system

Abstract

The system according to this embodiment aims to simplify the insurance selection process and reduce the burden on users. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and an efficiency unit. The reception unit responds immediately to user inquiries and facilitates contract procedures. The analysis unit analyzes the user's lifestyle and health condition based on the information received by the reception unit and proposes the optimal insurance plan. The provision unit clearly states the advantages and disadvantages of the insurance plan proposed by the analysis unit, enabling the user to make an informed decision. The efficiency unit improves the overall efficiency of insurance selection based on the information provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the insurance selection process is complicated and burdensome for users.

[0005] The system according to the embodiment aims to simplify the insurance selection process and reduce the burden on users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an efficiency unit. The reception unit responds immediately to user inquiries and facilitates contract procedures. The analysis unit analyzes the user's lifestyle and health condition based on the information received by the reception unit and proposes the optimal insurance plan. The provision unit clearly outlines the advantages and disadvantages of the insurance plan proposed by the analysis unit, enabling the user to make an informed decision. The efficiency unit improves the overall efficiency of insurance selection based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can simplify the insurance selection process and reduce the burden on the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An insurance selection support system according to an embodiment of the present invention is a system that uses AI technology to simplify the insurance selection process for consumers and reduce their burden. This insurance selection support system is equipped with real-time consultation and procedural support functions to respond immediately to user questions and to ensure smooth contract procedures. As a result, users can receive quick and accurate suggestions, improving convenience. The insurance selection support system also provides an individual needs analysis function that analyzes and proposes the optimal insurance plan based on the user's lifestyle and health condition. As a result, users can select the insurance plan that is best suited to them. Furthermore, it is equipped with a transparency assurance function that clearly shows the advantages and disadvantages of insurance plans, enabling users to make informed choices. As a result, users can choose insurance with peace of mind. It also provides an efficiency promotion function that provides information more quickly and accurately than human agents, improving the overall efficiency of insurance selection. As a result, users can easily choose insurance, and operating costs are reduced by AI. In this way, it is expected that the AI ​​agent will innovate insurance selection and provide new value to consumers, thereby establishing a firm position in the market. For example, the insurance selection support system is equipped with real-time consultation and procedural support functions to respond immediately to user questions and to ensure smooth contract procedures. This allows users to receive quick and accurate suggestions, improving convenience. Next, the insurance selection support system provides an individual needs analysis function that analyzes and proposes the optimal insurance plan based on the user's lifestyle and health condition. This allows users to select the insurance plan that is best suited to them. Furthermore, it has a transparency function that clearly shows the advantages and disadvantages of insurance plans, enabling users to make informed choices. This allows users to choose insurance with peace of mind. It also provides efficiency-promoting functions that provide information faster and more accurately than human agents, increasing the overall efficiency of insurance selection. This makes it easier for users to choose insurance, and operating costs are reduced by AI. In this way, it is expected that the AI ​​agent will revolutionize insurance selection and provide new value to consumers, thereby establishing a solid position in the market.This allows the insurance selection support system to simplify the user's insurance selection process and reduce their burden.

[0029] The insurance selection support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an efficiency unit. The reception unit responds immediately to user inquiries and facilitates smooth contract procedures. For example, the reception unit provides real-time answers to user inquiries. The reception unit can also manage the progress of contract procedures and support users in completing the procedures smoothly. Furthermore, the reception unit can analyze the content of user inquiries and collect information to provide appropriate answers. For example, the reception unit can quickly search for relevant information in response to user inquiries and provide answers. The analysis unit analyzes the user's lifestyle and health condition and proposes the optimal insurance plan. For example, the analysis unit collects information on the user's lifestyle and selects the optimal insurance plan based on their health condition. The analysis unit can also analyze data on the user's health condition and perform risk assessments. Furthermore, the analysis unit can evaluate the merits and demerits of insurance plans based on the user's lifestyle and health condition. For example, the analysis unit can determine whether a particular insurance plan is suitable based on the user's health condition. The Service Department clearly outlines the advantages and disadvantages of insurance plans proposed by the Analysis Department, enabling users to make informed choices. For example, the Service Department provides detailed explanations of insurance plans and organizes information in a user-friendly manner. It can also compare the advantages and disadvantages of insurance plans to support users in making the best choice. Furthermore, the Service Department can provide detailed information on insurance plans in response to user inquiries. For example, it evaluates and explains the cost-effectiveness and risks of insurance plans to users. The Efficiency Department improves the overall efficiency of insurance selection based on the information provided by the Service Department. For example, it optimizes each step of the insurance selection process to allow users to choose insurance quickly. It can also promote the automation of the insurance selection process to reduce the user burden. Furthermore, the Efficiency Department can reduce operating costs by streamlining the insurance selection process. For example, it automates each step of the insurance selection process to allow users to choose insurance quickly.As a result, the insurance selection support system according to this embodiment can simplify the user's insurance selection process and reduce their burden.

[0030] The reception department provides immediate responses to user inquiries and ensures smooth contract procedures. Specifically, the reception department utilizes chatbots and AI assistants to provide real-time answers to user questions. This allows users to ask questions 24 / 7 and receive quick responses. Furthermore, the reception department manages the progress of contract procedures and supports users in completing the process smoothly. For example, when a user applies for an insurance contract, the reception department automatically tracks the progress and reminds them of necessary document submissions and confirmations. The reception department can also analyze user questions and gather information to provide appropriate answers. For example, if a user asks about a specific insurance plan, the reception department quickly searches for relevant information and provides a detailed answer. This ensures that users have enough information to choose the insurance plan that best suits them. In addition, the reception department can provide answers optimized for individual users based on their past question history and behavioral data. This ensures that users receive consistent support and the insurance selection process proceeds more smoothly.

