System

Abstract

An object of a system according to an embodiment is to analyze personal data and propose an optimal career path or asset management strategy.SOLUTION: A system includes a data collection part, an analysis part, a carrier advice part, and an asset advice part. The data collection unit collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The carrier advice unit proposes a carrier path based on the data analyzed by the analysis unit. The asset advice unit proposes an asset management strategy based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately collect and analyze data to optimize individuals' career paths and asset management strategies, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze personal data and propose an optimal career path or asset management strategy. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a career advice unit, and an asset advice unit. The data collection unit collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The career advice unit proposes a career path based on the data analyzed by the analysis unit. The asset advice unit proposes an asset management strategy based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze personal data and suggest optimal career paths or asset management strategies. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The career development and asset formation support system according to an embodiment of the present invention is a system that uses generative AI to collect and analyze personal data and propose optimal strategies for career paths and asset management. As a result, the career development and asset formation support system can provide integrated support for an individual's career development and asset formation.

[0029] A career development and asset formation support system according to an embodiment includes a data collection unit, an analysis unit, a career advice unit, and an asset advice unit. The data collection unit collects personal data. For example, the data collection unit collects personal data such as a user's educational background, work history, skills, interests, goals, and financial situation. The data collection unit can also collect the user's social media activities and online behavior history. The data collection unit can also collect data based on the user's health data, family structure, and life events. The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit can analyze the user's emotional state in real time and collect data according to emotional fluctuations. The analysis unit can also analyze the user's social media activities and online behavior history to identify potential interests and skills. The analysis unit can also analyze the user's health data and provide career and asset formation advice based on the user's health condition. The career advice unit suggests a career path based on the data analyzed by the analysis unit. For example, the career advice unit can analyze the user's emotional state and suggest a career path based on the user's emotions. The career advice unit can also analyze the user's past projects and work results and, based on the results, suggest specific steps for career advancement. The career advice unit can also analyze the user's network and identify and introduce connections that are beneficial to career development. The asset advice unit proposes an asset management strategy based on the data analyzed by the analysis unit. For example, the asset advice unit can analyze the user's emotional state and suggest asset management based on the emotions. The asset advice unit can also analyze the user's consumption patterns and expenditure history and provide specific advice for reducing wasteful spending. The asset advice unit can also analyze the user's risk tolerance in detail and suggest a customized investment strategy based on the analysis. As a result, the career development and asset formation support system according to the embodiment can provide integrated support for an individual's career development and asset formation.

[0030] The data collection unit can collect personal data such as the user's educational background, work history, skills, interests, goals, and financial situation. The data collection unit collects, for example, personal data such as the user's educational background, work history, skills, interests, goals, and financial situation. For example, the data collection unit collects information such as the user's educational background and work history, current skill set, future career goals, and current asset status. By collecting detailed personal data about the user, more accurate advice can be provided.

[0031] The analysis unit can analyze a user's social media activity or online behavior history to identify potential interests and skills. For example, the analysis unit links the user's social media accounts and analyzes the content of posts and accounts followed. For example, a user who posts a lot about technology can be suggested an engineering-related career path. By identifying the user's potential interests and skills, the analysis unit can suggest more appropriate career paths and asset management strategies.

[0032] The analysis unit can collect the user's health data and provide career or asset formation advice based on the user's health condition. The analysis unit collects data from, for example, a fitness tracker or smartwatch and analyzes the user's health condition. For example, if a user is not getting enough exercise, the analysis unit can provide lifestyle improvement advice to maintain health. This allows the system to suggest more appropriate career paths and asset management strategies by providing advice based on the user's health condition.

[0033] The analysis unit can collect data based on the user's family structure or life events and provide advice according to the user's life stage. For example, the analysis unit provides a form for inputting the user's family structure and life events, and customizes advice based on that data. For example, a user who is about to get married can be provided with advice on post-marriage career plans and asset management. This makes it possible to propose more appropriate career paths and asset management strategies by providing advice according to the user's life stage.

[0034] The analysis unit can collect data on the user's hobbies or leisure activities and make career and asset formation suggestions based on that data. For example, the analysis unit provides a form for inputting data on the user's hobbies and leisure activities, and customizes career and asset formation advice based on that data. For example, for a user who enjoys outdoor activities, related occupations and investment destinations are suggested. In this way, by providing advice based on the user's hobbies and leisure activities, more appropriate career paths and asset management strategies can be suggested.

