System

Abstract

An object of a system according to an embodiment is to analyze risk information of a user and recommend an optimal insurance.SOLUTION: A system includes a reception unit, an analysis unit, and a recommendation unit. The reception unit inputs risk information of a user in a question form. The analysis unit analyzes the risk information input by the receiving unit, and identifies a risk type of the user. The recommendation unit recommends an appropriate insurance based on the risk type specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze users' risk information and recommend optimal insurance, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze risk information of a user and recommend optimal insurance. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit inputs risk information of a user in the form of a question. The analysis unit analyzes the risk information input by the reception unit and identifies the user's risk type. The recommendation unit recommends appropriate insurance based on the risk type identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze risk information of a user and recommend optimal insurance. [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 recommendation system according to an embodiment of the present invention is a system in which a user's risk information is input in the form of a question, which is analyzed by a generation AI, and the system recommends the most suitable insurance. In the insurance recommendation system, a user inputs their own risk information in the form of a question, and a generation AI analyzes the information to identify the risk type and recommend the most suitable insurance. For example, if the user lives in an area where their home is at risk of flooding, the generation AI recommends flood insurance. Also, if the user has a high health risk, the generation AI recommends health insurance. In this way, the insurance recommendation system can easily find the insurance that best suits the user's risk. This allows the insurance recommendation system to efficiently analyze the user's risk information and recommend the most suitable insurance. For example, if the user lives in an area where their home is at risk of flooding, the generation AI recommends flood insurance, allowing the user to find the most suitable insurance for themselves. Also, if the user has a high health risk, the generation AI recommends health insurance, allowing the user to find the most suitable insurance for themselves.

[0029] The insurance recommendation system according to the embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives user risk information in the form of a question. The user's risk information includes, but is not limited to, information about the user's living environment, health status, and occupation. For example, the reception unit allows the user to input, for example, "My home is in a flood-risk area." The reception unit also allows the user to input, for example, "My health risk is high." The analysis unit uses a generation AI to analyze the risk information received by the reception unit and identify the user's risk type. For example, the generation AI identifies the risk type based on information about the user's living environment, health status, occupation, and the like. For example, if the user inputs, "My home is in a flood-risk area," the generation AI analyzes the information and determines that the user has a high flood risk. Also, if the user inputs, "My health risk is high," the generation AI analyzes the information and determines that the user has a high health risk. The recommendation unit uses the generation AI to recommend optimal insurance based on the risk type identified by the analysis unit. For example, if the generation AI determines that the user has a high flood risk, it recommends flood insurance. Furthermore, if the generation AI determines that the user has a high health risk, it recommends health insurance. This allows the insurance recommendation system according to the embodiment to efficiently analyze the user's risk information and recommend optimal insurance.

[0030] The reception unit can input information about the user's living environment, health condition, and occupation in the form of a question. The reception unit, for example, inputs the user's living environment information in the form of a question. For example, the user can input, "My home is in an area at risk of flooding." The reception unit can also input the user's health condition information in the form of a question. For example, the user can input, "My health risk is high." The reception unit can also input the user's occupation information in the form of a question. For example, the user can input, "My occupation is in the construction industry." This allows for the collection of detailed information about the user, enabling more accurate risk analysis.

[0031] The analysis unit can identify the risk type based on information such as the user's living environment, health condition, and occupation. The analysis unit identifies the risk type based on, for example, the user's living environment information. For example, if the user inputs "My home is in an area at risk of flooding," the analysis unit analyzes the information and determines that the flood risk is high. The analysis unit can also identify the risk type based on the user's health condition information. For example, if the user inputs "My health risk is high," the analysis unit analyzes the information and determines that the health risk is high. The analysis unit can also identify the risk type based on the user's occupation information. For example, if the user inputs "My occupation is construction work," the analysis unit analyzes the information and determines that the risk related to the construction industry is high. This makes it possible to accurately identify the risk type based on the user's detailed information.

[0032] The recommendation unit can recommend appropriate insurance based on the identified risk type. For example, if the flood risk is determined to be high, the recommendation unit recommends flood insurance. For example, if the user inputs "my home is in an area at risk of flooding," the recommendation unit analyzes the information, determines that the flood risk is high, and recommends flood insurance. The recommendation unit can also recommend health insurance if the health risk is determined to be high. For example, if the user inputs "high health risk," the recommendation unit analyzes the information, determines that the health risk is high, and recommends health insurance. This makes it possible to recommend insurance that is optimal for the user's risk type.

[0033] The analysis unit can recommend flood insurance to users who live in areas at risk of flooding. For example, if a user inputs that "my home is in an area at risk of flooding," the analysis unit analyzes that information and determines that the flood risk is high. For example, the analysis unit can evaluate flood risk based on past flood history and geographical conditions. If the analysis unit determines that the flood risk is high, it recommends flood insurance. For example, it can recommend the most appropriate flood insurance by taking into account the coverage, premiums, and applicable conditions of the flood insurance. This makes it possible to recommend appropriate insurance to users who live in areas at high risk of flooding.

[0034] The analysis unit can recommend health insurance if there is a health risk. For example, if a user inputs "high health risk," the analysis unit analyzes the information and determines that the health risk is high. For example, the analysis unit can evaluate health risk based on health checkup results and lifestyle habits. If the analysis unit determines that the health risk is high, it recommends health insurance. For example, it recommends the most suitable health insurance, taking into consideration the health insurance coverage, premiums, and applicable conditions. This makes it possible to recommend appropriate insurance to users with high health risks.

[0035] The reception unit can analyze the user's past response history and select an appropriate question format. The reception unit presents the most appropriate question format, for example, based on the response format (multiple choice, free text, etc.) selected by the user in the past. For example, if the user previously responded in multiple choice format, the reception unit presents the question in a similar format. The reception unit can also analyze the user's response to a specific question format from the user's past response history and select the most appropriate format. For example, if the user previously provided detailed information in free text format, the reception unit presents the question in a similar format. The reception unit can also preferentially present related questions by referring to the content of the user's past responses. For example, if the user previously responded that "my home is in an area at risk of flooding," the reception unit presents related questions based on that information. In this way, information can be efficiently collected by providing the most appropriate question format based on the user's past response history.

