System

Abstract

An object of a system according to an embodiment is to enable a user to casually consult insurance and propose an optimal insurance plan.SOLUTION: A system includes a collection unit, a proposal unit, a provision unit, and a new proposal unit. The collection unit collects a family structure and anxiety matters of a user. The proposal unit proposes an insurance plan based on the information collected by the collection unit. The providing unit visually provides the insurance plan proposed by the proposal unit. The new proposal unit proposes a new insurance plan to the insurance company based on the information collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, insurance consultations and proposals were often conducted face-to-face, leaving a lack of an environment where users could easily seek advice.

[0005] The system according to the embodiment aims to allow users to easily consult about insurance and to propose the most suitable insurance plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a proposal unit, a provision unit, and a new proposal unit. The collection unit collects information about the user's family structure and concerns. The proposal unit proposes an insurance plan based on the information collected by the collection unit. The provision unit visually provides the insurance plan proposed by the proposal unit. The new proposal unit proposes a new insurance plan to an insurance company based on the information collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily consult about insurance and propose the most suitable insurance plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An insurance consultation system according to an embodiment of the present invention collects information about a user's family structure and concerns, and then a generation AI proposes and visually presents the optimal insurance plan. The insurance consultation system collects information about a user's family structure and concerns, and then a generation AI proposes the optimal insurance plan. This proposal is quickly provided in the form of an estimate or a graphical representation. This creates a relaxed atmosphere for users to consult without feeling pressured by sales. It also solves the shortage of salespeople at insurance companies, allowing the generation AI to propose new insurance plans to insurance companies based on the consultation content. For example, in an insurance consultation system, a user initiates a conversation with a generation AI. The generation AI asks questions such as, "What is your family structure?" or "What concerns do you have?" The user responds, and the generation AI collects the information. Next, the generation AI proposes the optimal insurance plan based on the collected information. For example, the system may suggest, "Based on your family structure and concerns, this insurance plan is suitable." This proposal is quickly provided in the form of an estimate or a graphical representation. For example, the insurance coverage and costs can be visually displayed in graphs and charts. Furthermore, the generation AI can propose new insurance plans to insurance companies based on the consultation content. For example, the system provides the insurance company with feedback such as, "Since many users have this kind of family structure and concerns, we suggest developing this kind of insurance plan." This allows the insurance consultation system to make it easier for users to consult about insurance, and helps insurance companies solve their salesperson shortage and develop new insurance plans.

[0029] An insurance consultation system according to an embodiment includes a collection unit, a proposal unit, a provision unit, and a new proposal unit. The collection unit collects information about a user's family structure and concerns. For example, the collection unit analyzes the user's conversation and collects the information about the family structure and concerns. The collection unit can also use a generation AI to analyze the user's conversation and collect the information about the family structure and concerns. For example, the collection unit converts the user's conversation into text data using speech recognition technology and extracts the information about the family structure and concerns using natural language processing technology. The proposal unit proposes an insurance plan based on the information collected by the collection unit. The proposal unit uses a generation AI to generate an optimal insurance plan based on the collected information. For example, the proposal unit uses an algorithm to generate an insurance plan based on the user's family structure and concerns. The proposal unit can also use a generation AI to generate an insurance plan based on the collected information. The provision unit visually provides the insurance plan proposed by the proposal unit. The provision unit can also use the generation AI to visually provide the generated insurance plan in the form of a graph or chart. For example, the providing unit visually displays insurance coverage and costs using graphs and charts. The new suggestion unit proposes a new insurance plan to the insurance company based on the information collected by the collecting unit. The new suggestion unit can also use generation AI to propose a new insurance plan to the insurance company based on the collected information. For example, the new suggestion unit provides feedback to the insurance company such as, "Since many users have this kind of family structure and concerns, we propose developing this kind of insurance plan." In this way, the insurance consultation system according to the embodiment collects the user's family structure and concerns, proposes an optimal insurance plan, and visually presents it, allowing the user to easily consult about insurance.

[0030] The collection unit can analyze the user's conversation and collect information about family structure and concerns. The collection unit can convert the user's conversation into text data using, for example, voice recognition technology. For example, the collection unit automatically analyzes the speech using voice recognition software and saves it as text. The collection unit can also extract the family structure and concerns from the text data using natural language processing technology. For example, the collection unit analyzes the text data and extracts keywords and phrases related to the family structure and concerns. The collection unit can also analyze the user's conversation and collect the family structure and concerns using a generation AI. For example, the collection unit inputs the user's conversation data into the generation AI, which then extracts the family structure and concerns. In this way, the family structure and concerns can be accurately collected by analyzing the user's conversation.

[0031] The proposal unit can generate an insurance plan based on the collected information. The proposal unit, for example, uses an algorithm to analyze the collected information and generate an optimal insurance plan. For example, the proposal unit generates an insurance plan based on the user's family structure and concerns. The proposal unit can also use a generation AI to generate an insurance plan based on the collected information. For example, the proposal unit inputs the collected information into the generation AI, which then generates an optimal insurance plan. The proposal unit can also use data analysis technology to analyze the collected information and generate an insurance plan. For example, the proposal unit uses data analysis technology to generate an insurance plan based on the user's family structure and concerns. In this way, an insurance plan suitable for the user can be proposed by generating an optimal insurance plan based on the collected information.

[0032] The provision unit can visually provide the generated insurance plan in the form of a graph or chart. For example, the provision unit visually provides the generated insurance plan in the form of a graph or chart. For example, the provision unit visually shows the insurance coverage and costs in the form of a graph or chart. The provision unit can also visually provide the generated insurance plan using a generation AI. For example, the provision unit inputs insurance plan data into the generation AI, which then generates a graph or chart. The provision unit can also adjust the visual presentation method. For example, the provision unit adjusts the visual presentation method based on the user's emotions. In this way, visually providing the generated insurance plan makes it easier for the user to understand.

[0033] The new proposal unit can propose a new insurance plan to the insurance company based on the collected information. For example, the new proposal unit proposes a new insurance plan to the insurance company based on the collected information. For example, the new proposal unit provides feedback to the insurance company such as, "Since many users have this kind of family structure and concerns, we propose developing this kind of insurance plan." The new proposal unit can also use a generation AI to propose a new insurance plan based on the collected information. For example, the new proposal unit inputs the collected information into the generation AI, which then proposes a new insurance plan. The new proposal unit can also use data analysis technology to analyze the collected information and propose a new insurance plan. For example, the new proposal unit uses data analysis technology to propose a new insurance plan based on the user's family structure and concerns. This allows the insurance company to develop a new insurance plan by proposing a new insurance plan based on the collected information.

