System

Abstract

An object of a system according to an embodiment is to provide highly accurate money planning on the basis of individual situations and desires of employees.SOLUTION: A system includes an individual situation collection part, an analysis part, a welfare consideration part, an advice provision part, and a packaging part. An individual situation collection part collects the individual situation and desire of the employee. The analysis part analyzes the individual situations and desires of the employees collected by the individual situation collection part. The benefits consideration section considers the company's own benefits and systems. The advice providing unit provides advice tailored to an individual based on the information obtained by the analysis unit and the welfare consideration unit. The packaging unit packages the advice provided by the advice providing 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 has the drawback of making it difficult to provide accurate financial planning based on employees' individual circumstances and wishes.

[0005] The system according to the embodiment aims to provide highly accurate financial planning based on the individual circumstances and wishes of employees. [Means for solving the problem]

[0006] The system according to the embodiment includes an individual situation collection unit, an analysis unit, a benefits consideration unit, an advice provision unit, and a packaging unit. The individual situation collection unit collects the individual situations and wishes of employees. The analysis unit analyzes the individual situations and wishes of employees collected by the individual situation collection unit. The benefits consideration unit considers the company's unique benefits and systems. The advice provision unit provides advice tailored to each individual based on the information obtained by the analysis unit and the benefits consideration unit. The packaging unit packages the advice provided by the advice provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide highly accurate financial planning based on the individual circumstances and wishes of employees. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The money planning system according to an embodiment of the present invention provides highly accurate money planning based on the individual circumstances and wishes of each employee. This allows the money planning system to improve employee satisfaction and promote employee retention within the company. Furthermore, by selling this money planning system in a package, it can also contribute to increased profits for the company.

[0029] A financial planning system according to an embodiment includes an individual situation collection unit, an analysis unit, a benefits consideration unit, an advice provision unit, and a packaging unit. The individual situation collection unit collects information about each employee's individual situation and aspirations. For example, the information includes the employee's age, family structure, income, expenses, and future goals. The individual situation collection unit can also predict the employee's life events (marriage, childbirth, job change, etc.) and propose future financial plans based on the predicted events. The analysis unit analyzes the employee's individual situation and aspirations collected by the individual situation collection unit. For example, the analysis can be performed using data mining, statistical analysis, or machine learning algorithms. The benefits consideration unit considers the company's unique benefits and systems. For example, the system takes into account the company's retirement pension system, health insurance, and education support programs. The advice provision unit provides personalized advice based on the information obtained by the analysis unit and the benefits consideration unit. For example, if an employee asks, "How should I prepare for my child's education expenses?", the system provides specific advice. The packaging unit packages the advice provided by the advice provision unit. For example, money planning proposals based on the individual circumstances and wishes of employees can be packaged and sold to companies. This allows the money planning system according to the embodiment to provide highly accurate money planning based on the individual circumstances and wishes of employees. For example, this can improve employee satisfaction and encourage employees to stay with the company. Furthermore, by purchasing this package, companies can provide highly accurate money planning to their employees.

[0030] The individual situation collection unit can predict an employee's life events and propose a future financial plan based on them. The individual situation collection unit, for example, analyzes the employee's past data and predicts life events such as marriage and childbirth. For example, it predicts the next likely event based on the employee's age, family composition, and history of past life events. It also proposes a future financial plan based on the predicted life events. For example, if marriage is predicted, it proposes plans for wedding expenses and purchasing a new home. It also updates the employee's life event predictions in real time and adjusts the financial plan based on the latest information. For example, if an employee reports a new life event, it immediately updates the plan. This makes it possible to provide a future financial plan based on the employee's life events.

[0031] The individual status collection unit collects employee health data and can provide a financial plan based on the employee's health condition. The individual status collection unit collects employee health data, for example, from fitness trackers or health checkup results, and analyzes the employee's health condition. For example, the health condition is evaluated based on data such as heart rate, exercise volume, and blood pressure. The unit also proposes an optimal financial plan based on the employee's health condition. For example, an investment plan for maintaining health is proposed for an employee in good health. The unit also monitors health data in real time and adjusts the financial plan according to changes in the employee's health condition. For example, if the employee's health condition worsens, a medical expense preparation plan is proposed. This makes it possible to provide a financial plan based on the employee's health condition.

[0032] The employee benefit consideration department can analyze the usage history of a company's employee benefit programs, identify the most effective programs, and propose them to employees. The employee benefit consideration department, for example, registers and analyzes the usage history of a company's employee benefit programs in a database. For example, it determines which programs employees use and to what extent. It also identifies the most effective employee benefit programs based on the usage history and proposes them to employees. For example, it prioritizes the proposal of programs that are used most frequently. It also regularly updates the employee benefit program usage history, and updates the analysis results when a new program is introduced. For example, it evaluates the effectiveness of a new program and proposes it to employees. This makes it possible to propose the most effective programs to employees based on the usage history of the company's employee benefit programs.

[0033] The employee benefits consideration department can take into account the company's financial situation, predict future changes to employee benefits, and provide a financial plan based on that. The employee benefits consideration department, for example, analyzes the company's financial situation and predicts future changes to employee benefits. For example, the department makes predictions based on the company's revenue, expenses, and investment plans. The department also proposes an optimal financial plan based on the predicted future changes to employee benefits. For example, if changes to the retirement benefit system are predicted, the department proposes a plan to prepare for living expenses after retirement. The department also regularly monitors the company's financial situation and updates the predicted changes to employee benefits based on the latest information. For example, if the company's revenue increases, the department predicts the introduction of a new employee benefits program. This makes it possible to predict future changes to employee benefits based on the company's financial situation and provide a financial plan based on that.