[0036] The reception unit can analyze information input in the form of a question in real time and dynamically generate the next question. For example, if the user answers "yes," the reception unit generates a related, more detailed question based on the answer. For example, if the user answers "yes" to the question "Is your home located in an area at risk of flooding?", the reception unit makes subsequent questions related to flood risk. Furthermore, if the user answers "no," the reception unit can also generate a different question based on the answer. For example, if the user answers "no" to the question "Are you at high risk for health?", the reception unit makes subsequent questions unrelated to health risk. Furthermore, the reception unit can analyze the content of the user's answer in real time and optimize the next question. For example, if the user answers "I work in construction," the reception unit generates the next question based on that information. In this way, information can be collected efficiently by analyzing information in real time and dynamically generating the next question.

[0037] The reception unit can acquire information using voice input or image recognition when inputting information about the user's living environment, health condition, and occupation. For example, the reception unit acquires information by the user vocally inputting, for example, "My home is in a flood-risk area." For example, the reception unit converts voice data into text data using voice recognition technology. The reception unit can also acquire health condition information by the user uploading an image of a health checkup certificate. For example, the reception unit can analyze the image data using image recognition technology to extract health condition information. The reception unit can also acquire occupation information by the user uploading a photo of their workplace. For example, the reception unit can analyze the image data using image recognition technology to extract occupation information. This allows the user to easily input information using voice input or image recognition.

[0038] The reception unit can acquire information entered in the form of a question in conjunction with the user's geographic location information. For example, when a user enters their current location, the reception unit automatically acquires GPS information to simplify input. For example, when a user enters "my home is in a flood-risk area," the reception unit automatically acquires risk information for that area based on the GPS information. The reception unit can also suggest optimal candidate locations based on the geographic location information when a user enters their home address. For example, when a user enters "my home is in a flood-risk area," the reception unit suggests areas with a high flood risk based on the geographic location information. The reception unit can also update the user's current location in real time and present related questions when the user uses the app while on the move. For example, if a user enters "my home is in a flood-risk area," the reception unit presents related questions based on that information. In this way, by linking with geographic location information, the user's input can be simplified and information can be collected efficiently.

[0039] The reception unit can acquire information entered in the form of a question in conjunction with the user's social media activity. The reception unit, for example, presents related questions based on the location where the user checked in on social media. For example, if the user inputs "my home is in an area at risk of flooding," the reception unit presents related questions based on that information. The reception unit can also analyze the content of the user's social media posts and generate related questions. For example, if the user inputs "high health risk," the reception unit generates related questions based on that information. The reception unit can also present related questions based on the activities of the user's friends on social media. For example, if the user inputs "my occupation is construction work," the reception unit presents related questions based on that information. In this way, detailed information about the user can be collected by linking with social media activity.

[0040] The reception unit can customize information input in the form of a question by reflecting the user's past feedback. The reception unit customizes the content and order of questions based on, for example, feedback provided by the user in the past. For example, if the user inputs "my home is in an area at risk of flooding," the reception unit customizes related questions based on that information. The reception unit can also analyze responses to specific question formats from the user's past feedback and select an optimal format. For example, if the user inputs "high health risk," the reception unit customizes related questions based on that information. The reception unit can also prioritize related questions by referring to content of answers provided by the user in the past. For example, if the user inputs "my occupation is construction work," the reception unit customizes related questions based on that information. In this way, the reception unit can provide the user with an optimal question format by reflecting past feedback.

[0041] During analysis, the analysis unit can improve the accuracy of identifying the risk type by referring to the user's past risk information. The analysis unit improves the accuracy of identifying the risk type, for example, based on risk information provided by the user in the past. For example, if the user inputs "My home is in an area at risk of flooding," the analysis unit identifies the risk type based on that information by referring to past flood history and geographical conditions. The analysis unit can also analyze specific risk patterns from the user's past risk information to identify the risk type. For example, if the user inputs "I have high health risks," the analysis unit can identify the risk type based on that information by referring to past health checkup results and lifestyle habits. The analysis unit can also identify the risk type by referring to risk events the user has experienced in the past. For example, if the user inputs "I work in construction," the analysis unit can identify the risk type based on that information by referring to past risk events related to the construction industry. In this way, by referring to past risk information, the accuracy of identifying the risk type is improved.

[0042] During analysis, the analysis unit can identify the risk type by cross-referencing information such as the user's living environment, health condition, and occupation. The analysis unit, for example, identifies the risk type based on the user's living environment information. For example, if the user inputs "My home is in an area at risk of flooding," the analysis unit identifies the flood risk based on that information. The analysis unit can also identify the risk type based on the user's health condition information. For example, if the user inputs "My health risk is high," the analysis unit identifies the health risk based on that information. The analysis unit can also identify the risk type based on the user's occupation information. For example, if the user inputs "My occupation is construction work," the analysis unit identifies risks related to the construction industry based on that information. This cross-referencing improves the accuracy of identifying the risk type. Cross-referencing is performed, for example, using data correlation analysis or data integration methods.

[0043] During analysis, the analysis unit can evaluate the reliability of the user's answers and prioritize analysis of highly reliable information. The analysis unit, for example, prioritizes analysis of highly reliable information based on the user's answers. For example, if a user inputs "My home is in an area at risk of flooding," the analysis unit evaluates the reliability of that information and prioritizes analysis of highly reliable information. The analysis unit can also identify highly reliable information based on the user's past answer history. For example, if a user inputs "My health risk is high," the analysis unit evaluates the reliability of that information and prioritizes analysis of highly reliable information. The analysis unit can also evaluate the consistency of the user's answers and prioritize analysis of highly reliable information. For example, if a user inputs "My occupation is construction," the analysis unit evaluates the reliability of that information and prioritizes analysis of highly reliable information. In this way, by prioritizing analysis of highly reliable information, the accuracy of risk type identification is improved.

[0044] During analysis, the analysis unit can identify a risk type based on the user's geographical location information. The analysis unit, for example, considers the geographical risk of the area where the user lives to identify the risk type. For example, if the user inputs "my home is in an area at risk of flooding," the analysis unit identifies the risk type based on that information and taking the geographical risk into account. The analysis unit can also identify the risk type based on the geographical risk of the user's workplace. For example, if the user inputs "my occupation is construction," the analysis unit identifies the risk type based on that information and taking the geographical risk of the workplace into account. The analysis unit can also identify the risk type based on the geographical risk of places frequently visited by the user. For example, if the user inputs "high health risk," the analysis unit identifies the risk type based on that information and taking the geographical risk of places frequently visited into account. In this way, by taking geographical location information into account, the accuracy of identifying the risk type is improved.