[0035] The career advice unit can analyze the user's past projects and work results and, based on that, suggest specific steps for career advancement. The career advice unit, for example, analyzes the user's past projects and work results and, based on that, suggest specific steps for career advancement. For example, it can suggest the next step by utilizing the skills from a successful project. In this way, by suggesting specific steps for career advancement based on the user's past work results, it is possible to support more effective career development.

[0036] The career advice unit can analyze the user's network, identify personal connections that are beneficial to career development, and introduce them to the user. The career advice unit can, for example, analyze the user's LinkedIn connection information and identify personal connections that are beneficial to career development. For example, it can introduce experienced professionals who work in the same industry. In this way, the career advice unit can support the user's career development by analyzing the user's network, identifying useful personal connections, and introducing them to the user.

[0037] The career advice unit can compare career paths in different industries or occupations and suggest new career possibilities to the user. The career advice unit, for example, compares career paths in different industries or occupations and suggests new career possibilities to the user. For example, it can suggest a career change from a technical position to a managerial position. In this way, by comparing career paths in different industries or occupations, it can suggest new career possibilities to the user.

[0038] The career advice unit can recommend online courses or training programs according to the user's career goals. For example, the career advice unit recommends online courses or training programs according to the user's career goals. For example, the career advice unit suggests an online course for acquiring data science skills. This can support career development by recommending online courses or training programs according to the user's career goals.

[0039] The asset advice unit can analyze the user's consumption patterns and expenditure history and provide specific advice for reducing wasteful spending. For example, the asset advice unit can analyze the user's consumption patterns and expenditure history and provide specific advice for reducing wasteful spending. For example, the asset advice unit can suggest a periodic review of subscription services. In this way, by analyzing the user's consumption patterns and expenditure history, it is possible to reduce wasteful spending and support more effective asset formation.

[0040] The asset advice unit can analyze the user's risk tolerance in detail and propose a customized investment strategy based on that. The asset advice unit, for example, analyzes the user's risk tolerance in detail and proposes a customized investment strategy based on that. For example, for a user who likes risk, it proposes high-risk, high-return investments. In this way, by proposing a customized investment strategy based on the user's risk tolerance, it is possible to support more appropriate asset formation.

[0041] The asset advice unit can compare different investment products or performance and propose an optimal investment portfolio to the user. The asset advice unit, for example, compares the performance of different investment products and proposes an optimal investment portfolio to the user. For example, it compares investment destinations such as stocks, bonds, and real estate and proposes a balanced portfolio. In this way, by comparing the performance of different investment products, it is possible to propose an optimal investment portfolio to the user.

[0042] The asset advice unit can provide tax-saving measures or legal advice according to the user's asset formation goals. The asset advice unit, for example, proposes tax-saving measures according to the user's asset formation goals. For example, it proposes investment destinations and tax-saving methods that utilize tax incentives. In this way, by providing tax-saving measures and legal advice according to the user's asset formation goals, it is possible to support more effective asset formation.

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

[0044] The career development and asset formation support system may further include a hobby analysis unit that makes career and asset formation suggestions based on the user's hobbies and leisure activities. The hobby analysis unit, for example, provides a form for inputting data about the user's hobbies and leisure activities, and customizes career and asset formation advice based on that data. For example, for a user who enjoys outdoor activities, related occupations and investment destinations are suggested. This allows for the provision of advice based on the user's hobbies and leisure activities to suggest more appropriate career paths and asset management strategies.

[0045] The career development and asset formation support system may further include a life stage analysis unit that collects data based on the user's family structure and life events and provides advice according to the user's life stage. The life stage analysis unit, for example, provides a form for inputting the user's family structure and life events, and customizes advice based on that data. For example, a user who is about to get married may be provided with advice on post-marriage career plans and asset management. This allows the system to propose more appropriate career paths and asset management strategies by providing advice according to the user's life stage.

[0046] The career development and asset formation support system may further include a health analysis unit that collects the user's health data and provides career or asset formation advice based on the user's health condition. The health analysis unit may collect data from, for example, a fitness tracker or a smartwatch and analyze the user's health condition. For example, if a user is not getting enough exercise, the health analysis unit may provide advice on lifestyle improvements to maintain health. This allows the system to provide advice based on the user's health condition, thereby suggesting more appropriate career paths and asset management strategies.

