System

Abstract

An object of a system according to an embodiment is to efficiently collect and appropriately organize information necessary for activation and deactivation.SOLUTION: A system includes an interaction progress part, an information collection part, a latest activity note creation part, and a will creation part. The dialogue facilitator uses the generated AI to facilitate a dialogue with the user. The information collecting unit collects information necessary for activation and deactivation through the dialog progressed by the dialog progressing unit. A final activation note creation part creates a final activation note on the basis of the information collected by the information collection part. The will preparing section prepares a will based on the information collected by the information collecting section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently collect and properly organize the information necessary for end-of-life planning.

[0005] The system of the embodiment aims to efficiently collect and appropriately organize information necessary for end-of-life planning. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue progression unit, an information collection unit, an end-of-life planning notebook creation unit, and a will creation unit. The dialogue progression unit uses a generation AI to progress a dialogue with a user. The information collection unit collects information necessary for end-of-life planning through a dialogue progressed by the dialogue progression unit. The end-of-life planning notebook creation unit creates an end-of-life planning notebook based on the information collected by the information collection unit. The will creation unit creates a will based on the information collected by the information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and appropriately organize information necessary for end-of-life planning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 end-of-life planning support system according to an embodiment of the present invention is a system in which information necessary for end-of-life planning is collected through dialogue with the user, and a generation AI creates an end-of-life planning notebook and a will. As a result, the end-of-life planning support system allows the user to proceed with end-of-life planning through dialogue, and ultimately creates an end-of-life planning notebook and a will.

[0029] An end-of-life planning support system according to an embodiment includes a dialogue facilitator, an information collector, an end-of-life planning notebook creator, and a will creator. The dialogue facilitator uses a generation AI to conduct a dialogue with a user. For example, the generation AI asks the user questions such as, "Tell me about your assets" or "Is there a message you would like to convey to your family?" and records the user's responses. The generation AI can also analyze the user's tone of voice and speaking style to estimate the user's emotional state and adjust the dialogue accordingly. For example, if the user is relaxed, the dialogue can proceed smoothly. Furthermore, the generation AI can refer to the user's past dialogue history and generate questions tailored to individual needs and preferences. For example, it can ask questions related to topics in which the user has previously shown interest. The information collector collects information necessary for end-of-life planning through the dialogue conducted by the dialogue facilitator. For example, it collects information about the user's assets, messages to family members, funeral wishes, etc. The end-of-life planning notebook creator creates an end-of-life planning notebook based on the information collected by the information collector. For example, the generation AI writes a list of the user's assets and messages to family members in the end-of-life planning notebook based on the collected information. The will creation unit creates a will based on the information collected by the information collection unit. For example, the generation AI asks how the user's assets should be distributed and creates a will based on the answer. As a result, the end-of-life planning support system according to the embodiment can collect information necessary for end-of-life planning through dialogue with the user and create an end-of-life planning notebook and a will. For example, the user can organize asset information and messages to family members through dialogue, thereby reducing the mental burden. Furthermore, creating a will can clarify how assets will be distributed and prevent disputes between surviving family members.

[0030] The dialogue promoting unit can refer to the user's past dialogue history and generate questions that meet individual needs and preferences. For example, the dialogue promoting unit uses a generation AI to analyze the user's past dialogue history and generate questions that meet individual needs and preferences. For example, the dialogue promoting unit may ask questions related to topics in which the user has shown interest in the past. The dialogue promoting unit can also generate questions that are likely to interest the user based on the user's dialogue history. This makes it possible to generate questions that meet individual needs and preferences based on the user's past dialogue history.

[0031] The dialogue facilitator uses a camera to recognize the user's gestures and facial expressions during the dialogue, and can adjust the dialogue based on non-verbal feedback. For example, the generation AI uses a camera to recognize the user's gestures and facial expressions, and adjusts the dialogue based on non-verbal feedback. For example, if the user nods, it asks a question that shows understanding. The dialogue facilitator can also analyze the user's facial expressions to determine whether the user is interested. This allows the dialogue to be adjusted based on the user's gestures and facial expressions and non-verbal feedback.