[0045] During the analysis, the analysis unit can analyze the user's social media activity to identify the risk type. The analysis unit, for example, analyzes the user's social media posts to identify the risk type. For example, if a user inputs "my home is in an area at risk of flooding," the analysis unit analyzes the social media posts based on that information to identify the risk type. The analysis unit can also identify the risk type based on the user's social media check-in information. For example, if a user inputs "high health risk," the analysis unit analyzes the social media check-in information based on that information to identify the risk type. The analysis unit can also identify the risk type based on the activities of the user's friends on social media. For example, if a user inputs "my occupation is construction work," the analysis unit analyzes the activities of the user's friends on social media based on that information to identify the risk type. This improves the accuracy of identifying risk types by analyzing social media activity.

[0046] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit adjusts the analysis algorithm based on, for example, feedback provided by the user in the past. For example, if the user inputs "my home is in an area at risk of flooding," the analysis algorithm is adjusted based on that information and reflects past feedback. The analysis unit can also analyze specific risk patterns from the user's past feedback and adjust the analysis algorithm. For example, if the user inputs "high health risk," the analysis unit adjusts the analysis algorithm based on that information and reflects past feedback. The analysis unit can also adjust the analysis algorithm by referring to risk events the user has experienced in the past. For example, if the user inputs "my occupation is construction work," the analysis algorithm is adjusted based on that information and reflects past risk events. In this way, the accuracy of the analysis algorithm is improved by reflecting past feedback.

[0047] When making a recommendation, the recommendation unit can recommend the most suitable insurance by referring to the user's past insurance selection history. The recommendation unit, for example, recommends the most suitable insurance based on the insurance selected by the user in the past. For example, if the user inputs that "my home is in a flood-risk area," the recommendation unit recommends the most suitable insurance by referring to flood insurance selected in the past based on that information. The recommendation unit can also preferentially recommend a specific insurance type based on the user's past insurance selection history. For example, if the user inputs that "my health risk is high," the recommendation unit recommends the most suitable insurance by referring to health insurance selected in the past based on that information. The recommendation unit can also recommend the most suitable insurance based on feedback provided by the user in the past. For example, if the user inputs that "my occupation is construction work," the recommendation unit recommends the most suitable insurance by referring to past feedback based on that information. In this way, the recommendation unit can recommend the most suitable insurance by referring to the past insurance selection history.

[0048] When making a recommendation, the recommendation unit can customize insurance by taking into consideration information such as the user's living environment, health condition, and occupation. The recommendation unit customizes the most suitable insurance based on, for example, the user's living environment information. For example, if the user inputs that "my home is in an area at risk of flooding," the recommendation unit customizes flood insurance based on that information. The recommendation unit can also customize the most suitable insurance based on the user's health condition information. For example, if the user inputs that "my health risk is high," the recommendation unit customizes health insurance based on that information. The recommendation unit can also customize the most suitable insurance based on the user's occupation information. For example, if the user inputs that "my occupation is construction work," the recommendation unit customizes insurance related to the construction work based on that information. In this way, the most suitable insurance can be customized and recommended by taking into consideration detailed information about the user.

[0049] The recommendation unit can improve the insurance recommendation method by reflecting user feedback when making recommendations. The recommendation unit improves the insurance recommendation method based on, for example, feedback provided by the user in the past. For example, if the user inputs that "my home is in an area at risk of flooding," the recommendation unit improves the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also analyze reactions to specific insurance types from the user's past feedback and adjust the recommendation method. For example, if the user inputs that "my health risk is high," the recommendation unit adjusts the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also improve the insurance recommendation method by referring to risk events experienced by the user in the past. For example, if the user inputs that "my occupation is construction work," the recommendation unit improves the insurance recommendation method by reflecting past risk events based on that information. In this way, the recommendation unit can improve the insurance recommendation method by reflecting past feedback.

[0050] When making a recommendation, the recommendation unit can recommend the most suitable insurance by taking into account the user's geographical location information. The recommendation unit recommends the most suitable insurance by taking into account, for example, the geographical risk of the area where the user lives. For example, if the user inputs "my home is in an area at risk of flooding," the recommendation unit recommends the most suitable insurance by taking into account the geographical risk based on that information. The recommendation unit can also recommend the most suitable insurance by taking into account the geographical risk of the user's workplace. For example, if the user inputs "my occupation is construction," the recommendation unit recommends the most suitable insurance by taking into account the geographical risk of the workplace based on that information. The recommendation unit can also recommend the most suitable insurance by taking into account the geographical risk of places frequently visited by the user. For example, if the user inputs "high health risk," the recommendation unit recommends the most suitable insurance by taking into account the geographical risk of places frequently visited based on that information. In this way, the recommendation unit can recommend the most suitable insurance by taking into account the geographical location information.

[0051] When making a recommendation, the recommendation unit can analyze the user's social media activity and recommend relevant insurance. The recommendation unit, for example, analyzes the user's social media posts and recommends relevant insurance. For example, if the user inputs "my home is in a flood-risk area," the recommendation unit analyzes the social media posts based on that information and recommends relevant insurance. The recommendation unit can also recommend relevant insurance based on the user's social media check-in information. For example, if the user inputs "high health risk," the recommendation unit analyzes the social media check-in information based on that information and recommends relevant insurance. The recommendation unit can also recommend relevant insurance based on the activity of the user's friends on social media. For example, if the user inputs "my occupation is construction work," the recommendation unit analyzes the social media activity of the user based on that information and recommends relevant insurance. In this way, relevant insurance can be recommended by analyzing social media activity.

[0052] The recommendation unit can customize the insurance recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit customizes the insurance recommendation method based on, for example, feedback provided by the user in the past. For example, if the user inputs "my home is in an area at risk of flooding," the recommendation unit customizes the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also analyze the user's reaction to a specific insurance type from the user's past feedback and adjust the recommendation method. For example, if the user inputs "high health risk," the recommendation unit adjusts the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also customize the insurance recommendation method by referring to risk events the user has experienced in the past. For example, if the user inputs "my occupation is construction work," the recommendation unit customizes the insurance recommendation method by reflecting past risk events based on that information. In this way, the recommendation unit can customize the insurance recommendation method by reflecting past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When inputting the user's risk information, the reception unit can customize the questions by referring to the user's past insurance selection history. For example, if the user previously selected flood insurance, questions about flood risk can be presented with priority. Also, if the user previously selected health insurance, questions about health risk can be presented with priority. Furthermore, the content and order of questions can be adjusted based on feedback provided by the user in the past. In this way, by referring to the user's past insurance selection history, more appropriate questions can be presented.