[0034] The advice providing unit can analyze an employee's past financial behavior, predict future behavior, and provide advice. The advice providing unit, for example, collects and analyzes the employee's past financial behavior data. For example, it identifies behavioral patterns based on past income, expenses, investment history, etc. It also predicts future behavior based on past financial behavior and provides optimal advice. For example, it proposes future investment plans based on past investment patterns. It also regularly updates the financial behavior data and adjusts advice based on the latest information. For example, if new financial behavior occurs, it updates the advice content. This makes it possible to predict future behavior based on the employee's past financial behavior and provide optimal advice.

[0035] The advice providing unit can propose optimal insurance and investment plans based on the employee's family structure and life stage. The advice providing unit, for example, registers and analyzes the employee's family structure and life stage in a database. For example, it ascertains the number of family members, their ages, and their life stage (marriage, child-rearing, retirement, etc.). It also proposes optimal insurance and investment plans based on the family structure and life stage. For example, it proposes an education expense preparation plan for employees who are raising children. It also regularly updates the family structure and life stage data and adjusts the plans based on the latest information. For example, it updates the plans if there are any new changes in the family structure. This makes it possible to provide optimal insurance and investment plans based on the employee's family structure and life stage.

[0036] The packaging department can package money planning and sell it to companies. For example, the packaging department can incorporate the company's logo and colors into the money planning package to emphasize the brand image. For example, they can adopt a design that reflects the company's vision and mission. They can also customize the package contents based on the company's brand image. For example, they can propose a plan that matches the company's values ​​and culture. They can also promote the package to emphasize the brand image. For example, they can introduce the features of the package on the company's website and social media. This allows the packaging department to package money planning and sell it to companies.

[0037] The packaging department can periodically update the package contents to respond to the latest economic conditions and legal changes. The packaging department, for example, periodically reviews the package contents to respond to the latest economic conditions and legal changes. For example, it updates the plans based on tax reforms and fluctuations in the financial markets. It also collects information on economic conditions and legal changes and reflects this in the package contents. For example, it adjusts the plans based on the latest economic indicators and legal changes. It also notifies employees of updates to the package contents and provides them with the latest plans. For example, it distributes update information via email or an internal portal. This allows the package contents to be periodically updated to respond to the latest economic conditions and legal changes.

[0038] The packaging department can customize the money planning package to suit different industries and company sizes. For example, the packaging department customizes the money planning package to suit different industries. For example, it provides plans specialized for industries such as manufacturing, services, and IT. It also adjusts the package contents according to company size. For example, it provides a simple plan for small and medium-sized enterprises and a detailed plan for large companies. It also collects data on the industry and company size and customizes the package contents based on that data. For example, it proposes a plan that reflects the characteristics of the industry and the needs of the company size. This allows the money planning package to be customized to suit different industries and company sizes.

[0039] The packaging department can incorporate additional services into the packages. For example, the packaging department can incorporate financial seminars into money planning packages to provide employees with financial knowledge. For example, they can hold seminars on investments and savings. They can also add individual consultation services to packages to provide employees with opportunities to consult directly with experts. For example, they can set up individual consultations with financial planners. They can also regularly review the content of additional services and adjust them according to employees' needs. For example, they can add seminars on new financial products and services. By incorporating additional services into packages, they can provide more comprehensive money planning.

[0040] The advice providing unit can analyze an employee's career path and provide a financial plan based on future income predictions. The advice providing unit, for example, registers and analyzes the employee's career path in a database. For example, the advice providing unit identifies a career path based on past work history, skills, promotion history, etc. It also predicts future income based on the career path and proposes an optimal financial plan. For example, for an employee who is predicted to be promoted, it proposes an investment plan based on the income after promotion. It also regularly updates the career path data and adjusts the plan based on the latest information. For example, if a new skill is acquired, the income prediction is updated and the plan is adjusted. In this way, it is possible to provide a financial plan based on future income predictions based on the employee's career path.

[0041] The advice providing unit can propose optimal mortgage and renovation plans taking into consideration the employee's housing situation. The advice providing unit, for example, registers and analyzes the employee's housing situation in a database. For example, it determines whether the home is rented or owned, and whether or not the home has a mortgage. It also proposes optimal mortgage and renovation plans based on the housing situation. For example, it proposes renovation plans to employees who own their homes. It also regularly updates the housing situation data and adjusts the plans based on the latest information. For example, if a new home is purchased, the mortgage plan is updated. This makes it possible to provide optimal mortgage and renovation plans based on the employee's housing situation.

[0042] The advice providing unit can collect information about employees' hobbies and interests and propose investment and savings plans based on them. The advice providing unit, for example, collects information about employees' hobbies and interests through questionnaires and interviews and registers the information in a database. For example, it collects information about hobbies such as sports, traveling, and reading. It also proposes optimal investment and savings plans based on the hobbies and interests. For example, it proposes a savings plan for travel funds to an employee whose hobby is traveling. It also regularly updates the hobby and interest data and adjusts the plan if a new hobby or interest is discovered. For example, it proposes an investment plan that matches a newly started hobby. In this way, it is possible to provide investment and savings plans based on employees' hobbies and interests.

