The system according to this embodiment comprises a diagnostic unit, an advice unit, and a management unit. The diagnostic unit diagnoses the user's personality. The advice unit provides advice based on the personality diagnosed by the diagnostic unit. The management unit performs household finance management and health management based on the advice provided by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, individual advice and management based on the user's personality have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to an embodiment aims to provide individual advice based on the user's personality and perform household management and health management.

Means for Solving the Problems

[0006] The system according to an embodiment includes a diagnosis unit, an advice unit, and a management unit. The diagnosis unit diagnoses the user's personality. The advice unit provides advice based on the personality diagnosed by the diagnosis unit. The management unit performs household management and health management based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized advice based on the user's personality and can manage household finances and health. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The smartphone advice system according to an embodiment of the present invention is a system that provides advice and consultation after understanding the user's personality. This smartphone advice system diagnoses the user's personality, and the generating AI provides appropriate advice and consultation based on the user's personality. Furthermore, the smartphone advice system can also handle household budget management, dieting, and health management. For example, the smartphone advice system performs an MBTI (personality test) during initial setup to understand the user's personality. Next, the generating AI provides appropriate advice and consultation based on the user's personality. This advice and consultation is tailored to the user's personality and is easy for the user to understand. Furthermore, the smartphone advice system can also handle household budget management, dieting, and health management. For example, in household budget management, it analyzes the user's income and expenses and proposes the optimal saving method. In dieting and health management, it provides appropriate advice based on the user's records of meals and exercise. First, an MBTI (personality test) is performed during initial setup. At this time, the user diagnoses their personality type by answering a series of questions. For example, questions include whether the user is extroverted or introverted, and whether they are emotional or logical. This information is input into the generating AI. Next, the generating AI analyzes the input personality assessment results and provides advice and consultation based on the user's personality. For example, it provides proactive advice to extroverted users and cautious advice to introverted users. In this way, user-friendly advice and consultation are realized. Furthermore, the smartphone advice system can also handle household budget management, diet management, and health management. For example, in household budget management, it links the user's bank account and credit card information and automatically records income and expenses. The generating AI analyzes this information and proposes the best way to save money. For example, it provides specific advice on how to reduce unnecessary spending. In addition, in diet and health management, it links the user's records of meals and exercise. The generating AI analyzes this information and provides appropriate advice. For example, it proposes a balanced meal menu and an effective exercise plan. In this way, it supports the user's health management.This mechanism allows the smartphone advice system to understand the user's personality and provide advice and consultation accordingly. It can also handle household budget management, dieting, and health management. This enables users to lead more fulfilling lives. In short, the smartphone advice system understands the user's personality and provides appropriate advice and consultation.

[0029] The smartphone advice system according to this embodiment comprises a diagnostic unit, an advice unit, and a management unit. The diagnostic unit diagnoses the user's personality. The diagnostic unit diagnoses the user's personality using, for example, MBTI (personality test). The diagnostic unit diagnoses the personality type when the user answers a series of questions. For example, questions include whether the user is extroverted or introverted, and whether they are emotional or logical. The diagnostic unit inputs this information into a generating AI and analyzes it. The advice unit provides advice based on the personality diagnosed by the diagnostic unit. For example, the advice unit provides proactive advice to extroverted users and cautious advice to introverted users. The advice unit uses a generating AI to provide advice tailored to the user's personality. For example, the generating AI generates friendly advice based on the user's personality. The management unit performs household budget management and health management based on the advice provided by the advice unit. For example, in household budget management, the management unit analyzes the user's income and expenses and proposes the optimal saving method. The management department uses generative AI to analyze the user's income and expenses and provide specific advice to reduce unnecessary spending. In health management, the management department provides appropriate advice based on the user's records of diet and exercise. The management department uses generative AI to propose balanced meal menus and effective exercise plans. As a result, the smartphone advice system according to this embodiment can understand the user's personality and provide appropriate advice and consultation. Some or all of the above processing in the management department may be performed using generative AI or not. For example, the management department can input the user's income and expense data into the generative AI and have the generative AI propose the optimal way to save money.

[0030] The diagnostic unit diagnoses the user's personality. For example, the diagnostic unit uses the MBTI (a personality assessment tool) to diagnose the user's personality. Specifically, the diagnostic unit presents the user with a series of questions and diagnoses their personality type based on their answers. These questions include items such as whether the user is extroverted or introverted, feeling or logical, and planning or perceptive. These questions are designed to gain a detailed understanding of the user's behavior and thought patterns, and the answers are input into a generative AI. The generative AI analyzes these answers to identify the user's personality type. The generative AI uses natural language processing technology to understand the user's answers and classifies personality types using statistical models. For example, the generative AI analyzes the user's response patterns to determine whether they are more extroverted or introverted. It also evaluates whether they tend to have more emotional responses or more logical judgments. This allows the diagnostic unit to diagnose the user's personality with high accuracy and understand individual characteristics in detail. Furthermore, the diagnostic unit can refer to past diagnostic results and data from other users to perform even more accurate diagnoses. For example, by comparing data from users with the same personality type, specific tendencies and patterns can be identified, improving the reliability of the diagnostic results. This allows the diagnostic unit to accurately diagnose a user's personality and use that information to provide subsequent advice and management.

[0031] The advice unit provides advice based on the personality diagnosed by the diagnostic unit. Specifically, the advice unit uses generative AI to generate advice tailored to the user's personality. For example, it provides advice encouraging proactive behavior to extroverted users and advice recommending cautious behavior to introverted users. Based on the user's personality type, the generative AI generates friendly language and specific action plans. For example, it advises extroverted users to meet new people and participate in social events, while it suggests ways to relax in quiet environments and hobbies that can be enjoyed alone to introverted users. Furthermore, the advice unit continuously improves the content of its advice based on the user's past behavioral history and feedback. For example, by recording how users reacted to advice they received in the past and analyzing that data, it can provide more effective advice. The generative AI learns from this data and generates advice tailored to the user's preferences and tendencies. As a result, the advice unit can provide users with personalized, specific, and practical advice, improving their quality of life.