[0055] When analyzing a user's risk information, the analysis unit can identify the risk type by analyzing the user's social media activity. For example, if a user frequently posts about floods on social media, the analysis unit can determine that the user has a high flood risk based on that information. Also, if a user frequently posts about health, the analysis unit can determine that the user has a high health risk based on that information. Furthermore, the analysis unit can identify the risk type by taking into account the activities of the user's friends on social media. This improves the accuracy of identifying risk types by analyzing social media activity.

[0056] The reception unit can acquire information using voice input or image recognition when inputting information about the user's living environment, health condition, and occupation. For example, the information can be acquired by the user inputting by voice, "My home is in an area at risk of flooding." Health condition information can also be acquired by the user uploading an image of a health certificate. Furthermore, occupational information can also be acquired by the user uploading a photo of their workplace. This allows the user to easily input information using voice input or image recognition.

[0057] During analysis, the analysis unit can improve the accuracy of identifying the risk type by referring to the user's past risk information. For example, the accuracy of identifying the risk type can be improved based on risk information provided by the user in the past. The analysis unit can also analyze specific risk patterns from the user's past risk information to identify the risk type. Furthermore, the risk type can also be identified by referring to risk events the user has experienced in the past. In this way, the accuracy of identifying the risk type can be improved by referring to past risk information.

[0058] During analysis, the analysis unit can identify the risk type based on the user's geographic location information. For example, the risk type can be identified by taking into account the geographical risks of the area where the user lives. The risk type can also be identified by taking into account the geographical risks of the user's workplace. Furthermore, the risk type can also be identified by taking into account the geographical risks of places the user frequently visits. In this way, by taking into account the geographical location information, the accuracy of identifying the risk type can be improved.

[0059] During analysis, the analysis unit can evaluate the reliability of the user's answers and prioritize analysis of highly reliable information. For example, highly reliable information can be prioritized for analysis based on the user's answers. Highly reliable information can also be identified based on the user's past answer history. Furthermore, the consistency of the user's answers can be evaluated and highly reliable information can be prioritized for analysis. This prioritizes analysis of highly reliable information, improving the accuracy of risk type identification.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs the user's risk information in the form of a question. The user's risk information includes living environment, health status, occupation, etc. For example, the user can input "My home is in an area at risk of flooding" or "My health risk is high." Step 2: The analysis unit uses the generation AI to analyze the risk information entered by the reception unit and identify the user's risk type. For example, if the user enters "My home is in an area at risk of flooding," the analysis unit analyzes that information and determines that the flood risk is high. Also, if the user enters "My health risk is high," the analysis unit analyzes that information and determines that the health risk is high. Step 3: The recommendation unit uses the generative AI to recommend the most suitable insurance based on the risk type identified by the analysis unit. For example, if flood risk is determined to be high, flood insurance will be recommended, and if health risk is determined to be high, health insurance will be recommended.

[0062] (Example 2) An insurance recommendation system according to an embodiment of the present invention is a system in which a user's risk information is input in the form of a question, which is analyzed by a generation AI, and the system recommends the most suitable insurance. In the insurance recommendation system, a user inputs their own risk information in the form of a question, and a generation AI analyzes the information to identify the risk type and recommend the most suitable insurance. For example, if the user lives in an area where their home is at risk of flooding, the generation AI recommends flood insurance. Also, if the user has a high health risk, the generation AI recommends health insurance. In this way, the insurance recommendation system can easily find the insurance that best suits the user's risk. This allows the insurance recommendation system to efficiently analyze the user's risk information and recommend the most suitable insurance. For example, if the user lives in an area where their home is at risk of flooding, the generation AI recommends flood insurance, allowing the user to find the most suitable insurance for themselves. Also, if the user has a high health risk, the generation AI recommends health insurance, allowing the user to find the most suitable insurance for themselves.

[0063] The insurance recommendation system according to the embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives user risk information in the form of a question. The user's risk information includes, but is not limited to, information about the user's living environment, health status, and occupation. For example, the reception unit allows the user to input, for example, "My home is in a flood-risk area." The reception unit also allows the user to input, for example, "My health risk is high." The analysis unit uses a generation AI to analyze the risk information received by the reception unit and identify the user's risk type. For example, the generation AI identifies the risk type based on information about the user's living environment, health status, occupation, and the like. For example, if the user inputs, "My home is in a flood-risk area," the generation AI analyzes the information and determines that the user has a high flood risk. Also, if the user inputs, "My health risk is high," the generation AI analyzes the information and determines that the user has a high health risk. The recommendation unit uses the generation AI to recommend optimal insurance based on the risk type identified by the analysis unit. For example, if the generation AI determines that the user has a high flood risk, it recommends flood insurance. Furthermore, if the generation AI determines that the user has a high health risk, it recommends health insurance. This allows the insurance recommendation system according to the embodiment to efficiently analyze the user's risk information and recommend optimal insurance.

[0064] The reception unit can input information about the user's living environment, health condition, and occupation in the form of a question. The reception unit, for example, inputs the user's living environment information in the form of a question. For example, the user can input, "My home is in an area at risk of flooding." The reception unit can also input the user's health condition information in the form of a question. For example, the user can input, "My health risk is high." The reception unit can also input the user's occupation information in the form of a question. For example, the user can input, "My occupation is in the construction industry." This allows for the collection of detailed information about the user, enabling more accurate risk analysis.

[0065] The analysis unit can identify the risk type based on information such as the user's living environment, health condition, and occupation. The analysis unit identifies the risk type based on, for example, the user's living environment information. For example, if the user inputs "My home is in an area at risk of flooding," the analysis unit analyzes the information and determines that the flood risk is high. The analysis unit can also identify the risk type based on the user's health condition information. For example, if the user inputs "My health risk is high," the analysis unit analyzes the information and determines that the health risk is high. The analysis unit can also identify the risk type based on the user's occupation information. For example, if the user inputs "My occupation is construction work," the analysis unit analyzes the information and determines that the risk related to the construction industry is high. This makes it possible to accurately identify the risk type based on the user's detailed information.