[0034] The collection unit can analyze the user's past conversation history and select a question method. The collection unit, for example, analyzes the user's past conversation history using text mining technology. For example, the collection unit generates unique questions from the past conversation history. The collection unit can also analyze the past conversation history and select an optimal question method using natural language processing technology. For example, the collection unit analyzes past answer patterns and selects the most effective question method. The collection unit can also analyze the past conversation history and select a question method using a generation AI. For example, the collection unit inputs the past conversation history into the generation AI, which selects the optimal question method. In this way, by analyzing the past conversation history, unique questions can be asked, enabling effective information collection.

[0035] When collecting conversations, the collection unit can filter information based on the user's current living situation or areas of interest. For example, the collection unit filters information based on the user's current living situation. For example, if the user has recently moved, the collection unit asks questions related to the user's new residence. The collection unit can also filter information based on the user's areas of interest. For example, if the user has a particular hobby, the collection unit asks questions related to that hobby. The collection unit can also use the generation AI to filter information based on the user's current living situation or areas of interest. For example, the collection unit inputs data on the user's living situation and areas of interest into the generation AI, and the generation AI filters relevant information. In this way, by filtering information based on the user's living situation and areas of interest, more relevant information can be collected.

[0036] When collecting conversations, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. For example, the collection unit automatically analyzes the voice using voice recognition software and saves it as text. Furthermore, if the user uses text input, the collection unit can also collect information using natural language processing technology. For example, the collection unit analyzes text data and extracts keywords and phrases related to family structure and concerns. Furthermore, if the user provides images, the collection unit can also collect information using image analysis technology. For example, the collection unit analyzes image data and extracts related information. This improves the accuracy of information collection by selecting the optimal collection means depending on the user's input method.

[0037] When collecting conversations, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit collects information about insurance plans and services specific to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting information related to the user's travel destination. For example, the collection unit collects information about insurance plans and emergency services at the travel destination. Furthermore, if the user plans to move to a specific area, the collection unit can prioritize collecting information related to that area. For example, the collection unit collects information about insurance plans and services related to the area to which the user is moving. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0038] When collecting conversations, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit poses related questions based on information shared by the user on social media. For example, the collection unit analyzes the content of the user's social media posts and generates questions related to related topics. The collection unit can also analyze the user's social media activities and collect information related to topics of interest. For example, the collection unit analyzes the user's "likes" and comments and collects information related to topics of interest. The collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit generates related questions based on information shared by the user's friends. In this way, related information can be effectively collected by analyzing the user's social media activities.

[0039] When collecting conversations, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the content and timing of questions based on feedback provided by the user in the past. For example, the collection unit prioritizes the use of collection methods that the user has preferred in the past. The collection unit can also analyze the user's past feedback and suggest the optimal collection method. For example, the collection unit analyzes the user's feedback and selects the optimal question method and timing. The collection unit can also customize the collection method by using the generation AI to reflect the user's past feedback. For example, the collection unit inputs the user's feedback data into the generation AI, and the generation AI suggests the optimal collection method. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0040] The proposal unit can adjust the level of detail of the insurance plan when making a proposal. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of the insurance plan. For example, the proposal unit provides a detailed explanation for insurance plans with a high level of importance. The proposal unit can also provide a concise explanation for insurance plans with a low level of importance. For example, the proposal unit gradually adjusts the level of detail of the proposal according to the importance. The proposal unit can also adjust the level of detail of the insurance plan using a generation AI. For example, the proposal unit inputs data of the insurance plan into the generation AI, and the generation AI adjusts the level of detail. In this way, by adjusting the level of detail of the proposal according to the importance of the insurance plan, it is possible to provide information that is suitable for the user.

[0041] When making a proposal, the proposal unit can apply a proposal algorithm depending on the category of the insurance plan. The proposal unit, for example, applies different proposal algorithms depending on the category of the insurance plan. For example, in the case of life insurance, the proposal unit makes a proposal based on family composition and health condition. In addition, in the case of automobile insurance, the proposal unit can also make a proposal based on the vehicle type and driving history. For example, the proposal unit proposes an optimal insurance plan based on the vehicle type and driving history. In addition, in the case of home insurance, the proposal unit can also make a proposal based on the type and location of the residence. For example, the proposal unit proposes an optimal insurance plan based on the type and location of the residence. In this way, by applying a proposal algorithm depending on the category of the insurance plan, more appropriate proposals can be made.

[0042] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, analyzes the user's past proposal results and improves the accuracy of the proposal. For example, the suggestion unit makes similar proposals based on proposals that the user has accepted in the past. The suggestion unit can also adjust to avoid proposals that the user has rejected in the past. For example, the suggestion unit makes optimal proposals based on past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the generation AI by referring to the user's past proposal results. For example, the suggestion unit inputs past proposal results into the generation AI, and the generation AI makes optimal suggestions. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0043] The proposal unit can determine the priority of insurance plans when they are proposed. The proposal unit determines the priority of proposals, for example, based on the time of submission of the insurance plans. For example, the proposal unit prioritizes insurance plans with upcoming submission deadlines. The proposal unit can also postpone insurance plans with distant submission deadlines. For example, the proposal unit gradually adjusts the priority of proposals depending on the time of submission. The proposal unit can also determine the priority of insurance plans using a generation AI. For example, the proposal unit inputs insurance plan data into the generation AI, and the generation AI determines the priority. In this way, by determining the priority of proposals based on the time of submission of the insurance plans, important proposals can be given priority.

[0044] The suggestion unit can adjust the order of insurance plans when making a suggestion. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the insurance plans. For example, the suggestion unit prioritizes suggesting insurance plans related to the user's family structure. The suggestion unit can also prioritize suggesting insurance plans related to the user's concerns. For example, the suggestion unit gradually adjusts the order of suggestions according to the relevance of the insurance plans. The suggestion unit can also adjust the order of insurance plans using a generation AI. For example, the suggestion unit inputs insurance plan data into the generation AI, which then adjusts the order. In this way, by adjusting the order of suggestions based on the relevance of the insurance plans, suggestions that are most relevant to the user can be made with priority.

[0045] When making a proposal, the suggestion unit can adjust the use of technical terms according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terms in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about insurance, the suggestion unit makes a proposal that uses a lot of technical terms. In addition, if the user is not knowledgeable about insurance, the suggestion unit can also explain in simple terms. For example, the suggestion unit gradually adjusts the use of technical terms in the proposal according to the user's level of expertise. In addition, the suggestion unit can also adjust the use of technical terms using a generation AI. For example, the suggestion unit inputs the user's level of expertise into the generation AI, and the generation AI adjusts the use of technical terms. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0046] The providing unit can adjust the level of detail of the insurance plan when providing it. The providing unit adjusts the visual level of detail based on, for example, the importance of the insurance plan. For example, the providing unit provides detailed graphs and charts for insurance plans with high importance. The providing unit can also provide simple graphs and charts for insurance plans with low importance. For example, the providing unit gradually adjusts the visual level of detail according to the importance. The providing unit can also adjust the level of detail of the insurance plan using a generating AI. For example, the providing unit inputs data of the insurance plan into the generating AI, and the generating AI adjusts the level of detail. In this way, by adjusting the visual level of detail according to the importance of the insurance plan, it is possible to provide important information to the user in detail.