[0043] The advice providing unit can analyze the employee's social media activity and provide a financial plan that suits their lifestyle. The advice providing unit, for example, analyzes the employee's social media activity to understand their lifestyle and interests. For example, it analyzes the content of posts and the accounts they follow. It also proposes a financial plan that suits their lifestyle based on their social media activity. For example, it proposes a plan to purchase outdoor equipment to an employee who frequently engages in outdoor activities. It also regularly updates the social media activity data and adjusts the plan according to changes in their lifestyle. For example, it updates the plan if a new hobby or interest is discovered. In this way, it is possible to provide a financial plan that suits their lifestyle based on the employee's social media activity.

[0044] The advice providing department can regularly monitor employee satisfaction and propose specific measures to improve satisfaction. For example, the advice providing department periodically monitors employee satisfaction through questionnaires and interviews. For example, it regularly conducts satisfaction surveys and registers the results in a database. It also proposes specific measures to improve satisfaction based on the satisfaction data. For example, it proposes improving employee benefits and the work environment. It also regularly updates the satisfaction data and adjusts measures based on the latest information. For example, it reviews measures based on new satisfaction survey results. In this way, it is possible to regularly monitor employee satisfaction and propose specific measures to improve satisfaction.

[0045] The advice providing department can introduce measures that refer to the success stories of other companies in order to improve employee satisfaction. The advice providing department, for example, collects success stories of other companies and identifies measures to improve employee satisfaction. For example, it investigates cases of employee benefit programs and work environment improvements at other companies in the same industry. It also introduces measures to improve employee satisfaction based on the success stories. For example, it makes a proposal to introduce measures that have received high praise from other companies into the company. It also regularly updates data on the success stories of other companies and adjusts measures based on the latest information. For example, if a new success story is discovered, it reviews the measures. In this way, employee satisfaction can be improved by introducing measures that refer to the success stories of other companies.

[0046] The advice providing department can collect feedback by utilizing internal communication tools in order to improve employee satisfaction. The advice providing department, for example, uses internal communication tools to collect feedback from employees. For example, it uses internal chat or survey functions. It also proposes measures to improve employee satisfaction based on the feedback. For example, it proposes improvements to employee benefit programs that reflect employee opinions. It also regularly updates the feedback data and adjusts the measures based on the latest information. For example, if new feedback is collected, it reviews the measures. In this way, it is possible to collect feedback by utilizing internal communication tools and propose measures to improve employee satisfaction.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The money planning system can also collect information about employees' hobbies and interests and propose investment and savings plans based on them. For example, employees' hobbies and interests can be collected through questionnaires or interviews, and the information can be registered in a database. For example, hobbies such as sports, travel, and reading can be collected. The system can then propose optimal investment and savings plans based on the hobbies and interests. For example, an employee whose hobby is traveling can be proposed a savings plan for travel funds. The hobby and interest data can also be updated regularly, and plans can be adjusted if new hobbies or interests are discovered. For example, an investment plan tailored to a newly started hobby can be proposed. This makes it possible to provide investment and savings plans based on employees' hobbies and interests.

[0049] The money planning system can also analyze employees' social media activity to provide a money plan that suits their lifestyle. For example, an employee's social media activity can be analyzed to understand their lifestyle and interests. For example, the content of their posts and the accounts they follow can be analyzed. The system can then propose a money plan that suits their lifestyle based on their social media activity. For example, an outdoor equipment purchase plan can be proposed for an employee who frequently engages in outdoor activities. The system can also regularly update the social media activity data and adjust the plan according to changes in their lifestyle. For example, the plan can be updated if a new hobby or interest is discovered. This makes it possible to provide a money plan that suits an employee's lifestyle based on their social media activity.

[0050] The money planning system can also analyze an employee's career path and provide a money plan based on future income projections. For example, an employee's career path is registered in a database and analyzed. For example, a career path is identified based on past work history, skills, promotion history, etc. Future income is predicted based on the career path, and an optimal money plan is proposed. For example, for an employee who is predicted to be promoted, an investment plan based on the income after promotion is proposed. Career path data is also updated regularly, and the plan is adjusted based on the latest information. For example, if a new skill is acquired, the income projection is updated and the plan is adjusted. This makes it possible to provide a money plan based on future income projections based on the employee's career path.

[0051] The money planning system can also take into account an employee's housing situation and propose the most suitable mortgage and renovation plan. For example, the system registers and analyzes an employee's housing situation in a database. For example, it determines whether the home is rented or owned, and whether or not the home has a mortgage. It then proposes the most suitable mortgage and renovation plan based on the housing situation. For example, it proposes a renovation plan to an employee who owns their home. It also regularly updates the housing situation data and adjusts the plan based on the latest information. For example, if a new home is purchased, the mortgage plan is updated. This makes it possible to provide the most suitable mortgage and renovation plan based on the employee's housing situation.