[0066] The recommendation unit can recommend appropriate insurance based on the identified risk type. For example, if the flood risk is determined to be high, the recommendation unit recommends flood insurance. For example, if the user inputs "my home is in an area at risk of flooding," the recommendation unit analyzes the information, determines that the flood risk is high, and recommends flood insurance. The recommendation unit can also recommend health insurance if the health risk is determined to be high. For example, if the user inputs "high health risk," the recommendation unit analyzes the information, determines that the health risk is high, and recommends health insurance. This makes it possible to recommend insurance that is optimal for the user's risk type.

[0067] The analysis unit can recommend flood insurance to users who live in areas at risk of flooding. For example, if a user inputs that "my home is in an area at risk of flooding," the analysis unit analyzes that information and determines that the flood risk is high. For example, the analysis unit can evaluate flood risk based on past flood history and geographical conditions. If the analysis unit determines that the flood risk is high, it recommends flood insurance. For example, it can recommend the most appropriate flood insurance by taking into account the coverage, premiums, and applicable conditions of the flood insurance. This makes it possible to recommend appropriate insurance to users who live in areas at high risk of flooding.

[0068] The analysis unit can recommend health insurance if there is a health risk. For example, if a user inputs "high health risk," the analysis unit analyzes the information and determines that the health risk is high. For example, the analysis unit can evaluate health risk based on health checkup results and lifestyle habits. If the analysis unit determines that the health risk is high, it recommends health insurance. For example, it recommends the most suitable health insurance, taking into consideration the health insurance coverage, premiums, and applicable conditions. This makes it possible to recommend appropriate insurance to users with high health risks.

[0069] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit starts with simple questions and gradually moves to more detailed questions. For example, if the user answers "Yes" to the question "Is your home in an area at risk of flooding?", the reception unit can simplify the subsequent questions. If the user is relaxed, the reception unit can present detailed questions from the beginning to collect in-depth information. For example, if the user inputs "I have a high health risk," the subsequent questions can be more detailed. If the user is in a hurry, the reception unit can prioritize important questions to quickly collect information. For example, if the user inputs "I work in construction," the reception unit can narrow down the subsequent questions to important ones. By adjusting the order and content of questions according to the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The reception unit can analyze the user's past response history and select an appropriate question format. The reception unit presents the most appropriate question format, for example, based on the response format (multiple choice, free text, etc.) selected by the user in the past. For example, if the user previously responded in multiple choice format, the reception unit presents the question in a similar format. The reception unit can also analyze the user's response to a specific question format from the user's past response history and select the most appropriate format. For example, if the user previously provided detailed information in free text format, the reception unit presents the question in a similar format. The reception unit can also preferentially present related questions by referring to the content of the user's past responses. For example, if the user previously responded that "my home is in an area at risk of flooding," the reception unit presents related questions based on that information. In this way, information can be efficiently collected by providing the most appropriate question format based on the user's past response history.

[0071] The reception unit can analyze information input in the form of a question in real time and dynamically generate the next question. For example, if the user answers "yes," the reception unit generates a related, more detailed question based on the answer. For example, if the user answers "yes" to the question "Is your home located in an area at risk of flooding?", the reception unit makes subsequent questions related to flood risk. Furthermore, if the user answers "no," the reception unit can also generate a different question based on the answer. For example, if the user answers "no" to the question "Are you at high risk for health?", the reception unit makes subsequent questions unrelated to health risk. Furthermore, the reception unit can analyze the content of the user's answer in real time and optimize the next question. For example, if the user answers "I work in construction," the reception unit generates the next question based on that information. In this way, information can be collected efficiently by analyzing information in real time and dynamically generating the next question.

[0072] The reception unit can acquire information using voice input or image recognition when inputting information about the user's living environment, health condition, and occupation. For example, the reception unit acquires information by the user vocally inputting, for example, "My home is in a flood-risk area." For example, the reception unit converts voice data into text data using voice recognition technology. The reception unit can also acquire health condition information by the user uploading an image of a health checkup certificate. For example, the reception unit can analyze the image data using image recognition technology to extract health condition information. The reception unit can also acquire occupation information by the user uploading a photo of their workplace. For example, the reception unit can analyze the image data using image recognition technology to extract occupation information. This allows the user to easily input information using voice input or image recognition.

[0073] The reception unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user emotions. For example, if the user is nervous, the reception unit can start with easy questions and gradually increase the difficulty. For example, if the user answers "Yes" to the question "Is your home in an area at risk of flooding?", the reception unit can simplify subsequent questions. Furthermore, if the user is relaxed, the reception unit can present more difficult questions from the beginning. For example, if the user inputs "high health risk," the subsequent questions become more detailed. Furthermore, if the user is in a hurry, the reception unit can prioritize important questions to quickly collect information. For example, if the user inputs "I work in construction," the reception unit narrows the subsequent questions to important ones. By adjusting the difficulty of questions according to the user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The reception unit can acquire information entered in the form of a question in conjunction with the user's geographic location information. For example, when a user enters their current location, the reception unit automatically acquires GPS information to simplify input. For example, when a user enters "my home is in a flood-risk area," the reception unit automatically acquires risk information for that area based on the GPS information. The reception unit can also suggest optimal candidate locations based on the geographic location information when a user enters their home address. For example, when a user enters "my home is in a flood-risk area," the reception unit suggests areas with a high flood risk based on the geographic location information. The reception unit can also update the user's current location in real time and present related questions when the user uses the app while on the move. For example, if a user enters "my home is in a flood-risk area," the reception unit presents related questions based on that information. In this way, by linking with geographic location information, the user's input can be simplified and information can be collected efficiently.

[0075] The reception unit can acquire information entered in the form of a question in conjunction with the user's social media activity. The reception unit, for example, presents related questions based on the location where the user checked in on social media. For example, if the user inputs "my home is in an area at risk of flooding," the reception unit presents related questions based on that information. The reception unit can also analyze the content of the user's social media posts and generate related questions. For example, if the user inputs "high health risk," the reception unit generates related questions based on that information. The reception unit can also present related questions based on the activities of the user's friends on social media. For example, if the user inputs "my occupation is construction work," the reception unit presents related questions based on that information. In this way, detailed information about the user can be collected by linking with social media activity.

[0076] The reception unit can customize information input in the form of a question by reflecting the user's past feedback. The reception unit customizes the content and order of questions based on, for example, feedback provided by the user in the past. For example, if the user inputs "my home is in an area at risk of flooding," the reception unit customizes related questions based on that information. The reception unit can also analyze responses to specific question formats from the user's past feedback and select an optimal format. For example, if the user inputs "high health risk," the reception unit customizes related questions based on that information. The reception unit can also prioritize related questions by referring to content of answers provided by the user in the past. For example, if the user inputs "my occupation is construction work," the reception unit customizes related questions based on that information. In this way, the reception unit can provide the user with an optimal question format by reflecting past feedback.

