System

Abstract

To provide a system for promoting reunion between an insurance contractor and an insurance receiver.SOLUTION: A system includes a matching unit and a chat start unit. A matching part analyzes information on an insurance contractor and an insurance receiver, and matches a pair having a high possibility of reunion. A chat start part proposes reunion chat to the insurance contractor and the insurance receiver matched by the matching part.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 have had the problem that there are few opportunities for policyholders and beneficiaries to reunite, and there is a lack of means to promote reunion.

[0005] The system according to the embodiment aims to facilitate the reunification of policyholders and beneficiaries. [Means for solving the problem]

[0006] The system according to the embodiment includes a matching unit and a chat initiation unit. The matching unit analyzes information on the policyholder and the insurance recipient and matches pairs who are likely to reunite. The chat initiation unit proposes a reunion chat to the policyholder and the insurance recipient matched by the matching unit. [Effects of the Invention]

[0007] A system according to an embodiment can facilitate the reunification of policyholders and beneficiaries. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The chat system according to the embodiment of the present invention is a system that promotes reunion between an insurance policyholder and an insurance recipient and facilitates communication between them. As a result, the chat system can promote reunion between an insurance policyholder and an insurance recipient and facilitate communication between them.

[0029] A chat system according to an embodiment includes a matching unit and a chat initiation unit. The matching unit analyzes information about a policyholder and a beneficiary to match pairs who are likely to reunite. For example, the matching unit analyzes the policyholder's hobbies, interests, and past interaction history to find commonalities with the beneficiary. The matching unit also uses a generation AI to perform matching based on the profile information and past interaction history of the policyholder and the beneficiary. The matching unit can also analyze the emotional states of the policyholder and the beneficiary to match pairs who are emotionally compatible. For example, the generation AI analyzes past chat history and social media posts to match users who have a high level of positive emotions. The chat initiation unit proposes a reunion chat to the matched policyholder and the beneficiary. For example, the chat initiation unit generates a message such as "It's been a while. How have you been lately?" and starts the chat. The chat initiation unit also uses the generation AI to generate an appropriate message based on the suggested reunion chat message. The chat initiation unit can also analyze the past chat history of the policyholder and the beneficiary to suggest the optimal timing to start the reunion chat. For example, the suggestion is made based on the time period when the policyholder and the beneficiary frequently chatted in the past. In this way, the chat system according to the embodiment can promote reunion between the policyholder and the beneficiary and facilitate smooth communication.

[0030] The matching unit can analyze the past health data and life events of the policyholder and the insurance recipient to identify pairs who are likely to need to reunite. The matching unit, for example, analyzes the past health data of the policyholder and the insurance recipient to identify pairs who are likely to need to reunite. For example, it prioritizes matching between users whose health conditions are deteriorating. The matching unit can also analyze the life events of the policyholder and the insurance recipient to identify pairs who are likely to need to reunite. For example, it performs matching based on events such as marriage, childbirth, and job change. In this way, identifying pairs who are likely to need to reunite increases the significance of the reunion.

[0031] The matching unit can analyze the lifestyle rhythms and daily activity patterns of the policyholder and the insurance recipient, and propose the optimal timing for the reunion. For example, the matching unit analyzes the lifestyle rhythms of the policyholder and the insurance recipient, and proposes the optimal timing for the reunion. For example, if both are active during the same time period, it proposes that time period as the timing for the reunion. The matching unit can also analyze the daily activity patterns of the policyholder and the insurance recipient, and propose the optimal timing for the reunion. For example, it makes a proposal based on daily travel routes and activity frequency. By proposing the optimal timing for the reunion, the success rate of the reunion is improved.

[0032] The matching unit incorporates recommendation information from the family and friends of the policyholder and the insurance recipient, thereby achieving more reliable matching. The matching unit, for example, collects recommendation information from the family and friends of the policyholder and the insurance recipient and reflects it in matching. For example, it gives priority to matching to partners recommended by family. The matching unit can also use social media data to collect recommendation information from family and friends. For example, it analyzes recommendation comments on social media and reflects this in matching. In this way, by incorporating recommendation information from family and friends, it is possible to achieve more reliable matching.

[0033] The matching unit can suggest common events and activities based on the hobbies and interests of the policyholder and the recipient of insurance. For example, the matching unit analyzes the hobbies and interests of the policyholder and the recipient of insurance and suggests common events and activities. For example, if both are interested in music, it can suggest attending a concert. The matching unit can also collect and suggest event information based on the hobbies and interests of the policyholder and the recipient of insurance. For example, it can make suggestions based on information about sporting events or book clubs. In this way, suggesting common events and activities increases the chances of reunions.

[0034] The chat initiation unit can analyze the past chat history of the policyholder and the insurance recipient, and suggest the optimal timing to start a reunion chat. The chat initiation unit, for example, analyzes the past chat history of the policyholder and the insurance recipient, and suggests the optimal timing to start a reunion chat. For example, the chat initiation unit makes a suggestion based on the time periods when the policyholder and the insurance recipient frequently chatted in the past. The chat initiation unit can also analyze the content of the chat history of the policyholder and the insurance recipient, and suggest a topic suitable for starting a reunion chat. For example, the chat initiation unit makes a suggestion based on events or hobbies that were discussed in past chats. In this way, by suggesting the optimal timing to start a reunion chat, the success rate of the reunion is improved.

