system

Abstract

The system according to this embodiment aims to generate and provide end-of-life planning plans based on an individual's values ​​and desires. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects an individual's life log data. The analysis unit analyzes the data collected by the collection unit and infers the individual's values ​​and desires. The generation unit generates an end-of-life plan based on the analysis results obtained by the analysis unit. The provision unit provides the end-of-life plan generated by the generation unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to generate an end-of-life plan based on an individual's values and wishes, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide an end-of-life plan based on an individual's values and wishes.

Means for Solving the Problems

[0007] The system according to this embodiment can generate and provide end-of-life planning plans based on an individual's values ​​and desires. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The life log analysis system according to an embodiment of the present invention is a system in which an AI analyzes an individual's life log data to infer their values ​​and desires. This life log analysis system collects an individual's life log data, and by analyzing it with an AI, it infers the individual's values ​​and desires and proposes a comprehensive life plan and end-of-life plan from before death to after death. For example, the life log analysis system collects an individual's life log data. In this process, it collects data that indicates the individual's behavior and state, such as SNS posts, location information, and health data. For example, it can grasp the individual's hobbies and interests from the content of SNS posts and identify their daily activity range from location information. From health data, it can grasp the individual's health status and lifestyle habits. Next, the life log analysis system has an AI analyze the collected life log data. The AI ​​analyzes the collected data and infers the individual's values ​​and desires. For example, it can analyze the individual's values ​​and interests from the content of SNS posts and grasp their daily activity patterns from location information. From health data, it can analyze the individual's health status and lifestyle habits. Based on the analysis results, the AI ​​infers the individual's values ​​and desires. This generates an end-of-life plan that respects the individual's wishes. For example, the system proposes end-of-life planning based on the individual's wishes, such as how they want to spend their final days and how they want to leave memories for their family. Furthermore, the life log analysis system proposes methods for managing and transferring digital assets. For example, it proposes methods for organizing and transferring digital assets left behind by the individual, such as SNS accounts and cloud storage data. This reduces the burden on family members and ensures that the individual's wishes are reliably passed on. The life log analysis system also automatically generates life timelines and memory albums based on life logs. This allows users to reflect on their lives and supports self-realization. For example, by extracting important events and memories from life log data and creating life timelines and memory albums, users can reflect on their lives. Finally, the life log analysis system continuously optimizes end-of-life planning based on daily data updates. This allows the system to provide end-of-life planning plans that respond to changes in the user's lifestyle and values.For example, the system updates end-of-life planning plans based on changes in the user's health and lifestyle, providing the most suitable plan. This mechanism ensures that end-of-life planning respects the individual's wishes and reduces the burden on surviving family members. It also supports users in reflecting on their lives and achieving self-realization, contributing to the creation of a sense of purpose. In this way, the life log analysis system can realize end-of-life planning that respects the individual's wishes and reduce the burden on surviving family members.

[0029] The life log analysis system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects an individual's life log data. The collection unit collects, for example, SNS posts, location information, and health data. The collection unit can, for example, understand an individual's hobbies and interests from the content of SNS posts and identify their daily activity range from location information. The collection unit can understand an individual's health status and lifestyle habits from health data. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the content of SNS posts into AI, and the AI ​​can extract hobbies and interests. The analysis unit analyzes the data collected by the collection unit and infers an individual's values ​​and desires. The analysis unit can, for example, analyze an individual's values ​​and interests from the content of SNS posts and understand their daily activity patterns from location information. The analysis unit can analyze an individual's health status and lifestyle habits from health data. Some or all of the above-described processes in the analysis unit may, for example, be performed using, for example, AI, or without AI. For example, the analysis unit inputs the collected data into an AI, which can infer values ​​and desires. The generation unit generates an end-of-life plan based on the analysis results obtained by the analysis unit. The generation unit proposes an end-of-life plan based on the individual's wishes, such as how the individual wants to face the end of their life and how they want to leave memories for their family. Some or all of the above processing in the generation unit may be performed using an AI, or without an AI. For example, the generation unit can input the analysis results into an AI, which can generate an end-of-life plan. The provision unit provides the end-of-life plan generated by the generation unit. The provision unit, for example, presents the generated end-of-life plan to the user, allowing the user to select one. Some or all of the above processing in the provision unit may be performed using an AI, or without an AI. For example, the provision unit can input the generated end-of-life plan into an AI, which can present it to the user. Thus, the life log analysis system according to the embodiment can propose a comprehensive life plan and end-of-life plan by analyzing an individual's life log data and inferring their values ​​and desires.

[0030] The data collection unit collects personal life log data. For example, it collects social media posts, location information, and health data. Specifically, it identifies an individual's hobbies and interests from social media posts and their daily activity range from location information. Social media posts are analyzed using text analysis techniques to extract keywords and sentiment, revealing the individual's interests and concerns. For example, if there are many posts about a particular sport or music, it can be determined that the individual has a strong interest in that field. Location information is analyzed using GPS data to identify places the individual frequently visits and their movement patterns. This allows for an understanding of the individual's daily activity range and living area. Health data is obtained from wearable devices and smartphone sensors, collecting information such as heart rate, steps, and sleep patterns. This data is used to gain a detailed understanding of the individual's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input social media posts into an AI, which can then extract hobbies and interests. The AI ​​uses natural language processing technology to analyze the content of posts and automatically classify individuals' interests and preferences. Location information and health data are also analyzed by the AI, allowing for an automatic understanding of individual behavioral patterns and health status. This enables the data collection unit to efficiently gather data from diverse data sources and comprehensively understand an individual's life log.

[0031] The analysis department analyzes data collected by the collection department to infer an individual's values ​​and desires. For example, the analysis department analyzes an individual's values ​​and interests from the content of social media posts and understands their daily behavior patterns from location information. Specifically, it analyzes the content of social media posts using text mining techniques to extract an individual's values ​​and interests. For example, if there are many posts expressing positive emotions, it can be inferred that the individual has optimistic values. Location information is used to analyze places an individual frequently visits and their movement patterns to understand their daily behavior patterns. For example, if an individual frequently visits a particular cafe or park, it can be determined that that place holds significant meaning for them. Health data is used to analyze an individual's health status and lifestyle in detail. For example, by analyzing heart rate and sleep patterns, it is possible to understand an individual's stress level and daily rhythm. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the collected data into an AI, which can then infer values ​​and desires. Using machine learning algorithms, AI learns patterns from collected data and accurately infers individuals' values ​​and desires. This allows the analytics department to quickly and accurately analyze collected data and understand individuals' values ​​and desires. Furthermore, the analytics department can utilize historical data and statistical information to analyze long-term trends and changes. This enables continuous monitoring of changes in individuals' values ​​and desires, leading to more accurate analyses.