[0032] The dialogue progression unit can visualize the information provided by the user in real time as the dialogue progresses and present it in a form that is easy for the user to understand. For example, the dialogue progression unit visualizes the information provided by the user in real time as the dialogue progresses using a generation AI and presents it in a form that is easy for the user to understand. For example, a list of assets can be displayed in a graph or table. The dialogue progression unit can also visually organize the information provided by the user to support the progress of the dialogue. This allows the information provided by the user to be visualized in real time and presented in a form that is easy for the user to understand.

[0033] The end-of-life notebook creation unit can automatically extract the user's life events and important occurrences and reflect them in the end-of-life notebook. For example, the end-of-life notebook creation unit uses a generation AI to automatically extract the user's life events and important occurrences and reflect them in the end-of-life notebook. For example, it can list important occurrences such as marriage and childbirth. The end-of-life notebook creation unit can also organize the user's life events in chronological order and record them in the end-of-life notebook. This allows the user's life events and important occurrences to be automatically extracted and reflected in the end-of-life notebook.

[0034] The end-of-life notebook creation unit can analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, the end-of-life notebook creation unit uses a generation AI to analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, it can include content based on the user's religion or beliefs. The end-of-life notebook creation unit can also collect the user's opinions and create the end-of-life notebook based on them to reflect the user's values ​​and beliefs. This makes it possible to create a personalized end-of-life notebook based on the user's values ​​and beliefs.

[0035] The end-of-life notebook creation unit can collect feedback from the user's family and friends and complement the end-of-life notebook based on that feedback. For example, the generation AI in the end-of-life notebook creation unit collects feedback from the user's family and friends and complements the end-of-life notebook based on that feedback. For example, it can reflect the opinions of the family. The end-of-life notebook creation unit can also make revisions to reflect the user's wishes based on feedback from the user's family and friends. This allows the end-of-life notebook to be complemented based on feedback from the user's family and friends.

[0036] The end-of-life notebook creation unit can collect information related to the user's hobbies and interests and add it to the end-of-life notebook. For example, the generation AI can collect information related to the user's hobbies and interests and add it to the end-of-life notebook. For example, it can record information about the user's favorite sports or music. The end-of-life notebook creation unit can also collect the user's opinions and create the end-of-life notebook based on them to reflect the user's hobbies and interests. This allows information related to the user's hobbies and interests to be collected and added to the end-of-life notebook.

[0037] The will creation unit can analyze the user's asset information in detail and propose the optimal distribution method. For example, the generation AI in the will creation unit analyzes the user's asset information in detail and proposes the optimal distribution method. For example, the distribution method is determined based on the type and value of the asset. The will creation unit can also propose a distribution method that meets legal requirements based on the user's asset information. This allows the user's asset information to be analyzed in detail and the optimal distribution method to be proposed.

[0038] The will creation unit can personalize the contents of the will by taking into consideration the user's family structure and relationships. For example, the generation AI in the will creation unit personalizes the contents of the will by taking into consideration the user's family structure and relationships. For example, it determines how assets will be distributed based on the number of family members and their relationships. The will creation unit can also create a will that reflects the family's wishes based on the user's family structure and relationships. This makes it possible to personalize the contents of the will by taking into consideration the user's family structure and relationships.

[0039] The will creation unit can link the user's property information with other legal documents to make the contents of the will consistent. For example, the generation AI in the will creation unit links the user's property information with other legal documents to make the contents of the will consistent. For example, it ensures consistency with existing contracts and insurance policies. The will creation unit can also create a will that maintains legal consistency based on the user's property information. This allows the user's property information to be linked with other legal documents to make the contents of the will consistent.

[0040] The will creation unit can visually display the contents of the user's will, allowing the user to confirm them in an easy-to-understand manner. For example, the will creation unit uses a generation AI to visually display the contents of the user's will, allowing the user to confirm them in an easy-to-understand manner. For example, the method of asset distribution can be displayed in a graph or chart. The will creation unit can also organize and display information so that the user can easily visually confirm the contents of the will. This allows the contents of the user's will to be visually displayed, allowing the user to confirm them in an easy-to-understand manner.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The dialogue promotion unit can refer to the user's past dialogue history and generate questions that meet individual needs and preferences. For example, the generation AI analyzes the user's past dialogue history and generates questions that meet individual needs and preferences. For example, it may ask questions related to topics in which the user has shown interest in the past. The dialogue promotion unit can also generate questions that are likely to interest the user based on the user's dialogue history. This makes it possible to generate questions that meet individual needs and preferences based on the user's past dialogue history.