[0035] The chat initiation unit can generate a message that references common memories or episodes between the policyholder and the insurance recipient. The chat initiation unit, for example, analyzes common memories or episodes between the policyholder and the insurance recipient and generates a message that references them. For example, it generates a message such as, "Do you remember the trip we took together last time?" The chat initiation unit can also generate a message that references common memories or episodes based on the past interaction history between the policyholder and the insurance recipient. For example, it generates a message based on past events or shared experiences. This makes it possible to smoothly start a reunion chat by referencing common memories or episodes.

[0036] The chat initiation unit can suggest topics based on the common hobbies and interests of the policyholder and the insurance recipient. For example, the chat initiation unit analyzes the common hobbies and interests of the policyholder and the insurance recipient and suggests topics based on them. For example, if both are interested in movies, it can suggest topics about recent movies. The chat initiation unit can also use a generation AI to suggest topics based on the hobbies and interests of the policyholder and the insurance recipient. For example, the generation AI can extract and suggest topics based on hobbies and interests. This allows for a smooth start to the reunion chat by suggesting topics based on common hobbies and interests.

[0037] The chat initiation unit can suggest the next step based on the past interaction history between the policyholder and the insurance recipient. The chat initiation unit, for example, analyzes the past interaction history between the policyholder and the insurance recipient and suggests the next step. For example, it may suggest attending an event that was previously discussed. The chat initiation unit can also use a generation AI to suggest the next step based on the interaction history between the policyholder and the insurance recipient. For example, the generation AI analyzes the content of past messages and the frequency of conversations and suggests the next step. In this way, by suggesting the next step based on the past interaction history, the success rate of reunion is improved.

[0038] The system can analyze the chat content between the policyholder and the insurance recipient in real time and make optimal suggestions. For example, the system uses a generation AI to analyze the chat content between the policyholder and the insurance recipient in real time and make optimal suggestions. For example, if the topic of travel comes up, the system will suggest a travel destination. The system can also analyze the chat content between the policyholder and the insurance recipient and suggest opportunities for reunions. For example, if the topic of "I've been wanting to go on a trip lately" comes up in the chat, the generation AI will suggest "How about going on a trip together?" This allows the system to analyze the chat content in real time and make optimal suggestions, improving the success rate of reunions.

[0039] When analyzing chat content, the system can refer to the past interaction history between the policyholder and the insurance recipient and suggest opportunities for reunion. For example, when analyzing chat content, the system can refer to the past interaction history between the policyholder and the insurance recipient and suggest opportunities for reunion. For example, it can suggest attending an event that was previously discussed. The system can also use a generation AI to suggest opportunities for reunion based on the interaction history between the policyholder and the insurance recipient. For example, the generation AI can analyze the content of past messages and the frequency of conversations and suggest opportunities for reunion. In this way, by referring to the past interaction history and suggesting opportunities for reunion, the success rate of reunions can be improved.

[0040] When analyzing chat content, the system can make suggestions based on the common hobbies and interests of the policyholder and the recipient of the insurance. For example, when analyzing chat content, the system analyzes the common hobbies and interests of the policyholder and the recipient of the insurance and makes suggestions based on that. For example, if both are interested in movies, the system can suggest recent movies. The system can also use generative AI to make suggestions based on the hobbies and interests of the policyholder and the recipient of the insurance. For example, the generative AI can extract topics based on hobbies and interests and make suggestions. This improves the success rate of reunions by making suggestions based on common hobbies and interests.

[0041] When analyzing chat content, the system can take into account the lifestyle rhythms and daily behavior patterns of the policyholder and the insurance recipient, and make optimal suggestions. For example, when analyzing chat content, the system can analyze the lifestyle rhythms of the policyholder and the insurance recipient, and make suggestions based on that. For example, if both are active during the same time period, the system can make suggestions appropriate for that time period. The system can also analyze the daily behavior patterns of the policyholder and the insurance recipient, and make optimal suggestions. For example, the system can make suggestions based on daily travel routes and activity frequency. This improves the success rate of reunions by making suggestions that take into account lifestyle rhythms and behavior patterns.

[0042] The system can analyze the schedules of the policyholder and the beneficiary in real time and propose the optimal date and time for the reunion. For example, the system can use generative AI to analyze the schedules of the policyholder and the beneficiary in real time and propose the optimal date and time for the reunion. For example, it can find a common free time based on the calendar information of both parties. The system can also analyze the schedules of the policyholder and the beneficiary and propose the optimal date and time for the reunion. For example, it can adjust the schedules of both parties and propose a common free time. This improves the success rate of reunions by analyzing schedules in real time and proposing the optimal date and time for the reunion.

[0043] When adjusting schedules, the system can consider the lifestyle rhythms and daily activity patterns of the policyholder and the insurance recipient to propose the optimal reunion date and time. For example, when adjusting schedules, the system analyzes the lifestyle rhythms of the policyholder and the insurance recipient and proposes a reunion date and time based on that. For example, if both parties are active during the same time period, that time period will be proposed. The system can also analyze the daily activity patterns of the policyholder and the insurance recipient and propose the optimal reunion date and time. For example, the system makes proposals based on daily travel routes and activity frequency. This improves the success rate of reunions by proposing a reunion date and time that takes into account lifestyle rhythms and activity patterns.