[0032] The Management Department manages household finances and health based on advice provided by the Advice Department. Specifically, the Management Department uses generative AI to analyze the user's income and expenses and propose optimal saving methods. For example, it inputs the user's income and expense data into the generative AI and provides specific advice to reduce unnecessary spending. The generative AI analyzes the user's income and expense patterns and proposes specific ways to save money. For example, it reviews monthly fixed and variable expenses and provides specific action plans to reduce unnecessary spending. In addition, the Management Department also uses generative AI to analyze the user's diet and exercise records and provide appropriate advice for health management. For example, it proposes balanced meal menus and effective exercise plans. The generative AI analyzes the user's diet and exercise data and evaluates nutritional balance and the effectiveness of exercise. This allows the Management Department to provide specific advice to maintain and improve the user's health. Furthermore, the Management Department continuously improves the content of the advice based on user feedback. For example, by recording how the user reacted to the proposed saving methods and health management plans and analyzing that data, it is possible to provide more effective advice. This allows the management department to support users in all aspects of their lives and improve their quality of life.

[0033] The diagnostic unit can diagnose a user's personality based on the MBTI. For example, the diagnostic unit diagnoses a personality type when the user answers a series of questions. These questions may include whether the user is extroverted or introverted, feeling or logical, etc. The diagnostic unit inputs this information into a generating AI for analysis. This enables a personality diagnosis based on the MBTI. Some or all of the above processing in the diagnostic unit may be performed using the generating AI, or not. For example, the diagnostic unit can input the user's response data into the generating AI and have the generating AI execute the personality diagnosis results.

[0034] The advice unit can provide appropriate advice to the user based on the diagnostic results. For example, the advice unit provides proactive advice to extroverted users and cautious advice to introverted users. The advice unit uses generative AI to provide advice tailored to the user's personality. For example, the generative AI generates friendly advice based on the user's personality. This ensures that appropriate advice is provided based on the diagnostic results. Some or all of the above-described processes in the advice unit may be performed using generative AI or not. For example, the advice unit can input the diagnostic results into the generative AI and have the generative AI generate appropriate advice.

[0035] The management department can perform household budget management. For example, the management department can analyze the user's income and expenses and propose the best saving methods. The management department uses generative AI to analyze the user's income and expenses and provide specific advice to reduce unnecessary spending. This enables household budget management. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input the user's income and expense data into the generative AI and have the generative AI propose the best saving methods.

[0036] The management department can perform health management. For example, the management department can provide appropriate advice based on the user's diet and exercise records. The management department can use generative AI to propose balanced meal menus and effective exercise plans. This enables health management. Some or all of the above processes in the management department may be performed using generative AI, or they may not. For example, the management department can input the user's diet and exercise data into the generative AI and have the generative AI generate appropriate advice.

[0037] The management department can analyze users' income and expenses and propose optimal saving methods. For example, the management department can propose optimal saving methods considering the balance of users' income and expenses. The management department can use generative AI to analyze users' income and expenses and provide specific advice to reduce unnecessary spending. This makes it possible to propose saving methods based on income and expense analysis. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input user income and expense data into the generative AI and have the generative AI propose optimal saving methods.

[0038] The management department can provide appropriate advice based on the user's diet and exercise records. For example, the management department can analyze the user's diet and exercise records and propose balanced meal menus and effective exercise plans. The management department uses generative AI to analyze the user's diet and exercise data and provide appropriate advice. This ensures that appropriate advice is provided based on the diet and exercise records. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input the user's diet and exercise data into the generative AI and have the generative AI generate appropriate advice.

[0039] The diagnostic unit can analyze the user's past behavioral history and make corrections to improve the accuracy of the personality assessment. For example, the diagnostic unit can adjust the questions by referring to the results of personality assessments the user has taken in the past. The diagnostic unit analyzes the user's past behavioral patterns and reflects them in the diagnostic results. The diagnostic unit makes corrections to improve the accuracy of the assessment based on the user's past response tendencies. This makes it possible to improve the accuracy of the personality assessment based on past behavioral history. Some or all of the above processes in the diagnostic unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the diagnostic unit can input the user's past behavioral data into a generative AI and have the generative AI perform the corrections to the personality assessment.

[0040] The diagnostic unit can customize the results of the personality assessment based on the user's living environment and occupation. For example, if the user is a student, the diagnostic unit will add questions related to academics. If the user is employed, the diagnostic unit will add questions related to their occupation. The diagnostic unit will adjust the questions according to the user's living environment (urban, rural, etc.). This makes it possible to customize the personality assessment results according to the user's living environment and occupation. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the diagnostic unit can input the user's living environment and occupation data into a generative AI and have the generative AI perform the customization of the personality assessment results.

[0041] The diagnostic unit can analyze the user's social media activity during personality assessment and reflect this in the assessment results. For example, the diagnostic unit can analyze the content of the user's social media posts and reflect this in the personality assessment. The diagnostic unit can analyze the user's social media friendships and reflect this in the assessment results. The diagnostic unit can improve the accuracy of the personality assessment based on the user's frequency of social media activity. This makes it possible to reflect personality assessment results based on social media activity. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the diagnostic unit can input the user's social media data into a generative AI and have the generative AI perform the reflection of the assessment results.