[0052] The money planning system can also regularly monitor employee satisfaction and propose specific measures to improve it. For example, employee satisfaction can be monitored regularly through questionnaires and interviews. For example, satisfaction surveys can be conducted regularly and the results can be registered in a database. Specific measures to improve satisfaction can be proposed based on the satisfaction data. For example, improvements can be made to employee benefits and the work environment. The satisfaction data can also be regularly updated and measures can be adjusted based on the latest information. For example, measures can be reviewed based on new satisfaction survey results. This makes it possible to regularly monitor employee satisfaction and propose specific measures to improve satisfaction.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The Individual Status Collection Unit collects information about each employee's individual situation and aspirations. For example, it collects information such as the employee's age, family structure, income, expenses, and future goals. It can also predict the employee's life events (marriage, childbirth, job change, etc.) and propose future financial plans based on these. Step 2: The analysis unit analyzes the individual situations and preferences of employees collected by the individual situation collection unit, for example, using data mining, statistical analysis, and machine learning algorithms. Step 3: The employee benefits department considers the company's unique employee benefits and programs, such as retirement plans, health insurance, and educational assistance programs. Step 4: The advice section provides personalized advice based on the information obtained by the analysis section and the employee benefits consideration section. For example, if an employee asks, "How should I prepare for my child's education expenses?", specific advice will be provided. Step 5: The packaging department packages the advice provided by the advice providing department. For example, it packages financial planning proposals based on the individual circumstances and wishes of employees and sells them to companies.

[0055] (Example 2) The money planning system according to an embodiment of the present invention provides highly accurate money planning based on the individual circumstances and wishes of each employee. This allows the money planning system to improve employee satisfaction and promote employee retention within the company. Furthermore, by selling this money planning system in a package, it can also contribute to increased profits for the company.

[0056] A financial planning system according to an embodiment includes an individual situation collection unit, an analysis unit, a benefits consideration unit, an advice provision unit, and a packaging unit. The individual situation collection unit collects information about each employee's individual situation and aspirations. For example, the information includes the employee's age, family structure, income, expenses, and future goals. The individual situation collection unit can also predict the employee's life events (marriage, childbirth, job change, etc.) and propose future financial plans based on the predicted events. The analysis unit analyzes the employee's individual situation and aspirations collected by the individual situation collection unit. For example, the analysis can be performed using data mining, statistical analysis, or machine learning algorithms. The benefits consideration unit considers the company's unique benefits and systems. For example, the system takes into account the company's retirement pension system, health insurance, and education support programs. The advice provision unit provides personalized advice based on the information obtained by the analysis unit and the benefits consideration unit. For example, if an employee asks, "How should I prepare for my child's education expenses?", the system provides specific advice. The packaging unit packages the advice provided by the advice provision unit. For example, money planning proposals based on the individual circumstances and wishes of employees can be packaged and sold to companies. This allows the money planning system according to the embodiment to provide highly accurate money planning based on the individual circumstances and wishes of employees. For example, this can improve employee satisfaction and encourage employees to stay with the company. Furthermore, by purchasing this package, companies can provide highly accurate money planning to their employees.

[0057] The individual situation collection unit can predict an employee's life events and propose a future financial plan based on them. The individual situation collection unit, for example, analyzes the employee's past data and predicts life events such as marriage and childbirth. For example, it predicts the next likely event based on the employee's age, family composition, and history of past life events. It also proposes a future financial plan based on the predicted life events. For example, if marriage is predicted, it proposes plans for wedding expenses and purchasing a new home. It also updates the employee's life event predictions in real time and adjusts the financial plan based on the latest information. For example, if an employee reports a new life event, it immediately updates the plan. This makes it possible to provide a future financial plan based on the employee's life events.

[0058] The individual status collection unit collects employee health data and can provide a financial plan based on the employee's health condition. The individual status collection unit collects employee health data, for example, from fitness trackers or health checkup results, and analyzes the employee's health condition. For example, the health condition is evaluated based on data such as heart rate, exercise volume, and blood pressure. The unit also proposes an optimal financial plan based on the employee's health condition. For example, an investment plan for maintaining health is proposed for an employee in good health. The unit also monitors health data in real time and adjusts the financial plan according to changes in the employee's health condition. For example, if the employee's health condition worsens, a medical expense preparation plan is proposed. This makes it possible to provide a financial plan based on the employee's health condition.

[0059] The individual situation collection unit can use the emotion estimation function to analyze the stress level of an employee and propose a financial plan that helps reduce stress. The individual situation collection unit, for example, uses the emotion estimation function to analyze the stress level of an employee. For example, it analyzes facial expressions and tone of voice to calculate a stress score. It also proposes a financial plan that helps reduce stress based on the stress level. For example, it proposes an investment plan related to relaxation and hobbies. It also monitors stress levels in real time and immediately provides a stress reduction plan if stress increases. For example, it suggests using a relaxation facility when stress increases. In this way, it is possible to provide a financial plan based on the employee's stress level.

[0060] The employee benefit consideration department can analyze the usage history of a company's employee benefit programs, identify the most effective programs, and propose them to employees. The employee benefit consideration department, for example, registers and analyzes the usage history of a company's employee benefit programs in a database. For example, it determines which programs employees use and to what extent. It also identifies the most effective employee benefit programs based on the usage history and proposes them to employees. For example, it prioritizes the proposal of programs that are used most frequently. It also regularly updates the employee benefit program usage history, and updates the analysis results when a new program is introduced. For example, it evaluates the effectiveness of a new program and proposes it to employees. This makes it possible to propose the most effective programs to employees based on the usage history of the company's employee benefit programs.