[0044] When arranging schedules, the system can suggest a reunion date and time based on the common hobbies and interests of the policyholder and the recipient. For example, when arranging schedules, the system analyzes the common hobbies and interests of the policyholder and the recipient, and suggests a reunion date and time based on that. For example, if both are interested in movies, the system suggests a reunion date and time that coincides with a movie screening time. The system can also use a generation AI to suggest a reunion date and time based on the hobbies and interests of the policyholder and the recipient. For example, the generation AI collects event information based on hobbies and interests and makes suggestions. This improves the success rate of reunions by suggesting a reunion date and time based on common hobbies and interests.

[0045] When arranging schedules, the system can refer to the past interaction history between the policyholder and the insurance recipient to suggest the optimal reunion date and time. For example, when arranging schedules, the system can refer to the past interaction history between the policyholder and the insurance recipient to suggest the optimal reunion date and time. For example, the system can suggest a reunion date and time that coincides with the date and time of an event that was previously discussed. The system can also use generation AI to suggest a reunion date and time based on the interaction history between the policyholder and the insurance recipient. For example, the generation AI can analyze the content of past messages and the frequency of conversations to suggest a reunion date and time. This improves the success rate of reunions by referring to past interaction history and suggesting the optimal reunion date and time.

[0046] When following up after a reunion, the system can refer to the past interaction history between the policyholder and the recipient and suggest the next reunion. For example, when following up after a reunion, the system can refer to the past interaction history between the policyholder and the recipient and suggest the next reunion. For example, it can suggest attending an event that was previously discussed. The system can also use the generation AI to suggest the next reunion based on the interaction history between the policyholder and the recipient. For example, the generation AI can analyze the content of past messages and the frequency of conversations and suggest the next reunion. In this way, by referring to the past interaction history and suggesting the next reunion, the effect of the reunion can be sustained.

[0047] During a follow-up after a reunion, the system can suggest the next reunion based on the common hobbies and interests of the policyholder and the recipient. For example, during a follow-up after a reunion, the system analyzes the common hobbies and interests of the policyholder and the recipient, and suggests the next reunion based on that. For example, if both are interested in movies, the system can suggest going to the movies next time. The system can also use generative AI to suggest the next reunion based on the hobbies and interests of the policyholder and the recipient. For example, the generative AI collects event information based on hobbies and interests and makes suggestions. This allows the effect of the reunion to be sustained by suggesting the next reunion based on common hobbies and interests.

[0048] The system can consider the lifestyle rhythms and daily activity patterns of the policyholder and the beneficiary when following up after a reunion and suggest the optimal next reunion. For example, when following up after a reunion, the system can analyze the lifestyle rhythms of the policyholder and the beneficiary and suggest the next reunion based on that. For example, if both parties are active during the same time period, the system can suggest the next reunion at that time. The system can also analyze the daily activity patterns of the policyholder and the beneficiary and suggest the optimal next reunion. For example, the system can make suggestions based on daily travel routes and activity frequency. In this way, the effectiveness of the reunion can be sustained by suggesting the next reunion taking into account the lifestyle rhythms and activity patterns.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The matching unit can also analyze the occupations and expertise of the policyholder and the beneficiary, and match pairs with common occupational interests. For example, matching users who work in the same industry can encourage them to talk about their occupations. The matching unit can also collect information about events and seminars related to the occupations of the policyholder and the beneficiary, and suggest participation in events that share common interests. This increases the success rate of reunions by matching pairs with common occupational interests.

[0051] The matching unit can also analyze the past travel history and travel destination preferences of the policyholder and the insurance recipient to match pairs with similar travel interests. For example, matching users who are interested in the same travel destinations can encourage them to talk about travel. The matching unit can also propose travel plans based on the travel destination preferences of the policyholder and the insurance recipient. This increases the success rate of reunions by matching pairs with similar travel interests.

[0052] The matching unit can also analyze the food preferences and allergy information of the policyholder and the recipient, and match pairs with similar food preferences. For example, matching users who like the same food can encourage conversation about food. The matching unit can also suggest information about restaurants and cafes based on the food preferences of the policyholder and the recipient. This improves the success rate of reunions by matching pairs with similar food preferences.

[0053] The matching unit can also analyze information about the pets of the policyholder and the recipient and match users who own pets with each other. For example, matching users who own the same type of pet can encourage them to talk about pets. The matching unit can also suggest events and activity information related to the pets of the policyholder and the recipient. This improves the success rate of reunions by matching users who own pets with each other.