[0043] The dialogue facilitator uses a camera to recognize the user's gestures and facial expressions during the dialogue, and can adjust the dialogue based on non-verbal feedback. For example, the generation AI uses a camera to recognize the user's gestures and facial expressions, and adjusts the dialogue based on non-verbal feedback. For example, if the user nods, it asks a question that shows understanding. The dialogue facilitator can also analyze the user's facial expressions to determine whether the user is interested. This allows it to recognize the user's gestures and facial expressions and adjust the dialogue based on non-verbal feedback.

[0044] The dialogue progression unit can visualize the information provided by the user in real time as the dialogue progresses and present it in a form that is easy for the user to understand. For example, the generation AI can visualize the information provided by the user in real time as the dialogue progresses and present it in a form that is easy for the user to understand. For example, it can display a list of assets in a graph or table. The dialogue progression unit can also visually organize the information provided by the user to support the progress of the dialogue. This allows the information provided by the user to be visualized in real time and presented in a form that is easy for the user to understand.

[0045] The end-of-life notebook creation unit can automatically extract the user's life events and important events and reflect them in the end-of-life notebook. For example, the generation AI can automatically extract the user's life events and important events and reflect them in the end-of-life notebook. For example, it can list important events such as marriage and childbirth. The end-of-life notebook creation unit can also organize the user's life events in chronological order and record them in the end-of-life notebook. This allows the user's life events and important events to be automatically extracted and reflected in the end-of-life notebook.

[0046] The end-of-life notebook creation unit can analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, the generation AI can analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, it can include content based on the user's religion or beliefs. The end-of-life notebook creation unit can also collect the user's opinions and create the end-of-life notebook based on them to reflect the user's values ​​and beliefs. This makes it possible to create a personalized end-of-life notebook based on the user's values ​​and beliefs.

[0047] The end-of-life notebook creation unit can collect feedback from the user's family and friends and complement the end-of-life notebook based on that feedback. For example, the generation AI collects feedback from the user's family and friends and complements the end-of-life notebook based on that feedback. For example, it can reflect the opinions of family members. The end-of-life notebook creation unit can also make revisions to reflect the user's wishes based on feedback from the user's family and friends. This allows the end-of-life notebook to be complemented based on feedback from the user's family and friends.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The dialogue progression unit uses the generation AI to progress the dialogue with the user. The generation AI asks the user questions such as "Tell me about your assets" and "Is there a message you would like to convey to your family?" and records the user's responses. The generation AI can also analyze the user's tone of voice and speaking style to estimate the user's emotional state and adjust the dialogue progression accordingly. For example, if the user is relaxed, the dialogue will proceed smoothly. Furthermore, the generation AI can refer to the user's past dialogue history and generate questions that meet individual needs and preferences. Step 2: The information collection unit collects information necessary for end-of-life planning through the dialogue facilitated by the dialogue management unit, such as the user's financial information, messages to family, and funeral wishes. Step 3: The end-of-life notebook creation unit creates an end-of-life notebook based on the information collected by the information collection unit. For example, the generation AI may write a list of the user's assets and a message to their family in the end-of-life notebook based on the collected information. Step 4: The will creation unit creates a will based on the information collected by the information collection unit. For example, the generation AI asks the user how to distribute their assets and creates a will based on the user's answers.

[0050] (Example 2) The end-of-life planning support system according to an embodiment of the present invention is a system in which information necessary for end-of-life planning is collected through dialogue with the user, and a generation AI creates an end-of-life planning notebook and a will. As a result, the end-of-life planning support system allows the user to proceed with end-of-life planning through dialogue, and ultimately creates an end-of-life planning notebook and a will.