[0032] The generation unit generates end-of-life planning plans based on the analysis results obtained by the analysis unit. The generation unit proposes end-of-life planning plans based on the individual's wishes, such as how the individual wants to face the end of their life and how they want to leave memories for their family. Specifically, it uses AI to process the analysis results in order to generate end-of-life planning plans that reflect the individual's values ​​and desires. For example, if an individual wishes for a natural burial, it proposes a specific plan based on that wish. The AI ​​can generate the most suitable plan for the individual by referring to past data and end-of-life planning plans of other users. Some or all of the processing described above in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the analysis results into the AI, and the AI ​​can generate the end-of-life planning plan. The AI ​​uses natural language generation technology to automatically create a specific plan based on the individual's wishes. This allows the generation unit to quickly and accurately generate end-of-life planning plans that reflect the individual's values ​​and desires. Furthermore, the generation unit can continuously improve the accuracy and satisfaction of the plan by presenting the generated plan to the user and collecting feedback. This allows the generation unit to provide an optimal end-of-life planning plan based on the individual's wishes, thereby increasing user satisfaction.

[0033] The service provider provides the end-of-life planning plan generated by the generation unit. For example, the service provider presents the generated end-of-life planning plan to the user, allowing the user to select one. Specifically, it presents the generated end-of-life planning plan to the user in an easy-to-understand manner, offering options. The service provider presents the plan to the user, for example, through a web application or mobile application. The user can review the presented plan and select one that suits their needs. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the generated end-of-life planning plan into an AI, which can then present to the user. The AI ​​can learn from the user's reactions and selection history, and propose a more appropriate plan. This allows the service provider to provide the user with the optimal end-of-life planning plan, increasing user satisfaction. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and satisfaction of the plan. For example, it can improve the next proposal based on feedback on the plan selected by the user. The service provider can also reliably transmit information using multiple communication methods. For example, by using not only web and mobile applications but also email and SMS in combination, important information can be reliably delivered. This allows the service provider to deliver end-of-life planning plans to users quickly and reliably, thereby increasing user satisfaction.

[0034] The data collection unit can collect social media posts, location information, health data, and more. For example, the data collection unit can understand an individual's hobbies and interests from the content of their social media posts. For example, the data collection unit can identify an individual's daily activity range from their location information. For example, the data collection unit can understand an individual's health status and lifestyle from their health data. By collecting data that indicates an individual's behavior and condition, more detailed life log data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of social media posts into a generating AI, which can then extract hobbies and interests.

[0035] The data collection unit can analyze the user's past life log data and select the optimal collection method. For example, the data collection unit may prioritize collecting data from devices and apps that the user has frequently used in the past. For example, the data collection unit may analyze the user's past behavior patterns and determine the optimal collection timing. For example, the data collection unit may select the most efficient collection method based on the user's past data collection history. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past life log data into a generating AI, which can then select the optimal collection method.

[0036] The data collection unit can filter the life log data based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the data collection unit will prioritize collecting health data. For example, if the user is traveling, the data collection unit will prioritize collecting location information. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. This allows for the collection of more relevant data by filtering the data based on the user's areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest into a generating AI, which can then filter the data.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifelog data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of information about the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of information about the area around the user's home. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data when collecting lifelog data. For example, the data collection unit can analyze the content a user frequently posts on social media and collect relevant data. For example, the data collection unit can collect relevant data based on information about accounts a user follows. For example, the data collection unit can collect relevant data based on information about online communities a user participates in. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into a generating AI, which can then collect relevant data.

[0039] The analysis department can analyze collected data and infer an individual's values ​​and desires. For example, the analysis department can analyze an individual's values ​​and interests from the content of their social media posts. For example, the analysis department can understand their daily behavioral patterns from location information. For example, the analysis department can analyze an individual's health status and lifestyle from health data. In this way, by analyzing the collected data, an individual's values ​​and desires can be inferred. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into AI, which can then infer values ​​and desires.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can analyze high-importance data in detail and low-importance data simply. For example, the analysis unit can prioritize the analysis of high-importance data and postpone the analysis of low-importance data. For example, the analysis unit can allocate more resources to the analysis of high-importance data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to evaluate health status to health data. For example, the analysis unit can apply an algorithm to analyze behavioral patterns to location data. For example, the analysis unit can apply an algorithm to perform sentiment analysis to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply different analysis algorithms.

[0042] The generation unit can generate end-of-life plans based on an individual's values ​​and desires. For example, the generation unit proposes end-of-life plans based on the individual's wishes, such as how they want to face the end of their life or how they want to leave memories for their surviving family. By generating end-of-life plans based on an individual's values ​​and desires, it is possible to provide end-of-life plans that respect the individual's wishes. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input values ​​and desires into a generation AI, and the generation AI can generate an end-of-life plan.

[0043] The generation unit can analyze the user's past behavioral history to select the optimal plan when generating an end-of-life plan. For example, the generation unit can select the optimal plan based on the end-of-life plans the user has made in the past. For example, the generation unit can select the most efficient end-of-life plan from the user's past behavioral history. For example, the generation unit can analyze the user's past behavioral history and select the most suitable end-of-life plan. This allows for the provision of an efficient end-of-life plan by selecting the optimal plan based on past behavioral history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past behavioral history into a generation AI, which can then select the optimal plan.

[0044] The generation unit can customize the end-of-life plan based on the user's current living situation when generating it. For example, the generation unit can generate an end-of-life plan that takes health into consideration based on the user's health condition. For example, the generation unit can generate an end-of-life plan that takes work into consideration based on the user's work situation. For example, the generation unit can generate an end-of-life plan that takes family circumstances into consideration based on the user's family situation. By customizing the plan based on the current living situation, a more appropriate end-of-life plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the current living situation into a generation AI, and the generation AI can customize the plan.

[0045] The service provider can provide the generated end-of-life plan. For example, the service provider can present the generated end-of-life plan to the user and allow the user to select one. By providing the generated end-of-life plan, the service provider can provide the user with an appropriate end-of-life plan. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated end-of-life plan into AI, and the AI ​​can present it to the user.

[0046] The service provider can select the optimal service delivery method by referring to the user's past feedback when providing end-of-life planning plans. For example, the service provider can prioritize providing the service delivery method that the user has preferred in the past. For example, the service provider can analyze the user's past feedback and select the service delivery method that yields the highest satisfaction. For example, the service provider can customize the service delivery method based on the user's past feedback. This improves user satisfaction by selecting the optimal service delivery method based on past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past feedback into a generating AI, which can then select the optimal service delivery method.

[0047] The service provider can select the optimal delivery method when providing end-of-life planning plans, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. If the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. If the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. In this way, by considering device information, the service provider can provide the most suitable delivery method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information into a generating AI, which can then select the optimal delivery method.