[0054] The matching unit can also analyze information about the volunteer activities and social contributions of the policyholder and the insurance recipient, and match pairs who share an interest in common social contribution activities. For example, by matching users who participate in the same volunteer activities, they can get excited about talking about social contributions. The matching unit can also suggest event and project information related to the social contribution activities of the policyholder and the insurance recipient. This improves the success rate of reunions by matching pairs who share an interest in common social contribution activities.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The matching unit analyzes the information of the policyholder and the beneficiary and matches pairs with a high probability of reunion. Specifically, it analyzes the policyholder's hobbies, interests, and past interaction history to find commonalities with the beneficiary. Generative AI is also used to match the policyholder and the beneficiary based on their profile information and past interaction history. Furthermore, it is possible to analyze the emotional state of the policyholder and the beneficiary and match pairs with emotional compatibility. For example, the generative AI analyzes past chat history and social media posts to match users with a high level of positive emotions. Step 2: The chat initiation unit proposes a reunion chat to the matched policyholder and beneficiary. Specifically, it generates a message such as "It's been a while. How have you been lately?" and starts the chat. It also uses a generation AI to generate an appropriate message based on the suggested reunion chat message. Furthermore, it can analyze the past chat history of the policyholder and beneficiary and suggest the optimal time to start the reunion chat. For example, it can make suggestions based on the time of day when they frequently chatted in the past.

[0057] (Example 2) The chat system according to the embodiment of the present invention is a system that promotes reunion between an insurance policyholder and an insurance recipient and facilitates communication between them. As a result, the chat system can promote reunion between an insurance policyholder and an insurance recipient and facilitate communication between them.

[0058] A chat system according to an embodiment includes a matching unit and a chat initiation unit. The matching unit analyzes information about a policyholder and a beneficiary to match pairs who are likely to reunite. For example, the matching unit analyzes the policyholder's hobbies, interests, and past interaction history to find commonalities with the beneficiary. The matching unit also uses a generation AI to perform matching based on the profile information and past interaction history of the policyholder and the beneficiary. The matching unit can also analyze the emotional states of the policyholder and the beneficiary to match pairs who are emotionally compatible. For example, the generation AI analyzes past chat history and social media posts to match users who have a high level of positive emotions. The chat initiation unit proposes a reunion chat to the matched policyholder and the beneficiary. For example, the chat initiation unit generates a message such as "It's been a while. How have you been lately?" and starts the chat. The chat initiation unit also uses the generation AI to generate an appropriate message based on the suggested reunion chat message. The chat initiation unit can also analyze the past chat history of the policyholder and the beneficiary to suggest the optimal timing to start the reunion chat. For example, the suggestion is made based on the time period when the policyholder and the beneficiary frequently chatted in the past. In this way, the chat system according to the embodiment can promote reunion between the policyholder and the beneficiary and facilitate smooth communication.

[0059] The matching unit analyzes the emotional state of the policyholder and the insured person, and can match pairs with good emotional compatibility. For example, to analyze the emotional state of the policyholder and the insured person, the generative AI analyzes past chat history and social media posts. For example, it matches users with many positive emotions. This improves the success rate of reunions by matching pairs with good emotional compatibility.

[0060] The matching unit can analyze the past health data and life events of the policyholder and the insurance recipient to identify pairs who are likely to need to reunite. The matching unit, for example, analyzes the past health data of the policyholder and the insurance recipient to identify pairs who are likely to need to reunite. For example, it prioritizes matching between users whose health conditions are deteriorating. The matching unit can also analyze the life events of the policyholder and the insurance recipient to identify pairs who are likely to need to reunite. For example, it performs matching based on events such as marriage, childbirth, and job change. In this way, identifying pairs who are likely to need to reunite increases the significance of the reunion.

[0061] The matching unit can analyze the lifestyle rhythms and daily activity patterns of the policyholder and the insurance recipient, and propose the optimal timing for the reunion. For example, the matching unit analyzes the lifestyle rhythms of the policyholder and the insurance recipient, and proposes the optimal timing for the reunion. For example, if both are active during the same time period, it proposes that time period as the timing for the reunion. The matching unit can also analyze the daily activity patterns of the policyholder and the insurance recipient, and propose the optimal timing for the reunion. For example, it makes a proposal based on daily travel routes and activity frequency. By proposing the optimal timing for the reunion, the success rate of the reunion is improved.

[0062] The matching unit incorporates recommendation information from the family and friends of the policyholder and the insurance recipient, thereby achieving more reliable matching. The matching unit, for example, collects recommendation information from the family and friends of the policyholder and the insurance recipient and reflects it in matching. For example, it gives priority to matching to partners recommended by family. The matching unit can also use social media data to collect recommendation information from family and friends. For example, it analyzes recommendation comments on social media and reflects this in matching. In this way, by incorporating recommendation information from family and friends, it is possible to achieve more reliable matching.

[0063] The matching unit can suggest common events and activities based on the hobbies and interests of the policyholder and the recipient of insurance. For example, the matching unit analyzes the hobbies and interests of the policyholder and the recipient of insurance and suggests common events and activities. For example, if both are interested in music, it can suggest attending a concert. The matching unit can also collect and suggest event information based on the hobbies and interests of the policyholder and the recipient of insurance. For example, it can make suggestions based on information about sporting events or book clubs. In this way, suggesting common events and activities increases the chances of reunions.