[0051] An end-of-life planning support system according to an embodiment includes a dialogue facilitator, an information collector, an end-of-life planning notebook creator, and a will creator. The dialogue facilitator uses a generation AI to conduct a dialogue with a user. For example, the generation AI asks the user questions such as, "Tell me about your assets" or "Is there a message you would like to convey to your family?" and records the user's responses. The generation AI can also analyze the user's tone of voice and speaking style to estimate the user's emotional state and adjust the dialogue accordingly. For example, if the user is relaxed, the dialogue can proceed smoothly. Furthermore, the generation AI can refer to the user's past dialogue history and generate questions tailored to individual needs and preferences. For example, it can ask questions related to topics in which the user has previously shown interest. The information collector collects information necessary for end-of-life planning through the dialogue conducted by the dialogue facilitator. For example, it collects information about the user's assets, messages to family members, funeral wishes, etc. The end-of-life planning notebook creator creates an end-of-life planning notebook based on the information collected by the information collector. For example, the generation AI writes a list of the user's assets and messages to family members in the end-of-life planning notebook based on the collected information. The will creation unit creates a will based on the information collected by the information collection unit. For example, the generation AI asks how the user's assets should be distributed and creates a will based on the answer. As a result, the end-of-life planning support system according to the embodiment can collect information necessary for end-of-life planning through dialogue with the user and create an end-of-life planning notebook and a will. For example, the user can organize asset information and messages to family members through dialogue, thereby reducing the mental burden. Furthermore, creating a will can clarify how assets will be distributed and prevent disputes between surviving family members.

[0052] The dialogue progression unit can analyze the user's tone of voice and speaking style, estimate the user's emotional state, and adjust the progress of the dialogue. For example, the dialogue progression unit uses a generation AI to analyze the user's tone of voice and speaking style in real time to estimate the user's emotional state. For example, it can determine the user's emotions based on the pitch, speed, and intonation of the voice, and if the user is relaxed, it can allow the dialogue to proceed smoothly. Furthermore, if the user is feeling stressed, the dialogue progression unit can ask questions to help the user relax. This makes it possible to adjust the progress of the dialogue according to the user's emotional state.

[0053] The dialogue promoting unit can refer to the user's past dialogue history and generate questions that meet individual needs and preferences. For example, the dialogue promoting unit uses a generation AI to analyze the user's past dialogue history and generate questions that meet individual needs and preferences. For example, the dialogue promoting unit may ask questions related to topics in which the user has shown interest in the past. The dialogue promoting unit can also generate questions that are likely to interest the user based on the user's dialogue history. This makes it possible to generate questions that meet individual needs and preferences based on the user's past dialogue history.

[0054] The dialogue promoting unit can use the emotion estimation function to avoid questions that cause the user to feel emotionally burdened and to proceed with the dialogue in a relaxed state. The dialogue promoting unit can, for example, use the emotion estimation function to avoid questions that cause the user to feel emotionally burdened. For example, if the user is feeling stressed, the dialogue promoting unit can ask questions to relax the user. The dialogue promoting unit can also use the emotion estimation function to monitor the user's emotional state so that the dialogue can proceed in a relaxed state. This allows the dialogue to proceed in a relaxed state by avoiding questions that cause the user to feel emotionally burdened.

[0055] The dialogue facilitator uses a camera to recognize the user's gestures and facial expressions during the dialogue, and can adjust the dialogue based on non-verbal feedback. For example, the generation AI uses a camera to recognize the user's gestures and facial expressions, and adjusts the dialogue based on non-verbal feedback. For example, if the user nods, it asks a question that shows understanding. The dialogue facilitator can also analyze the user's facial expressions to determine whether the user is interested. This allows the dialogue to be adjusted based on the user's gestures and facial expressions and non-verbal feedback.

[0056] The dialogue progression unit can visualize the information provided by the user in real time as the dialogue progresses and present it in a form that is easy for the user to understand. For example, the dialogue progression unit visualizes the information provided by the user in real time as the dialogue progresses using a generation AI and presents it in a form that is easy for the user to understand. For example, a list of assets can be displayed in a graph or table. The dialogue progression unit can also visually organize the information provided by the user to support the progress of the dialogue. This allows the information provided by the user to be visualized in real time and presented in a form that is easy for the user to understand.

[0057] The dialogue promoting unit can use the emotion estimation function to monitor the stress level felt by the user during the dialogue and suggest a break at an appropriate time. The dialogue promoting unit, for example, uses the emotion estimation function to monitor the stress level felt by the user during the dialogue. For example, if stress increases, the dialogue promoting unit can suggest a break. The dialogue promoting unit can also ask questions to encourage relaxation in order to reduce the user's stress level. This makes it possible to monitor the stress level felt by the user during the dialogue and suggest a break at an appropriate time.