[0047] The providing unit can apply a visual representation according to the category of the insurance plan when providing the information. The providing unit, for example, applies different visual representations according to the category of the insurance plan. For example, in the case of life insurance, the providing unit provides graphs and charts based on family composition and health condition. Furthermore, in the case of automobile insurance, the providing unit can also provide graphs and charts based on the vehicle type and driving history. For example, the providing unit provides an optimal visual representation based on the vehicle type and driving history. Furthermore, in the case of home insurance, the providing unit can also provide graphs and charts based on the type and location of the residence. For example, the providing unit provides an optimal visual representation based on the type and location of the residence. In this way, by applying a visual representation according to the category of the insurance plan, it is possible to provide information that is easy for the user to understand.

[0048] The providing unit can improve visual accuracy by referring to the user's past visual provision results when providing the visual representation. The providing unit, for example, analyzes the user's past visual provision results to improve visual accuracy. For example, the providing unit provides similar graphs or charts based on visual representations that the user has previously preferred. The providing unit can also make adjustments to avoid visual representations that the user has previously rejected. For example, the providing unit provides an optimal visual representation based on the past visual provision results. The providing unit can also improve visual accuracy by using the generation AI by referring to the user's past visual provision results. For example, the providing unit inputs the past visual provision results into the generation AI, and the generation AI provides the optimal visual representation. In this way, visual accuracy is improved by referring to the user's past visual provision results.

[0049] The provision unit can determine the visual priority at the time of provision based on the time of submission of the insurance plan. The provision unit determines the visual priority based on, for example, the time of submission of the insurance plan. For example, the provision unit visually provides insurance plans with an upcoming submission deadline with priority. The provision unit can also postpone insurance plans with a distant submission deadline. For example, the provision unit gradually adjusts the visual priority according to the time of submission. The provision unit can also determine the priority of the insurance plan using a generation AI. For example, the provision unit inputs insurance plan data into the generation AI, and the generation AI determines the priority. In this way, by determining the visual priority based on the time of submission of the insurance plan, important information can be provided preferentially.

[0050] The providing unit can adjust the order of the insurance plans when providing them. The providing unit adjusts the visual order based on, for example, the relevance of the insurance plans. For example, the providing unit visually provides insurance plans related to the user's family structure with priority. The providing unit can also visually provide insurance plans related to the user's concerns with priority. For example, the providing unit gradually adjusts the visual order according to the relevance of the insurance plans. The providing unit can also adjust the order of the insurance plans using a generating AI. For example, the providing unit inputs data of the insurance plans into the generating AI, and the generating AI adjusts the order. In this way, by adjusting the visual order based on the relevance of the insurance plans, it is possible to provide information that is most relevant to the user with priority.

[0051] The providing unit can adjust the use of technical terms during provision according to the user's level of expertise. The providing unit, for example, adjusts the use of visual technical terms according to the user's level of expertise. For example, if the user is knowledgeable about insurance, the providing unit provides visual information that makes extensive use of technical terms. Furthermore, if the user is not knowledgeable about insurance, the providing unit can provide visual information that explains things in simple terms. For example, the providing unit gradually adjusts the use of technical terms in the visual information according to the user's level of expertise. Furthermore, the providing unit can also adjust the use of technical terms using a generation AI. For example, the providing unit inputs the user's level of expertise into the generation AI, which then adjusts the use of technical terms. In this way, by adjusting the use of visual technical terms according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0052] When proposing a new insurance plan, the proposal unit can analyze the content of the user's past consultations and select a proposal method. The proposal unit, for example, analyzes the content of the user's past consultations using text mining technology. For example, the proposal unit proposes a new insurance plan that does not overlap with the content of the past consultations. The proposal unit can also analyze the content of the past consultations using natural language processing technology and select the optimal proposal method. For example, the proposal unit analyzes the content of the past consultations and selects the most effective proposal method. The proposal unit can also analyze the content of the past consultations using a generation AI and select a proposal method. For example, the proposal unit inputs the content of the past consultations into the generation AI, and the generation AI selects the optimal proposal method. In this way, it becomes possible to propose a new insurance plan that does not overlap with the content of the past consultations by analyzing the content of the user's past consultations.

[0053] When proposing a new insurance plan, the suggestion unit can customize the means of suggestion based on the user's current living situation. The suggestion unit customizes the means of suggestion based on the user's current living situation, for example. For example, if the user has recently moved, the suggestion unit can suggest an insurance plan related to the new residence. Furthermore, if the user has a specific hobby, the suggestion unit can also suggest an insurance plan related to the hobby. For example, the suggestion unit can suggest an insurance plan related to the hobby. Furthermore, if the user has experienced a specific life event, the suggestion unit can also suggest an insurance plan related to the event. For example, the suggestion unit can suggest an insurance plan related to the life event. In this way, by customizing the means of suggestion based on the user's current living situation, it is possible to make the most suitable suggestion for the user.

[0054] The proposal unit can improve the proposal method by reflecting user feedback when proposing a new insurance plan. For example, the proposal unit adjusts the content and timing of the proposal based on feedback provided by the user in the past. For example, the proposal unit prioritizes the use of a proposal method that the user has preferred in the past. The proposal unit can also analyze the user's past feedback and propose an optimal proposal method. For example, the proposal unit analyzes the user's feedback and selects the optimal proposal method. The proposal unit can also improve the proposal method by reflecting user feedback using a generation AI. For example, the proposal unit inputs user feedback data into the generation AI, which then proposes the optimal proposal method. In this way, the proposal method can be improved by reflecting user feedback, enabling more appropriate proposals.

[0055] When proposing a new insurance plan, the suggestion unit can select a suggestion method taking into account the user's geographical location information. For example, if the user lives in a specific area, the suggestion unit can suggest a new insurance plan related to that area. For example, the suggestion unit can suggest information about insurance plans and services specific to that area. Furthermore, if the user is traveling, the suggestion unit can also suggest a new insurance plan related to the user's travel destination. For example, the suggestion unit can suggest information about insurance plans and emergency services for the travel destination. Furthermore, if the user plans to move to a specific area, the suggestion unit can also suggest a new insurance plan related to that area. For example, the suggestion unit can suggest information about insurance plans and services related to the area to which the user is moving. In this way, by taking the user's geographical location information into account, it is possible to suggest a new insurance plan that is highly relevant.