[0061] The employee benefits consideration department can take into account the company's financial situation, predict future changes to employee benefits, and provide a financial plan based on that. The employee benefits consideration department, for example, analyzes the company's financial situation and predicts future changes to employee benefits. For example, the department makes predictions based on the company's revenue, expenses, and investment plans. The department also proposes an optimal financial plan based on the predicted future changes to employee benefits. For example, if changes to the retirement benefit system are predicted, the department proposes a plan to prepare for living expenses after retirement. The department also regularly monitors the company's financial situation and updates the predicted changes to employee benefits based on the latest information. For example, if the company's revenue increases, the department predicts the introduction of a new employee benefits program. This makes it possible to predict future changes to employee benefits based on the company's financial situation and provide a financial plan based on that.

[0062] The employee benefits consideration unit can use the emotion estimation function to identify the employee benefits program that provides the most satisfaction to employees and propose a financial plan based on that. The employee benefits consideration unit, for example, uses the emotion estimation function to identify the employee benefits program that provides the most satisfaction. For example, it analyzes the emotion scores when using the employee benefits program. It also proposes the optimal financial plan based on the employee benefits program that provides the most satisfaction. For example, it prioritizes the proposal of programs with high satisfaction. It also periodically updates the emotion estimation data and adjusts the proposed employee benefits program according to changes in employee satisfaction. For example, when a new program is introduced, it evaluates satisfaction and updates the proposed content. This makes it possible to provide a financial plan based on the employee benefits program that provides the most satisfaction.

[0063] The advice providing unit can analyze an employee's past financial behavior, predict future behavior, and provide advice. The advice providing unit, for example, collects and analyzes the employee's past financial behavior data. For example, it identifies behavioral patterns based on past income, expenses, investment history, etc. It also predicts future behavior based on past financial behavior and provides optimal advice. For example, it proposes future investment plans based on past investment patterns. It also regularly updates the financial behavior data and adjusts advice based on the latest information. For example, if new financial behavior occurs, it updates the advice content. This makes it possible to predict future behavior based on the employee's past financial behavior and provide optimal advice.

[0064] The advice providing unit can propose optimal insurance and investment plans based on the employee's family structure and life stage. The advice providing unit, for example, registers and analyzes the employee's family structure and life stage in a database. For example, it ascertains the number of family members, their ages, and their life stage (marriage, child-rearing, retirement, etc.). It also proposes optimal insurance and investment plans based on the family structure and life stage. For example, it proposes an education expense preparation plan for employees who are raising children. It also regularly updates the family structure and life stage data and adjusts the plans based on the latest information. For example, it updates the plans if there are any new changes in the family structure. This makes it possible to provide optimal insurance and investment plans based on the employee's family structure and life stage.

[0065] The advice providing unit can use the emotion estimation function to analyze the employee's anxieties and hopes and provide specific advice based on the analysis. The advice providing unit, for example, uses the emotion estimation function to analyze the employee's anxieties and hopes. For example, it analyzes facial expressions and voice tone to calculate scores for the anxieties and hopes. It also provides optimal advice based on the anxieties and hopes. For example, it proposes a low-risk investment plan to an employee with high anxiety. It also periodically updates the emotion estimation data and adjusts the advice according to changes in the employee's anxieties and hopes. For example, if new anxieties or hopes arise, it updates the advice content. This makes it possible to provide specific advice based on the employee's anxieties and hopes.

[0066] The packaging department can package money planning and sell it to companies. For example, the packaging department can incorporate the company's logo and colors into the money planning package to emphasize the brand image. For example, they can adopt a design that reflects the company's vision and mission. They can also customize the package contents based on the company's brand image. For example, they can propose a plan that matches the company's values ​​and culture. They can also promote the package to emphasize the brand image. For example, they can introduce the features of the package on the company's website and social media. This allows the packaging department to package money planning and sell it to companies.

[0067] The packaging department can periodically update the package contents to respond to the latest economic conditions and legal changes. The packaging department, for example, periodically reviews the package contents to respond to the latest economic conditions and legal changes. For example, it updates the plans based on tax reforms and fluctuations in the financial markets. It also collects information on economic conditions and legal changes and reflects this in the package contents. For example, it adjusts the plans based on the latest economic indicators and legal changes. It also notifies employees of updates to the package contents and provides them with the latest plans. For example, it distributes update information via email or an internal portal. This allows the package contents to be periodically updated to respond to the latest economic conditions and legal changes.

[0068] The packaging unit can use the emotion estimation function to evaluate the acceptability of a package and propose optimal package contents. The packaging unit, for example, uses the emotion estimation function to evaluate the acceptability of a package. For example, it analyzes the emotion score when the package is used. It also proposes optimal package contents based on the acceptability. For example, it preferentially proposes plans with high emotion scores. It also periodically updates the emotion estimation data and adjusts the package contents according to changes in employees' emotional reactions. For example, when a new plan is introduced, it evaluates the emotional reactions and updates the proposed contents. This makes it possible to evaluate the acceptability of a package and propose optimal package contents.

[0069] The packaging department can customize the money planning package to suit different industries and company sizes. For example, the packaging department customizes the money planning package to suit different industries. For example, it provides plans specialized for industries such as manufacturing, services, and IT. It also adjusts the package contents according to company size. For example, it provides a simple plan for small and medium-sized enterprises and a detailed plan for large companies. It also collects data on the industry and company size and customizes the package contents based on that data. For example, it proposes a plan that reflects the characteristics of the industry and the needs of the company size. This allows the money planning package to be customized to suit different industries and company sizes.