[0058] The end-of-life notebook creation unit can automatically extract the user's life events and important occurrences and reflect them in the end-of-life notebook. For example, the end-of-life notebook creation unit uses a generation AI to automatically extract the user's life events and important occurrences and reflect them in the end-of-life notebook. For example, it can list important occurrences such as marriage and childbirth. The end-of-life notebook creation unit can also organize the user's life events in chronological order and record them in the end-of-life notebook. This allows the user's life events and important occurrences to be automatically extracted and reflected in the end-of-life notebook.

[0059] The end-of-life notebook creation unit can analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, the end-of-life notebook creation unit uses a generation AI to analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, it can include content based on the user's religion or beliefs. The end-of-life notebook creation unit can also collect the user's opinions and create the end-of-life notebook based on them to reflect the user's values ​​and beliefs. This makes it possible to create a personalized end-of-life notebook based on the user's values ​​and beliefs.

[0060] The end-of-life planning notebook creation unit can use the emotion estimation function to highlight information that the user holds particularly important and prioritize writing it down in the end-of-life planning notebook. The end-of-life planning notebook creation unit can, for example, use the emotion estimation function to highlight information that the user holds particularly important and prioritize writing it down in the end-of-life planning notebook. For example, it can prioritize writing down content that the user has a strong emotional reaction to. The end-of-life planning notebook creation unit can also monitor the user's emotional state and reflect information that the user feels is important in the end-of-life planning notebook. This allows information that the user holds particularly important to be highlighted and prioritized in the end-of-life planning notebook.

[0061] The end-of-life notebook creation unit can collect feedback from the user's family and friends and complement the end-of-life notebook based on that feedback. For example, the generation AI in the end-of-life notebook creation unit collects feedback from the user's family and friends and complements the end-of-life notebook based on that feedback. For example, it can reflect the opinions of the family. The end-of-life notebook creation unit can also make revisions to reflect the user's wishes based on feedback from the user's family and friends. This allows the end-of-life notebook to be complemented based on feedback from the user's family and friends.

[0062] The end-of-life notebook creation unit can collect information related to the user's hobbies and interests and add it to the end-of-life notebook. For example, the generation AI can collect information related to the user's hobbies and interests and add it to the end-of-life notebook. For example, it can record information about the user's favorite sports or music. The end-of-life notebook creation unit can also collect the user's opinions and create the end-of-life notebook based on them to reflect the user's hobbies and interests. This allows information related to the user's hobbies and interests to be collected and added to the end-of-life notebook.

[0063] The end-of-life notebook creation unit can use the emotion estimation function to analyze the emotions the user feels about the contents of the end-of-life notebook and add content that elicits positive emotions. The end-of-life notebook creation unit can, for example, use the emotion estimation function to analyze the emotions the user feels about the contents of the end-of-life notebook and add content that elicits positive emotions. For example, it can record episodes that make the user feel happy. The end-of-life notebook creation unit can also monitor the user's emotional state and reflect information that makes the user feel positive emotions in the end-of-life notebook. This allows the user to analyze the emotions the user feels about the contents of the end-of-life notebook and add content that elicits positive emotions.

[0064] The will creation unit can analyze the user's asset information in detail and propose the optimal distribution method. For example, the generation AI in the will creation unit analyzes the user's asset information in detail and proposes the optimal distribution method. For example, the distribution method is determined based on the type and value of the asset. The will creation unit can also propose a distribution method that meets legal requirements based on the user's asset information. This allows the user's asset information to be analyzed in detail and the optimal distribution method to be proposed.

[0065] The will creation unit can personalize the contents of the will by taking into consideration the user's family structure and relationships. For example, the generation AI in the will creation unit personalizes the contents of the will by taking into consideration the user's family structure and relationships. For example, it determines how assets will be distributed based on the number of family members and their relationships. The will creation unit can also create a will that reflects the family's wishes based on the user's family structure and relationships. This makes it possible to personalize the contents of the will by taking into consideration the user's family structure and relationships.

[0066] The will creation unit can use the emotion estimation function to make suggestions to alleviate the anxiety and concerns the user feels about the contents of the will. The will creation unit can, for example, use the emotion estimation function to make suggestions to alleviate the anxiety and concerns the user feels about the contents of the will. For example, the will creation unit can identify parts that make the user feel anxious and add explanations to reassure the user. The will creation unit can also monitor the user's emotional state and suggest will contents that will reassure the user. This makes it possible to make suggestions to alleviate the anxiety and concerns the user feels about the contents of the will.