[0056] When proposing a new insurance plan, the suggestion unit can analyze the user's social media activity to provide a means for making suggestions. For example, the suggestion unit can suggest a new related insurance plan based on information shared by the user on social media. For example, the suggestion unit can analyze the content of the user's social media posts and suggest a new insurance plan related to a related topic. The suggestion unit can also analyze the user's social media activity and suggest a new insurance plan related to a topic of interest. For example, the suggestion unit can analyze the user's "likes" and comments and suggest a new insurance plan related to a topic of interest. The suggestion unit can also suggest a new related insurance plan based on the activity of the user's friends on social media. For example, the suggestion unit can suggest a new related insurance plan based on information shared by the user's friends. In this way, it is possible to suggest a new insurance plan that is highly relevant by analyzing the user's social media activity.

[0057] When proposing a new insurance plan, the proposal unit can customize the proposal method by reflecting the user's past feedback. For example, the proposal unit adjusts the content and timing of the proposal based on feedback provided by the user in the past. For example, the proposal unit prioritizes the use of a proposal method that the user has preferred in the past. The proposal unit can also analyze the user's past feedback and propose an optimal proposal method. For example, the proposal unit analyzes the user's feedback and selects the optimal proposal method. The proposal unit can also customize the proposal method by reflecting the user's past feedback using a generation AI. For example, the proposal unit inputs the user's feedback data into the generation AI, which then proposes the optimal proposal method. In this way, the proposal method can be optimized by reflecting the user's past feedback, enabling more appropriate proposals.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can collect information about the user's health condition and lifestyle, in addition to the user's family structure and concerns. For example, the collection unit can collect information about the user's health checkup results and exercise habits and propose an insurance plan based on this information. The collection unit can also collect information about the user's life events (marriage, childbirth, moving, etc.) and propose insurance plans corresponding to these events. Furthermore, the collection unit can collect information about the user's hobbies and interests and customize insurance plans based on this information. This makes it possible to propose insurance plans that meet the diverse needs of users.

[0060] The providing unit can customize the visual presentation of the generated insurance plan based on the user's visual preferences. For example, the providing unit creates graphs and charts using the user's preferred colors and fonts. The providing unit can also provide information in a format that is visually easy for the user to understand. For example, if the user prefers text over graphs, the providing unit provides the information in text format. Furthermore, the providing unit can adjust the layout of the information according to the user's visual preferences. This allows the provision of information that is easy for the user to see and understand.

[0061] The collection unit can collect information about the user's financial situation in addition to the user's family structure and concerns. For example, the collection unit collects information about the user's income, expenses, and savings status, and proposes an insurance plan based on this information. The collection unit can also collect information about the user's investment status and asset management, and customize an insurance plan based on this information. Furthermore, the collection unit can collect information about the user's future goals (e.g., children's education expenses and retirement living expenses) and propose an insurance plan that corresponds to these goals. This makes it possible to propose an optimal insurance plan based on the user's financial situation.

[0062] When visually providing the generated insurance plan, the providing unit can provide it in a format optimized for the user's device. For example, the providing unit provides information in a layout optimized for mobile devices such as smartphones and tablets. The providing unit can also adjust the way the information is displayed depending on the screen size and resolution of the device used by the user. Furthermore, the providing unit can customize the way the information is displayed depending on the settings of the user's device (e.g., dark mode or light mode). This makes it possible to provide visual information optimized for the user's device.

[0063] The collection unit can collect information about the user's occupation and work situation, in addition to the user's family structure and concerns. For example, the collection unit collects information about the user's occupation, working hours, and work location, and proposes an insurance plan based on this information. The collection unit can also collect information about the user's occupational risks and working environment, and customize an insurance plan based on this information. Furthermore, the collection unit can collect information about the user's career plan and future career goals, and propose an insurance plan that corresponds to these goals. This makes it possible to propose an optimal insurance plan based on the user's occupation and work situation.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the user's family structure and concerns. For example, the collection unit analyzes the user's conversation, converts the conversation into text data using voice recognition technology, and extracts the family structure and concerns using natural language processing technology. Using generative AI makes it possible to collect this information efficiently. Step 2: The proposal unit proposes an insurance plan based on the information collected by the collection unit. The proposal unit uses a generation AI to generate an optimal insurance plan based on the collected information. For example, the proposal unit uses an algorithm to generate an insurance plan based on the user's family structure and concerns. Step 3: The provision unit visually provides the insurance plan proposed by the proposal unit. The provision unit can also use the generation AI to visually provide the generated insurance plan in the form of graphs or charts. For example, the insurance coverage and costs can be visually displayed in graphs or charts. Step 4: The new proposal unit proposes a new insurance plan to the insurance company based on the information collected by the collection unit. The new proposal unit can also use generative AI to propose a new insurance plan to the insurance company based on the collected information. For example, it provides feedback to the insurance company such as, "Since there are many users with this family structure and concerns, we propose developing this type of insurance plan."

[0066] (Example 2) An insurance consultation system according to an embodiment of the present invention collects information about a user's family structure and concerns, and then a generation AI proposes and visually presents the optimal insurance plan. The insurance consultation system collects information about a user's family structure and concerns, and then a generation AI proposes the optimal insurance plan. This proposal is quickly provided in the form of an estimate or a graphical representation. This creates a relaxed atmosphere for users to consult without feeling pressured by sales. It also solves the shortage of salespeople at insurance companies, allowing the generation AI to propose new insurance plans to insurance companies based on the consultation content. For example, in an insurance consultation system, a user initiates a conversation with a generation AI. The generation AI asks questions such as, "What is your family structure?" or "What concerns do you have?" The user responds, and the generation AI collects the information. Next, the generation AI proposes the optimal insurance plan based on the collected information. For example, the system may suggest, "Based on your family structure and concerns, this insurance plan is suitable." This proposal is quickly provided in the form of an estimate or a graphical representation. For example, the insurance coverage and costs can be visually displayed in graphs and charts. Furthermore, the generation AI can propose new insurance plans to insurance companies based on the consultation content. For example, the system provides the insurance company with feedback such as, "Since many users have this kind of family structure and concerns, we suggest developing this kind of insurance plan." This allows the insurance consultation system to make it easier for users to consult about insurance, and helps insurance companies solve their salesperson shortage and develop new insurance plans.

[0067] An insurance consultation system according to an embodiment includes a collection unit, a proposal unit, a provision unit, and a new proposal unit. The collection unit collects information about a user's family structure and concerns. For example, the collection unit analyzes the user's conversation and collects the information about the family structure and concerns. The collection unit can also use a generation AI to analyze the user's conversation and collect the information about the family structure and concerns. For example, the collection unit converts the user's conversation into text data using speech recognition technology and extracts the information about the family structure and concerns using natural language processing technology. The proposal unit proposes an insurance plan based on the information collected by the collection unit. The proposal unit uses a generation AI to generate an optimal insurance plan based on the collected information. For example, the proposal unit uses an algorithm to generate an insurance plan based on the user's family structure and concerns. The proposal unit can also use a generation AI to generate an insurance plan based on the collected information. The provision unit visually provides the insurance plan proposed by the proposal unit. The provision unit can also use the generation AI to visually provide the generated insurance plan in the form of a graph or chart. For example, the providing unit visually displays insurance coverage and costs using graphs and charts. The new suggestion unit proposes a new insurance plan to the insurance company based on the information collected by the collecting unit. The new suggestion unit can also use generation AI to propose a new insurance plan to the insurance company based on the collected information. For example, the new suggestion unit provides feedback to the insurance company such as, "Since many users have this kind of family structure and concerns, we propose developing this kind of insurance plan." In this way, the insurance consultation system according to the embodiment collects the user's family structure and concerns, proposes an optimal insurance plan, and visually presents it, allowing the user to easily consult about insurance.