[0031] The analytics department analyzes users' lifestyles and health conditions to propose the most suitable insurance plan. Specifically, the analytics department collects information on users' lifestyles and selects the most suitable insurance plan based on their health condition. For example, it collects data on users' daily exercise, diet, smoking, and drinking habits, and uses this information to perform risk assessments. The analytics department can also analyze data on users' health conditions and perform risk assessments. For example, it can assess the risk of specific diseases based on users' past health checkup results and medical history, and propose an insurance plan accordingly. Furthermore, the analytics department can evaluate the advantages and disadvantages of insurance plans based on users' lifestyles and health conditions. For example, if a user is healthy, it will propose a low-risk insurance plan, and conversely, if the user has a high health risk, it will propose a more comprehensive insurance plan. This allows users to choose the insurance plan that best suits their situation. In addition, the analytics department can utilize AI to quickly analyze large amounts of data and propose the most suitable insurance plan to users in real time. This allows users to make insurance selections based on quick and accurate information.

[0032] The service provider clearly outlines the advantages and disadvantages of the insurance plans proposed by the analysis department, enabling users to make informed choices. Specifically, the service provider provides detailed explanations of the insurance plans and organizes the information in a way that is easy for users to understand. For example, they provide materials that clearly summarize the content, coverage, costs, and risks of the insurance plans. The service provider can also compare the advantages and disadvantages of insurance plans and support users in making the best choice. For example, they provide tools for comparing multiple insurance plans, allowing users to choose the plan that best suits their needs. Furthermore, the service provider can provide detailed information about insurance plans in response to user inquiries. For example, if a user requests detailed information about a particular insurance plan, the service provider will evaluate the cost-effectiveness and risks of that plan and explain them to the user. This allows users to feel confident in their choices. In addition, the service provider can collect user feedback and use it to improve insurance plans. For example, if a user is dissatisfied with an insurance plan, the service provider will review and improve the plan based on that feedback. This ensures that the service provider can always provide users with the best possible insurance plan.

[0033] The Efficiency Department improves the overall efficiency of insurance selection based on the information provided by the Provision Department. Specifically, the Efficiency Department optimizes each step of the insurance selection process to enable users to choose insurance quickly. For example, it automates each step of the insurance selection process to enable users to quickly obtain the necessary information. The Efficiency Department can also reduce the burden on users by promoting the automation of the insurance selection process. For example, it can build a system that automatically collects and provides the information necessary when users choose an insurance plan. Furthermore, the Efficiency Department can reduce operating costs by streamlining the insurance selection process. For example, by automating each step of the insurance selection process, it can reduce human resources and improve operational efficiency. In this way, the Efficiency Department can not only enable users to choose insurance quickly but also contribute to reducing operating costs. In addition, the Efficiency Department can continuously improve the insurance selection process based on user feedback. For example, if users are dissatisfied with the insurance selection process, the process will be reviewed and improved based on that feedback. In this way, the Efficiency Department can continue to provide users with the optimal insurance selection process at all times.

[0034] The reception desk can analyze a user's past question history and select the most appropriate response. For example, the reception desk can prepare relevant information in advance based on the types of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and respond to questions that are frequently asked during specific time periods based on the user's past question history. This enables optimal responses based on past question history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI select the most appropriate response.

[0035] The reception unit can filter questions based on the user's current situation and areas of interest when questions are received. For example, when a user enters their current situation, the reception unit will prioritize displaying relevant questions. The reception unit can also filter and display relevant questions based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate questions according to the user's current situation. This enables filtering of questions according to the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0036] The reception desk can prioritize questions based on the user's geographical location when receiving inquiries. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. The reception desk can also provide region-specific information based on the user's geographical location. Furthermore, if the user is traveling, the reception desk can prioritize questions related to their travel destination. This enables priority answering of questions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant questions.

[0037] The reception desk can analyze the user's social media activity when a question is received and retrieve relevant questions. For example, the reception desk can retrieve questions related to topics of interest from the user's social media activity. The reception desk can also suggest relevant questions based on the user's recent posts. Furthermore, the reception desk can analyze the user's social media activity and retrieve questions related to trends. This makes it possible to retrieve questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant questions.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's lifestyle and health status during the analysis. For example, if the user's health status is important, the analysis unit will perform an analysis that includes detailed health information. Furthermore, if the user's lifestyle is important, the analysis unit can also perform an analysis that includes lifestyle-related information. In addition, if both the user's health status and lifestyle are important, the analysis unit can perform an analysis that includes information from both in a balanced manner. This allows for adjustment of the level of detail of the analysis according to the user's lifestyle and health status. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user lifestyle and health status data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the user's category during analysis. For example, if the user is young, the analysis unit can apply an analysis algorithm for young people. Similarly, if the user is elderly, the analysis unit can apply an analysis algorithm for the elderly. Furthermore, if the user is engaged in a specific occupation, the analysis unit can apply an analysis algorithm related to that occupation. This enables the application of analysis algorithms tailored to the user's category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the user's submission date. For example, if the user submitted earlier, the analysis unit will prioritize the analysis. If the user submitted later, the analysis unit can also perform the analysis in the normal order. Furthermore, the analysis unit can adjust the priority of analysis based on the user's submission date. This makes it possible to determine the priority of analysis based on the user's submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, if a user is highly relevant, the analysis unit will prioritize that analysis. Conversely, if a user is less relevant, the analysis unit can perform the analysis in the normal order. Furthermore, the analysis unit can adjust the order of analysis based on user relevance. This makes it possible to adjust the order of analysis based on user relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The delivery unit can adjust the level of detail provided based on the importance of the insurance plan at the time of delivery. For example, if the insurance plan is highly important, the delivery unit will provide detailed information. Conversely, if the insurance plan is less important, the delivery unit may provide concise information. Furthermore, the delivery unit can adjust the level of detail provided based on the importance of the insurance plan. This makes it possible to adjust the level of detail provided according to the importance of the insurance plan. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input insurance plan importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0043] The service provider can apply different service provision algorithms depending on the insurance plan category at the time of provision. For example, if the insurance plan is medical insurance, the service provider can apply a service provision algorithm specialized for medical insurance. Furthermore, if the insurance plan is life insurance, the service provider can apply a service provision algorithm specialized for life insurance. In addition, if the insurance plan is automobile insurance, the service provider can apply a service provision algorithm specialized for automobile insurance. This makes it possible to apply service provision algorithms according to the insurance plan category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input insurance plan category data into a generating AI and have the generating AI execute the application of different service provision algorithms.