[0042] The diagnostic unit can customize the personality assessment results by taking into account the user's geographical location information. For example, if the user lives in an urban area, the diagnostic unit will add questions related to urban life. If the user lives in a rural area, the diagnostic unit will add questions related to rural life. The diagnostic unit customizes the assessment results based on the user's geographical location information. This makes it possible to customize personality assessment results based on geographical location information. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the diagnostic unit can input the user's geographical location data into a generative AI and have the generative AI perform the customization of the assessment results.

[0043] The advice unit can select the most appropriate advice by referring to the user's past advice history when providing advice. For example, the advice unit can provide the most appropriate advice for the current situation based on the advice the user has received in the past. The advice unit analyzes the user's past advice history and selects effective advice. The advice unit provides the most appropriate advice by referring to the results of the advice the user has received in the past. This makes it possible to select the most appropriate advice based on past advice history. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input the user's past advice history data into a generation AI and have the generation AI perform the selection of the most appropriate advice.

[0044] The advice unit can customize the advice it provides based on the user's current lifestyle and goals. For example, if the user is on a diet, the advice unit will provide advice on diet and exercise. If the user is stressed at work, the advice unit will provide advice on stress management. If the user wants to start a new hobby, the advice unit will provide advice on that hobby. This makes it possible to customize the advice to suit the user's current lifestyle and goals. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's lifestyle and goal data into a generative AI and have the generative AI perform the customization of the advice.

[0045] The advice unit can analyze the user's social media activity and provide relevant advice when offering advice. For example, the advice unit can provide advice based on goals shared by the user on social media. The advice unit analyzes the user's social media activities and provides relevant advice. The advice unit considers the user's social media friendships to provide appropriate advice. This makes it possible to provide relevant advice based on social media activity. Some or all of the above processing in the advice unit may be performed using a generative AI, or not. For example, the advice unit can input the user's social media data into a generative AI and have the generative AI generate relevant advice.

[0046] The advice unit can provide optimal advice by considering the user's geographical location information when providing advice. For example, if the user lives in an urban area, the advice unit will provide advice related to urban life. If the user lives in a rural area, the advice unit will provide advice related to rural life. The advice unit provides optimal advice based on the user's geographical location information. This makes it possible to provide optimal advice based on geographical location information. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input the user's geographical location data into a generation AI and have the generation AI perform the generation of optimal advice.

[0047] The management department can analyze a user's past income and expenditure history to suggest optimal saving methods during household budget management. For example, the management department can analyze a user's past spending patterns and suggest ways to reduce unnecessary spending. The management department can suggest optimal saving methods considering the balance between the user's income and expenditures. The management department can suggest effective saving methods based on the user's past successful saving examples. This makes it possible to suggest optimal saving methods based on past income and expenditure history. Some or all of the above processes in the management department may be performed using a generation AI, or they may be performed without a generation AI. For example, the management department can input the user's past income and expenditure data into a generation AI and have the generation AI suggest optimal saving methods.

[0048] The management department can select the optimal health management method by referring to the user's past health data during health management. For example, the management department can propose an appropriate health management method based on the user's past health checkup results. The management department can analyze the user's past exercise history and propose an optimal exercise plan. The management department can refer to the user's past meal records and propose a balanced meal plan. This makes it possible to select the optimal health management method based on past health data. Some or all of the above processes in the management department may be performed using a generation AI, or they may be performed without a generation AI. For example, the management department can input the user's past health data into a generation AI and have the generation AI perform the selection of the optimal health management method.

[0049] The management unit can suggest optimal saving methods when managing household finances, taking into account the user's geographical location information. For example, if the user lives in an urban area, the management unit will suggest saving methods suitable for urban living. If the user lives in a rural area, the management unit will suggest saving methods suitable for rural living. The management unit suggests optimal saving methods based on the user's geographical location information. This makes it possible to suggest optimal saving methods based on geographical location information. Some or all of the above processing in the management unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the management unit can input the user's geographical location data into a generative AI and have the generative AI suggest optimal saving methods.

[0050] The management department can analyze a user's social media activity and provide relevant health advice during health management. For example, the management department can provide advice based on health goals shared by the user on social media. The management department analyzes the user's social media activity and provides relevant health advice. The management department considers the user's social media friendships to provide appropriate health advice. This makes it possible to provide relevant health advice based on social media activity. Some or all of the above processes in the management department may be performed using or without a generative AI. For example, the management department can input the user's social media data into a generative AI and have the generative AI generate relevant health advice.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The smartphone advice system can also monitor the user's sleep patterns and provide appropriate advice. For example, the diagnostic unit collects the user's sleep data and analyzes the quality and quantity of sleep. The advice unit provides the user with advice on improving sleep based on the sleep data. For example, it may advise on relaxation methods before bedtime or how to create a suitable sleep environment. The management unit can propose specific plans to improve sleep quality as part of health management, based on the user's sleep data. This can improve the user's sleep quality and overall health.

[0053] The smartphone advice system can also analyze the user's hobbies and interests and provide advice based on that analysis. For example, the diagnostic unit collects and analyzes data on the user's hobbies and interests. The advice unit then suggests new hobbies and introduces related activities based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information on learning a new instrument or attending concerts. The management unit can take the user's hobbies and interests into consideration and propose specific plans to improve their quality of life. This can lead to a more fulfilling life and increased satisfaction for the user.

[0054] The smartphone advice system can also monitor users' exercise habits and provide appropriate advice. For example, the diagnostic unit collects the user's exercise data and analyzes the frequency and intensity of exercise. Based on the exercise data, the advice unit suggests improvements to the user's exercise habits and proposes new exercise plans. For example, it can advise on how many times a week exercise is appropriate and what types of exercise are effective. Based on the user's exercise data, the management unit can propose specific plans to improve exercise habits as part of health management. This can lead to improved exercise habits and an overall improvement in the user's health.