[0048] The Management Department can manage digital assets. The Management Department proposes methods for organizing and transferring digital assets left by individuals, such as social media accounts and cloud storage data. This reduces the burden on surviving family members and ensures the reliable transmission of the individual's wishes. Some or all of the above processes performed by the Management Department may be carried out using AI, or not. For example, the Management Department can input digital asset data into a generating AI, which can then propose management methods.

[0049] The management department can analyze a user's past digital activities and select the optimal management method when managing digital heritage. For example, the management department can prioritize managing digital services that the user has frequently used in the past. The management department can analyze a user's past digital activities and select the optimal management method. For example, the management department can select the most efficient management method based on a user's past digital activity history. This enables efficient management of digital heritage by selecting the optimal management method based on past digital activities. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past digital activities into a generating AI, and the generating AI can select the optimal management method.

[0050] The management department can select the optimal management method when managing digital assets, taking into account the user's geographical location information. For example, if the user is in a specific region, the management department will prioritize managing digital assets related to that region. For example, if the user is traveling, the management department can manage digital assets based on information about the travel destination. For example, if the user is at home, the management department can manage digital assets based on information about the area around the user's home. This allows for more appropriate management of digital assets by considering geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input geographical location information into a generating AI, which can then select the optimal management method.

[0051] The Life Timeline and Memory Album Generation Unit can generate life timelines and memory albums based on life logs. For example, the Life Timeline and Memory Album Generation Unit extracts important events and memories from life log data and creates a life timeline and memory album. By generating life timelines and memory albums based on life logs, it can support users in reflecting on their lives and achieving self-realization. Some or all of the above-described processes in the Life Timeline and Memory Album Generation Unit may be performed using AI, for example, or without AI. For example, the Life Timeline and Memory Album Generation Unit can input life log data into a generating AI, which can then generate a life timeline and memory album.

[0052] The life timeline and memory album generation unit can analyze the user's past important events and select the optimal generation method when generating a life timeline or memory album. For example, the life timeline and memory album generation unit generates the optimal life timeline or memory album based on important events the user has experienced in the past. For example, the life timeline and memory album generation unit analyzes the user's past important events and selects the most efficient generation method. For example, the life timeline and memory album generation unit can generate the most suitable life timeline or memory album based on the user's past important events. This makes it possible to efficiently generate life timelines and memory albums by selecting the optimal generation method based on important past events. Some or all of the above processing in the life timeline and memory album generation unit may be performed using AI, for example, or without AI. For example, the life timeline and memory album generation unit can input important past events into a generation AI, which can then select the optimal generation method.

[0053] The life timeline and memory album generation unit can select the optimal generation method by considering the user's geographical location information when generating the life timeline and memory album. For example, if the user is in a specific region, the life timeline and memory album generation unit will generate a life timeline and memory album related to that region. For example, if the user is traveling, the life timeline and memory album generation unit can generate a life timeline and memory album based on information about the travel destination. For example, if the user is at home, the life timeline and memory album generation unit can generate a life timeline and memory album based on information about the area around the user's home. In this way, by considering geographical location information, a more appropriate life timeline and memory album can be generated. Some or all of the above processing in the life timeline and memory album generation unit may be performed using AI, for example, or without AI. For example, the life timeline and memory album generation unit can input geographical location information into a generation AI, which can then select the optimal generation method.

[0054] The optimization unit can optimize end-of-life planning plans based on daily data updates. For example, the optimization unit updates the end-of-life planning plan in response to changes in the user's health condition and lifestyle, providing the optimal plan. By optimizing the end-of-life planning plan based on daily data updates, it is possible to provide an end-of-life planning plan that responds to changes in the user's lifestyle and values. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input daily data updates into a generating AI, which can then optimize the end-of-life planning plan.

[0055] The optimization unit can select the optimal optimization method when optimizing an end-of-life planning plan by referring to the user's past data update history. For example, the optimization unit selects the optimal optimization method based on the user's past data update history. For example, the optimization unit analyzes the user's past data update history and selects the most efficient optimization method. For example, the optimization unit can select the most suitable optimization method based on the user's past data update history. This makes it possible to optimize an end-of-life planning plan efficiently by selecting the optimal optimization method based on past data update history. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past data update history into a generating AI, and the generating AI can select the optimal optimization method.

[0056] The optimization unit can select the optimal optimization method when optimizing an end-of-life planning plan, taking into account the user's device information. For example, if the user is using a smartphone, the optimization unit can provide an optimization method that matches the screen size. For example, if the user is using a tablet, the optimization unit can provide an optimization method optimized for a larger screen. For example, if the user is using a smartwatch, the optimization unit can provide a concise and highly visible optimization method. In this way, by taking device information into account, the optimal optimization method can be provided to the user. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input device information into a generating AI, and the generating AI can select the optimal optimization method.

[0057] The optimization unit can select the optimal optimization method when optimizing an end-of-life plan, taking into account the user's health condition. For example, the optimization unit can provide an optimization method that takes health into consideration, depending on the user's health condition. For example, if the user is unwell, the optimization unit can provide an optimization method that prioritizes rest. For example, if the user is seeking healthy exercise, the optimization unit can provide an optimization method that incorporates exercise. This allows for the provision of a more appropriate end-of-life plan by taking health conditions into account. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the health condition into a generating AI, which can then select the optimal optimization method.

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

[0059] The data collection unit can analyze the user's past life log data and select the optimal collection method. For example, it can prioritize collecting data from devices and apps that the user has frequently used in the past. It can analyze the user's past behavior patterns to determine the optimal collection timing. Based on the user's past data collection history, it can select the most efficient collection method. This enables efficient data collection by selecting the optimal collection method based on past data.

[0060] The data collection unit can filter lifelog data based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, health data will be prioritized. If the user is traveling, location information will be prioritized. If the user is at work, work-related data will be prioritized. This allows for the collection of more relevant data by filtering it based on the user's areas of interest.

[0061] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifelog data. For example, if the user is in a specific region, it can prioritize the collection of data related to that region. If the user is traveling, it can prioritize the collection of information about their travel destination. If the user is at home, it can prioritize the collection of information about their home area. In this way, by considering geographical location, it can prioritize the collection of highly relevant data.

[0062] The analysis department can adjust the level of detail in its analysis based on the importance of the data. For example, it can analyze highly important data in detail and less important data simply. Alternatively, it can prioritize the analysis of highly important data and postpone the analysis of less important data. More resources can be allocated to the analysis of highly important data. By adjusting the level of detail in the analysis based on the importance of the data, more efficient analysis becomes possible.