[0044] The service provider can determine the priority of service provision based on the submission date of the insurance plan at the time of provision. For example, if the insurance plan is submitted early, the service provider will provide it preferentially. If the insurance plan is submitted late, the service provider can provide it in the normal order. Furthermore, the service provider can adjust the priority of service provision based on the submission date of the insurance plan. This makes it possible to determine the priority of service provision based on the submission date of the insurance plan. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input insurance plan submission date data into a generating AI and have the generating AI perform the determination of the priority of service provision.

[0045] The delivery unit can adjust the order of delivery based on the relevance of insurance plans at the time of delivery. For example, the delivery unit will prioritize delivery if the insurance plans are highly relevant. Conversely, if the insurance plans are less relevant, the delivery unit can deliver them in the normal order. Furthermore, the delivery unit can adjust the order of delivery based on the relevance of insurance plans. This makes it possible to adjust the order of delivery based on the relevance of insurance plans. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input insurance plan relevance data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0046] The efficiency optimization unit can analyze the user's past behavior during the optimization process to select the optimal optimization method. For example, the efficiency optimization unit can analyze the user's past behavior patterns and propose the most efficient procedure. It can also select the optimal method based on the optimization methods the user has used in the past. Furthermore, the efficiency optimization unit can propose the optimal optimization method for a specific time period based on the user's past behavior history. This makes it possible to select the optimal optimization method based on the user's past behavior. Some or all of the above processing in the efficiency optimization unit may be performed using AI, for example, or without AI. For example, the efficiency optimization unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal optimization method.

[0047] The efficiency improvement unit can customize the means of efficiency improvement based on the user's current situation during the efficiency improvement process. For example, when the user inputs their current situation, the efficiency improvement unit proposes the optimal means of efficiency improvement. The efficiency improvement unit can also customize the means of efficiency improvement according to the user's current situation. Furthermore, the efficiency improvement unit can adjust the means of efficiency improvement in real time based on the user's current situation. This makes it possible to customize the means of efficiency improvement according to the user's current situation. Some or all of the above processing in the efficiency improvement unit may be performed using AI, for example, or without using AI. For example, the efficiency improvement unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of efficiency improvement.

[0048] The efficiency improvement unit can select the optimal efficiency improvement method by considering the user's geographical location information during the efficiency improvement process. For example, if the user is in a specific region, the efficiency improvement unit can provide efficiency improvement methods relevant to that region. The efficiency improvement unit can also propose region-specific efficiency improvement methods based on the user's geographical location information. Furthermore, if the user is traveling, the efficiency improvement unit can provide efficiency improvement methods relevant to the travel destination. This makes it possible to select the optimal efficiency improvement method based on the user's geographical location information. Some or all of the above processing in the efficiency improvement unit may be performed using AI, for example, or without AI. For example, the efficiency improvement unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal efficiency improvement method.

[0049] The efficiency improvement unit can analyze the user's social media activity and propose efficiency improvements during the optimization process. For example, the efficiency improvement unit can propose efficiency improvements related to topics of interest based on the user's social media activity. It can also propose relevant efficiency improvements based on the user's recent posts. Furthermore, the efficiency improvement unit can analyze the user's social media activity and propose efficiency improvements related to trends. This makes it possible to propose efficiency improvements based on the user's social media activity. Some or all of the above processing in the efficiency improvement unit may be performed using AI, for example, or without AI. For example, the efficiency improvement unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of efficiency improvements.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analytics department can monitor users' health data in real time and adjust insurance plan recommendations according to changes in their health status. For example, if a user's health deteriorates, it can suggest more comprehensive medical insurance. Conversely, if their health improves, it can suggest a plan with lower premiums. Furthermore, it can provide advice on preventive medicine based on the user's health data. This enables the proposal of insurance plans tailored to the user's health condition.

[0052] The service provider can analyze a user's purchase history and make new recommendations based on previously purchased insurance plans. For example, if a user has previously purchased health insurance, a similar plan can be suggested. Similarly, if a user has previously purchased life insurance, a plan related to life insurance can be suggested. Furthermore, based on the user's purchase history, an upgrade to a specific insurance plan can be suggested. This enables the provision of insurance plans tailored to the user's purchase history.

[0053] The efficiency optimization department can analyze users' device usage and propose the most suitable efficiency measures. For example, if a user frequently uses a smartphone, it can suggest procedures via a mobile app. If the user uses a PC, it can suggest procedures via a website. Furthermore, if the user uses a tablet, it can suggest procedures optimized for tablets. This makes it possible to propose efficiency measures tailored to the user's device usage.