[0055] The smartphone advice system can also analyze the user's learning style and provide learning advice based on that analysis. For example, the diagnostic unit collects the user's learning data and analyzes the efficiency and effectiveness of their learning. The advice unit then proposes effective learning methods and plans to the user based on the learning data. For instance, if the user has a visual learning style, the system will suggest learning methods that utilize visual aids. The management unit can then propose specific plans to achieve long-term learning goals based on the user's learning data. This improves the user's learning efficiency and maximizes their learning outcomes.

[0056] The smartphone advice system can further support users' travel planning and provide appropriate advice. For example, the diagnostic unit collects and analyzes the user's travel preferences and past travel history. The advice unit then suggests optimal travel destinations and plans based on the travel data. For instance, if the user prefers nature, it will suggest destinations rich in nature. The management unit can provide specific advice on travel preparation and health management during travel, based on the user's travel data. This can improve the user's travel experience and increase their satisfaction.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The diagnostic unit diagnoses the user's personality. The diagnostic unit diagnoses the user's personality using, for example, MBTI (a personality assessment). The user answers a series of questions to determine their personality type. These questions may include, for example, whether they are extroverted or introverted, feeling or logical, etc. The diagnostic unit inputs this information into a generating AI for analysis. Step 2: The advice unit provides advice based on the personality diagnosed by the diagnostic unit. For example, the advice unit provides proactive advice to extroverted users and cautious advice to introverted users. Generative AI is used to provide advice tailored to the user's personality. For example, the generative AI generates friendly advice based on the user's personality. Step 3: The Management Department manages household finances and health based on the advice provided by the Advice Department. For example, in household finance management, the Management Department analyzes the user's income and expenses and proposes optimal saving methods. Using generative AI, it analyzes the user's income and expenses and provides specific advice to reduce unnecessary spending. In health management, it provides appropriate advice based on the user's records of meals and exercise. Using generative AI, it proposes balanced meal menus and effective exercise plans.

[0059] (Example of form 2) The smartphone advice system according to an embodiment of the present invention is a system that provides advice and consultation after understanding the user's personality. This smartphone advice system diagnoses the user's personality, and the generating AI provides appropriate advice and consultation based on the user's personality. Furthermore, the smartphone advice system can also handle household budget management, dieting, and health management. For example, the smartphone advice system performs an MBTI (personality test) during initial setup to understand the user's personality. Next, the generating AI provides appropriate advice and consultation based on the user's personality. This advice and consultation is tailored to the user's personality and is easy for the user to understand. Furthermore, the smartphone advice system can also handle household budget management, dieting, and health management. For example, in household budget management, it analyzes the user's income and expenses and proposes the optimal saving method. In dieting and health management, it provides appropriate advice based on the user's records of meals and exercise. First, an MBTI (personality test) is performed during initial setup. At this time, the user diagnoses their personality type by answering a series of questions. For example, questions include whether the user is extroverted or introverted, and whether they are emotional or logical. This information is input into the generating AI. Next, the generating AI analyzes the input personality assessment results and provides advice and consultation based on the user's personality. For example, it provides proactive advice to extroverted users and cautious advice to introverted users. In this way, user-friendly advice and consultation are realized. Furthermore, the smartphone advice system can also handle household budget management, diet management, and health management. For example, in household budget management, it links the user's bank account and credit card information and automatically records income and expenses. The generating AI analyzes this information and proposes the best way to save money. For example, it provides specific advice on how to reduce unnecessary spending. In addition, in diet and health management, it links the user's records of meals and exercise. The generating AI analyzes this information and provides appropriate advice. For example, it proposes a balanced meal menu and an effective exercise plan. In this way, it supports the user's health management.This mechanism allows the smartphone advice system to understand the user's personality and provide advice and consultation accordingly. It can also handle household budget management, dieting, and health management. This enables users to lead more fulfilling lives. In short, the smartphone advice system understands the user's personality and provides appropriate advice and consultation.

[0060] The smartphone advice system according to this embodiment comprises a diagnostic unit, an advice unit, and a management unit. The diagnostic unit diagnoses the user's personality. The diagnostic unit diagnoses the user's personality using, for example, MBTI (personality test). The diagnostic unit diagnoses the personality type when the user answers a series of questions. For example, questions include whether the user is extroverted or introverted, and whether they are emotional or logical. The diagnostic unit inputs this information into a generating AI and analyzes it. The advice unit provides advice based on the personality diagnosed by the diagnostic unit. For example, the advice unit provides proactive advice to extroverted users and cautious advice to introverted users. The advice unit uses a generating AI to provide advice tailored to the user's personality. For example, the generating AI generates friendly advice based on the user's personality. The management unit performs household budget management and health management based on the advice provided by the advice unit. For example, in household budget management, the management unit analyzes the user's income and expenses and proposes the optimal saving method. The management department uses generative AI to analyze the user's income and expenses and provide specific advice to reduce unnecessary spending. In health management, the management department provides appropriate advice based on the user's records of diet and exercise. The management department uses generative AI to propose balanced meal menus and effective exercise plans. As a result, the smartphone advice system according to this embodiment can understand the user's personality and provide appropriate advice and consultation. Some or all of the above processing in the management department may be performed using generative AI or not. For example, the management department can input the user's income and expense data into the generative AI and have the generative AI propose the optimal way to save money.