[0063] The generation unit can analyze the user's past behavioral history to select the optimal plan when generating an end-of-life planning plan. For example, it can select the optimal plan based on the end-of-life planning plans the user has made in the past. It can select the most efficient end-of-life planning plan from the user's past behavioral history. By analyzing the user's past behavioral history, it can select the most suitable end-of-life planning plan. In this way, by selecting the optimal plan based on past behavioral history, it can provide an efficient end-of-life planning plan.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects personal life log data. For example, it collects social media posts, location information, and health data. The data collection unit understands an individual's hobbies and interests from social media posts and identifies their daily activity range from location information. It can also understand an individual's health status and lifestyle habits from health data. These processes may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit to infer individual values ​​and desires. For example, it can analyze an individual's values ​​and interests from the content of their social media posts, and understand their daily behavioral patterns from location data. It can also analyze an individual's health status and lifestyle habits from health data. These processes may or may not be performed using AI. Step 3: The generation unit generates an end-of-life plan based on the analysis results obtained by the analysis unit. For example, it proposes an end-of-life plan based on the individual's wishes, such as how they want to face the end of their life and how they want to leave memories for their family. These processes may or may not be performed using AI. Step 4: The provisioning unit provides the end-of-life plan generated by the generation unit. For example, it presents the generated end-of-life plan to the user and allows the user to make a selection. These processes may or may not be performed using AI.

[0066] (Example of form 2)The life log analysis system according to an embodiment of the present invention is a system in which an AI analyzes an individual's life log data to infer their values ​​and desires. This life log analysis system collects an individual's life log data, and by analyzing it with an AI, it infers the individual's values ​​and desires and proposes a comprehensive life plan and end-of-life plan from before death to after death. For example, the life log analysis system collects an individual's life log data. In this process, it collects data that indicates the individual's behavior and state, such as SNS posts, location information, and health data. For example, it can grasp the individual's hobbies and interests from the content of SNS posts and identify their daily activity range from location information. From health data, it can grasp the individual's health status and lifestyle habits. Next, the life log analysis system has an AI analyze the collected life log data. The AI ​​analyzes the collected data and infers the individual's values ​​and desires. For example, it can analyze the individual's values ​​and interests from the content of SNS posts and grasp their daily activity patterns from location information. From health data, it can analyze the individual's health status and lifestyle habits. Based on the analysis results, the AI ​​infers the individual's values ​​and desires. This generates an end-of-life plan that respects the individual's wishes. For example, the system proposes end-of-life planning based on the individual's wishes, such as how they want to spend their final days and how they want to leave memories for their family. Furthermore, the life log analysis system proposes methods for managing and transferring digital assets. For example, it proposes methods for organizing and transferring digital assets left behind by the individual, such as SNS accounts and cloud storage data. This reduces the burden on family members and ensures that the individual's wishes are reliably passed on. The life log analysis system also automatically generates life timelines and memory albums based on life logs. This allows users to reflect on their lives and supports self-realization. For example, by extracting important events and memories from life log data and creating life timelines and memory albums, users can reflect on their lives. Finally, the life log analysis system continuously optimizes end-of-life planning based on daily data updates. This allows the system to provide end-of-life planning plans that respond to changes in the user's lifestyle and values.For example, the system updates end-of-life planning plans based on changes in the user's health and lifestyle, providing the most suitable plan. This mechanism ensures that end-of-life planning respects the individual's wishes and reduces the burden on surviving family members. It also supports users in reflecting on their lives and achieving self-realization, contributing to the creation of a sense of purpose. In this way, the life log analysis system can realize end-of-life planning that respects the individual's wishes and reduce the burden on surviving family members.

[0067] The life log analysis system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects an individual's life log data. The collection unit collects, for example, SNS posts, location information, and health data. The collection unit can, for example, understand an individual's hobbies and interests from the content of SNS posts and identify their daily activity range from location information. The collection unit can understand an individual's health status and lifestyle habits from health data. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the content of SNS posts into AI, and the AI ​​can extract hobbies and interests. The analysis unit analyzes the data collected by the collection unit and infers an individual's values ​​and desires. The analysis unit can, for example, analyze an individual's values ​​and interests from the content of SNS posts and understand their daily activity patterns from location information. The analysis unit can analyze an individual's health status and lifestyle habits from health data. Some or all of the above-described processes in the analysis unit may, for example, be performed using, for example, AI, or without AI. For example, the analysis unit inputs the collected data into an AI, which can infer values ​​and desires. The generation unit generates an end-of-life plan based on the analysis results obtained by the analysis unit. The generation unit proposes an end-of-life plan based on the individual's wishes, such as how the individual wants to face the end of their life and how they want to leave memories for their family. Some or all of the above processing in the generation unit may be performed using an AI, or without an AI. For example, the generation unit can input the analysis results into an AI, which can generate an end-of-life plan. The provision unit provides the end-of-life plan generated by the generation unit. The provision unit, for example, presents the generated end-of-life plan to the user, allowing the user to select one. Some or all of the above processing in the provision unit may be performed using an AI, or without an AI. For example, the provision unit can input the generated end-of-life plan into an AI, which can present it to the user. Thus, the life log analysis system according to the embodiment can propose a comprehensive life plan and end-of-life plan by analyzing an individual's life log data and inferring their values ​​and desires.

[0068] The data collection unit collects personal life log data. For example, it collects social media posts, location information, and health data. Specifically, it identifies an individual's hobbies and interests from social media posts and their daily activity range from location information. Social media posts are analyzed using text analysis techniques to extract keywords and sentiment, revealing the individual's interests and concerns. For example, if there are many posts about a particular sport or music, it can be determined that the individual has a strong interest in that field. Location information is analyzed using GPS data to identify places the individual frequently visits and their movement patterns. This allows for an understanding of the individual's daily activity range and living area. Health data is obtained from wearable devices and smartphone sensors, collecting information such as heart rate, steps, and sleep patterns. This data is used to gain a detailed understanding of the individual's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input social media posts into an AI, which can then extract hobbies and interests. The AI ​​uses natural language processing technology to analyze the content of posts and automatically classify individuals' interests and preferences. Location information and health data are also analyzed by the AI, allowing for an automatic understanding of individual behavioral patterns and health status. This enables the data collection unit to efficiently gather data from diverse data sources and comprehensively understand an individual's life log.