[0077] The analysis unit can estimate the user's emotions and adjust the risk type identification method based on the estimated user emotions. For example, if the user is nervous, the analysis unit simplifies the risk type identification and provides a quick result. For example, if the user inputs "My home is in a flood-prone area," the analysis unit simplifies and identifies the risk type based on that information. The analysis unit can also identify the risk type based on detailed information if the user is relaxed. For example, if the user inputs "My health risk is high," the analysis unit identifies a detailed risk type based on that information. The analysis unit can also prioritize analysis of important risk information and quickly identify the risk type if the user is in a hurry. For example, if the user inputs "My occupation is construction," the analysis unit prioritizes analysis of important risk information based on that information. This allows the risk type identification method to be adjusted according to the user's emotions, thereby identifying a more appropriate risk type. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] During analysis, the analysis unit can improve the accuracy of identifying the risk type by referring to the user's past risk information. The analysis unit improves the accuracy of identifying the risk type, for example, based on risk information provided by the user in the past. For example, if the user inputs "My home is in an area at risk of flooding," the analysis unit identifies the risk type based on that information by referring to past flood history and geographical conditions. The analysis unit can also analyze specific risk patterns from the user's past risk information to identify the risk type. For example, if the user inputs "I have high health risks," the analysis unit can identify the risk type based on that information by referring to past health checkup results and lifestyle habits. The analysis unit can also identify the risk type by referring to risk events the user has experienced in the past. For example, if the user inputs "I work in construction," the analysis unit can identify the risk type based on that information by referring to past risk events related to the construction industry. In this way, by referring to past risk information, the accuracy of identifying the risk type is improved.

[0079] During analysis, the analysis unit can identify the risk type by cross-referencing information such as the user's living environment, health condition, and occupation. The analysis unit, for example, identifies the risk type based on the user's living environment information. For example, if the user inputs "My home is in an area at risk of flooding," the analysis unit identifies the flood risk based on that information. The analysis unit can also identify the risk type based on the user's health condition information. For example, if the user inputs "My health risk is high," the analysis unit identifies the health risk based on that information. The analysis unit can also identify the risk type based on the user's occupation information. For example, if the user inputs "My occupation is construction work," the analysis unit identifies risks related to the construction industry based on that information. This cross-referencing improves the accuracy of identifying the risk type. Cross-referencing is performed, for example, using data correlation analysis or data integration methods.

[0080] During analysis, the analysis unit can evaluate the reliability of the user's answers and prioritize analysis of highly reliable information. The analysis unit, for example, prioritizes analysis of highly reliable information based on the user's answers. For example, if a user inputs "My home is in an area at risk of flooding," the analysis unit evaluates the reliability of that information and prioritizes analysis of highly reliable information. The analysis unit can also identify highly reliable information based on the user's past answer history. For example, if a user inputs "My health risk is high," the analysis unit evaluates the reliability of that information and prioritizes analysis of highly reliable information. The analysis unit can also evaluate the consistency of the user's answers and prioritize analysis of highly reliable information. For example, if a user inputs "My occupation is construction," the analysis unit evaluates the reliability of that information and prioritizes analysis of highly reliable information. In this way, by prioritizing analysis of highly reliable information, the accuracy of risk type identification is improved.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user inputs "My home is in a flood-prone area," the analysis unit provides a simple display method based on that information. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user inputs "My health risk is high," the analysis unit provides a detailed display method based on that information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user inputs "My occupation is construction," the analysis unit provides a display method that focuses on the main points based on that information. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During analysis, the analysis unit can identify a risk type based on the user's geographical location information. The analysis unit, for example, considers the geographical risk of the area where the user lives to identify the risk type. For example, if the user inputs "my home is in an area at risk of flooding," the analysis unit identifies the risk type based on that information and taking the geographical risk into account. The analysis unit can also identify the risk type based on the geographical risk of the user's workplace. For example, if the user inputs "my occupation is construction," the analysis unit identifies the risk type based on that information and taking the geographical risk of the workplace into account. The analysis unit can also identify the risk type based on the geographical risk of places frequently visited by the user. For example, if the user inputs "high health risk," the analysis unit identifies the risk type based on that information and taking the geographical risk of places frequently visited into account. In this way, by taking geographical location information into account, the accuracy of identifying the risk type is improved.

[0083] During the analysis, the analysis unit can analyze the user's social media activity to identify the risk type. The analysis unit, for example, analyzes the user's social media posts to identify the risk type. For example, if a user inputs "my home is in an area at risk of flooding," the analysis unit analyzes the social media posts based on that information to identify the risk type. The analysis unit can also identify the risk type based on the user's social media check-in information. For example, if a user inputs "high health risk," the analysis unit analyzes the social media check-in information based on that information to identify the risk type. The analysis unit can also identify the risk type based on the activities of the user's friends on social media. For example, if a user inputs "my occupation is construction work," the analysis unit analyzes the activities of the user's friends on social media based on that information to identify the risk type. This improves the accuracy of identifying risk types by analyzing social media activity.

[0084] During analysis, the analysis unit can adjust the analysis algorithm by reflecting the user's past feedback. The analysis unit adjusts the analysis algorithm based on, for example, feedback provided by the user in the past. For example, if the user inputs "my home is in an area at risk of flooding," the analysis algorithm is adjusted based on that information and reflects past feedback. The analysis unit can also analyze specific risk patterns from the user's past feedback and adjust the analysis algorithm. For example, if the user inputs "high health risk," the analysis unit adjusts the analysis algorithm based on that information and reflects past feedback. The analysis unit can also adjust the analysis algorithm by referring to risk events the user has experienced in the past. For example, if the user inputs "my occupation is construction work," the analysis algorithm is adjusted based on that information and reflects past risk events. In this way, the accuracy of the analysis algorithm is improved by reflecting past feedback.