[0070] The packaging department can incorporate additional services into the packages. For example, the packaging department can incorporate financial seminars into money planning packages to provide employees with financial knowledge. For example, they can hold seminars on investments and savings. They can also add individual consultation services to packages to provide employees with opportunities to consult directly with experts. For example, they can set up individual consultations with financial planners. They can also regularly review the content of additional services and adjust them according to employees' needs. For example, they can add seminars on new financial products and services. By incorporating additional services into packages, they can provide more comprehensive money planning.

[0071] The packaging unit can use the emotion estimation function to analyze the emotional responses of package buyers and optimize sales strategies. The packaging unit, for example, uses the emotion estimation function to analyze the emotional responses of package buyers. For example, the emotional score at the time of purchase is analyzed to identify the purchase motivation. The packaging unit also proposes an optimal sales strategy based on the emotional responses. For example, a specific sales channel is utilized for buyers with strong positive emotions. The emotion estimation data is also periodically updated, and the sales strategy is adjusted according to changes in the emotional responses of buyers. For example, when implementing a new sales campaign, the emotional responses are evaluated and the strategy is adjusted. In this way, the emotional responses of package buyers can be analyzed and the sales strategy can be optimized.

[0072] The advice providing unit can analyze an employee's career path and provide a financial plan based on future income predictions. The advice providing unit, for example, registers and analyzes the employee's career path in a database. For example, the advice providing unit identifies a career path based on past work history, skills, promotion history, etc. It also predicts future income based on the career path and proposes an optimal financial plan. For example, for an employee who is predicted to be promoted, it proposes an investment plan based on the income after promotion. It also regularly updates the career path data and adjusts the plan based on the latest information. For example, if a new skill is acquired, the income prediction is updated and the plan is adjusted. In this way, it is possible to provide a financial plan based on future income predictions based on the employee's career path.

[0073] The advice providing unit can propose optimal mortgage and renovation plans taking into consideration the employee's housing situation. The advice providing unit, for example, registers and analyzes the employee's housing situation in a database. For example, it determines whether the home is rented or owned, and whether or not the home has a mortgage. It also proposes optimal mortgage and renovation plans based on the housing situation. For example, it proposes renovation plans to employees who own their homes. It also regularly updates the housing situation data and adjusts the plans based on the latest information. For example, if a new home is purchased, the mortgage plan is updated. This makes it possible to provide optimal mortgage and renovation plans based on the employee's housing situation.

[0074] The advice providing unit can use the emotion estimation function to suggest the most acceptable form of advice based on the employee's emotional state. For example, the advice providing unit uses the emotion estimation function to analyze the employee's emotional state and identify the most acceptable form of advice. For example, the advice providing unit selects the form of advice based on the emotion score. The advice providing unit also suggests the optimal form of advice based on the emotional state. For example, aggressive advice is suggested for an employee with strong positive emotions. The emotion estimation data is also periodically updated, and the form of advice is adjusted according to changes in the employee's emotional state. For example, if negative emotions are strong, gentle advice is suggested. In this way, the advice providing unit can suggest the most acceptable form of advice based on the employee's emotional state.

[0075] The advice providing unit can collect information about employees' hobbies and interests and propose investment and savings plans based on them. The advice providing unit, for example, collects information about employees' hobbies and interests through questionnaires and interviews and registers the information in a database. For example, it collects information about hobbies such as sports, traveling, and reading. It also proposes optimal investment and savings plans based on the hobbies and interests. For example, it proposes a savings plan for travel funds to an employee whose hobby is traveling. It also regularly updates the hobby and interest data and adjusts the plan if a new hobby or interest is discovered. For example, it proposes an investment plan that matches a newly started hobby. In this way, it is possible to provide investment and savings plans based on employees' hobbies and interests.

[0076] The advice providing unit can analyze the employee's social media activity and provide a financial plan that suits their lifestyle. The advice providing unit, for example, analyzes the employee's social media activity to understand their lifestyle and interests. For example, it analyzes the content of posts and the accounts they follow. It also proposes a financial plan that suits their lifestyle based on their social media activity. For example, it proposes a plan to purchase outdoor equipment to an employee who frequently engages in outdoor activities. It also regularly updates the social media activity data and adjusts the plan according to changes in their lifestyle. For example, it updates the plan if a new hobby or interest is discovered. In this way, it is possible to provide a financial plan that suits their lifestyle based on the employee's social media activity.

[0077] The advice providing unit can use the emotion estimation function to monitor the emotional state of the employee in real time and propose a financial plan according to the emotion. The advice providing unit, for example, uses the emotion estimation function to monitor the emotional state of the employee in real time. For example, it analyzes facial expressions and tone of voice to calculate an emotion score. It also proposes an optimal financial plan based on the emotional state. For example, when positive emotions are strong, it proposes a high-risk investment plan. It also adjusts the financial plan in real time according to changes in the emotional state. For example, when negative emotions are strong, it proposes a low-risk savings plan. In this way, it is possible to provide an optimal financial plan in real time based on the emotional state of the employee.