[0054] The analysis department can adjust insurance plan suggestions based on the user's life events. For example, if a user gets married, it can suggest a family-oriented insurance plan. If a user has children, it can suggest a child-oriented insurance plan. Furthermore, if a user retires, it can suggest an insurance plan suitable for retirement. This makes it possible to propose insurance plans tailored to the user's life events.

[0055] The efficiency optimization unit can analyze a user's past behavior to select the optimal efficiency method. For example, it can analyze a user's past behavior patterns and propose the most efficient procedure. It can also select the optimal method based on efficiency methods the user has used in the past. Furthermore, it can propose the most suitable efficiency method for a specific time period based on the user's past behavior history. This makes it possible to select the optimal efficiency method based on the user's past behavior.

[0056] The efficiency improvement unit can select the optimal efficiency improvement method by considering the user's geographical location. For example, if the user is in a specific region, it can provide efficiency improvement methods relevant to that region. It can also suggest region-specific efficiency improvement methods based on the user's geographical location. Furthermore, if the user is traveling, it can provide efficiency improvement methods relevant to their travel destination. This makes it possible to select the optimal efficiency improvement method based on the user's geographical location.

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

[0058] Step 1: The reception department will respond to user inquiries immediately and ensure smooth contract procedures. For example, they will provide real-time answers to user questions, manage the progress of contract procedures, and support users in completing the process smoothly. They will also analyze user questions and collect information to provide appropriate answers. Step 2: The analysis department analyzes the user's lifestyle and health status and proposes the optimal insurance plan. For example, it collects information on the user's lifestyle and selects the optimal insurance plan based on their health status. It also analyzes data on the user's health status and performs a risk assessment. Step 3: The service provider clearly outlines the advantages and disadvantages of the insurance plan proposed by the analysis department, enabling users to make informed decisions. For example, they provide detailed explanations of the insurance plans and organize the information in a way that is easy for users to understand. They also compare the advantages and disadvantages of the insurance plans to help users make the best choice. Step 4: The Efficiency Department will improve the overall efficiency of insurance selection based on the information provided by the Provision Department. For example, they will optimize each step of the insurance selection process to allow users to choose insurance quickly. They will also promote the automation of the insurance selection process to reduce the burden on users.

[0059] (Example of form 2) An insurance selection support system according to an embodiment of the present invention is a system that uses AI technology to simplify the insurance selection process for consumers and reduce their burden. This insurance selection support system is equipped with real-time consultation and procedural support functions to respond immediately to user questions and to ensure smooth contract procedures. As a result, users can receive quick and accurate suggestions, improving convenience. The insurance selection support system also provides an individual needs analysis function that analyzes and proposes the optimal insurance plan based on the user's lifestyle and health condition. As a result, users can select the insurance plan that is best suited to them. Furthermore, it is equipped with a transparency assurance function that clearly shows the advantages and disadvantages of insurance plans, enabling users to make informed choices. As a result, users can choose insurance with peace of mind. It also provides an efficiency promotion function that provides information more quickly and accurately than human agents, improving the overall efficiency of insurance selection. As a result, users can easily choose insurance, and operating costs are reduced by AI. In this way, it is expected that the AI ​​agent will innovate insurance selection and provide new value to consumers, thereby establishing a firm position in the market. For example, the insurance selection support system is equipped with real-time consultation and procedural support functions to respond immediately to user questions and to ensure smooth contract procedures. This allows users to receive quick and accurate suggestions, improving convenience. Next, the insurance selection support system provides an individual needs analysis function that analyzes and proposes the optimal insurance plan based on the user's lifestyle and health condition. This allows users to select the insurance plan that is best suited to them. Furthermore, it has a transparency function that clearly shows the advantages and disadvantages of insurance plans, enabling users to make informed choices. This allows users to choose insurance with peace of mind. It also provides efficiency-promoting functions that provide information faster and more accurately than human agents, increasing the overall efficiency of insurance selection. This makes it easier for users to choose insurance, and operating costs are reduced by AI. In this way, it is expected that the AI ​​agent will revolutionize insurance selection and provide new value to consumers, thereby establishing a solid position in the market.This allows the insurance selection support system to simplify the user's insurance selection process and reduce their burden.

[0060] The insurance selection support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an efficiency unit. The reception unit responds immediately to user inquiries and facilitates smooth contract procedures. For example, the reception unit provides real-time answers to user inquiries. The reception unit can also manage the progress of contract procedures and support users in completing the procedures smoothly. Furthermore, the reception unit can analyze the content of user inquiries and collect information to provide appropriate answers. For example, the reception unit can quickly search for relevant information in response to user inquiries and provide answers. The analysis unit analyzes the user's lifestyle and health condition and proposes the optimal insurance plan. For example, the analysis unit collects information on the user's lifestyle and selects the optimal insurance plan based on their health condition. The analysis unit can also analyze data on the user's health condition and perform risk assessments. Furthermore, the analysis unit can evaluate the merits and demerits of insurance plans based on the user's lifestyle and health condition. For example, the analysis unit can determine whether a particular insurance plan is suitable based on the user's health condition. The Service Department clearly outlines the advantages and disadvantages of insurance plans proposed by the Analysis Department, enabling users to make informed choices. For example, the Service Department provides detailed explanations of insurance plans and organizes information in a user-friendly manner. It can also compare the advantages and disadvantages of insurance plans to support users in making the best choice. Furthermore, the Service Department can provide detailed information on insurance plans in response to user inquiries. For example, it evaluates and explains the cost-effectiveness and risks of insurance plans to users. The Efficiency Department improves the overall efficiency of insurance selection based on the information provided by the Service Department. For example, it optimizes each step of the insurance selection process to allow users to choose insurance quickly. It can also promote the automation of the insurance selection process to reduce the user burden. Furthermore, the Efficiency Department can reduce operating costs by streamlining the insurance selection process. For example, it automates each step of the insurance selection process to allow users to choose insurance quickly.As a result, the insurance selection support system according to this embodiment can simplify the user's insurance selection process and reduce their burden.