[0085] The recommendation unit can estimate the user's emotions and adjust the insurance recommendation method based on the estimated user emotions. For example, if the user is nervous, the recommendation unit can provide a simple, highly visible insurance recommendation method. For example, if the user inputs "My home is in a flood-risk area," the recommendation unit can provide a simple insurance recommendation method based on that information. Furthermore, if the user is relaxed, the recommendation unit can provide an insurance recommendation method that includes detailed information. For example, if the user inputs "My health risk is high," the recommendation unit can provide a detailed insurance recommendation method based on that information. Furthermore, if the user is in a hurry, the recommendation unit can provide an insurance recommendation method that focuses on the key points. For example, if the user inputs "My occupation is construction," the recommendation unit can provide an insurance recommendation method that focuses on the key points based on that information. This allows the insurance recommendation method to be adjusted according to the user's emotions, thereby recommending more appropriate insurance. Emotion estimation is achieved using an emotion estimation function, for example, 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.

[0086] When making a recommendation, the recommendation unit can recommend the most suitable insurance by referring to the user's past insurance selection history. The recommendation unit, for example, recommends the most suitable insurance based on the insurance selected by the user in the past. For example, if the user inputs that "my home is in a flood-risk area," the recommendation unit recommends the most suitable insurance by referring to flood insurance selected in the past based on that information. The recommendation unit can also preferentially recommend a specific insurance type based on the user's past insurance selection history. For example, if the user inputs that "my health risk is high," the recommendation unit recommends the most suitable insurance by referring to health insurance selected in the past based on that information. The recommendation unit can also recommend the most suitable insurance based on feedback provided by the user in the past. For example, if the user inputs that "my occupation is construction work," the recommendation unit recommends the most suitable insurance by referring to past feedback based on that information. In this way, the recommendation unit can recommend the most suitable insurance by referring to the past insurance selection history.

[0087] When making a recommendation, the recommendation unit can customize insurance by taking into consideration information such as the user's living environment, health condition, and occupation. The recommendation unit customizes the most suitable insurance based on, for example, the user's living environment information. For example, if the user inputs that "my home is in an area at risk of flooding," the recommendation unit customizes flood insurance based on that information. The recommendation unit can also customize the most suitable insurance based on the user's health condition information. For example, if the user inputs that "my health risk is high," the recommendation unit customizes health insurance based on that information. The recommendation unit can also customize the most suitable insurance based on the user's occupation information. For example, if the user inputs that "my occupation is construction work," the recommendation unit customizes insurance related to the construction work based on that information. In this way, the most suitable insurance can be customized and recommended by taking into consideration detailed information about the user.

[0088] The recommendation unit can improve the insurance recommendation method by reflecting user feedback when making recommendations. The recommendation unit improves the insurance recommendation method based on, for example, feedback provided by the user in the past. For example, if the user inputs that "my home is in an area at risk of flooding," the recommendation unit improves the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also analyze reactions to specific insurance types from the user's past feedback and adjust the recommendation method. For example, if the user inputs that "my health risk is high," the recommendation unit adjusts the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also improve the insurance recommendation method by referring to risk events experienced by the user in the past. For example, if the user inputs that "my occupation is construction work," the recommendation unit improves the insurance recommendation method by reflecting past risk events based on that information. In this way, the recommendation unit can improve the insurance recommendation method by reflecting past feedback.

[0089] The recommendation unit can estimate the user's emotions and prioritize insurance policies based on the estimated user emotions. For example, if the user is nervous, the recommendation unit prioritizes recommending important insurance policies. For example, if the user inputs "My home is in a flood-prone area," the recommendation unit prioritizes recommending important insurance policies based on that information. The recommendation unit can also prioritize recommending insurance policies with detailed information if the user is relaxed. For example, if the user inputs "My health risk is high," the recommendation unit prioritizes recommending detailed insurance policies based on that information. The recommendation unit can also prioritize recommending insurance policies that focus on the essentials if the user is in a hurry. For example, if the user inputs "My occupation is construction," the recommendation unit prioritizes recommending insurance policies that focus on the essentials based on that information. This allows the system to prioritize insurance policies based on the user's emotions, thereby recommending more appropriate insurance policies. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] When making a recommendation, the recommendation unit can recommend the most suitable insurance by taking into account the user's geographical location information. The recommendation unit recommends the most suitable insurance by taking into account, for example, the geographical risk of the area where the user lives. For example, if the user inputs "my home is in an area at risk of flooding," the recommendation unit recommends the most suitable insurance by taking into account the geographical risk based on that information. The recommendation unit can also recommend the most suitable insurance by taking into account the geographical risk of the user's workplace. For example, if the user inputs "my occupation is construction," the recommendation unit recommends the most suitable insurance by taking into account the geographical risk of the workplace based on that information. The recommendation unit can also recommend the most suitable insurance by taking into account the geographical risk of places frequently visited by the user. For example, if the user inputs "high health risk," the recommendation unit recommends the most suitable insurance by taking into account the geographical risk of places frequently visited based on that information. In this way, the recommendation unit can recommend the most suitable insurance by taking into account the geographical location information.

[0091] When making a recommendation, the recommendation unit can analyze the user's social media activity and recommend relevant insurance. The recommendation unit, for example, analyzes the user's social media posts and recommends relevant insurance. For example, if the user inputs "my home is in a flood-risk area," the recommendation unit analyzes the social media posts based on that information and recommends relevant insurance. The recommendation unit can also recommend relevant insurance based on the user's social media check-in information. For example, if the user inputs "high health risk," the recommendation unit analyzes the social media check-in information based on that information and recommends relevant insurance. The recommendation unit can also recommend relevant insurance based on the activity of the user's friends on social media. For example, if the user inputs "my occupation is construction work," the recommendation unit analyzes the social media activity of the user based on that information and recommends relevant insurance. In this way, relevant insurance can be recommended by analyzing social media activity.