[0078] The advice providing unit can analyze the emotional state of the employee using the emotion estimation function and propose measures to improve emotional satisfaction. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the employee. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. It also proposes measures to improve emotional satisfaction based on the emotional state. For example, it proposes a relaxation program to reduce stress. It also periodically updates the emotion estimation data and adjusts the measures according to changes in the employee's emotional state. For example, if the emotion score is low, it proposes measures to strengthen emotional support. In this way, it is possible to propose measures to improve emotional satisfaction based on the employee's emotional state.

[0079] The advice providing department can regularly monitor employee satisfaction and propose specific measures to improve satisfaction. For example, the advice providing department periodically monitors employee satisfaction through questionnaires and interviews. For example, it regularly conducts satisfaction surveys and registers the results in a database. It also proposes specific measures to improve satisfaction based on the satisfaction data. For example, it proposes improving employee benefits and the work environment. It also regularly updates the satisfaction data and adjusts measures based on the latest information. For example, it reviews measures based on new satisfaction survey results. In this way, it is possible to regularly monitor employee satisfaction and propose specific measures to improve satisfaction.

[0080] The advice providing department can introduce measures that refer to the success stories of other companies in order to improve employee satisfaction. The advice providing department, for example, collects success stories of other companies and identifies measures to improve employee satisfaction. For example, it investigates cases of employee benefit programs and work environment improvements at other companies in the same industry. It also introduces measures to improve employee satisfaction based on the success stories. For example, it makes a proposal to introduce measures that have received high praise from other companies into the company. It also regularly updates data on the success stories of other companies and adjusts measures based on the latest information. For example, if a new success story is discovered, it reviews the measures. In this way, employee satisfaction can be improved by introducing measures that refer to the success stories of other companies.

[0081] The advice providing department can collect feedback by utilizing internal communication tools in order to improve employee satisfaction. The advice providing department, for example, uses internal communication tools to collect feedback from employees. For example, it uses internal chat or survey functions. It also proposes measures to improve employee satisfaction based on the feedback. For example, it proposes improvements to employee benefit programs that reflect employee opinions. It also regularly updates the feedback data and adjusts the measures based on the latest information. For example, if new feedback is collected, it reviews the measures. In this way, it is possible to collect feedback by utilizing internal communication tools and propose measures to improve employee satisfaction.

[0082] The advice providing unit can use the emotion estimation function to identify and implement the most effective satisfaction improvement measures based on the emotional reactions of employees. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional reactions of employees and identify the most effective satisfaction improvement measures. For example, it evaluates the effectiveness of the measures based on the emotional scores. It also proposes and implements optimal satisfaction improvement measures based on the emotional reactions. For example, it prioritizes the introduction of measures with high emotional scores. It also regularly updates the emotion estimation data and adjusts the measures according to changes in the emotional reactions of employees. For example, when a new measure is introduced, it evaluates the emotional reactions and reviews the measure. In this way, it is possible to identify and implement the most effective satisfaction improvement measures based on the emotional reactions of employees.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The money planning system can also collect information about employees' hobbies and interests and propose investment and savings plans based on them. For example, employees' hobbies and interests can be collected through questionnaires or interviews, and the information can be registered in a database. For example, hobbies such as sports, travel, and reading can be collected. The system can then propose optimal investment and savings plans based on the hobbies and interests. For example, an employee whose hobby is traveling can be proposed a savings plan for travel funds. The hobby and interest data can also be updated regularly, and plans can be adjusted if new hobbies or interests are discovered. For example, an investment plan tailored to a newly started hobby can be proposed. This makes it possible to provide investment and savings plans based on employees' hobbies and interests.

[0085] The money planning system can also analyze employees' social media activity to provide a money plan that suits their lifestyle. For example, an employee's social media activity can be analyzed to understand their lifestyle and interests. For example, the content of their posts and the accounts they follow can be analyzed. The system can then propose a money plan that suits their lifestyle based on their social media activity. For example, an outdoor equipment purchase plan can be proposed for an employee who frequently engages in outdoor activities. The system can also regularly update the social media activity data and adjust the plan according to changes in their lifestyle. For example, the plan can be updated if a new hobby or interest is discovered. This makes it possible to provide a money plan that suits an employee's lifestyle based on their social media activity.

[0086] The money planning system can also analyze an employee's career path and provide a money plan based on future income projections. For example, an employee's career path is registered in a database and analyzed. For example, a career path is identified based on past work history, skills, promotion history, etc. Future income is predicted based on the career path, and an optimal money plan is proposed. For example, for an employee who is predicted to be promoted, an investment plan based on the income after promotion is proposed. Career path data is also updated regularly, and the plan is adjusted based on the latest information. For example, if a new skill is acquired, the income projection is updated and the plan is adjusted. This makes it possible to provide a money plan based on future income projections based on the employee's career path.

[0087] The money planning system can also take into account an employee's housing situation and propose the most suitable mortgage and renovation plan. For example, the system registers and analyzes an employee's housing situation in a database. For example, it determines whether the home is rented or owned, and whether or not the home has a mortgage. It then proposes the most suitable mortgage and renovation plan based on the housing situation. For example, it proposes a renovation plan to an employee who owns their home. It also regularly updates the housing situation data and adjusts the plan based on the latest information. For example, if a new home is purchased, the mortgage plan is updated. This makes it possible to provide the most suitable mortgage and renovation plan based on the employee's housing situation.