[0064] The matching unit can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient, and perform matching that is emotionally satisfying. The matching unit, for example, uses the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient. For example, it matches users with strong positive emotions. The matching unit can also use the emotion estimation function to analyze the emotional states of the policyholder and the insurance recipient in real time, and perform matching that is emotionally satisfying. For example, it uses an emotion analysis algorithm to classify needs based on emotional states and reflect them in matching. This allows for matching that is emotionally satisfying, thereby improving the success rate of reunions.

[0065] The chat initiation unit can analyze the past chat history of the policyholder and the insurance recipient, and suggest the optimal timing to start a reunion chat. The chat initiation unit, for example, analyzes the past chat history of the policyholder and the insurance recipient, and suggests the optimal timing to start a reunion chat. For example, the chat initiation unit makes a suggestion based on the time periods when the policyholder and the insurance recipient frequently chatted in the past. The chat initiation unit can also analyze the content of the chat history of the policyholder and the insurance recipient, and suggest a topic suitable for starting a reunion chat. For example, the chat initiation unit makes a suggestion based on events or hobbies that were discussed in past chats. In this way, by suggesting the optimal timing to start a reunion chat, the success rate of the reunion is improved.

[0066] The chat initiation unit can generate a message that references common memories or episodes between the policyholder and the insurance recipient. The chat initiation unit, for example, analyzes common memories or episodes between the policyholder and the insurance recipient and generates a message that references them. For example, it generates a message such as, "Do you remember the trip we took together last time?" The chat initiation unit can also generate a message that references common memories or episodes based on the past interaction history between the policyholder and the insurance recipient. For example, it generates a message based on past events or shared experiences. This makes it possible to smoothly start a reunion chat by referencing common memories or episodes.

[0067] The chat initiation unit can analyze the emotional states of the policyholder and the insurance recipient in real time and generate an optimal message. The chat initiation unit, for example, analyzes the emotional states of the policyholder and the insurance recipient in real time and generates an optimal message. For example, if positive emotions are strong, an upbeat message is generated. The chat initiation unit can also use an emotion estimation function to analyze the emotional states of the policyholder and the insurance recipient and generate an emotionally appropriate message. For example, an emotion analysis algorithm is used to generate a message based on the emotional state. In this way, by analyzing the emotional state in real time and generating an optimal message, the start of a reunion chat can be made smooth.

[0068] The chat initiation unit can suggest topics based on the common hobbies and interests of the policyholder and the insurance recipient. For example, the chat initiation unit analyzes the common hobbies and interests of the policyholder and the insurance recipient and suggests topics based on them. For example, if both are interested in movies, it can suggest topics about recent movies. The chat initiation unit can also use a generation AI to suggest topics based on the hobbies and interests of the policyholder and the insurance recipient. For example, the generation AI can extract and suggest topics based on hobbies and interests. This allows for a smooth start to the reunion chat by suggesting topics based on common hobbies and interests.

[0069] The chat initiation unit can suggest the next step based on the past interaction history between the policyholder and the insurance recipient. The chat initiation unit, for example, analyzes the past interaction history between the policyholder and the insurance recipient and suggests the next step. For example, it may suggest attending an event that was previously discussed. The chat initiation unit can also use a generation AI to suggest the next step based on the interaction history between the policyholder and the insurance recipient. For example, the generation AI analyzes the content of past messages and the frequency of conversations and suggests the next step. In this way, by suggesting the next step based on the past interaction history, the success rate of reunion is improved.

[0070] The chat initiation unit can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient and start a reunion chat that is emotionally satisfying. The chat initiation unit, for example, uses the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient and start a reunion chat. For example, if positive emotions are strong, it can suggest a cheerful topic. The chat initiation unit can also use the emotion estimation function to analyze the emotional states of the policyholder and the insurance recipient in real time and generate an emotionally appropriate message. For example, it uses an emotion analysis algorithm to generate a message based on the emotional state. This can start a reunion chat that is emotionally satisfying, thereby improving the success rate of the reunion.

[0071] The system can analyze the chat content between the policyholder and the insurance recipient in real time and make optimal suggestions. For example, the system uses a generation AI to analyze the chat content between the policyholder and the insurance recipient in real time and make optimal suggestions. For example, if the topic of travel comes up, the system will suggest a travel destination. The system can also analyze the chat content between the policyholder and the insurance recipient and suggest opportunities for reunions. For example, if the topic of "I've been wanting to go on a trip lately" comes up in the chat, the generation AI will suggest "How about going on a trip together?" This allows the system to analyze the chat content in real time and make optimal suggestions, improving the success rate of reunions.

[0072] The system can take into account the emotional state of the policyholder and the insurance recipient when analyzing chat content and make emotionally positive suggestions. For example, the system can take into account the emotional state of the policyholder and the insurance recipient when analyzing chat content and make emotionally positive suggestions. For example, if positive emotions are strong, the system can suggest fun events. The system can also use an emotion estimation function to analyze the emotional state of the policyholder and the insurance recipient in real time and make emotionally appropriate suggestions. For example, an emotion analysis algorithm can be used to make suggestions based on the emotional state. This takes into account the emotional state and makes emotionally positive suggestions, thereby improving the success rate of reunions.