[0068] The collection unit can analyze the user's conversation and collect information about family structure and concerns. The collection unit can convert the user's conversation into text data using, for example, voice recognition technology. For example, the collection unit automatically analyzes the speech using voice recognition software and saves it as text. The collection unit can also extract the family structure and concerns from the text data using natural language processing technology. For example, the collection unit analyzes the text data and extracts keywords and phrases related to the family structure and concerns. The collection unit can also analyze the user's conversation and collect the family structure and concerns using a generation AI. For example, the collection unit inputs the user's conversation data into the generation AI, which then extracts the family structure and concerns. In this way, the family structure and concerns can be accurately collected by analyzing the user's conversation.

[0069] The proposal unit can generate an insurance plan based on the collected information. The proposal unit, for example, uses an algorithm to analyze the collected information and generate an optimal insurance plan. For example, the proposal unit generates an insurance plan based on the user's family structure and concerns. The proposal unit can also use a generation AI to generate an insurance plan based on the collected information. For example, the proposal unit inputs the collected information into the generation AI, which then generates an optimal insurance plan. The proposal unit can also use data analysis technology to analyze the collected information and generate an insurance plan. For example, the proposal unit uses data analysis technology to generate an insurance plan based on the user's family structure and concerns. In this way, an insurance plan suitable for the user can be proposed by generating an optimal insurance plan based on the collected information.

[0070] The provision unit can visually provide the generated insurance plan in the form of a graph or chart. For example, the provision unit visually provides the generated insurance plan in the form of a graph or chart. For example, the provision unit visually shows the insurance coverage and costs in the form of a graph or chart. The provision unit can also visually provide the generated insurance plan using a generation AI. For example, the provision unit inputs insurance plan data into the generation AI, which then generates a graph or chart. The provision unit can also adjust the visual presentation method. For example, the provision unit adjusts the visual presentation method based on the user's emotions. In this way, visually providing the generated insurance plan makes it easier for the user to understand.

[0071] The new proposal unit can propose a new insurance plan to the insurance company based on the collected information. For example, the new proposal unit proposes a new insurance plan to the insurance company based on the collected information. For example, the new proposal unit provides feedback to the insurance company such as, "Since many users have this kind of family structure and concerns, we propose developing this kind of insurance plan." The new proposal unit can also use a generation AI to propose a new insurance plan based on the collected information. For example, the new proposal unit inputs the collected information into the generation AI, which then proposes a new insurance plan. The new proposal unit can also use data analysis technology to analyze the collected information and propose a new insurance plan. For example, the new proposal unit uses data analysis technology to propose a new insurance plan based on the user's family structure and concerns. This allows the insurance company to develop a new insurance plan by proposing a new insurance plan based on the collected information.

[0072] The collection unit can estimate the user's emotions and adjust the content or timing of questions based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows more appropriate information to be collected by adjusting the content and timing 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. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit can analyze the user's past conversation history and select a question method. The collection unit, for example, analyzes the user's past conversation history using text mining technology. For example, the collection unit generates unique questions from the past conversation history. The collection unit can also analyze the past conversation history and select an optimal question method using natural language processing technology. For example, the collection unit analyzes past answer patterns and selects the most effective question method. The collection unit can also analyze the past conversation history and select a question method using a generation AI. For example, the collection unit inputs the past conversation history into the generation AI, which selects the optimal question method. In this way, by analyzing the past conversation history, unique questions can be asked, enabling effective information collection.

[0074] When collecting conversations, the collection unit can filter information based on the user's current living situation or areas of interest. For example, the collection unit filters information based on the user's current living situation. For example, if the user has recently moved, the collection unit asks questions related to the user's new residence. The collection unit can also filter information based on the user's areas of interest. For example, if the user has a particular hobby, the collection unit asks questions related to that hobby. The collection unit can also use the generation AI to filter information based on the user's current living situation or areas of interest. For example, the collection unit inputs data on the user's living situation and areas of interest into the generation AI, and the generation AI filters relevant information. In this way, by filtering information based on the user's living situation and areas of interest, more relevant information can be collected.

[0075] When collecting conversations, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. For example, the collection unit automatically analyzes the voice using voice recognition software and saves it as text. Furthermore, if the user uses text input, the collection unit can also collect information using natural language processing technology. For example, the collection unit analyzes text data and extracts keywords and phrases related to family structure and concerns. Furthermore, if the user provides images, the collection unit can also collect information using image analysis technology. For example, the collection unit analyzes image data and extracts related information. This improves the accuracy of information collection by selecting the optimal collection means depending on the user's input method.

[0076] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows important information to be collected preferentially by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When collecting conversations, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit collects information about insurance plans and services specific to that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting information related to the user's travel destination. For example, the collection unit collects information about insurance plans and emergency services at the travel destination. Furthermore, if the user plans to move to a specific area, the collection unit can prioritize collecting information related to that area. For example, the collection unit collects information about insurance plans and services related to the area to which the user is moving. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0078] When collecting conversations, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit poses related questions based on information shared by the user on social media. For example, the collection unit analyzes the content of the user's social media posts and generates questions related to related topics. The collection unit can also analyze the user's social media activities and collect information related to topics of interest. For example, the collection unit analyzes the user's "likes" and comments and collects information related to topics of interest. The collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit generates related questions based on information shared by the user's friends. In this way, related information can be effectively collected by analyzing the user's social media activities.

[0079] When collecting conversations, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the content and timing of questions based on feedback provided by the user in the past. For example, the collection unit prioritizes the use of collection methods that the user has preferred in the past. The collection unit can also analyze the user's past feedback and suggest the optimal collection method. For example, the collection unit analyzes the user's feedback and selects the optimal question method and timing. The collection unit can also customize the collection method by using the generation AI to reflect the user's past feedback. For example, the collection unit inputs the user's feedback data into the generation AI, and the generation AI suggests the optimal collection method. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0080] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate suggestions to be made by adjusting the way the suggestion is expressed based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The proposal unit can adjust the level of detail of the insurance plan when making a proposal. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of the insurance plan. For example, the proposal unit provides a detailed explanation for insurance plans with a high level of importance. The proposal unit can also provide a concise explanation for insurance plans with a low level of importance. For example, the proposal unit gradually adjusts the level of detail of the proposal according to the importance. The proposal unit can also adjust the level of detail of the insurance plan using a generation AI. For example, the proposal unit inputs data of the insurance plan into the generation AI, and the generation AI adjusts the level of detail. In this way, by adjusting the level of detail of the proposal according to the importance of the insurance plan, it is possible to provide information that is suitable for the user.