[0061] The reception department provides immediate responses to user inquiries and ensures smooth contract procedures. Specifically, the reception department utilizes chatbots and AI assistants to provide real-time answers to user questions. This allows users to ask questions 24 / 7 and receive quick responses. Furthermore, the reception department manages the progress of contract procedures and supports users in completing the process smoothly. For example, when a user applies for an insurance contract, the reception department automatically tracks the progress and reminds them of necessary document submissions and confirmations. The reception department can also analyze user questions and gather information to provide appropriate answers. For example, if a user asks about a specific insurance plan, the reception department quickly searches for relevant information and provides a detailed answer. This ensures that users have enough information to choose the insurance plan that best suits them. In addition, the reception department can provide answers optimized for individual users based on their past question history and behavioral data. This ensures that users receive consistent support and the insurance selection process proceeds more smoothly.

[0062] The analytics department analyzes users' lifestyles and health conditions to propose the most suitable insurance plan. Specifically, the analytics department collects information on users' lifestyles and selects the most suitable insurance plan based on their health condition. For example, it collects data on users' daily exercise, diet, smoking, and drinking habits, and uses this information to perform risk assessments. The analytics department can also analyze data on users' health conditions and perform risk assessments. For example, it can assess the risk of specific diseases based on users' past health checkup results and medical history, and propose an insurance plan accordingly. Furthermore, the analytics department can evaluate the advantages and disadvantages of insurance plans based on users' lifestyles and health conditions. For example, if a user is healthy, it will propose a low-risk insurance plan, and conversely, if the user has a high health risk, it will propose a more comprehensive insurance plan. This allows users to choose the insurance plan that best suits their situation. In addition, the analytics department can utilize AI to quickly analyze large amounts of data and propose the most suitable insurance plan to users in real time. This allows users to make insurance selections based on quick and accurate information.

[0063] The service provider clearly outlines the advantages and disadvantages of the insurance plans proposed by the analysis department, enabling users to make informed choices. Specifically, the service provider provides detailed explanations of the insurance plans and organizes the information in a way that is easy for users to understand. For example, they provide materials that clearly summarize the content, coverage, costs, and risks of the insurance plans. The service provider can also compare the advantages and disadvantages of insurance plans and support users in making the best choice. For example, they provide tools for comparing multiple insurance plans, allowing users to choose the plan that best suits their needs. Furthermore, the service provider can provide detailed information about insurance plans in response to user inquiries. For example, if a user requests detailed information about a particular insurance plan, the service provider will evaluate the cost-effectiveness and risks of that plan and explain them to the user. This allows users to feel confident in their choices. In addition, the service provider can collect user feedback and use it to improve insurance plans. For example, if a user is dissatisfied with an insurance plan, the service provider will review and improve the plan based on that feedback. This ensures that the service provider can always provide users with the best possible insurance plan.

[0064] The Efficiency Department improves the overall efficiency of insurance selection based on the information provided by the Provision Department. Specifically, the Efficiency Department optimizes each step of the insurance selection process to enable users to choose insurance quickly. For example, it automates each step of the insurance selection process to enable users to quickly obtain the necessary information. The Efficiency Department can also reduce the burden on users by promoting the automation of the insurance selection process. For example, it can build a system that automatically collects and provides the information necessary when users choose an insurance plan. Furthermore, the Efficiency Department can reduce operating costs by streamlining the insurance selection process. For example, by automating each step of the insurance selection process, it can reduce human resources and improve operational efficiency. In this way, the Efficiency Department can not only enable users to choose insurance quickly but also contribute to reducing operating costs. In addition, the Efficiency Department can continuously improve the insurance selection process based on user feedback. For example, if users are dissatisfied with the insurance selection process, the process will be reviewed and improved based on that feedback. In this way, the Efficiency Department can continue to provide users with the optimal insurance selection process at all times.

[0065] The reception desk can estimate the user's emotions and adjust its response to questions based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can respond to questions in a gentle tone to provide reassurance. If the user is in a hurry, the reception desk can answer questions quickly and concisely to save time. Furthermore, if the user is agitated, the reception desk can respond to questions in a calm tone to calm them down. This enables responses that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0066] The reception desk can analyze a user's past question history and select the most appropriate response. For example, the reception desk can prepare relevant information in advance based on the types of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and respond to questions that are frequently asked during specific time periods based on the user's past question history. This enables optimal responses based on past question history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI select the most appropriate response.

[0067] The reception unit can filter questions based on the user's current situation and areas of interest when questions are received. For example, when a user enters their current situation, the reception unit will prioritize displaying relevant questions. The reception unit can also filter and display relevant questions based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate questions according to the user's current situation. This enables filtering of questions according to the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0068] The reception desk can estimate the user's emotions and determine the priority of questions to address based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize urgent questions. If the user is relaxed, the reception desk can also address normal questions in order. Furthermore, if the user is agitated, the reception desk can prioritize important questions. This makes it possible to determine the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The reception desk can prioritize questions based on the user's geographical location when receiving inquiries. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. The reception desk can also provide region-specific information based on the user's geographical location. Furthermore, if the user is traveling, the reception desk can prioritize questions related to their travel destination. This enables priority answering of questions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant questions.