[0073] When analyzing chat content, the system can refer to the past interaction history between the policyholder and the insurance recipient and suggest opportunities for reunion. For example, when analyzing chat content, the system can refer to the past interaction history between the policyholder and the insurance recipient and suggest opportunities for reunion. For example, it can suggest attending an event that was previously discussed. The system can also use a generation AI to suggest opportunities for reunion based on the interaction history between the policyholder and the insurance recipient. For example, the generation AI can analyze the content of past messages and the frequency of conversations and suggest opportunities for reunion. In this way, by referring to the past interaction history and suggesting opportunities for reunion, the success rate of reunions can be improved.

[0074] When analyzing chat content, the system can make suggestions based on the common hobbies and interests of the policyholder and the recipient of the insurance. For example, when analyzing chat content, the system analyzes the common hobbies and interests of the policyholder and the recipient of the insurance and makes suggestions based on that. For example, if both are interested in movies, the system can suggest recent movies. The system can also use generative AI to make suggestions based on the hobbies and interests of the policyholder and the recipient of the insurance. For example, the generative AI can extract topics based on hobbies and interests and make suggestions. This improves the success rate of reunions by making suggestions based on common hobbies and interests.

[0075] When analyzing chat content, the system can take into account the lifestyle rhythms and daily behavior patterns of the policyholder and the insurance recipient, and make optimal suggestions. For example, when analyzing chat content, the system can analyze the lifestyle rhythms of the policyholder and the insurance recipient, and make suggestions based on that. For example, if both are active during the same time period, the system can make suggestions appropriate for that time period. The system can also analyze the daily behavior patterns of the policyholder and the insurance recipient, and make optimal suggestions. For example, the system can make suggestions based on daily travel routes and activity frequency. This improves the success rate of reunions by making suggestions that take into account lifestyle rhythms and behavior patterns.

[0076] The system can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient and make emotionally satisfying proposals. For example, the system can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient and make optimal proposals. For example, if positive emotions are strong, the system can make suggestions about fun events. The system can also use the emotion estimation function to analyze the emotional state of the policyholder and the insurance recipient in real time and make emotionally appropriate proposals. For example, the system can use an emotion analysis algorithm to make suggestions based on the emotional state. This improves the success rate of reunions by making emotionally satisfying proposals.

[0077] The system can analyze the schedules of the policyholder and the beneficiary in real time and propose the optimal date and time for the reunion. For example, the system can use generative AI to analyze the schedules of the policyholder and the beneficiary in real time and propose the optimal date and time for the reunion. For example, it can find a common free time based on the calendar information of both parties. The system can also analyze the schedules of the policyholder and the beneficiary and propose the optimal date and time for the reunion. For example, it can adjust the schedules of both parties and propose a common free time. This improves the success rate of reunions by analyzing schedules in real time and proposing the optimal date and time for the reunion.

[0078] The system can consider the emotional state of the policyholder and the insured when arranging the schedule, and propose an emotionally positive reunion date and time. For example, the system considers the emotional state of the policyholder and the insured when arranging the schedule, and proposes an emotionally positive reunion date and time. For example, it proposes a time period when positive emotions are strong. The system can also use an emotion estimation function to analyze the emotional state of the policyholder and the insured in real time, and propose an emotionally appropriate reunion date and time. For example, it uses an emotion analysis algorithm to propose a reunion date and time based on the emotional state. In this way, by considering the emotional state and proposing an emotionally positive reunion date and time, the success rate of the reunion is improved.

[0079] When adjusting schedules, the system can consider the lifestyle rhythms and daily activity patterns of the policyholder and the insurance recipient to propose the optimal reunion date and time. For example, when adjusting schedules, the system analyzes the lifestyle rhythms of the policyholder and the insurance recipient and proposes a reunion date and time based on that. For example, if both parties are active during the same time period, that time period will be proposed. The system can also analyze the daily activity patterns of the policyholder and the insurance recipient and propose the optimal reunion date and time. For example, the system makes proposals based on daily travel routes and activity frequency. This improves the success rate of reunions by proposing a reunion date and time that takes into account lifestyle rhythms and activity patterns.

[0080] When arranging schedules, the system can suggest a reunion date and time based on the common hobbies and interests of the policyholder and the recipient. For example, when arranging schedules, the system analyzes the common hobbies and interests of the policyholder and the recipient, and suggests a reunion date and time based on that. For example, if both are interested in movies, the system suggests a reunion date and time that coincides with a movie screening time. The system can also use a generation AI to suggest a reunion date and time based on the hobbies and interests of the policyholder and the recipient. For example, the generation AI collects event information based on hobbies and interests and makes suggestions. This improves the success rate of reunions by suggesting a reunion date and time based on common hobbies and interests.

[0081] When arranging schedules, the system can refer to the past interaction history between the policyholder and the insurance recipient to suggest the optimal reunion date and time. For example, when arranging schedules, the system can refer to the past interaction history between the policyholder and the insurance recipient to suggest the optimal reunion date and time. For example, the system can suggest a reunion date and time that coincides with the date and time of an event that was previously discussed. The system can also use generation AI to suggest a reunion date and time based on the interaction history between the policyholder and the insurance recipient. For example, the generation AI can analyze the content of past messages and the frequency of conversations to suggest a reunion date and time. This improves the success rate of reunions by referring to past interaction history and suggesting the optimal reunion date and time.