[0047] The career development and asset formation support system may further include a consumption analysis unit that analyzes the user's consumption patterns and expenditure history and provides specific advice for reducing wasteful spending. The consumption analysis unit, for example, analyzes the user's consumption patterns and expenditure history and provides specific advice for reducing wasteful spending. For example, the consumption analysis unit may suggest a periodic review of subscription services. In this way, by analyzing the user's consumption patterns and expenditure history, it is possible to reduce wasteful spending and support more effective asset formation.

[0048] The career development and asset formation support system may further include an investment comparison unit that compares different investment products or performance and proposes an optimal investment portfolio to the user. The investment comparison unit, for example, compares the performance of different investment products and proposes an optimal investment portfolio to the user. For example, it compares investment destinations such as stocks, bonds, and real estate and proposes a balanced portfolio. In this way, it is possible to propose an optimal investment portfolio to the user by comparing the performance of different investment products.

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

[0050] Step 1: The data collection unit collects personal data, such as the user's educational background, work history, skills, interests, goals, and financial situation. It can also collect data based on the user's social media activity, online behavior, health data, family structure, and life events. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, it analyzes the user's emotional state in real time and collects data according to emotional fluctuations. It also analyzes social media activity and online behavior history to identify potential interests and skills. It also analyzes health data and provides career and asset formation advice based on the user's health condition. Step 3: The career advice unit suggests career paths based on the data analyzed by the analysis unit. For example, it analyzes the user's emotional state and suggests career paths based on their emotions. It also analyzes past projects and work results and suggests specific steps for career advancement based on that. It also analyzes the user's network and identifies and introduces connections that will be beneficial for career development. Step 4: The asset advice unit proposes asset management strategies based on the data analyzed by the analysis unit. For example, it analyzes the user's emotional state and proposes asset management strategies based on their emotions. It also analyzes consumption patterns and expenditure history to provide specific advice on reducing wasteful spending. It also analyzes the user's risk tolerance in detail and proposes customized investment strategies based on that analysis.

[0051] (Example 2) The career development and asset formation support system according to an embodiment of the present invention is a system that uses generative AI to collect and analyze personal data and propose optimal strategies for career paths and asset management. As a result, the career development and asset formation support system can provide integrated support for an individual's career development and asset formation.

[0052] A career development and asset formation support system according to an embodiment includes a data collection unit, an analysis unit, a career advice unit, and an asset advice unit. The data collection unit collects personal data. For example, the data collection unit collects personal data such as a user's educational background, work history, skills, interests, goals, and financial situation. The data collection unit can also collect the user's social media activities and online behavior history. The data collection unit can also collect data based on the user's health data, family structure, and life events. The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit can analyze the user's emotional state in real time and collect data according to emotional fluctuations. The analysis unit can also analyze the user's social media activities and online behavior history to identify potential interests and skills. The analysis unit can also analyze the user's health data and provide career and asset formation advice based on the user's health condition. The career advice unit suggests a career path based on the data analyzed by the analysis unit. For example, the career advice unit can analyze the user's emotional state and suggest a career path based on the user's emotions. The career advice unit can also analyze the user's past projects and work results and, based on the results, suggest specific steps for career advancement. The career advice unit can also analyze the user's network and identify and introduce connections that are beneficial to career development. The asset advice unit proposes an asset management strategy based on the data analyzed by the analysis unit. For example, the asset advice unit can analyze the user's emotional state and suggest asset management based on the emotions. The asset advice unit can also analyze the user's consumption patterns and expenditure history and provide specific advice for reducing wasteful spending. The asset advice unit can also analyze the user's risk tolerance in detail and suggest a customized investment strategy based on the analysis. As a result, the career development and asset formation support system according to the embodiment can provide integrated support for an individual's career development and asset formation.

[0053] The data collection unit can collect personal data such as the user's educational background, work history, skills, interests, goals, and financial situation. The data collection unit collects, for example, personal data such as the user's educational background, work history, skills, interests, goals, and financial situation. For example, the data collection unit collects information such as the user's educational background and work history, current skill set, future career goals, and current asset status. By collecting detailed personal data about the user, more accurate advice can be provided.

[0054] The analysis unit can analyze the user's emotional state in real time and collect data according to emotional fluctuations. For example, the analysis unit analyzes the user's emotional state in real time through a camera or microphone while the user is using an application. For example, the analysis unit analyzes facial expressions and voice tone and provides advice to relax if the user is feeling stressed. In this way, by collecting data according to the user's emotional state, more appropriate advice can be provided.