[0061] The diagnostic unit diagnoses the user's personality. For example, the diagnostic unit uses the MBTI (a personality assessment tool) to diagnose the user's personality. Specifically, the diagnostic unit presents the user with a series of questions and diagnoses their personality type based on their answers. These questions include items such as whether the user is extroverted or introverted, feeling or logical, and planning or perceptive. These questions are designed to gain a detailed understanding of the user's behavior and thought patterns, and the answers are input into a generative AI. The generative AI analyzes these answers to identify the user's personality type. The generative AI uses natural language processing technology to understand the user's answers and classifies personality types using statistical models. For example, the generative AI analyzes the user's response patterns to determine whether they are more extroverted or introverted. It also evaluates whether they tend to have more emotional responses or more logical judgments. This allows the diagnostic unit to diagnose the user's personality with high accuracy and understand individual characteristics in detail. Furthermore, the diagnostic unit can refer to past diagnostic results and data from other users to perform even more accurate diagnoses. For example, by comparing data from users with the same personality type, specific tendencies and patterns can be identified, improving the reliability of the diagnostic results. This allows the diagnostic unit to accurately diagnose a user's personality and use that information to provide subsequent advice and management.

[0062] The advice unit provides advice based on the personality diagnosed by the diagnostic unit. Specifically, the advice unit uses generative AI to generate advice tailored to the user's personality. For example, it provides advice encouraging proactive behavior to extroverted users and advice recommending cautious behavior to introverted users. Based on the user's personality type, the generative AI generates friendly language and specific action plans. For example, it advises extroverted users to meet new people and participate in social events, while it suggests ways to relax in quiet environments and hobbies that can be enjoyed alone to introverted users. Furthermore, the advice unit continuously improves the content of its advice based on the user's past behavioral history and feedback. For example, by recording how users reacted to advice they received in the past and analyzing that data, it can provide more effective advice. The generative AI learns from this data and generates advice tailored to the user's preferences and tendencies. As a result, the advice unit can provide users with personalized, specific, and practical advice, improving their quality of life.

[0063] The Management Department manages household finances and health based on advice provided by the Advice Department. Specifically, the Management Department uses generative AI to analyze the user's income and expenses and propose optimal saving methods. For example, it inputs the user's income and expense data into the generative AI and provides specific advice to reduce unnecessary spending. The generative AI analyzes the user's income and expense patterns and proposes specific ways to save money. For example, it reviews monthly fixed and variable expenses and provides specific action plans to reduce unnecessary spending. In addition, the Management Department also uses generative AI to analyze the user's diet and exercise records and provide appropriate advice for health management. For example, it proposes balanced meal menus and effective exercise plans. The generative AI analyzes the user's diet and exercise data and evaluates nutritional balance and the effectiveness of exercise. This allows the Management Department to provide specific advice to maintain and improve the user's health. Furthermore, the Management Department continuously improves the content of the advice based on user feedback. For example, by recording how the user reacted to the proposed saving methods and health management plans and analyzing that data, it is possible to provide more effective advice. This allows the management department to support users in all aspects of their lives and improve their quality of life.

[0064] The diagnostic unit can diagnose a user's personality based on the MBTI. For example, the diagnostic unit diagnoses a personality type when the user answers a series of questions. These questions may include whether the user is extroverted or introverted, feeling or logical, etc. The diagnostic unit inputs this information into a generating AI for analysis. This enables a personality diagnosis based on the MBTI. Some or all of the above processing in the diagnostic unit may be performed using the generating AI, or not. For example, the diagnostic unit can input the user's response data into the generating AI and have the generating AI execute the personality diagnosis results.

[0065] The advice unit can provide appropriate advice to the user based on the diagnostic results. For example, the advice unit provides proactive advice to extroverted users and cautious advice to introverted users. The advice unit uses generative AI to provide advice tailored to the user's personality. For example, the generative AI generates friendly advice based on the user's personality. This ensures that appropriate advice is provided based on the diagnostic results. Some or all of the above-described processes in the advice unit may be performed using generative AI or not. For example, the advice unit can input the diagnostic results into the generative AI and have the generative AI generate appropriate advice.

[0066] The management department can perform household budget management. For example, the management department can analyze the user's income and expenses and propose the best saving methods. The management department uses generative AI to analyze the user's income and expenses and provide specific advice to reduce unnecessary spending. This enables household budget management. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input the user's income and expense data into the generative AI and have the generative AI propose the best saving methods.

[0067] The management department can perform health management. For example, the management department can provide appropriate advice based on the user's diet and exercise records. The management department can use generative AI to propose balanced meal menus and effective exercise plans. This enables health management. Some or all of the above processes in the management department may be performed using generative AI, or they may not. For example, the management department can input the user's diet and exercise data into the generative AI and have the generative AI generate appropriate advice.

[0068] The management department can analyze users' income and expenses and propose optimal saving methods. For example, the management department can propose optimal saving methods considering the balance of users' income and expenses. The management department can use generative AI to analyze users' income and expenses and provide specific advice to reduce unnecessary spending. This makes it possible to propose saving methods based on income and expense analysis. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input user income and expense data into the generative AI and have the generative AI propose optimal saving methods.

[0069] The management department can provide appropriate advice based on the user's diet and exercise records. For example, the management department can analyze the user's diet and exercise records and propose balanced meal menus and effective exercise plans. The management department uses generative AI to analyze the user's diet and exercise data and provide appropriate advice. This ensures that appropriate advice is provided based on the diet and exercise records. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input the user's diet and exercise data into the generative AI and have the generative AI generate appropriate advice.

[0070] The diagnostic unit can estimate the user's emotions and dynamically change the content of personality assessment questions based on the estimated emotions. For example, if the user is stressed, the diagnostic unit will prioritize simple and easy-to-answer questions. If the user is relaxed, the diagnostic unit will conduct a personality assessment that includes detailed questions. If the user is in a hurry, the diagnostic unit will provide a set of questions that can be completed in a short time. This makes it possible to change the content of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using or without a generative AI. For example, the diagnostic unit can input user emotion data into a generative AI and have the generative AI dynamically change the content of the questions.