[0069] The analysis department analyzes data collected by the collection department to infer an individual's values ​​and desires. For example, the analysis department analyzes an individual's values ​​and interests from the content of social media posts and understands their daily behavior patterns from location information. Specifically, it analyzes the content of social media posts using text mining techniques to extract an individual's values ​​and interests. For example, if there are many posts expressing positive emotions, it can be inferred that the individual has optimistic values. Location information is used to analyze places an individual frequently visits and their movement patterns to understand their daily behavior patterns. For example, if an individual frequently visits a particular cafe or park, it can be determined that that place holds significant meaning for them. Health data is used to analyze an individual's health status and lifestyle in detail. For example, by analyzing heart rate and sleep patterns, it is possible to understand an individual's stress level and daily rhythm. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the collected data into an AI, which can then infer values ​​and desires. Using machine learning algorithms, AI learns patterns from collected data and accurately infers individuals' values ​​and desires. This allows the analytics department to quickly and accurately analyze collected data and understand individuals' values ​​and desires. Furthermore, the analytics department can utilize historical data and statistical information to analyze long-term trends and changes. This enables continuous monitoring of changes in individuals' values ​​and desires, leading to more accurate analyses.

[0070] The generation unit generates end-of-life planning plans based on the analysis results obtained by the analysis unit. The generation unit proposes end-of-life planning plans based on the individual's wishes, such as how the individual wants to face the end of their life and how they want to leave memories for their family. Specifically, it uses AI to process the analysis results in order to generate end-of-life planning plans that reflect the individual's values ​​and desires. For example, if an individual wishes for a natural burial, it proposes a specific plan based on that wish. The AI ​​can generate the most suitable plan for the individual by referring to past data and end-of-life planning plans of other users. Some or all of the processing described above in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the analysis results into the AI, and the AI ​​can generate the end-of-life planning plan. The AI ​​uses natural language generation technology to automatically create a specific plan based on the individual's wishes. This allows the generation unit to quickly and accurately generate end-of-life planning plans that reflect the individual's values ​​and desires. Furthermore, the generation unit can continuously improve the accuracy and satisfaction of the plan by presenting the generated plan to the user and collecting feedback. This allows the generation unit to provide an optimal end-of-life planning plan based on the individual's wishes, thereby increasing user satisfaction.

[0071] The service provider provides the end-of-life planning plan generated by the generation unit. For example, the service provider presents the generated end-of-life planning plan to the user, allowing the user to select one. Specifically, it presents the generated end-of-life planning plan to the user in an easy-to-understand manner, offering options. The service provider presents the plan to the user, for example, through a web application or mobile application. The user can review the presented plan and select one that suits their needs. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the generated end-of-life planning plan into an AI, which can then present to the user. The AI ​​can learn from the user's reactions and selection history, and propose a more appropriate plan. This allows the service provider to provide the user with the optimal end-of-life planning plan, increasing user satisfaction. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and satisfaction of the plan. For example, it can improve the next proposal based on feedback on the plan selected by the user. The service provider can also reliably transmit information using multiple communication methods. For example, by using not only web and mobile applications but also email and SMS in combination, important information can be reliably delivered. This allows the service provider to deliver end-of-life planning plans to users quickly and reliably, thereby increasing user satisfaction.

[0072] The data collection unit can collect social media posts, location information, health data, and more. For example, the data collection unit can understand an individual's hobbies and interests from the content of their social media posts. For example, the data collection unit can identify an individual's daily activity range from their location information. For example, the data collection unit can understand an individual's health status and lifestyle from their health data. By collecting data that indicates an individual's behavior and condition, more detailed life log data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of social media posts into a generating AI, which can then extract hobbies and interests.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of life log data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is busy, the data collection unit can adjust the collection timing to match the user's daily rhythm. This reduces the user's burden and allows for the collection of more detailed data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0074] The data collection unit can analyze the user's past life log data and select the optimal collection method. For example, the data collection unit may prioritize collecting data from devices and apps that the user has frequently used in the past. For example, the data collection unit may analyze the user's past behavior patterns and determine the optimal collection timing. For example, the data collection unit may select the most efficient collection method based on the user's past data collection history. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past life log data into a generating AI, which can then select the optimal collection method.

[0075] The data collection unit can filter the life log data based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the data collection unit will prioritize collecting health data. For example, if the user is traveling, the data collection unit will prioritize collecting location information. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. This allows for the collection of more relevant data by filtering the data based on the user's areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest into a generating AI, which can then filter the data.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to relaxation. For example, if the user is having fun, the data collection unit may prioritize collecting data related to hobbies and entertainment. For example, if the user is tired, the data collection unit may prioritize collecting data related to rest. This allows for the collection of more appropriate data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of data.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifelog data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of information about the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of information about the area around the user's home. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0078] The data collection unit can analyze a user's social media activity and collect relevant data when collecting lifelog data. For example, the data collection unit can analyze the content a user frequently posts on social media and collect relevant data. For example, the data collection unit can collect relevant data based on information about accounts a user follows. For example, the data collection unit can collect relevant data based on information about online communities a user participates in. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into a generating AI, which can then collect relevant data.

[0079] The analysis department can analyze collected data and infer an individual's values ​​and desires. For example, the analysis department can analyze an individual's values ​​and interests from the content of their social media posts. For example, the analysis department can understand their daily behavioral patterns from location information. For example, the analysis department can analyze an individual's health status and lifestyle from health data. In this way, by analyzing the collected data, an individual's values ​​and desires can be inferred. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into AI, which can then infer values ​​and desires.

[0080] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing data related to stress reduction. For example, if the user is relaxed, the analysis unit will analyze data related to relaxation in detail. For example, if the user is excited, the analysis unit can prioritize analyzing data related to excitement. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can analyze high-importance data in detail and low-importance data simply. For example, the analysis unit can prioritize the analysis of high-importance data and postpone the analysis of low-importance data. For example, the analysis unit can allocate more resources to the analysis of high-importance data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to evaluate health status to health data. For example, the analysis unit can apply an algorithm to analyze behavioral patterns to location data. For example, the analysis unit can apply an algorithm to perform sentiment analysis to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply different analysis algorithms.

[0083] The generation unit can generate end-of-life plans based on an individual's values ​​and desires. For example, the generation unit proposes end-of-life plans based on the individual's wishes, such as how they want to face the end of their life or how they want to leave memories for their surviving family. By generating end-of-life plans based on an individual's values ​​and desires, it is possible to provide end-of-life plans that respect the individual's wishes. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input values ​​and desires into a generation AI, and the generation AI can generate an end-of-life plan.

[0084] The generation unit can estimate the user's emotions and adjust the method of generating the end-of-life plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate an end-of-life plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate an end-of-life plan that emphasizes the shortest route. If the user is excited, the generation unit can generate an end-of-life plan with visually stimulating effects. By adjusting the generation method according to the user's emotions, a more appropriate end-of-life plan can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can estimate the emotions and adjust the generation method.