[0082] When making a proposal, the proposal unit can apply a proposal algorithm depending on the category of the insurance plan. The proposal unit, for example, applies different proposal algorithms depending on the category of the insurance plan. For example, in the case of life insurance, the proposal unit makes a proposal based on family composition and health condition. In addition, in the case of automobile insurance, the proposal unit can also make a proposal based on the vehicle type and driving history. For example, the proposal unit proposes an optimal insurance plan based on the vehicle type and driving history. In addition, in the case of home insurance, the proposal unit can also make a proposal based on the type and location of the residence. For example, the proposal unit proposes an optimal insurance plan based on the type and location of the residence. In this way, by applying a proposal algorithm depending on the category of the insurance plan, more appropriate proposals can be made.

[0083] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, analyzes the user's past proposal results and improves the accuracy of the proposal. For example, the suggestion unit makes similar proposals based on proposals that the user has accepted in the past. The suggestion unit can also adjust to avoid proposals that the user has rejected in the past. For example, the suggestion unit makes optimal proposals based on past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the generation AI by referring to the user's past proposal results. For example, the suggestion unit inputs past proposal results into the generation AI, and the generation AI makes optimal suggestions. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0084] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The proposal unit can determine the priority of insurance plans when they are proposed. The proposal unit determines the priority of proposals, for example, based on the time of submission of the insurance plans. For example, the proposal unit prioritizes insurance plans with upcoming submission deadlines. The proposal unit can also postpone insurance plans with distant submission deadlines. For example, the proposal unit gradually adjusts the priority of proposals depending on the time of submission. The proposal unit can also determine the priority of insurance plans using a generation AI. For example, the proposal unit inputs insurance plan data into the generation AI, and the generation AI determines the priority. In this way, by determining the priority of proposals based on the time of submission of the insurance plans, important proposals can be given priority.

[0086] The suggestion unit can adjust the order of insurance plans when making a suggestion. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the insurance plans. For example, the suggestion unit prioritizes suggesting insurance plans related to the user's family structure. The suggestion unit can also prioritize suggesting insurance plans related to the user's concerns. For example, the suggestion unit gradually adjusts the order of suggestions according to the relevance of the insurance plans. The suggestion unit can also adjust the order of insurance plans using a generation AI. For example, the suggestion unit inputs insurance plan data into the generation AI, which then adjusts the order. In this way, by adjusting the order of suggestions based on the relevance of the insurance plans, suggestions that are most relevant to the user can be made with priority.

[0087] When making a proposal, the suggestion unit can adjust the use of technical terms according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terms in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about insurance, the suggestion unit makes a proposal that uses a lot of technical terms. In addition, if the user is not knowledgeable about insurance, the suggestion unit can also explain in simple terms. For example, the suggestion unit gradually adjusts the use of technical terms in the proposal according to the user's level of expertise. In addition, the suggestion unit can also adjust the use of technical terms using a generation AI. For example, the suggestion unit inputs the user's level of expertise into the generation AI, and the generation AI adjusts the use of technical terms. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0088] The providing unit can estimate the user's emotion and adjust the visual presentation method based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on heart rate fluctuations. This enables the provision of information that is easy for the user to view by adjusting the visual presentation method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The providing unit can adjust the level of detail of the insurance plan when providing it. The providing unit adjusts the visual level of detail based on, for example, the importance of the insurance plan. For example, the providing unit provides detailed graphs and charts for insurance plans with high importance. The providing unit can also provide simple graphs and charts for insurance plans with low importance. For example, the providing unit gradually adjusts the visual level of detail according to the importance. The providing unit can also adjust the level of detail of the insurance plan using a generating AI. For example, the providing unit inputs data of the insurance plan into the generating AI, and the generating AI adjusts the level of detail. In this way, by adjusting the visual level of detail according to the importance of the insurance plan, it is possible to provide important information to the user in detail.

[0090] The providing unit can apply a visual representation according to the category of the insurance plan when providing the information. The providing unit, for example, applies different visual representations according to the category of the insurance plan. For example, in the case of life insurance, the providing unit provides graphs and charts based on family composition and health condition. Furthermore, in the case of automobile insurance, the providing unit can also provide graphs and charts based on the vehicle type and driving history. For example, the providing unit provides an optimal visual representation based on the vehicle type and driving history. Furthermore, in the case of home insurance, the providing unit can also provide graphs and charts based on the type and location of the residence. For example, the providing unit provides an optimal visual representation based on the type and location of the residence. In this way, by applying a visual representation according to the category of the insurance plan, it is possible to provide information that is easy for the user to understand.

[0091] The providing unit can improve visual accuracy by referring to the user's past visual provision results when providing the visual representation. The providing unit, for example, analyzes the user's past visual provision results to improve visual accuracy. For example, the providing unit provides similar graphs or charts based on visual representations that the user has previously preferred. The providing unit can also make adjustments to avoid visual representations that the user has previously rejected. For example, the providing unit provides an optimal visual representation based on the past visual provision results. The providing unit can also improve visual accuracy by using the generation AI by referring to the user's past visual provision results. For example, the providing unit inputs the past visual provision results into the generation AI, and the generation AI provides the optimal visual representation. In this way, visual accuracy is improved by referring to the user's past visual provision results.

[0092] The providing unit can estimate the user's emotion and adjust the length of visual presentation based on the estimated user emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on heart rate fluctuations. This makes it possible to provide appropriate information to the user by adjusting the length of visual presentation according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The provision unit can determine the visual priority at the time of provision based on the time of submission of the insurance plan. The provision unit determines the visual priority based on, for example, the time of submission of the insurance plan. For example, the provision unit visually provides insurance plans with an upcoming submission deadline with priority. The provision unit can also postpone insurance plans with a distant submission deadline. For example, the provision unit gradually adjusts the visual priority according to the time of submission. The provision unit can also determine the priority of the insurance plan using a generation AI. For example, the provision unit inputs insurance plan data into the generation AI, and the generation AI determines the priority. In this way, by determining the visual priority based on the time of submission of the insurance plan, important information can be provided preferentially.

[0094] The providing unit can adjust the order of the insurance plans when providing them. The providing unit adjusts the visual order based on, for example, the relevance of the insurance plans. For example, the providing unit visually provides insurance plans related to the user's family structure with priority. The providing unit can also visually provide insurance plans related to the user's concerns with priority. For example, the providing unit gradually adjusts the visual order according to the relevance of the insurance plans. The providing unit can also adjust the order of the insurance plans using a generating AI. For example, the providing unit inputs data of the insurance plans into the generating AI, and the generating AI adjusts the order. In this way, by adjusting the visual order based on the relevance of the insurance plans, it is possible to provide information that is most relevant to the user with priority.