[0070] The reception desk can analyze the user's social media activity when a question is received and retrieve relevant questions. For example, the reception desk can retrieve questions related to topics of interest from the user's social media activity. The reception desk can also suggest relevant questions based on the user's recent posts. Furthermore, the reception desk can analyze the user's social media activity and retrieve questions related to trends. This makes it possible to retrieve questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant questions.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will use a simple and easy-to-understand presentation. If the user is relaxed, the analysis unit may also use a presentation that includes detailed information. Furthermore, if the user is excited, the analysis unit may also use a visually appealing presentation. This allows for adjustment of the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's lifestyle and health status during the analysis. For example, if the user's health status is important, the analysis unit will perform an analysis that includes detailed health information. Furthermore, if the user's lifestyle is important, the analysis unit can also perform an analysis that includes lifestyle-related information. In addition, if both the user's health status and lifestyle are important, the analysis unit can perform an analysis that includes information from both in a balanced manner. This allows for adjustment of the level of detail of the analysis according to the user's lifestyle and health status. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user lifestyle and health status data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the user's category during analysis. For example, if the user is young, the analysis unit can apply an analysis algorithm for young people. Similarly, if the user is elderly, the analysis unit can apply an analysis algorithm for the elderly. Furthermore, if the user is engaged in a specific occupation, the analysis unit can apply an analysis algorithm related to that occupation. This enables the application of analysis algorithms tailored to the user's category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with more detailed information. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually appealing effects. This allows for adjustment of the analysis length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The analysis unit can determine the priority of analysis based on the user's submission date. For example, if the user submitted earlier, the analysis unit will prioritize the analysis. If the user submitted later, the analysis unit can also perform the analysis in the normal order. Furthermore, the analysis unit can adjust the priority of analysis based on the user's submission date. This makes it possible to determine the priority of analysis based on the user's submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0076] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, if a user is highly relevant, the analysis unit will prioritize that analysis. Conversely, if a user is less relevant, the analysis unit can perform the analysis in the normal order. Furthermore, the analysis unit can adjust the order of analysis based on user relevance. This makes it possible to adjust the order of analysis based on user relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The service provider can estimate the user's emotions and adjust the presentation of the service based on the estimated emotions. For example, if the user is feeling anxious, the service provider will use a simple and easy-to-understand presentation. If the user is relaxed, the service provider may also use a presentation that includes detailed information. Furthermore, if the user is excited, the service provider may also use a visually appealing presentation. This allows for adjustment of the presentation of the service according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The delivery unit can adjust the level of detail provided based on the importance of the insurance plan at the time of delivery. For example, if the insurance plan is highly important, the delivery unit will provide detailed information. Conversely, if the insurance plan is less important, the delivery unit may provide concise information. Furthermore, the delivery unit can adjust the level of detail provided based on the importance of the insurance plan. This makes it possible to adjust the level of detail provided according to the importance of the insurance plan. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input insurance plan importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0079] The service provider can apply different service provision algorithms depending on the insurance plan category at the time of provision. For example, if the insurance plan is medical insurance, the service provider can apply a service provision algorithm specialized for medical insurance. Furthermore, if the insurance plan is life insurance, the service provider can apply a service provision algorithm specialized for life insurance. In addition, if the insurance plan is automobile insurance, the service provider can apply a service provision algorithm specialized for automobile insurance. This makes it possible to apply service provision algorithms according to the insurance plan category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input insurance plan category data into a generating AI and have the generating AI execute the application of different service provision algorithms.

[0080] The delivery unit can estimate the user's emotions and adjust the length of the delivery based on the estimated emotions. For example, if the user is in a hurry, the delivery unit can provide a short, concise delivery. If the user is relaxed, the delivery unit can provide a longer delivery with more detailed information. Furthermore, if the user is excited, the delivery unit can provide a delivery with visually appealing effects. This allows for adjustment of the delivery length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The service provider can determine the priority of service provision based on the submission date of the insurance plan at the time of provision. For example, if the insurance plan is submitted early, the service provider will provide it preferentially. If the insurance plan is submitted late, the service provider can provide it in the normal order. Furthermore, the service provider can adjust the priority of service provision based on the submission date of the insurance plan. This makes it possible to determine the priority of service provision based on the submission date of the insurance plan. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input insurance plan submission date data into a generating AI and have the generating AI perform the determination of the priority of service provision.

[0082] The delivery unit can adjust the order of delivery based on the relevance of insurance plans at the time of delivery. For example, the delivery unit will prioritize delivery if the insurance plans are highly relevant. Conversely, if the insurance plans are less relevant, the delivery unit can deliver them in the normal order. Furthermore, the delivery unit can adjust the order of delivery based on the relevance of insurance plans. This makes it possible to adjust the order of delivery based on the relevance of insurance plans. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input insurance plan relevance data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0083] The efficiency unit can estimate the user's emotions and adjust the efficiency method based on the estimated emotions. For example, if the user is feeling anxious, the efficiency unit can provide a simple and easy-to-understand efficiency method. If the user is relaxed, the efficiency unit can also provide an efficiency method that includes detailed information. Furthermore, if the user is excited, the efficiency unit can provide a visually appealing efficiency method. This makes it possible to adjust the efficiency method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or not using AI. For example, the efficiency unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The efficiency optimization unit can analyze the user's past behavior during the optimization process to select the optimal optimization method. For example, the efficiency optimization unit can analyze the user's past behavior patterns and propose the most efficient procedure. It can also select the optimal method based on the optimization methods the user has used in the past. Furthermore, the efficiency optimization unit can propose the optimal optimization method for a specific time period based on the user's past behavior history. This makes it possible to select the optimal optimization method based on the user's past behavior. Some or all of the above processing in the efficiency optimization unit may be performed using AI, for example, or without AI. For example, the efficiency optimization unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal optimization method.