[0055] The analysis unit can analyze a user's social media activity or online behavior history to identify potential interests and skills. For example, the analysis unit links the user's social media accounts and analyzes the content of posts and accounts followed. For example, a user who posts a lot about technology can be suggested an engineering-related career path. By identifying the user's potential interests and skills, the analysis unit can suggest more appropriate career paths and asset management strategies.

[0056] The analysis unit can collect the user's health data and provide career or asset formation advice based on the user's health condition. The analysis unit collects data from, for example, a fitness tracker or smartwatch and analyzes the user's health condition. For example, if a user is not getting enough exercise, the analysis unit can provide lifestyle improvement advice to maintain health. This allows the system to suggest more appropriate career paths and asset management strategies by providing advice based on the user's health condition.

[0057] The analysis unit can collect data based on the user's family structure or life events and provide advice according to the user's life stage. For example, the analysis unit provides a form for inputting the user's family structure and life events, and customizes advice based on that data. For example, a user who is about to get married can be provided with advice on post-marriage career plans and asset management. This makes it possible to propose more appropriate career paths and asset management strategies by providing advice according to the user's life stage.

[0058] The analysis unit can collect data on the user's hobbies or leisure activities and make career and asset formation suggestions based on that data. For example, the analysis unit provides a form for inputting data on the user's hobbies and leisure activities, and customizes career and asset formation advice based on that data. For example, for a user who enjoys outdoor activities, related occupations and investment destinations are suggested. In this way, by providing advice based on the user's hobbies and leisure activities, more appropriate career paths and asset management strategies can be suggested.

[0059] The analysis unit can use the emotion estimation function to analyze the emotion of the user when entering data and provide an interface for eliciting positive emotions. For example, the analysis unit can analyze the emotion of the user in real time using a camera or microphone when entering data and provide an interface for eliciting positive emotions. For example, if the user smiles, an encouraging message can be displayed. This allows more effective advice to be provided by eliciting positive emotions when the user enters data.

[0060] The career advice unit can analyze the user's emotional state and suggest a career path based on the emotion. For example, the career advice unit analyzes the user's emotional state and suggests a career path when the user has strong positive emotions. For example, when the user is confident, it suggests a challenging career path. In this way, by suggesting a career path based on the user's emotional state, it is possible to support more appropriate career development.

[0061] The career advice unit can analyze the user's past projects and work results and, based on that, suggest specific steps for career advancement. The career advice unit, for example, analyzes the user's past projects and work results and, based on that, suggest specific steps for career advancement. For example, it can suggest the next step by utilizing the skills from a successful project. In this way, by suggesting specific steps for career advancement based on the user's past work results, it is possible to support more effective career development.

[0062] The career advice unit can analyze the user's network, identify personal connections that are beneficial to career development, and introduce them to the user. The career advice unit can, for example, analyze the user's LinkedIn connection information and identify personal connections that are beneficial to career development. For example, it can introduce experienced professionals who work in the same industry. In this way, the career advice unit can support the user's career development by analyzing the user's network, identifying useful personal connections, and introducing them to the user.

[0063] The career advice unit can compare career paths in different industries or occupations and suggest new career possibilities to the user. The career advice unit, for example, compares career paths in different industries or occupations and suggests new career possibilities to the user. For example, it can suggest a career change from a technical position to a managerial position. In this way, by comparing career paths in different industries or occupations, it can suggest new career possibilities to the user.

[0064] The career advice unit can recommend online courses or training programs according to the user's career goals. For example, the career advice unit recommends online courses or training programs according to the user's career goals. For example, the career advice unit suggests an online course for acquiring data science skills. This can support career development by recommending online courses or training programs according to the user's career goals.

[0065] The career advice unit can use the emotion estimation function to analyze the emotions of the user when receiving career advice and provide advice to elicit positive emotions. For example, the career advice unit can use the emotion estimation function to analyze the emotions of the user when receiving career advice and provide advice to elicit positive emotions. For example, if the user is feeling anxious, an encouraging message can be displayed. This can elicit positive emotions when the user receives career advice, thereby supporting more effective career development.

[0066] The asset advice unit can analyze the user's emotional state and suggest asset management based on the emotion. For example, the asset advice unit analyzes the user's emotional state and makes asset management suggestions when the user has strong positive emotions. For example, when the user is confident, it suggests high-risk investments. In this way, asset management suggestions based on the user's emotional state can be made, thereby supporting more appropriate asset formation.