[0095] The providing unit can adjust the use of technical terms during provision according to the user's level of expertise. The providing unit, for example, adjusts the use of visual technical terms according to the user's level of expertise. For example, if the user is knowledgeable about insurance, the providing unit provides visual information that makes extensive use of technical terms. Furthermore, if the user is not knowledgeable about insurance, the providing unit can provide visual information that explains things in simple terms. For example, the providing unit gradually adjusts the use of technical terms in the visual information according to the user's level of expertise. Furthermore, the providing unit can also adjust the use of technical terms using a generation AI. For example, the providing unit inputs the user's level of expertise into the generation AI, which then adjusts the use of technical terms. In this way, by adjusting the use of visual technical terms according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0096] The suggestion unit can estimate the user's emotions and adjust the method for proposing a new insurance plan based on the estimated user emotions. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations. This enables the proposal method for a new insurance plan to be adjusted according to the user's emotions, thereby making it possible to make an appropriate proposal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0097] When proposing a new insurance plan, the proposal unit can analyze the content of the user's past consultations and select a proposal method. The proposal unit, for example, analyzes the content of the user's past consultations using text mining technology. For example, the proposal unit proposes a new insurance plan that does not overlap with the content of the past consultations. The proposal unit can also analyze the content of the past consultations using natural language processing technology and select the optimal proposal method. For example, the proposal unit analyzes the content of the past consultations and selects the most effective proposal method. The proposal unit can also analyze the content of the past consultations using a generation AI and select a proposal method. For example, the proposal unit inputs the content of the past consultations into the generation AI, and the generation AI selects the optimal proposal method. In this way, it becomes possible to propose a new insurance plan that does not overlap with the content of the past consultations by analyzing the content of the user's past consultations.

[0098] When proposing a new insurance plan, the suggestion unit can customize the means of suggestion based on the user's current living situation. The suggestion unit customizes the means of suggestion based on the user's current living situation, for example. For example, if the user has recently moved, the suggestion unit can suggest an insurance plan related to the new residence. Furthermore, if the user has a specific hobby, the suggestion unit can also suggest an insurance plan related to the hobby. For example, the suggestion unit can suggest an insurance plan related to the hobby. Furthermore, if the user has experienced a specific life event, the suggestion unit can also suggest an insurance plan related to the event. For example, the suggestion unit can suggest an insurance plan related to the life event. In this way, by customizing the means of suggestion based on the user's current living situation, it is possible to make the most suitable suggestion for the user.

[0099] The proposal unit can improve the proposal method by reflecting user feedback when proposing a new insurance plan. For example, the proposal unit adjusts the content and timing of the proposal based on feedback provided by the user in the past. For example, the proposal unit prioritizes the use of a proposal method that the user has preferred in the past. The proposal unit can also analyze the user's past feedback and propose an optimal proposal method. For example, the proposal unit analyzes the user's feedback and selects the optimal proposal method. The proposal unit can also improve the proposal method by reflecting user feedback using a generation AI. For example, the proposal unit inputs user feedback data into the generation AI, which then proposes the optimal proposal method. In this way, the proposal method can be improved by reflecting user feedback, enabling more appropriate proposals.

[0100] The suggestion unit can estimate the user's emotions and prioritize new insurance plans based on the estimated user emotions. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations. This allows the prioritization of new insurance plans based on the user's emotions, thereby prioritizing proposals that are important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] When proposing a new insurance plan, the suggestion unit can select a suggestion method taking into account the user's geographical location information. For example, if the user lives in a specific area, the suggestion unit can suggest a new insurance plan related to that area. For example, the suggestion unit can suggest information about insurance plans and services specific to that area. Furthermore, if the user is traveling, the suggestion unit can also suggest a new insurance plan related to the user's travel destination. For example, the suggestion unit can suggest information about insurance plans and emergency services for the travel destination. Furthermore, if the user plans to move to a specific area, the suggestion unit can also suggest a new insurance plan related to that area. For example, the suggestion unit can suggest information about insurance plans and services related to the area to which the user is moving. In this way, by taking the user's geographical location information into account, it is possible to suggest a new insurance plan that is highly relevant.

[0102] When proposing a new insurance plan, the suggestion unit can analyze the user's social media activity to provide a means for making suggestions. For example, the suggestion unit can suggest a new related insurance plan based on information shared by the user on social media. For example, the suggestion unit can analyze the content of the user's social media posts and suggest a new insurance plan related to a related topic. The suggestion unit can also analyze the user's social media activity and suggest a new insurance plan related to a topic of interest. For example, the suggestion unit can analyze the user's "likes" and comments and suggest a new insurance plan related to a topic of interest. The suggestion unit can also suggest a new related insurance plan based on the activity of the user's friends on social media. For example, the suggestion unit can suggest a new related insurance plan based on information shared by the user's friends. In this way, it is possible to suggest a new insurance plan that is highly relevant by analyzing the user's social media activity.

[0103] When proposing a new insurance plan, the proposal unit can customize the proposal method by reflecting the user's past feedback. For example, the proposal unit adjusts the content and timing of the proposal based on feedback provided by the user in the past. For example, the proposal unit prioritizes the use of a proposal method that the user has preferred in the past. The proposal unit can also analyze the user's past feedback and propose an optimal proposal method. For example, the proposal unit analyzes the user's feedback and selects the optimal proposal method. The proposal unit can also customize the proposal method by reflecting the user's past feedback using a generation AI. For example, the proposal unit inputs the user's feedback data into the generation AI, which then proposes the optimal proposal method. In this way, the proposal method can be optimized by reflecting the user's past feedback, enabling more appropriate proposals. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, proposal unit, provision unit, and new proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a user's conversation using the microphone 38B of the smart device 14 and analyzes it using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal insurance plan based on the collected information. The provision unit visually provides the insurance plan using, for example, the display 40A of the smart device 14. The new proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a new insurance plan to an insurance company based on the collected information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, provision unit, and new proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a user's conversation using the microphone 238 of the smart glasses 214 and analyzes it using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal insurance plan based on the collected information. The provision unit visually provides the insurance plan using, for example, the display of the smart glasses 214. The new proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a new insurance plan to an insurance company based on the collected information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, proposal unit, provision unit, and new proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's conversation using the microphone 238 of the headset type terminal 314 and analyzes it using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal insurance plan based on the collected information. The provision unit visually provides the insurance plan using, for example, the display 343 of the headset type terminal 314. The new proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a new insurance plan to the insurance company based on the collected information. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, proposal unit, provision unit, and new proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's conversation using the microphone 238 of the robot 414 and analyzes it using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal insurance plan based on the collected information. The provision unit visually provides the insurance plan using, for example, the display of the robot 414. The new proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a new insurance plan to an insurance company based on the collected information.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can collect information about the user's health condition and lifestyle, in addition to the user's family structure and concerns. For example, the collection unit can collect information about the user's health checkup results and exercise habits and propose an insurance plan based on this information. The collection unit can also collect information about the user's life events (marriage, childbirth, moving, etc.) and propose insurance plans corresponding to these events. Furthermore, the collection unit can collect information about the user's hobbies and interests and customize insurance plans based on this information. This makes it possible to propose insurance plans that meet the diverse needs of users.