[0092] The recommendation unit can customize the insurance recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit customizes the insurance recommendation method based on, for example, feedback provided by the user in the past. For example, if the user inputs "my home is in an area at risk of flooding," the recommendation unit customizes the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also analyze the user's reaction to a specific insurance type from the user's past feedback and adjust the recommendation method. For example, if the user inputs "high health risk," the recommendation unit adjusts the insurance recommendation method by reflecting past feedback based on that information. The recommendation unit can also customize the insurance recommendation method by referring to risk events the user has experienced in the past. For example, if the user inputs "my occupation is construction work," the recommendation unit customizes the insurance recommendation method by reflecting past risk events based on that information. In this way, the recommendation unit can customize the insurance recommendation method by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, and recommendation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and a user can input risk information using a touch panel 38A or a microphone 38B. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the user's risk information using a generation AI to identify a risk type. The recommendation unit is realized by the identification processing unit 290 of the data processing device 12, and recommends optimal insurance based on the risk type identified by the analysis unit. For example, the insurance recommendation results can be displayed to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and recommendation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input risk information through voice input. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the user's risk information using a generation AI to identify a risk type. The recommendation unit is realized by the identification processing unit 290 of the data processing device 12, and recommends optimal insurance based on the risk type identified by the analysis unit. For example, the insurance recommendation results can be provided to the user by voice through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and recommendation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input risk information through voice input. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the user's risk information using a generation AI to identify a risk type. The recommendation unit is realized by the identification processing unit 290 of the data processing device 12, and recommends optimal insurance based on the risk type identified by the analysis unit. For example, the insurance recommendation results can be displayed to the user via the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user can input risk information through voice input. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the user's risk information using a generation AI to identify a risk type. The recommendation unit is realized by the identification processing unit 290 of the data processing device 12, and recommends optimal insurance based on the risk type identified by the analysis unit. For example, the insurance recommendation results can be provided to the user by voice via the speaker 240 of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When inputting the user's risk information, the reception unit can customize the questions by referring to the user's past insurance selection history. For example, if the user previously selected flood insurance, questions about flood risk can be presented with priority. Also, if the user previously selected health insurance, questions about health risk can be presented with priority. Furthermore, the content and order of questions can be adjusted based on feedback provided by the user in the past. In this way, by referring to the user's past insurance selection history, more appropriate questions can be presented.

[0095] When analyzing a user's risk information, the analysis unit can identify the risk type by analyzing the user's social media activity. For example, if a user frequently posts about floods on social media, the analysis unit can determine that the user has a high flood risk based on that information. Also, if a user frequently posts about health, the analysis unit can determine that the user has a high health risk based on that information. Furthermore, the analysis unit can identify the risk type by taking into account the activities of the user's friends on social media. This improves the accuracy of identifying risk types by analyzing social media activity.

[0096] The recommendation unit can estimate the user's emotions and adjust the insurance recommendation method based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible insurance recommendation method can be provided. If the user is relaxed, an insurance recommendation method including detailed information can be provided. Also, if the user is in a hurry, an insurance recommendation method that focuses on the main points can be provided. In this way, by adjusting the insurance recommendation method according to the user's emotions, more appropriate insurance can be recommended.

[0097] The reception unit can acquire information using voice input or image recognition when inputting information about the user's living environment, health condition, and occupation. For example, the information can be acquired by the user inputting by voice, "My home is in an area at risk of flooding." Health condition information can also be acquired by the user uploading an image of a health certificate. Furthermore, occupational information can also be acquired by the user uploading a photo of their workplace. This allows the user to easily input information using voice input or image recognition.

[0098] During analysis, the analysis unit can improve the accuracy of identifying the risk type by referring to the user's past risk information. For example, the accuracy of identifying the risk type can be improved based on risk information provided by the user in the past. The analysis unit can also analyze specific risk patterns from the user's past risk information to identify the risk type. Furthermore, the risk type can also be identified by referring to risk events the user has experienced in the past. In this way, the accuracy of identifying the risk type can be improved by referring to past risk information.

[0099] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can start with simple questions and gradually move on to more detailed questions. If the user is relaxed, the reception unit can present detailed questions from the beginning to collect in-depth information. Also, if the user is in a hurry, the reception unit can present important questions first to quickly collect information. In this way, by adjusting the order and content of questions according to the user's emotions, more appropriate information can be collected.

[0100] During analysis, the analysis unit can identify the risk type based on the user's geographic location information. For example, the risk type can be identified by taking into account the geographical risks of the area where the user lives. The risk type can also be identified by taking into account the geographical risks of the user's workplace. Furthermore, the risk type can also be identified by taking into account the geographical risks of places the user frequently visits. In this way, by taking into account the geographical location information, the accuracy of identifying the risk type can be improved.

[0101] The recommendation unit can estimate the user's emotions and determine the priority of insurance policies based on the estimated user emotions. For example, if the user is nervous, important insurance policies can be recommended with priority. If the user is relaxed, insurance policies that include detailed information can be recommended with priority. Also, if the user is in a hurry, insurance policies that focus on the main points can be recommended with priority. In this way, by determining the priority of insurance policies according to the user's emotions, more appropriate insurance policies can be recommended.

[0102] During analysis, the analysis unit can evaluate the reliability of the user's answers and prioritize analysis of highly reliable information. For example, highly reliable information can be prioritized for analysis based on the user's answers. Highly reliable information can also be identified based on the user's past answer history. Furthermore, the consistency of the user's answers can be evaluated and highly reliable information can be prioritized for analysis. This prioritizes analysis of highly reliable information, improving the accuracy of risk type identification.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit inputs the user's risk information in the form of a question. The user's risk information includes living environment, health status, occupation, etc. For example, the user can input "My home is in an area at risk of flooding" or "My health risk is high." Step 2: The analysis unit uses the generation AI to analyze the risk information entered by the reception unit and identify the user's risk type. For example, if the user enters "My home is in an area at risk of flooding," the analysis unit analyzes that information and determines that the flood risk is high. Also, if the user enters "My health risk is high," the analysis unit analyzes that information and determines that the health risk is high. Step 3: The recommendation unit uses the generative AI to recommend the most suitable insurance based on the risk type identified by the analysis unit. For example, if flood risk is determined to be high, flood insurance will be recommended, and if health risk is determined to be high, health insurance will be recommended.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, in order to avoid confusion and to 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 reception unit for inputting user risk information in the form of a question; an analysis unit that analyzes the risk information input by the reception unit and identifies a risk type of the user; a recommendation unit that recommends appropriate insurance based on the risk type identified by the analysis unit. A system characterized by:

2. The reception unit Enter information about the user's living environment, health status, and occupation in the form of questions 2. The system of claim 1.

3. The analysis unit Identify risk types based on information such as the user's living environment, health status, and occupation 2. The system of claim 1.

4. The recommendation unit Recommend appropriate insurance based on identified risk types 2. The system of claim 1.

5. The analysis unit If you live in a flood-risk area, we recommend flood insurance.

2. The system of claim 1.

6. The analysis unit If you have health risks, we recommend health insurance.

2. The system of claim 1.

7. The reception unit Infer user sentiment and adjust the order and content of questions based on the estimated sentiment 2. The system of claim 1.

8. The reception unit Analyze the user's past answer history and select the appropriate question format 2. The system of claim 1.

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