[0085] The generation unit can analyze the user's past behavioral history to select the optimal plan when generating an end-of-life plan. For example, the generation unit can select the optimal plan based on the end-of-life plans the user has made in the past. For example, the generation unit can select the most efficient end-of-life plan from the user's past behavioral history. For example, the generation unit can analyze the user's past behavioral history and select the most suitable end-of-life plan. This allows for the provision of an efficient end-of-life plan by selecting the optimal plan based on past behavioral history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past behavioral history into a generation AI, which can then select the optimal plan.

[0086] The generation unit can customize the end-of-life plan based on the user's current living situation when generating it. For example, the generation unit can generate an end-of-life plan that takes health into consideration based on the user's health condition. For example, the generation unit can generate an end-of-life plan that takes work into consideration based on the user's work situation. For example, the generation unit can generate an end-of-life plan that takes family circumstances into consideration based on the user's family situation. By customizing the plan based on the current living situation, a more appropriate end-of-life plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the current living situation into a generation AI, and the generation AI can customize the plan.

[0087] The service provider can provide the generated end-of-life plan. For example, the service provider can present the generated end-of-life plan to the user and allow the user to select one. By providing the generated end-of-life plan, the service provider can provide the user with an appropriate end-of-life plan. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated end-of-life plan into AI, and the AI ​​can present it to the user.

[0088] The service provider can estimate the user's emotions and adjust the method of delivering the end-of-life plan based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver the end-of-life plan at a leisurely pace. If the user is in a hurry, the service provider can deliver the end-of-life plan quickly. If the user is excited, the service provider can deliver the end-of-life plan with visually stimulating effects. By adjusting the delivery method according to the user's emotions, a more appropriate end-of-life plan can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the delivery method.

[0089] The service provider can select the optimal service delivery method by referring to the user's past feedback when providing end-of-life planning plans. For example, the service provider can prioritize providing the service delivery method that the user has preferred in the past. For example, the service provider can analyze the user's past feedback and select the service delivery method that yields the highest satisfaction. For example, the service provider can customize the service delivery method based on the user's past feedback. This improves user satisfaction by selecting the optimal service delivery method based on past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past feedback into a generating AI, which can then select the optimal service delivery method.

[0090] The service provider can estimate the user's emotions and adjust the order in which end-of-life planning plans are provided based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize providing end-of-life planning plans related to stress reduction. For example, if the user is relaxed, the service provider will prioritize providing end-of-life planning plans related to relaxation. For example, if the user is excited, the service provider can prioritize providing end-of-life planning plans related to excitement. By adjusting the order of provision according to the user's emotions, a more appropriate end-of-life planning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI, which can estimate emotions and adjust the order of provision.

[0091] The service provider can select the optimal delivery method when providing end-of-life planning plans, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. If the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. If the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. In this way, by considering device information, the service provider can provide the most suitable delivery method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information into a generating AI, which can then select the optimal delivery method.

[0092] The Management Department can manage digital assets. The Management Department proposes methods for organizing and transferring digital assets left by individuals, such as social media accounts and cloud storage data. This reduces the burden on surviving family members and ensures the reliable transmission of the individual's wishes. Some or all of the above processes performed by the Management Department may be carried out using AI, or not. For example, the Management Department can input digital asset data into a generating AI, which can then propose management methods.

[0093] The management unit can estimate the user's emotions and adjust the digital legacy management method based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple management method to reduce the burden. For example, if the user is relaxed, the management unit can provide detailed management options and suggest a customizable management method. For example, if the user is in a hurry, the management unit can provide a way to manage the digital legacy quickly. This allows for more appropriate digital legacy management by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the management method.

[0094] The management department can analyze a user's past digital activities and select the optimal management method when managing digital heritage. For example, the management department can prioritize managing digital services that the user has frequently used in the past. The management department can analyze a user's past digital activities and select the optimal management method. For example, the management department can select the most efficient management method based on a user's past digital activity history. This enables efficient management of digital heritage by selecting the optimal management method based on past digital activities. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past digital activities into a generating AI, and the generating AI can select the optimal management method.

[0095] The management unit can estimate the user's emotions and determine the management priority of digital assets based on the estimated user emotions. For example, if the user is stressed, the management unit will prioritize managing digital assets related to stress reduction. For example, if the user is relaxed, the management unit will prioritize managing digital assets related to relaxation. For example, if the user is excited, the management unit can prioritize managing digital assets related to excitement. This allows for more appropriate management of digital assets by determining management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI, which can estimate emotions and determine management priorities.

[0096] The management department can select the optimal management method when managing digital assets, taking into account the user's geographical location information. For example, if the user is in a specific region, the management department will prioritize managing digital assets related to that region. For example, if the user is traveling, the management department can manage digital assets based on information about the travel destination. For example, if the user is at home, the management department can manage digital assets based on information about the area around the user's home. This allows for more appropriate management of digital assets by considering geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input geographical location information into a generating AI, which can then select the optimal management method.

[0097] The Life Timeline and Memory Album Generation Unit can generate life timelines and memory albums based on life logs. For example, the Life Timeline and Memory Album Generation Unit extracts important events and memories from life log data and creates a life timeline and memory album. By generating life timelines and memory albums based on life logs, it can support users in reflecting on their lives and achieving self-realization. Some or all of the above-described processes in the Life Timeline and Memory Album Generation Unit may be performed using AI, for example, or without AI. For example, the Life Timeline and Memory Album Generation Unit can input life log data into a generating AI, which can then generate a life timeline and memory album.

[0098] The life timeline and memory album generation unit can estimate the user's emotions and adjust the generation method of the life timeline and memory album based on the estimated user emotions. For example, if the user is relaxed, the life timeline and memory album generation unit will generate a life timeline and memory album that progresses at a leisurely pace. For example, if the user is in a hurry, the life timeline and memory album generation unit will generate a life timeline and memory album that emphasizes the shortest route. For example, if the user is excited, the life timeline and memory album generation unit can generate a life timeline and memory album with visually stimulating effects. In this way, by adjusting the generation method according to the user's emotions, a more appropriate life timeline and memory album can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the life timeline and memory album generation unit may be performed using AI, for example, or without using AI. For example, the life timeline and memory album generation unit can input user emotional data into a generation AI, which can then estimate the emotions and adjust the generation method.

[0099] The life timeline and memory album generation unit can analyze the user's past important events and select the optimal generation method when generating a life timeline or memory album. For example, the life timeline and memory album generation unit generates the optimal life timeline or memory album based on important events the user has experienced in the past. For example, the life timeline and memory album generation unit analyzes the user's past important events and selects the most efficient generation method. For example, the life timeline and memory album generation unit can generate the most suitable life timeline or memory album based on the user's past important events. This makes it possible to efficiently generate life timelines and memory albums by selecting the optimal generation method based on important past events. Some or all of the above processing in the life timeline and memory album generation unit may be performed using AI, for example, or without AI. For example, the life timeline and memory album generation unit can input important past events into a generation AI, which can then select the optimal generation method.