[0106] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, the suggestion unit makes suggestions when the user is relaxed, making the user more likely to accept the suggestions. The suggestion unit can also refrain from making suggestions when the user is feeling stressed. Furthermore, the suggestion unit can adjust the content of the suggestions according to the user's emotions. For example, if the user is feeling anxious, the suggestion unit makes suggestions that give the user a sense of security. This makes it possible to make suggestions that take the user's emotions into consideration.

[0107] The providing unit can customize the visual presentation of the generated insurance plan based on the user's visual preferences. For example, the providing unit creates graphs and charts using the user's preferred colors and fonts. The providing unit can also provide information in a format that is visually easy for the user to understand. For example, if the user prefers text over graphs, the providing unit provides the information in text format. Furthermore, the providing unit can adjust the layout of the information according to the user's visual preferences. This allows the provision of information that is easy for the user to see and understand.

[0108] When proposing a new insurance plan based on the collected information, the new suggestion unit can estimate the user's emotions and adjust the content of the proposal based on the estimated emotions. For example, if the user is feeling anxious, the new suggestion unit can suggest an insurance plan that gives the user a sense of security. Also, if the user is excited, the new suggestion unit can suggest an insurance plan with reduced risk. Furthermore, the new suggestion unit can adjust the timing of the proposal according to the user's emotions. This makes it possible to propose a new insurance plan that takes the user's emotions into consideration.

[0109] The collection unit can collect information about the user's financial situation in addition to the user's family structure and concerns. For example, the collection unit collects information about the user's income, expenses, and savings status, and proposes an insurance plan based on this information. The collection unit can also collect information about the user's investment status and asset management, and customize an insurance plan based on this information. Furthermore, the collection unit can collect information about the user's future goals (e.g., children's education expenses and retirement living expenses) and propose an insurance plan that corresponds to these goals. This makes it possible to propose an optimal insurance plan based on the user's financial situation.

[0110] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make the suggestion using casual expressions. On the other hand, if the user is nervous, the suggestion unit can make the suggestion using formal expressions. Furthermore, the suggestion unit can adjust the tone of the suggestion according to the user's emotions. For example, if the user is feeling anxious, the suggestion unit can make the suggestion in a tone that gives a sense of security. This makes it possible to make suggestions that take the user's emotions into consideration.

[0111] When visually providing the generated insurance plan, the providing unit can provide it in a format optimized for the user's device. For example, the providing unit provides information in a layout optimized for mobile devices such as smartphones and tablets. The providing unit can also adjust the way the information is displayed depending on the screen size and resolution of the device used by the user. Furthermore, the providing unit can customize the way the information is displayed depending on the settings of the user's device (e.g., dark mode or light mode). This makes it possible to provide visual information optimized for the user's device.

[0112] When proposing a new insurance plan based on the collected information, the new suggestion unit can estimate the user's emotions and determine the priority of the proposals based on the estimated emotions. For example, if the user is feeling anxious, the new suggestion unit can preferentially suggest insurance plans that give the user a sense of security. Also, if the user is excited, the new suggestion unit can preferentially suggest insurance plans with reduced risk. Furthermore, the new suggestion unit can adjust the timing of the proposals according to the user's emotions. This makes it possible to propose new insurance plans that take the user's emotions into consideration.

[0113] The collection unit can collect information about the user's occupation and work situation, in addition to the user's family structure and concerns. For example, the collection unit collects information about the user's occupation, working hours, and work location, and proposes an insurance plan based on this information. The collection unit can also collect information about the user's occupational risks and working environment, and customize an insurance plan based on this information. Furthermore, the collection unit can collect information about the user's career plan and future career goals, and propose an insurance plan that corresponds to these goals. This makes it possible to propose an optimal insurance plan based on the user's occupation and work situation.

[0114] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, the suggestion unit can make detailed suggestions when the user is relaxed. On the other hand, the suggestion unit can make concise suggestions when the user is nervous. Furthermore, the suggestion unit can adjust the content of the suggestions according to the user's emotions. For example, if the user is feeling anxious, the suggestion unit can make suggestions that give the user a sense of security. This makes it possible to make suggestions that take the user's emotions into consideration.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects the user's family structure and concerns. For example, the collection unit analyzes the user's conversation, converts the conversation into text data using voice recognition technology, and extracts the family structure and concerns using natural language processing technology. Using generative AI makes it possible to collect this information efficiently. Step 2: The proposal unit proposes an insurance plan based on the information collected by the collection unit. The proposal unit uses a generation AI to generate an optimal insurance plan based on the collected information. For example, the proposal unit uses an algorithm to generate an insurance plan based on the user's family structure and concerns. Step 3: The provision unit visually provides the insurance plan proposed by the proposal unit. The provision unit can also use the generation AI to visually provide the generated insurance plan in the form of graphs or charts. For example, the insurance coverage and costs can be visually displayed in graphs or charts. Step 4: The new proposal unit proposes a new insurance plan to the insurance company based on the information collected by the collection unit. The new proposal unit can also use generative AI to propose a new insurance plan to the insurance company based on the collected information. For example, it provides feedback to the insurance company such as, "Since there are many users with this family structure and concerns, we propose developing this type of insurance plan."

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about the user's family structure and concerns; a proposal unit that proposes insurance plans based on the information collected by the collection unit; a providing unit that visually provides the insurance plan proposed by the proposing unit; a new proposal unit that proposes a new insurance plan to an insurance company based on the information collected by the collection unit. A system characterized by:

2. The collecting unit Analyze user conversations to collect information about family structure and concerns 2. The system of claim 1.

3. The proposal unit Generate insurance plans based on collected information 2. The system of claim 1.

4. The providing unit Provide a visual representation of the generated insurance plan in a graph or chart 2. The system of claim 1.

5. The proposal unit Propose new insurance plans to insurance companies based on the collected information 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the content or timing of questions based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past conversation history and select the question method 2. The system of claim 1.

8. The collecting unit As conversations are collected, they can be filtered based on the user's current life situation or interests.

2. The system of claim 1.

9. The collecting unit When collecting conversations, select the optimal collection method depending on the user's input method 2. The system of claim 1.

10. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

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