[0082] The system can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient, and propose an emotionally satisfying reunion date and time. For example, the system can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient, and propose an optimal reunion date and time. For example, it can propose a time period when positive emotions are strong. The system can also use the emotion estimation function to analyze the emotional state of the policyholder and the insurance recipient in real time, and propose an emotionally appropriate reunion date and time. For example, it can use an emotion analysis algorithm to propose a reunion date and time based on the emotional state. This improves the success rate of reunions by proposing an emotionally satisfying reunion date and time.

[0083] The system can analyze the emotional state of the policyholder and the insurance recipient after the reunion and generate an optimal follow-up message. For example, the system uses generation AI to analyze the emotional state of the policyholder and the insurance recipient after the reunion and generate an optimal follow-up message. For example, if positive emotions are strong, a message suggesting the next reunion is generated. The system can also use an emotion estimation function to analyze the emotional state of the policyholder and the insurance recipient after the reunion in real time and generate an emotionally appropriate follow-up message. For example, an emotion analysis algorithm is used to generate a message based on the emotional state. In this way, the effectiveness of the reunion is sustained by analyzing the emotional state after the reunion and generating an optimal follow-up message.

[0084] When following up after a reunion, the system can refer to the past interaction history between the policyholder and the recipient and suggest the next reunion. For example, when following up after a reunion, the system can refer to the past interaction history between the policyholder and the recipient and suggest the next reunion. For example, it can suggest attending an event that was previously discussed. The system can also use the generation AI to suggest the next reunion based on the interaction history between the policyholder and the recipient. For example, the generation AI can analyze the content of past messages and the frequency of conversations and suggest the next reunion. In this way, by referring to the past interaction history and suggesting the next reunion, the effect of the reunion can be sustained.

[0085] The system can analyze the emotional states of the policyholder and the beneficiary in real time during follow-ups after the reunion and generate optimal follow-up messages. For example, the system can analyze the emotional states of the policyholder and the beneficiary in real time during follow-ups after the reunion and generate optimal follow-up messages. For example, if positive emotions are strong, a message suggesting the next reunion is generated. The system can also use an emotion estimation function to analyze the emotional states of the policyholder and the beneficiary after the reunion and generate emotionally appropriate follow-up messages. For example, an emotion analysis algorithm is used to generate messages based on the emotional states. In this way, the effectiveness of the reunion is sustained by analyzing the emotional states in real time and generating optimal follow-up messages.

[0086] During a follow-up after a reunion, the system can suggest the next reunion based on the common hobbies and interests of the policyholder and the recipient. For example, during a follow-up after a reunion, the system analyzes the common hobbies and interests of the policyholder and the recipient, and suggests the next reunion based on that. For example, if both are interested in movies, the system can suggest going to the movies next time. The system can also use generative AI to suggest the next reunion based on the hobbies and interests of the policyholder and the recipient. For example, the generative AI collects event information based on hobbies and interests and makes suggestions. This allows the effect of the reunion to be sustained by suggesting the next reunion based on common hobbies and interests.

[0087] The system can consider the lifestyle rhythms and daily activity patterns of the policyholder and the beneficiary when following up after a reunion and suggest the optimal next reunion. For example, when following up after a reunion, the system can analyze the lifestyle rhythms of the policyholder and the beneficiary and suggest the next reunion based on that. For example, if both parties are active during the same time period, the system can suggest the next reunion at that time. The system can also analyze the daily activity patterns of the policyholder and the beneficiary and suggest the optimal next reunion. For example, the system can make suggestions based on daily travel routes and activity frequency. In this way, the effectiveness of the reunion can be sustained by suggesting the next reunion taking into account the lifestyle rhythms and activity patterns.

[0088] The system can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient, and suggest the next reunion that will be emotionally satisfying. For example, the system can use the emotion estimation function to analyze the emotional needs of the policyholder and the insurance recipient, and suggest the optimal next reunion. For example, the system can suggest the next reunion during a time when positive emotions are strong. The system can also use the emotion estimation function to analyze the emotional state of the policyholder and the insurance recipient in real time, and suggest the next reunion that is emotionally appropriate. For example, the system can use an emotion analysis algorithm to suggest a reunion date and time based on the emotional state. In this way, the effect of the reunion can be sustained by suggesting the next reunion that will be emotionally satisfying.

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

[0090] The matching unit can also analyze the occupations and expertise of the policyholder and the beneficiary, and match pairs with common occupational interests. For example, matching users who work in the same industry can encourage them to talk about their occupations. The matching unit can also collect information about events and seminars related to the occupations of the policyholder and the beneficiary, and suggest participation in events that share common interests. This increases the success rate of reunions by matching pairs with common occupational interests.

[0091] The matching unit can also analyze the past travel history and travel destination preferences of the policyholder and the insurance recipient to match pairs with similar travel interests. For example, matching users who are interested in the same travel destinations can encourage them to talk about travel. The matching unit can also propose travel plans based on the travel destination preferences of the policyholder and the insurance recipient. This increases the success rate of reunions by matching pairs with similar travel interests.