[0071] The diagnostic unit can analyze the user's past behavioral history and make corrections to improve the accuracy of the personality assessment. For example, the diagnostic unit can adjust the questions by referring to the results of personality assessments the user has taken in the past. The diagnostic unit analyzes the user's past behavioral patterns and reflects them in the diagnostic results. The diagnostic unit makes corrections to improve the accuracy of the assessment based on the user's past response tendencies. This makes it possible to improve the accuracy of the personality assessment based on past behavioral history. Some or all of the above processes in the diagnostic unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the diagnostic unit can input the user's past behavioral data into a generative AI and have the generative AI perform the corrections to the personality assessment.

[0072] The diagnostic unit can customize the results of the personality assessment based on the user's living environment and occupation. For example, if the user is a student, the diagnostic unit will add questions related to academics. If the user is employed, the diagnostic unit will add questions related to their occupation. The diagnostic unit will adjust the questions according to the user's living environment (urban, rural, etc.). This makes it possible to customize the personality assessment results according to the user's living environment and occupation. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the diagnostic unit can input the user's living environment and occupation data into a generative AI and have the generative AI perform the customization of the personality assessment results.

[0073] The diagnostic unit can estimate the user's emotions and adjust the feedback method of the diagnostic results based on the estimated user emotions. For example, if the user is tense, the diagnostic unit will provide feedback in gentle words. If the user is relaxed, the diagnostic unit will provide detailed feedback. If the user is in a hurry, the diagnostic unit will provide concise feedback. This allows for adjustment of the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using or without a generative AI. For example, the diagnostic unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the feedback method.

[0074] The diagnostic unit can analyze the user's social media activity during personality assessment and reflect this in the assessment results. For example, the diagnostic unit can analyze the content of the user's social media posts and reflect this in the personality assessment. The diagnostic unit can analyze the user's social media friendships and reflect this in the assessment results. The diagnostic unit can improve the accuracy of the personality assessment based on the user's frequency of social media activity. This makes it possible to reflect personality assessment results based on social media activity. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the diagnostic unit can input the user's social media data into a generative AI and have the generative AI perform the reflection of the assessment results.

[0075] The diagnostic unit can customize the personality assessment results by taking into account the user's geographical location information. For example, if the user lives in an urban area, the diagnostic unit will add questions related to urban life. If the user lives in a rural area, the diagnostic unit will add questions related to rural life. The diagnostic unit customizes the assessment results based on the user's geographical location information. This makes it possible to customize personality assessment results based on geographical location information. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the diagnostic unit can input the user's geographical location data into a generative AI and have the generative AI perform the customization of the assessment results.

[0076] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is sad, the advice unit will provide advice that includes words of encouragement. If the user is happy, the advice unit will provide advice that includes positive expressions. If the user is angry, the advice unit will provide advice in calm language. This allows for adjustment of the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice unit may be performed using the generative AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0077] The advice unit can select the most appropriate advice by referring to the user's past advice history when providing advice. For example, the advice unit can provide the most appropriate advice for the current situation based on the advice the user has received in the past. The advice unit analyzes the user's past advice history and selects effective advice. The advice unit provides the most appropriate advice by referring to the results of the advice the user has received in the past. This makes it possible to select the most appropriate advice based on past advice history. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input the user's past advice history data into a generation AI and have the generation AI perform the selection of the most appropriate advice.

[0078] The advice unit can customize the advice it provides based on the user's current lifestyle and goals. For example, if the user is on a diet, the advice unit will provide advice on diet and exercise. If the user is stressed at work, the advice unit will provide advice on stress management. If the user wants to start a new hobby, the advice unit will provide advice on that hobby. This makes it possible to customize the advice to suit the user's current lifestyle and goals. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's lifestyle and goal data into a generative AI and have the generative AI perform the customization of the advice.

[0079] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user has an urgent problem, the advice unit will prioritize advice on that problem. If the user has long-term goals, the advice unit will prioritize advice on those goals. If the user has multiple problems, the advice unit will prioritize advice based on the intensity of the emotions. This makes it possible to determine the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using or without a generative AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI determine the priority of advice.

[0080] The advice unit can analyze the user's social media activity and provide relevant advice when offering advice. For example, the advice unit can provide advice based on goals shared by the user on social media. The advice unit analyzes the user's social media activities and provides relevant advice. The advice unit considers the user's social media friendships to provide appropriate advice. This makes it possible to provide relevant advice based on social media activity. Some or all of the above processing in the advice unit may be performed using a generative AI, or not. For example, the advice unit can input the user's social media data into a generative AI and have the generative AI generate relevant advice.

[0081] The advice unit can provide optimal advice by considering the user's geographical location information when providing advice. For example, if the user lives in an urban area, the advice unit will provide advice related to urban life. If the user lives in a rural area, the advice unit will provide advice related to rural life. The advice unit provides optimal advice based on the user's geographical location information. This makes it possible to provide optimal advice based on geographical location information. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input the user's geographical location data into a generation AI and have the generation AI perform the generation of optimal advice.

[0082] The management unit can estimate the user's emotions and adjust household and health management methods based on the estimated emotions. For example, if the user is stressed, the management unit will suggest a simple and easy-to-follow household management method. If the user is relaxed, the management unit will suggest a detailed household management method. If the user is motivated to manage their health, the management unit will suggest a proactive health management method. This makes it possible to adjust household and health management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using generative AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of household and health management methods.