[0100] The life timeline and memory album generation unit can estimate the user's emotions and determine the priority of the life timeline and memory album based on the estimated user emotions. For example, if the user is stressed, the life timeline and memory album generation unit will prioritize generating life timelines and memory albums related to stress reduction. For example, if the user is relaxed, the life timeline and memory album generation unit will prioritize generating life timelines and memory albums related to relaxation. For example, if the user is excited, the life timeline and memory album generation unit can prioritize generating life timelines and memory albums related to excitement. In this way, by determining priorities according to the user's emotions, more appropriate life timelines and memory albums can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the life timeline and memory album generation unit may be performed using AI, for example, or without using AI. For example, the life timeline and memory album generation unit can input user emotional data into a generating AI, which can then estimate emotions and determine priorities.

[0101] The life timeline and memory album generation unit can select the optimal generation method by considering the user's geographical location information when generating the life timeline and memory album. For example, if the user is in a specific region, the life timeline and memory album generation unit will generate a life timeline and memory album related to that region. For example, if the user is traveling, the life timeline and memory album generation unit can generate a life timeline and memory album based on information about the travel destination. For example, if the user is at home, the life timeline and memory album generation unit can generate a life timeline and memory album based on information about the area around the user's home. In this way, by considering geographical location information, a more appropriate life timeline and memory album can be generated. Some or all of the above processing in the life timeline and memory album generation unit may be performed using AI, for example, or without AI. For example, the life timeline and memory album generation unit can input geographical location information into a generation AI, which can then select the optimal generation method.

[0102] The optimization unit can optimize end-of-life planning plans based on daily data updates. For example, the optimization unit updates the end-of-life planning plan in response to changes in the user's health condition and lifestyle, providing the optimal plan. By optimizing the end-of-life planning plan based on daily data updates, it is possible to provide an end-of-life planning plan that responds to changes in the user's lifestyle and values. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input daily data updates into a generating AI, which can then optimize the end-of-life planning plan.

[0103] The optimization unit can estimate the user's emotions and adjust the optimization method of the end-of-life plan based on the estimated emotions. For example, if the user is relaxed, the optimization unit will optimize the end-of-life plan to proceed at a relaxed pace. If the user is in a hurry, the optimization unit will optimize the end-of-life plan to emphasize the shortest route. If the user is excited, the optimization unit can optimize the end-of-life plan to include visually stimulating effects. By adjusting the optimization method according to the user's emotions, a more appropriate end-of-life plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the optimization method.

[0104] The optimization unit can select the optimal optimization method when optimizing an end-of-life planning plan by referring to the user's past data update history. For example, the optimization unit selects the optimal optimization method based on the user's past data update history. For example, the optimization unit analyzes the user's past data update history and selects the most efficient optimization method. For example, the optimization unit can select the most suitable optimization method based on the user's past data update history. This makes it possible to optimize an end-of-life planning plan efficiently by selecting the optimal optimization method based on past data update history. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past data update history into a generating AI, and the generating AI can select the optimal optimization method.

[0105] The optimization unit can estimate the user's emotions and adjust the optimization frequency of the end-of-life plan based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing end-of-life plans related to stress reduction. For example, if the user is relaxed, the optimization unit will prioritize optimizing end-of-life plans related to relaxation. For example, if the user is excited, the optimization unit can prioritize optimizing end-of-life plans related to excitement. By adjusting the optimization frequency according to the user's emotions, a more appropriate end-of-life plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI, which can estimate emotions and adjust the optimization frequency.

[0106] The optimization unit can select the optimal optimization method when optimizing an end-of-life planning plan, taking into account the user's device information. For example, if the user is using a smartphone, the optimization unit can provide an optimization method that matches the screen size. For example, if the user is using a tablet, the optimization unit can provide an optimization method optimized for a larger screen. For example, if the user is using a smartwatch, the optimization unit can provide a concise and highly visible optimization method. In this way, by taking device information into account, the optimal optimization method can be provided to the user. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input device information into a generating AI, and the generating AI can select the optimal optimization method.

[0107] The optimization unit can select the optimal optimization method when optimizing an end-of-life plan, taking into account the user's health condition. For example, the optimization unit can provide an optimization method that takes health into consideration, depending on the user's health condition. For example, if the user is unwell, the optimization unit can provide an optimization method that prioritizes rest. For example, if the user is seeking healthy exercise, the optimization unit can provide an optimization method that incorporates exercise. This allows for the provision of a more appropriate end-of-life plan by taking health conditions into account. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the health condition into a generating AI, which can then select the optimal optimization method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data collection unit can estimate the user's emotions and adjust the timing of life log data collection based on those emotions. For example, if the user is stressed, the collection frequency can be reduced to lessen the user's burden. If the user is relaxed, the collection frequency can be increased to collect more detailed data. If the user is busy, the collection timing can be adjusted to match the user's daily rhythm. This allows for the collection of more detailed data while reducing the user's burden by adjusting the collection timing according to their emotions.

[0110] The data collection unit can analyze the user's past life log data and select the optimal collection method. For example, it can prioritize collecting data from devices and apps that the user has frequently used in the past. It can analyze the user's past behavior patterns to determine the optimal collection timing. Based on the user's past data collection history, it can select the most efficient collection method. This enables efficient data collection by selecting the optimal collection method based on past data.

[0111] The data collection unit can filter lifelog data based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, health data will be prioritized. If the user is traveling, location information will be prioritized. If the user is at work, work-related data will be prioritized. This allows for the collection of more relevant data by filtering it based on the user's areas of interest.

[0112] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting data related to relaxation. If the user is having fun, it can prioritize collecting data related to hobbies and entertainment. If the user is tired, it can prioritize collecting data related to rest. By prioritizing data according to the user's emotions, more appropriate data can be collected.

[0113] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifelog data. For example, if the user is in a specific region, it can prioritize the collection of data related to that region. If the user is traveling, it can prioritize the collection of information about their travel destination. If the user is at home, it can prioritize the collection of information about their home area. In this way, by considering geographical location, it can prioritize the collection of highly relevant data.

[0114] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, it can prioritize analyzing data related to stress reduction. If the user is relaxed, it can analyze data related to relaxation in detail. If the user is excited, it can prioritize analyzing data related to excitement. By adjusting the analysis method according to the user's emotions, more appropriate analysis becomes possible.

[0115] The analysis department can adjust the level of detail in its analysis based on the importance of the data. For example, it can analyze highly important data in detail and less important data simply. Alternatively, it can prioritize the analysis of highly important data and postpone the analysis of less important data. More resources can be allocated to the analysis of highly important data. By adjusting the level of detail in the analysis based on the importance of the data, more efficient analysis becomes possible.