[0085] The efficiency improvement unit can customize the means of efficiency improvement based on the user's current situation during the efficiency improvement process. For example, when the user inputs their current situation, the efficiency improvement unit proposes the optimal means of efficiency improvement. The efficiency improvement unit can also customize the means of efficiency improvement according to the user's current situation. Furthermore, the efficiency improvement unit can adjust the means of efficiency improvement in real time based on the user's current situation. This makes it possible to customize the means of efficiency improvement according to the user's current situation. Some or all of the above processing in the efficiency improvement unit may be performed using AI, for example, or without using AI. For example, the efficiency improvement unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of efficiency improvement.

[0086] The efficiency unit can estimate the user's emotions and determine efficiency priorities based on the estimated emotions. For example, if the user is feeling anxious, the efficiency unit will prioritize providing efficiency measures of high urgency. If the user is relaxed, the efficiency unit can also provide normal efficiency measures in order. Furthermore, if the user is excited, the efficiency unit can also prioritize providing important efficiency measures. This makes it possible to determine efficiency priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the efficiency unit may be performed using AI, or not using AI. For example, the efficiency unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The efficiency improvement unit can select the optimal efficiency improvement method by considering the user's geographical location information during the efficiency improvement process. For example, if the user is in a specific region, the efficiency improvement unit can provide efficiency improvement methods relevant to that region. The efficiency improvement unit can also propose region-specific efficiency improvement methods based on the user's geographical location information. Furthermore, if the user is traveling, the efficiency improvement unit can provide efficiency improvement methods relevant to the travel destination. This makes it possible to select the optimal efficiency improvement method based on the user's geographical location information. Some or all of the above processing in the efficiency improvement unit may be performed using AI, for example, or without AI. For example, the efficiency improvement unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal efficiency improvement method.

[0088] The efficiency improvement unit can analyze the user's social media activity and propose efficiency improvements during the optimization process. For example, the efficiency improvement unit can propose efficiency improvements related to topics of interest based on the user's social media activity. It can also propose relevant efficiency improvements based on the user's recent posts. Furthermore, the efficiency improvement unit can analyze the user's social media activity and propose efficiency improvements related to trends. This makes it possible to propose efficiency improvements based on the user's social media activity. Some or all of the above processing in the efficiency improvement unit may be performed using AI, for example, or without AI. For example, the efficiency improvement unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of efficiency improvements.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The reception desk can analyze the user's voice tone, estimate their emotions, and adjust its response accordingly. For example, if the user's voice is calm, it will respond normally, but if their voice is trembling, it will respond in a gentle, reassuring tone. If the user's voice sounds urgent, it will respond quickly and concisely. Furthermore, if the user's voice sounds agitated, it can respond in a calm tone to soothe them. This allows for responses tailored to the user's voice tone.

[0091] The analytics department can monitor users' health data in real time and adjust insurance plan recommendations according to changes in their health status. For example, if a user's health deteriorates, it can suggest more comprehensive medical insurance. Conversely, if their health improves, it can suggest a plan with lower premiums. Furthermore, it can provide advice on preventive medicine based on the user's health data. This enables the proposal of insurance plans tailored to the user's health condition.

[0092] The service provider can analyze a user's purchase history and make new recommendations based on previously purchased insurance plans. For example, if a user has previously purchased health insurance, a similar plan can be suggested. Similarly, if a user has previously purchased life insurance, a plan related to life insurance can be suggested. Furthermore, based on the user's purchase history, an upgrade to a specific insurance plan can be suggested. This enables the provision of insurance plans tailored to the user's purchase history.

[0093] The efficiency optimization department can analyze users' device usage and propose the most suitable efficiency measures. For example, if a user frequently uses a smartphone, it can suggest procedures via a mobile app. If the user uses a PC, it can suggest procedures via a website. Furthermore, if the user uses a tablet, it can suggest procedures optimized for tablets. This makes it possible to propose efficiency measures tailored to the user's device usage.

[0094] The reception desk can estimate the user's emotions and prioritize questions based on those estimates. For example, if the user is feeling anxious, urgent questions will be prioritized. If the user is relaxed, regular questions can be answered in order. Furthermore, if the user is agitated, important questions can be prioritized. This makes it possible to prioritize questions according to the user's emotions.

[0095] The analysis department can adjust insurance plan suggestions based on the user's life events. For example, if a user gets married, it can suggest a family-oriented insurance plan. If a user has children, it can suggest a child-oriented insurance plan. Furthermore, if a user retires, it can suggest an insurance plan suitable for retirement. This makes it possible to propose insurance plans tailored to the user's life events.

[0096] The service provider can estimate the user's emotions and adjust the presentation of the service based on those estimates. For example, if the user is feeling anxious, a simple and easy-to-understand presentation can be used. If the user is relaxed, a presentation containing detailed information can be used. Furthermore, if the user is excited, a visually appealing presentation can be used. This allows for adjustments to the presentation of the service according to the user's emotions.