[0067] The will creation unit can link the user's property information with other legal documents to make the contents of the will consistent. For example, the generation AI in the will creation unit links the user's property information with other legal documents to make the contents of the will consistent. For example, it ensures consistency with existing contracts and insurance policies. The will creation unit can also create a will that maintains legal consistency based on the user's property information. This allows the user's property information to be linked with other legal documents to make the contents of the will consistent.

[0068] The will creation unit can visually display the contents of the user's will, allowing the user to confirm them in an easy-to-understand manner. For example, the will creation unit uses a generation AI to visually display the contents of the user's will, allowing the user to confirm them in an easy-to-understand manner. For example, the method of asset distribution can be displayed in a graph or chart. The will creation unit can also organize and display information so that the user can easily visually confirm the contents of the will. This allows the contents of the user's will to be visually displayed, allowing the user to confirm them in an easy-to-understand manner.

[0069] The will creation unit can use the emotion estimation function to analyze the emotions the user feels about the contents of the will and add content that elicits positive emotions. The will creation unit can, for example, use the emotion estimation function to analyze the emotions the user feels about the contents of the will and add content that elicits positive emotions. For example, it can add a message that makes the user feel at ease. The will creation unit can also monitor the user's emotional state and reflect information that the user feels positive emotions in the will. This makes it possible to analyze the emotions the user feels about the contents of the will and add content that elicits positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The dialogue progression unit can analyze the user's tone of voice and speaking style, infer the user's emotional state, and adjust the progress of the dialogue. For example, the generation AI can analyze the user's tone of voice and speaking style in real time to infer the user's emotional state. For example, it can determine the user's emotions based on the pitch, speed, and intonation of the voice, and if the user is relaxed, it can allow the dialogue to proceed smoothly. In addition, if the user is feeling stressed, the dialogue progression unit can ask questions to help the user relax. This makes it possible to adjust the progress of the dialogue according to the user's emotional state.

[0072] The dialogue promotion unit can refer to the user's past dialogue history and generate questions that meet individual needs and preferences. For example, the generation AI analyzes the user's past dialogue history and generates questions that meet individual needs and preferences. For example, it may ask questions related to topics in which the user has shown interest in the past. The dialogue promotion unit can also generate questions that are likely to interest the user based on the user's dialogue history. This makes it possible to generate questions that meet individual needs and preferences based on the user's past dialogue history.

[0073] The dialogue promoting unit can use the emotion estimation function to avoid questions that cause the user to feel emotionally burdened and to proceed with the dialogue in a relaxed state. For example, the emotion estimation function can be used to avoid questions that cause the user to feel emotionally burdened. For example, if the user is feeling stressed, a question to relax the user can be asked. The dialogue promoting unit can also use the emotion estimation function to monitor the user's emotional state so that the dialogue can proceed in a relaxed state. This allows the dialogue to proceed in a relaxed state by avoiding questions that cause the user to feel emotionally burdened.

[0074] The dialogue facilitator uses a camera to recognize the user's gestures and facial expressions during the dialogue, and can adjust the dialogue based on non-verbal feedback. For example, the generation AI uses a camera to recognize the user's gestures and facial expressions, and adjusts the dialogue based on non-verbal feedback. For example, if the user nods, it asks a question that shows understanding. The dialogue facilitator can also analyze the user's facial expressions to determine whether the user is interested. This allows it to recognize the user's gestures and facial expressions and adjust the dialogue based on non-verbal feedback.

[0075] The dialogue progression unit can visualize the information provided by the user in real time as the dialogue progresses and present it in a form that is easy for the user to understand. For example, the generation AI can visualize the information provided by the user in real time as the dialogue progresses and present it in a form that is easy for the user to understand. For example, it can display a list of assets in a graph or table. The dialogue progression unit can also visually organize the information provided by the user to support the progress of the dialogue. This allows the information provided by the user to be visualized in real time and presented in a form that is easy for the user to understand.

[0076] The dialogue promoting unit can use the emotion estimation function to monitor the stress level felt by the user during the dialogue and suggest a break at an appropriate time. For example, the emotion estimation function is used to monitor the stress level felt by the user during the dialogue. For example, if stress increases, the dialogue promoting unit can suggest a break. The dialogue promoting unit can also ask questions to encourage relaxation in order to reduce the user's stress level. This makes it possible to monitor the stress level felt by the user during the dialogue and suggest a break at an appropriate time.