[0083] The management department can analyze a user's past income and expenditure history to suggest optimal saving methods during household budget management. For example, the management department can analyze a user's past spending patterns and suggest ways to reduce unnecessary spending. The management department can suggest optimal saving methods considering the balance between the user's income and expenditures. The management department can suggest effective saving methods based on the user's past successful saving examples. This makes it possible to suggest optimal saving methods based on past income and expenditure history. Some or all of the above processes in the management department may be performed using a generation AI, or they may be performed without a generation AI. For example, the management department can input the user's past income and expenditure data into a generation AI and have the generation AI suggest optimal saving methods.

[0084] The management department can select the optimal health management method by referring to the user's past health data during health management. For example, the management department can propose an appropriate health management method based on the user's past health checkup results. The management department can analyze the user's past exercise history and propose an optimal exercise plan. The management department can refer to the user's past meal records and propose a balanced meal plan. This makes it possible to select the optimal health management method based on past health data. Some or all of the above processes in the management department may be performed using a generation AI, or they may be performed without a generation AI. For example, the management department can input the user's past health data into a generation AI and have the generation AI perform the selection of the optimal health management method.

[0085] The management unit can estimate the user's emotions and determine priorities based on the estimated emotions. For example, if the user has an urgent financial problem, the management unit will prioritize financial management. If the user has a health problem, the management unit will prioritize health management. If the user has multiple problems, the management unit will determine priorities based on the intensity of the emotions. This makes it possible to determine management unit priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using generative AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the determination of management unit priorities.

[0086] The management unit can suggest optimal saving methods when managing household finances, taking into account the user's geographical location information. For example, if the user lives in an urban area, the management unit will suggest saving methods suitable for urban living. If the user lives in a rural area, the management unit will suggest saving methods suitable for rural living. The management unit suggests optimal saving methods based on the user's geographical location information. This makes it possible to suggest optimal saving methods based on geographical location information. Some or all of the above processing in the management unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the management unit can input the user's geographical location data into a generative AI and have the generative AI suggest optimal saving methods.

[0087] The management department can analyze a user's social media activity and provide relevant health advice during health management. For example, the management department can provide advice based on health goals shared by the user on social media. The management department analyzes the user's social media activity and provides relevant health advice. The management department considers the user's social media friendships to provide appropriate health advice. This makes it possible to provide relevant health advice based on social media activity. Some or all of the above processes in the management department may be performed using or without a generative AI. For example, the management department can input the user's social media data into a generative AI and have the generative AI generate relevant health advice.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The smartphone advice system can also monitor the user's sleep patterns and provide appropriate advice. For example, the diagnostic unit collects the user's sleep data and analyzes the quality and quantity of sleep. The advice unit provides the user with advice on improving sleep based on the sleep data. For example, it may advise on relaxation methods before bedtime or how to create a suitable sleep environment. The management unit can propose specific plans to improve sleep quality as part of health management, based on the user's sleep data. This can improve the user's sleep quality and overall health.

[0090] The smartphone advice system can also estimate the user's emotions and provide stress management advice based on those emotions. For example, the diagnostic unit collects the user's emotional data and evaluates their stress level. The advice unit suggests relaxation methods and stress-relieving activities according to the stress level, such as deep breathing, meditation, or light exercise. The management unit can monitor the user's stress level and propose a long-term stress management plan. This helps reduce the user's stress and maintain their mental health.

[0091] The smartphone advice system can also analyze the user's hobbies and interests and provide advice based on that analysis. For example, the diagnostic unit collects and analyzes data on the user's hobbies and interests. The advice unit then suggests new hobbies and introduces related activities based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information on learning a new instrument or attending concerts. The management unit can take the user's hobbies and interests into consideration and propose specific plans to improve their quality of life. This can lead to a more fulfilling life and increased satisfaction for the user.

[0092] The smartphone advice system can also estimate the user's emotions and provide communication advice based on those emotions. For example, the diagnostic unit collects the user's emotional data and analyzes their communication style and tendencies. The advice unit suggests effective communication methods according to the user's emotions. For instance, if the user is feeling nervous, it will advise on how to relax and speak at the appropriate time. The management unit can propose long-term plans to improve the user's communication skills. This can lead to improved communication abilities and smoother interpersonal relationships.

[0093] The smartphone advice system can also monitor users' exercise habits and provide appropriate advice. For example, the diagnostic unit collects the user's exercise data and analyzes the frequency and intensity of exercise. Based on the exercise data, the advice unit suggests improvements to the user's exercise habits and proposes new exercise plans. For example, it can advise on how many times a week exercise is appropriate and what types of exercise are effective. Based on the user's exercise data, the management unit can propose specific plans to improve exercise habits as part of health management. This can lead to improved exercise habits and an overall improvement in the user's health.

[0094] The smartphone advice system can also estimate the user's emotions and provide dietary advice based on those emotions. For example, the diagnostic unit collects the user's emotional data and analyzes the emotions that influence their food choices. The advice unit then suggests healthy food choices and meal timings based on those emotions. For instance, if the user is feeling stressed, it can provide ingredients and recipes that help relieve stress. The management unit can then propose a long-term meal plan based on the user's dietary data, supporting a healthy lifestyle. This can improve the user's eating habits and overall health.

[0095] The smartphone advice system can also analyze the user's learning style and provide learning advice based on that analysis. For example, the diagnostic unit collects the user's learning data and analyzes the efficiency and effectiveness of their learning. The advice unit then proposes effective learning methods and plans to the user based on the learning data. For instance, if the user has a visual learning style, the system will suggest learning methods that utilize visual aids. The management unit can then propose specific plans to achieve long-term learning goals based on the user's learning data. This improves the user's learning efficiency and maximizes their learning outcomes.