[0088] The money planning system can also regularly monitor employee satisfaction and propose specific measures to improve it. For example, employee satisfaction can be monitored regularly through questionnaires and interviews. For example, satisfaction surveys can be conducted regularly and the results can be registered in a database. Specific measures to improve satisfaction can be proposed based on the satisfaction data. For example, improvements can be made to employee benefits and the work environment. The satisfaction data can also be regularly updated and measures can be adjusted based on the latest information. For example, measures can be reviewed based on new satisfaction survey results. This makes it possible to regularly monitor employee satisfaction and propose specific measures to improve satisfaction.

[0089] The money planning system can also use an emotion estimation function to analyze employees' stress levels and propose money plans that help reduce stress. For example, the emotion estimation function can be used to analyze an employee's stress level. For example, facial expressions and tone of voice can be analyzed to calculate a stress score. Then, based on the stress level, a money plan that helps reduce stress can be proposed. For example, an investment plan related to relaxation or hobbies can be proposed. The system can also monitor stress levels in real time and immediately provide a stress reduction plan if stress increases. For example, it can suggest the use of relaxation facilities when stress increases. This makes it possible to provide a money plan based on the employee's stress level.

[0090] The money planning system can also use the emotion estimation function to identify the employee benefit program that provides the most satisfaction to employees and propose a money plan based on that. For example, the emotion estimation function can be used to identify the employee benefit program that provides the most satisfaction. For example, the emotion score obtained when using the benefit program can be analyzed. The system can then propose the optimal money plan based on the employee benefit program that provides the most satisfaction. For example, programs with high satisfaction can be proposed preferentially. The emotion estimation data can also be updated regularly to adjust the proposed benefit programs according to changes in employee satisfaction. For example, when a new program is introduced, satisfaction can be evaluated and the proposed content can be updated. This makes it possible to provide a money plan based on the employee benefit program that provides the most satisfaction.

[0091] The money planning system can also use an emotion estimation function to analyze employees' anxieties and hopes and provide specific advice based on them. For example, the emotion estimation function can be used to analyze employees' anxieties and hopes. For example, facial expressions and tone of voice can be analyzed to calculate scores for anxiety and hope. The system can then provide optimal advice based on these anxieties and hopes. For example, it can suggest low-risk investment plans to employees with high anxiety. The emotion estimation data can also be updated regularly to adjust advice according to changes in employees' anxieties and hopes. For example, if new anxieties or hopes arise, the advice content can be updated. This makes it possible to provide specific advice based on employees' anxieties and hopes.

[0092] The money planning system can also use an emotion estimation function to monitor the emotional state of employees in real time and propose a money plan based on their emotions. For example, the emotion estimation function can be used to monitor the emotional state of employees in real time. For example, facial expressions and tone of voice can be analyzed to calculate an emotion score. The system can then propose an optimal money plan based on the emotional state. For example, when positive emotions are strong, a high-risk investment plan can be proposed. The money plan can also be adjusted in real time according to changes in the emotional state. For example, when negative emotions are strong, a low-risk savings plan can be proposed. This makes it possible to provide an optimal money plan in real time based on the emotional state of employees.

[0093] The money planning system can further use an emotion estimation function to analyze the emotional state of employees and propose measures to improve their emotional satisfaction. For example, the emotion estimation function is used to analyze the emotional state of employees. For example, facial expressions and tone of voice are analyzed to calculate an emotion score. Then, measures to improve emotional satisfaction are proposed based on the emotional state. For example, a relaxation program to reduce stress is proposed. The emotion estimation data is also periodically updated, and measures are adjusted according to changes in the employee's emotional state. For example, if the emotion score is low, measures to strengthen emotional support are proposed. This makes it possible to propose measures to improve emotional satisfaction based on the employee's emotional state.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The Individual Status Collection Unit collects information about each employee's individual situation and aspirations. For example, it collects information such as the employee's age, family structure, income, expenses, and future goals. It can also predict the employee's life events (marriage, childbirth, job change, etc.) and propose future financial plans based on these. Step 2: The analysis unit analyzes the individual situations and preferences of employees collected by the individual situation collection unit, for example, using data mining, statistical analysis, and machine learning algorithms. Step 3: The employee benefits department considers the company's unique employee benefits and programs, such as retirement plans, health insurance, and educational assistance programs. Step 4: The advice section provides personalized advice based on the information obtained by the analysis section and the employee benefits consideration section. For example, if an employee asks, "How should I prepare for my child's education expenses?", specific advice will be provided. Step 5: The packaging department packages the advice provided by the advice providing department. For example, it packages financial planning proposals based on the individual circumstances and wishes of employees and sells them to companies.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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. an individual situation collection department that collects individual situations and wishes of employees; an analysis unit that analyzes the individual situations and wishes of the employees collected by the individual situation collection unit; The Employee Benefits Consideration Department considers the company's unique employee benefits and systems, an advice providing unit that provides advice tailored to each individual based on the information obtained by the analysis unit and the welfare consideration unit; a packaging unit that packages the advice provided by the advice providing unit. A system characterized by:

2. The individual status collection unit Predicting the employee's life events and proposing future financial plans based on them The system of claim 1 .

3. The individual status collection unit Collecting health data of said employees and providing financial plans based on their health status The system of claim 1 .

4. The individual status collection unit Analyze the stress level of the employee and propose a financial plan that will help reduce stress. The system of claim 1 .

5. The welfare consideration department Analyzing the usage history of a company's employee benefits programs, identifying the most effective programs, and proposing them to the employee The system of claim 1 .

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