[0077] The end-of-life notebook creation unit can automatically extract the user's life events and important events and reflect them in the end-of-life notebook. For example, the generation AI can automatically extract the user's life events and important events and reflect them in the end-of-life notebook. For example, it can list important events such as marriage and childbirth. The end-of-life notebook creation unit can also organize the user's life events in chronological order and record them in the end-of-life notebook. This allows the user's life events and important events to be automatically extracted and reflected in the end-of-life notebook.

[0078] The end-of-life notebook creation unit can analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, the generation AI can analyze the user's values ​​and beliefs and create a personalized end-of-life notebook based on them. For example, it can include content based on the user's religion or beliefs. The end-of-life notebook creation unit can also collect the user's opinions and create the end-of-life notebook based on them to reflect the user's values ​​and beliefs. This makes it possible to create a personalized end-of-life notebook based on the user's values ​​and beliefs.

[0079] The end-of-life notebook creation unit can use the emotion estimation function to highlight information that the user holds particularly important and prioritize writing it down in the end-of-life notebook. For example, the emotion estimation function can be used to highlight information that the user holds particularly important and prioritize writing it down in the end-of-life notebook. For example, the unit can prioritize writing down content that the user has a strong emotional reaction to. The end-of-life notebook creation unit can also monitor the user's emotional state and reflect information that the user feels is important in the end-of-life notebook. This allows the unit to highlight information that the user holds particularly important and prioritize writing it down in the end-of-life notebook.

[0080] The end-of-life notebook creation unit can collect feedback from the user's family and friends and complement the end-of-life notebook based on that feedback. For example, the generation AI collects feedback from the user's family and friends and complements the end-of-life notebook based on that feedback. For example, it can reflect the opinions of family members. The end-of-life notebook creation unit can also make revisions to reflect the user's wishes based on feedback from the user's family and friends. This allows the end-of-life notebook to be complemented based on feedback from the user's family and friends.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The dialogue progression unit uses the generation AI to progress the dialogue with the user. The generation AI asks the user questions such as "Tell me about your assets" and "Is there a message you would like to convey to your family?" and records the user's responses. The generation AI can also analyze the user's tone of voice and speaking style to estimate the user's emotional state and adjust the dialogue progression accordingly. For example, if the user is relaxed, the dialogue will proceed smoothly. Furthermore, the generation AI can refer to the user's past dialogue history and generate questions that meet individual needs and preferences. Step 2: The information collection unit collects information necessary for end-of-life planning through the dialogue facilitated by the dialogue management unit, such as the user's financial information, messages to family, and funeral wishes. Step 3: The end-of-life notebook creation unit creates an end-of-life notebook based on the information collected by the information collection unit. For example, the generation AI may write a list of the user's assets and a message to their family in the end-of-life notebook based on the collected information. Step 4: The will creation unit creates a will based on the information collected by the information collection unit. For example, the generation AI asks the user how to distribute their assets and creates a will based on the user's answers.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 dialogue progression unit that uses a generation AI to progress a dialogue with a user; an information collection unit that collects information necessary for end-of-life planning through the dialogue conducted by the dialogue promotion unit; A terminal care note creation unit that creates a terminal care note based on the information collected by the information collection unit; a will writing unit that writes a will based on the information collected by the information collecting unit. A system characterized by:

2. The dialogue promoting unit: Analyzing the user's tone of voice and speaking style, and estimating the user's emotional state to adjust the progress of the dialogue 2. The system of claim 1.

3. The dialogue promoting unit: The camera recognizes the user's gestures and facial expressions during the conversation and adjusts the conversation based on non-verbal feedback.

2. The system of claim 1.

4. The end-of-life note creation unit Automatically extract the user's life events and important happenings and reflect them in the end-of-life notebook.

2. The system of claim 1.

5. The end-of-life note creation unit Collecting feedback from the user's family and friends and using it to supplement the end-of-life notebook 2. The system of claim 1.

6. The will preparation unit Analyze the user's asset information in detail and propose the optimal distribution method 2. The system of claim 1.

7. The will preparation unit Providing suggestions to alleviate the anxiety and concerns the user feels about the contents of the will 2. The system of claim 1.

8. The will preparation unit Analyze the emotions felt by the user regarding the contents of the will, and add content that elicits positive emotions 2. The system of claim 1.

Images