[0067] The asset advice unit can analyze the user's consumption patterns and expenditure history and provide specific advice for reducing wasteful spending. For example, the asset advice unit can analyze the user's consumption patterns and expenditure history and provide specific advice for reducing wasteful spending. For example, the asset advice unit can suggest a periodic review of subscription services. In this way, by analyzing the user's consumption patterns and expenditure history, it is possible to reduce wasteful spending and support more effective asset formation.

[0068] The asset advice unit can analyze the user's risk tolerance in detail and propose a customized investment strategy based on that. The asset advice unit, for example, analyzes the user's risk tolerance in detail and proposes a customized investment strategy based on that. For example, for a user who likes risk, it proposes high-risk, high-return investments. In this way, by proposing a customized investment strategy based on the user's risk tolerance, it is possible to support more appropriate asset formation.

[0069] The asset advice unit can compare different investment products or performance and propose an optimal investment portfolio to the user. The asset advice unit, for example, compares the performance of different investment products and proposes an optimal investment portfolio to the user. For example, it compares investment destinations such as stocks, bonds, and real estate and proposes a balanced portfolio. In this way, by comparing the performance of different investment products, it is possible to propose an optimal investment portfolio to the user.

[0070] The asset advice unit can provide tax-saving measures or legal advice according to the user's asset formation goals. The asset advice unit, for example, proposes tax-saving measures according to the user's asset formation goals. For example, it proposes investment destinations and tax-saving methods that utilize tax incentives. In this way, by providing tax-saving measures and legal advice according to the user's asset formation goals, it is possible to support more effective asset formation.

[0071] The asset advice unit can use the emotion estimation function to analyze the emotions of the user when receiving asset management advice and provide advice to elicit positive emotions. For example, the asset advice unit can use the emotion estimation function to analyze the emotions of the user when receiving asset management advice and provide advice to elicit positive emotions. For example, if the user is feeling anxious, the asset advice unit can suggest low-risk investments. This can elicit positive emotions when the user receives asset management advice, thereby supporting more effective asset formation.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The career development and asset formation support system may further include a hobby analysis unit that makes career and asset formation suggestions based on the user's hobbies and leisure activities. The hobby analysis unit, for example, provides a form for inputting data about the user's hobbies and leisure activities, and customizes career and asset formation advice based on that data. For example, for a user who enjoys outdoor activities, related occupations and investment destinations are suggested. This allows for the provision of advice based on the user's hobbies and leisure activities to suggest more appropriate career paths and asset management strategies.

[0074] The career development and asset formation support system may further include a life stage analysis unit that collects data based on the user's family structure and life events and provides advice according to the user's life stage. The life stage analysis unit, for example, provides a form for inputting the user's family structure and life events, and customizes advice based on that data. For example, a user who is about to get married may be provided with advice on post-marriage career plans and asset management. This allows the system to propose more appropriate career paths and asset management strategies by providing advice according to the user's life stage.

[0075] The career development and asset formation support system may further include a health analysis unit that collects the user's health data and provides career or asset formation advice based on the user's health condition. The health analysis unit may collect data from, for example, a fitness tracker or a smartwatch and analyze the user's health condition. For example, if a user is not getting enough exercise, the health analysis unit may provide advice on lifestyle improvements to maintain health. This allows the system to provide advice based on the user's health condition, thereby suggesting more appropriate career paths and asset management strategies.

[0076] The career development and asset formation support system may further include a consumption analysis unit that analyzes the user's consumption patterns and expenditure history and provides specific advice for reducing wasteful spending. The consumption analysis unit, for example, analyzes the user's consumption patterns and expenditure history and provides specific advice for reducing wasteful spending. For example, the consumption analysis unit may suggest a periodic review of subscription services. In this way, by analyzing the user's consumption patterns and expenditure history, it is possible to reduce wasteful spending and support more effective asset formation.

[0077] The career development and asset formation support system may further include an investment comparison unit that compares different investment products or performance and proposes an optimal investment portfolio to the user. The investment comparison unit, for example, compares the performance of different investment products and proposes an optimal investment portfolio to the user. For example, it compares investment destinations such as stocks, bonds, and real estate and proposes a balanced portfolio. In this way, it is possible to propose an optimal investment portfolio to the user by comparing the performance of different investment products.