[0096] The smartphone advice system can also estimate the user's emotions and provide relaxation advice based on those emotions. For example, the diagnostic unit collects the user's emotional data and assesses their need for relaxation. The advice unit then suggests relaxation methods and creating a relaxing environment according to the user's emotions. For instance, if the user is tired, it might suggest relaxing music or aromatherapy. The management unit can then propose a long-term relaxation plan based on the user's relaxation data, supporting their mental health. This promotes relaxation and improves their overall mental well-being.

[0097] The smartphone advice system can further support users' travel planning and provide appropriate advice. For example, the diagnostic unit collects and analyzes the user's travel preferences and past travel history. The advice unit then suggests optimal travel destinations and plans based on the travel data. For instance, if the user prefers nature, it will suggest destinations rich in nature. The management unit can provide specific advice on travel preparation and health management during travel, based on the user's travel data. This can improve the user's travel experience and increase their satisfaction.

[0098] The smartphone advice system can also estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, the diagnostic unit collects the user's emotional data and evaluates the optimal timing for feedback. The advice unit adjusts the timing of feedback delivery according to the emotion. For example, it provides detailed feedback when the user is relaxed and concise feedback when the user is busy. The management unit can optimize the timing of feedback based on the user's emotional data to provide effective feedback. This improves the user's receptiveness to feedback and maximizes the effectiveness of the advice.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The diagnostic unit diagnoses the user's personality. The diagnostic unit diagnoses the user's personality using, for example, MBTI (a personality assessment). The user answers a series of questions to determine their personality type. These questions may include, for example, whether they are extroverted or introverted, feeling or logical, etc. The diagnostic unit inputs this information into a generating AI for analysis. Step 2: The advice unit provides advice based on the personality diagnosed by the diagnostic unit. For example, the advice unit provides proactive advice to extroverted users and cautious advice to introverted users. Generative AI is used to provide advice tailored to the user's personality. For example, the generative AI generates friendly advice based on the user's personality. Step 3: The Management Department manages household finances and health based on the advice provided by the Advice Department. For example, in household finance management, the Management Department analyzes the user's income and expenses and proposes optimal saving methods. Using generative AI, it analyzes the user's income and expenses and provides specific advice to reduce unnecessary spending. In health management, it provides appropriate advice based on the user's records of meals and exercise. Using generative AI, it proposes balanced meal menus and effective exercise plans.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the diagnostic unit, advice unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the smart device 14 and diagnoses the user's personality. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides advice based on the diagnosed personality. The management unit is implemented by the control unit 46A of the smart device 14 and performs household finance management and health management. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 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.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the diagnostic unit, advice unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the smart glasses 214 and diagnoses the user's personality. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides advice based on the diagnosed personality. The management unit is implemented by the control unit 46A of the smart glasses 214 and performs household finance management and health management. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the diagnostic unit, advice unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the headset terminal 314 and diagnoses the user's personality. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides advice based on the diagnosed personality. The management unit is implemented by the control unit 46A of the headset terminal 314 and performs household finance management and health management. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the diagnostic unit, advice unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the diagnostic unit is implemented by the control unit 46A of the robot 414 and diagnoses the user's personality. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides advice based on the diagnosed personality. The management unit is implemented by the control unit 46A of the robot 414 and performs household finance management and health management. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A diagnostic unit that assesses the user's personality, An advice unit that provides advice based on the personality diagnosed by the aforementioned diagnostic unit, The system includes a management unit that performs household finance management and health management based on the advice provided by the aforementioned advisory unit. A system characterized by the following features. (Note 2) The aforementioned diagnostic unit, Diagnose the user's personality based on MBTI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, Provide users with appropriate advice based on the diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Manage household finances The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Manage your health The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, It analyzes the user's income and expenses and suggests the best way to save money. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Based on the user's records of diet and exercise, it provides appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned diagnostic unit, It estimates the user's emotions and dynamically changes the content of the personality assessment questions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned diagnostic unit, We analyze the user's past behavior history and make adjustments to improve the accuracy of the personality assessment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned diagnostic unit, Customize the results of the personality assessment based on the user's living environment and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts the feedback method of the diagnostic results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned diagnostic unit, During the personality assessment, the user's social media activity will be analyzed and reflected in the assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned diagnostic unit, During personality assessments, the results are customized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, the system selects the most appropriate advice by referring to the user's past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, the advice is customized based on the user's current living situation and goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, It estimates the user's emotions and adjusts methods for managing household finances and health based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing household finances, the system analyzes the user's past income and spending history to suggest the most suitable saving methods. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During health management, the system selects the optimal health management method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, The system estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing household finances, the system suggests optimal saving methods while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, During health management, the system analyzes users' social media activity to provide relevant health advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A diagnostic unit that assesses the user's personality, An advice unit that provides advice based on the personality diagnosed by the aforementioned diagnostic unit, The system includes a management unit that performs household finance management and health management based on the advice provided by the aforementioned advisory unit. A system characterized by the following features.

2. The aforementioned diagnostic unit, Diagnose the user's personality based on MBTI. The system according to feature 1.

3. The aforementioned advice section, Provide users with appropriate advice based on the diagnostic results. The system according to feature 1.

4. The aforementioned management department, Manage household finances The system according to feature 1.

5. The aforementioned management department, Manage your health The system according to feature 1.

6. The aforementioned management department, It analyzes the user's income and expenses and suggests the best way to save money. The system according to feature 1.

7. The aforementioned management department, Based on the user's records of diet and exercise, it provides appropriate advice. The system according to feature 1.

8. The aforementioned diagnostic unit, It estimates the user's emotions and dynamically changes the content of the personality assessment questions based on the estimated emotions. The system according to feature 1.

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