[0092] The matching unit can also analyze the food preferences and allergy information of the policyholder and the recipient, and match pairs with similar food preferences. For example, matching users who like the same food can encourage conversation about food. The matching unit can also suggest information about restaurants and cafes based on the food preferences of the policyholder and the recipient. This improves the success rate of reunions by matching pairs with similar food preferences.

[0093] The matching unit can also analyze information about the pets of the policyholder and the recipient and match users who own pets with each other. For example, matching users who own the same type of pet can encourage them to talk about pets. The matching unit can also suggest events and activity information related to the pets of the policyholder and the recipient. This improves the success rate of reunions by matching users who own pets with each other.

[0094] The matching unit can also analyze information about the volunteer activities and social contributions of the policyholder and the insurance recipient, and match pairs who share an interest in common social contribution activities. For example, by matching users who participate in the same volunteer activities, they can get excited about talking about social contributions. The matching unit can also suggest event and project information related to the social contribution activities of the policyholder and the insurance recipient. This improves the success rate of reunions by matching pairs who share an interest in common social contribution activities.

[0095] The matching unit can also use the emotion estimation function of the policyholder and the insurance recipient to analyze their stress levels and match users who need to relax. For example, it can match users who are highly stressed and suggest activities that will help them relax. The matching unit can also use the emotion estimation function to suggest relaxation events and activities based on the stress levels of the policyholder and the insurance recipient. This improves the success rate of reunions by performing matching that takes stress levels into account.

[0096] The matching unit can also use the emotion estimation function of the policyholder and the insurance recipient to analyze their happiness levels and match users with high happiness levels. For example, users with many positive emotions can be matched together, allowing them to have fun talking about fun topics. The matching unit can also use the emotion estimation function to suggest events and activities based on the happiness levels of the policyholder and the insurance recipient. This improves the success rate of reunions by performing matching that takes happiness levels into account.

[0097] The matching unit can also use the emotion estimation function of the policyholder and the insurance recipient to analyze the emotional stability and match users with stable emotions. For example, it can match users with little emotional fluctuation and promote stable communication. The matching unit can also use the emotion estimation function to suggest events and activities based on the emotional stability of the policyholder and the insurance recipient. This improves the success rate of reunions by performing matching that takes emotional stability into account.

[0098] The matching unit can also use the emotion estimation function of the policyholder and the insurance recipient to analyze changes in emotion and match users with similar emotions. For example, it can match users with similar emotional changes to promote empathetic communication. The matching unit can also use the emotion estimation function to suggest events and activities based on changes in emotion between the policyholder and the insurance recipient. This improves the success rate of reunions by performing matching that takes emotional changes into account.

[0099] The matching unit can also use the emotion estimation function of the policyholder and the insurance recipient to analyze the intensity of their emotions and match users with strong emotions. For example, users with high emotional intensities can be matched together to engage in lively discussions of emotional topics. The matching unit can also use the emotion estimation function to suggest events and activities based on the emotional intensity of the policyholder and the insurance recipient. This improves the success rate of reunions by performing matching that takes into account the intensity of emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The matching unit analyzes the information of the policyholder and the beneficiary and matches pairs with a high probability of reunion. Specifically, it analyzes the policyholder's hobbies, interests, and past interaction history to find commonalities with the beneficiary. Generative AI is also used to match the policyholder and the beneficiary based on their profile information and past interaction history. Furthermore, it is possible to analyze the emotional state of the policyholder and the beneficiary and match pairs with emotional compatibility. For example, the generative AI analyzes past chat history and social media posts to match users with a high level of positive emotions. Step 2: The chat initiation unit proposes a reunion chat to the matched policyholder and beneficiary. Specifically, it generates a message such as "It's been a while. How have you been lately?" and starts the chat. It also uses a generation AI to generate an appropriate message based on the suggested reunion chat message. Furthermore, it can analyze the past chat history of the policyholder and beneficiary and suggest the optimal time to start the reunion chat. For example, it can make suggestions based on the time of day when they frequently chatted in the past.

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

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

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

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 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 matching unit that analyzes information on the policyholder and the beneficiary and matches pairs that are likely to reunite; a chat initiation unit that proposes a reunion chat to the policyholder and the insurance recipient matched by the matching unit; A system characterized by:

2. The matching unit Analyzing the emotional states of the policyholder and the insured and matching them to find emotionally compatible pairs. The system of claim 1 .

3. The matching unit Incorporating recommendations from family and friends of the policyholder and the beneficiary to achieve more reliable matching. The system of claim 1 .

4. The chat initiation unit Analyzing past chat history between the policyholder and the insured, and proposing the best timing to start a reunion chat. The system of claim 1 .

5. The system comprises: Analyzing chat contents between the policyholder and the insured in real time and making optimal proposals The system of claim 1 .

6. The system comprises: Analyzing the schedules of the policyholder and the insured in real time and proposing the optimal date and time for reunion. The system of claim 1 .

7. The system comprises: Analyzing the emotional state of the policyholder and the insured after reunion and generating an optimal follow-up message. The system of claim 1 .

8. The system comprises: To propose an emotionally positive reunion date and time by considering the emotional state of the policyholder and the insured when adjusting the schedule. The system of claim 1 .

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