[0116] The generation unit can estimate the user's emotions and adjust the method of generating the end-of-life plan based on those emotions. For example, if the user is relaxed, it can generate an end-of-life plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate an end-of-life plan that emphasizes the shortest route. If the user is excited, it can generate an end-of-life plan with visually stimulating effects. In this way, by adjusting the generation method according to the user's emotions, a more appropriate end-of-life plan can be generated.

[0117] The generation unit can analyze the user's past behavioral history to select the optimal plan when generating an end-of-life planning plan. For example, it can select the optimal plan based on the end-of-life planning plans the user has made in the past. It can select the most efficient end-of-life planning plan from the user's past behavioral history. By analyzing the user's past behavioral history, it can select the most suitable end-of-life planning plan. In this way, by selecting the optimal plan based on past behavioral history, it can provide an efficient end-of-life planning plan.

[0118] The service provider can estimate the user's emotions and adjust the delivery method of the end-of-life planning plan based on those emotions. For example, if the user is relaxed, the plan can be delivered at a leisurely pace. If the user is in a hurry, the plan can be delivered quickly. If the user is excited, the plan can be delivered with visually stimulating effects. By adjusting the delivery method according to the user's emotions, a more appropriate end-of-life planning plan can be provided.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects personal life log data. For example, it collects social media posts, location information, and health data. The data collection unit understands an individual's hobbies and interests from social media posts and identifies their daily activity range from location information. It can also understand an individual's health status and lifestyle habits from health data. These processes may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit to infer individual values ​​and desires. For example, it can analyze an individual's values ​​and interests from the content of their social media posts, and understand their daily behavioral patterns from location data. It can also analyze an individual's health status and lifestyle habits from health data. These processes may or may not be performed using AI. Step 3: The generation unit generates an end-of-life plan based on the analysis results obtained by the analysis unit. For example, it proposes an end-of-life plan based on the individual's wishes, such as how they want to face the end of their life and how they want to leave memories for their family. These processes may or may not be performed using AI. Step 4: The provisioning unit provides the end-of-life plan generated by the generation unit. For example, it presents the generated end-of-life plan to the user and allows the user to make a selection. These processes may or may not be performed using AI.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, management unit, life timeline and memory album generation unit, and optimization unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects life log data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and infers the individual's values ​​and desires. The generation unit generates an end-of-life plan with the specific processing unit 290 of the data processing unit 12. The provision unit presents the end-of-life plan generated by the control unit 46A of the smart device 14 to the user. The management unit manages the digital legacy with the specific processing unit 290 of the data processing unit 12. The life timeline and memory album generation unit generates a life timeline and memory album based on the life log data with the control unit 46A of the smart device 14. The optimization unit optimizes the end-of-life planning plan based on daily data updates, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, management unit, life timeline and memory album generation unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects life log data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and infers the individual's values ​​and desires. The generation unit generates an end-of-life plan with the specific processing unit 290 of the data processing unit 12. The provision unit presents the end-of-life plan generated by the control unit 46A of the smart glasses 214 to the user. The management unit manages the digital legacy with the specific processing unit 290 of the data processing unit 12. The life timeline and memory album generation unit generates a life timeline and memory album based on the life log data with the control unit 46A of the smart glasses 214. The optimization unit optimizes the end-of-life planning plan based on daily data updates, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, management unit, life timeline and memory album generation unit, and optimization unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects life log data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and infers the individual's values ​​and desires. The generation unit generates an end-of-life plan with the specific processing unit 290 of the data processing unit 12. The provision unit presents the end-of-life plan generated by the control unit 46A of the headset terminal 314 to the user. The management unit manages the digital legacy with the specific processing unit 290 of the data processing unit 12. The life timeline and memory album generation unit generates a life timeline and memory album based on the life log data with the control unit 46A of the headset terminal 314. The optimization unit optimizes the end-of-life planning plan based on daily data updates, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, management unit, life timeline and memory album generation unit, and optimization unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects life log data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and infers the individual's values ​​and desires. The generation unit generates an end-of-life plan with the specific processing unit 290 of the data processing unit 12. The provision unit presents the end-of-life plan generated by the control unit 46A of the robot 414 to the user. The management unit manages the digital legacy with the specific processing unit 290 of the data processing unit 12. The life timeline and memory album generation unit generates a life timeline and memory album based on the life log data with the control unit 46A of the robot 414. The optimization unit optimizes the end-of-life planning plan based on daily data updates, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A collection unit that collects personal life log data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that infers an individual's values ​​and desires, A generation unit generates an end-of-life planning plan based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides the end-of-life planning plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects social media posts, location information, health data, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of life log data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past life log data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When collecting life log data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting life log data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting lifelog data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The collected data is analyzed to infer individuals' values ​​and desires. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Creating an end-of-life plan based on individual values ​​and desires. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the method of generating end-of-life planning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating an end-of-life planning plan, the system analyzes the user's past behavioral history to select the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating an end-of-life planning plan, the plan is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, Provides a generated end-of-life planning plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way end-of-life planning is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing end-of-life planning services, we select the most suitable delivery method by referring to past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which end-of-life planning information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing end-of-life planning services, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) It has a management department that handles the management of digital heritage. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, It estimates user sentiment and adjusts how digital heritage is managed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing a digital legacy, analyze the user's past digital activities to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates user sentiment and determines digital legacy management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing digital assets, the optimal management method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 27) It features a generation unit that creates life timelines and memory albums based on life logs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is The system estimates the user's emotions and adjusts how life timelines and memory albums are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating a life timeline or memory album, the system analyzes the user's past important events to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is It estimates the user's emotions and determines the priority of life timelines and memory albums based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating a life timeline or photo album, the system selects the optimal generation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) It features an optimization unit that optimizes end-of-life planning based on daily data updates. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, It estimates the user's emotions and adjusts the optimization method of the end-of-life plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, When optimizing end-of-life planning, the system selects the optimal optimization method by referring to the user's past data update history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, It estimates the user's emotions and adjusts the optimization frequency of the end-of-life planning based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, When optimizing end-of-life planning, the optimal optimization method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The optimization unit, When optimizing end-of-life planning, the optimal optimization method is selected considering the user's health condition. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects personal life log data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that infers an individual's values ​​and desires, A generation unit generates an end-of-life planning plan based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides the end-of-life planning plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects social media posts, location information, health data, etc. The system according to feature 1.

3. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of life log data collection based on the estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past life log data and select the optimal collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting life log data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting life log data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is When collecting lifelog data, analyze users' social media activity and collect relevant data. The system according to feature 1.

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