[0097] The efficiency optimization unit can analyze a user's past behavior to select the optimal efficiency method. For example, it can analyze a user's past behavior patterns and propose the most efficient procedure. It can also select the optimal method based on efficiency methods the user has used in the past. Furthermore, it can propose the most suitable efficiency method for a specific time period based on the user's past behavior history. This makes it possible to select the optimal efficiency method based on the user's past behavior.

[0098] The delivery unit can estimate the user's emotions and adjust the length of the delivery based on those estimates. For example, if the user is in a hurry, a short, to-the-point delivery can be provided. If the user is relaxed, a longer delivery with more detailed information can be provided. Furthermore, if the user is excited, a delivery with visually appealing effects can be added. This allows for adjustment of the delivery length according to the user's emotions.

[0099] The efficiency improvement unit can select the optimal efficiency improvement method by considering the user's geographical location. For example, if the user is in a specific region, it can provide efficiency improvement methods relevant to that region. It can also suggest region-specific efficiency improvement methods based on the user's geographical location. Furthermore, if the user is traveling, it can provide efficiency improvement methods relevant to their travel destination. This makes it possible to select the optimal efficiency improvement method based on the user's geographical location.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception department will respond to user inquiries immediately and ensure smooth contract procedures. For example, they will provide real-time answers to user questions, manage the progress of contract procedures, and support users in completing the process smoothly. They will also analyze user questions and collect information to provide appropriate answers. Step 2: The analysis department analyzes the user's lifestyle and health status and proposes the optimal insurance plan. For example, it collects information on the user's lifestyle and selects the optimal insurance plan based on their health status. It also analyzes data on the user's health status and performs a risk assessment. Step 3: The service provider clearly outlines the advantages and disadvantages of the insurance plan proposed by the analysis department, enabling users to make informed decisions. For example, they provide detailed explanations of the insurance plans and organize the information in a way that is easy for users to understand. They also compare the advantages and disadvantages of the insurance plans to help users make the best choice. Step 4: The Efficiency Department will improve the overall efficiency of insurance selection based on the information provided by the Provision Department. For example, they will optimize each step of the insurance selection process to allow users to choose insurance quickly. They will also promote the automation of the insurance selection process to reduce the burden on users.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and efficiency unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which responds immediately to user inquiries and facilitates contract procedures. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's lifestyle and health status and proposes the optimal insurance plan. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, which clearly shows the advantages and disadvantages of insurance plans, enabling the user to make an informed choice. The efficiency unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which improves the overall efficiency of insurance selection. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and efficiency unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which responds immediately to user inquiries and facilitates contract procedures. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's lifestyle and health status and proposes the optimal insurance plan. The provision unit is implemented by the control unit 46A of the smart glasses 214, which clearly shows the advantages and disadvantages of insurance plans, enabling the user to make an informed choice. The efficiency unit is implemented by the specific processing unit 290 of the data processing unit 12, which improves the overall efficiency of insurance selection. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and efficiency unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which responds immediately to user inquiries and facilitates contract procedures. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's lifestyle and health status and proposes the optimal insurance plan. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which clearly shows the advantages and disadvantages of insurance plans, enabling the user to make an informed choice. The efficiency unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which improves the overall efficiency of insurance selection. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and efficiency unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which responds immediately to user inquiries and facilitates contract procedures. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's lifestyle and health condition and proposes the optimal insurance plan. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which clearly shows the advantages and disadvantages of insurance plans, enabling the user to make an informed choice. The efficiency unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which improves the overall efficiency of insurance selection. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) The reception department responds immediately to user inquiries and handles contract procedures smoothly. Based on the information received by the aforementioned reception department, the analysis department analyzes the user's lifestyle and health condition and proposes the optimal insurance plan. The provision department will clearly explain the advantages and disadvantages of the insurance plan proposed by the aforementioned analysis department, enabling users to make informed decisions. The system includes an efficiency-enhancing unit that improves the overall efficiency of insurance selection based on the information provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how it responds to questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past question history and select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes the questions to address based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving questions, we prioritize responding to highly relevant questions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When a question is submitted, the system analyzes the user's social media activity to obtain relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, the level of detail is adjusted based on the importance of the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the user category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, the analysis priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing the service, adjust the level of detail based on the importance of the insurance plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing insurance, different provisioning algorithms are applied depending on the insurance plan category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, we will determine the priority of provision based on when the insurance plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing services, the order of delivery will be adjusted based on the relevance of the insurance plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned efficiency improvement unit is It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned efficiency improvement unit is When optimizing processes, analyze users' past behavior to select the most optimal optimization method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned efficiency improvement unit is When optimizing, customize the optimization methods based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned efficiency improvement unit is It estimates user emotions and determines efficiency priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned efficiency improvement unit is When optimizing processes, the optimal optimization method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned efficiency improvement unit is When optimizing, we analyze users' social media activity and propose ways to improve efficiency. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception department responds immediately to user inquiries and handles contract procedures smoothly. Based on the information received by the aforementioned reception department, the analysis department analyzes the user's lifestyle and health condition and proposes the optimal insurance plan. The provision department will clearly explain the advantages and disadvantages of the insurance plan proposed by the aforementioned analysis department, enabling users to make informed decisions. The system includes an efficiency-enhancing unit that improves the overall efficiency of insurance selection based on the information provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how it responds to questions based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past question history and select the most appropriate response method. The system according to feature 1.

4. The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes the questions to address based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving questions, we prioritize responding to highly relevant questions by taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When a question is submitted, the system analyzes the user's social media activity to obtain relevant questions. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, the level of detail is adjusted based on the importance of the user's lifestyle and health status. The system according to feature 1.

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