[0078] The career development and asset formation support system may further include an emotion analysis unit that analyzes the user's emotional state and suggests a career path based on the emotion. The emotion analysis unit, for example, analyzes the user's emotional state and suggests a career path when the user has strong positive emotions. For example, when the user is confident, it suggests a challenging career path. This makes it possible to support more appropriate career development by suggesting a career path based on the user's emotional state.

[0079] The career development and asset formation support system may further include an emotion interface unit that uses the emotion estimation function to analyze the emotion of the user when entering data and provide an interface for eliciting positive emotions. For example, the emotion interface unit may analyze the emotion in real time using a camera or microphone when the user enters data and provide an interface for eliciting positive emotions. For example, if the user smiles, an encouraging message may be displayed. This allows for more effective advice to be provided by eliciting positive emotions when the user enters data.

[0080] The career development and asset formation support system may further include an emotion advice unit that uses the emotion estimation function to analyze the emotions of the user when receiving career advice and provides advice to elicit positive emotions. The emotion advice unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving career advice and provides advice to elicit positive emotions. For example, if the user is feeling anxious, an encouraging message may be displayed. This allows the user to elicit positive emotions when receiving career advice, thereby supporting more effective career development.

[0081] The career development and asset formation support system may further include an emotional asset advice unit that uses an emotion estimation function to analyze the emotions of the user when receiving asset management advice and provides advice to elicit positive emotions. The emotional asset advice unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving asset management advice and provides advice to elicit positive emotions. For example, if the user is feeling anxious, the emotional asset advice unit may suggest low-risk investments. This can elicit positive emotions when the user receives asset management advice, thereby supporting more effective asset formation.

[0082] The career development and asset formation support system can further include an emotional asset analysis unit that uses an emotion estimation function to analyze the user's emotional state and suggest asset management based on the emotion. The emotional asset analysis unit, for example, analyzes the user's emotional state and suggests asset management when the user has strong positive emotions. For example, when the user is confident, it suggests high-risk investments. In this way, asset management suggestions based on the user's emotional state can be made, thereby supporting more appropriate asset formation.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data collection unit collects personal data, such as the user's educational background, work history, skills, interests, goals, and financial situation. It can also collect data based on the user's social media activity, online behavior, health data, family structure, and life events. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, it analyzes the user's emotional state in real time and collects data according to emotional fluctuations. It also analyzes social media activity and online behavior history to identify potential interests and skills. It also analyzes health data and provides career and asset formation advice based on the user's health condition. Step 3: The career advice unit suggests career paths based on the data analyzed by the analysis unit. For example, it analyzes the user's emotional state and suggests career paths based on their emotions. It also analyzes past projects and work results and suggests specific steps for career advancement based on that. It also analyzes the user's network and identifies and introduces connections that will be beneficial for career development. Step 4: The asset advice unit proposes asset management strategies based on the data analyzed by the analysis unit. For example, it analyzes the user's emotional state and proposes asset management strategies based on their emotions. It also analyzes consumption patterns and expenditure history to provide specific advice on reducing wasteful spending. It also analyzes the user's risk tolerance in detail and proposes customized investment strategies based on that analysis.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that uses generative AI to collect and analyze personal data and propose optimal strategies for career paths or asset management. a data collection unit that collects personal data; an analysis unit that analyzes the personal data collected by the data collection unit; a career advice unit that proposes a career path based on the data analyzed by the analysis unit; an asset advice unit that proposes an asset management strategy based on the data analyzed by the analysis unit; A system characterized by:

2. The analysis unit Analyze the user's emotional state in real time and collect data according to emotional fluctuations 2. The system of claim 1.

3. The analysis unit Collects data based on the user's family structure or life events and provides advice according to their life stage 2. The system of claim 1.

4. The career advice unit Analyzes the user's emotional state and suggests career paths based on their emotions 2. The system of claim 1.

5. The asset advice unit Analyzes the user's emotional state and suggests asset management based on emotions 2. The system of claim 1.

6. The analysis unit Analyzing your social media activity or online history to identify potential interests and skills 2. The system of claim 1.

7. The career advice unit Analyzes users' past projects and work results, and suggests specific steps for career advancement based on that.

2. The system of claim 1.

8. The asset advice unit A detailed analysis of the user's risk tolerance and a customized investment strategy based on that.

2. The system of claim 1.

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