System

Abstract

An object of a system according to an embodiment is to organize endings based on a user's interest and to automatically generate a last will and testament.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a generation unit, and a storage unit. The collection unit collects information of a user. The analysis unit analyzes the information collected by the collection unit and specifies an interest of the user. The proposal unit proposes an ending based on the interest identified by the analysis unit. The generation unit automatically generates a last will and testament based on the ending proposed by the proposal unit. The storage unit stores the will document generated by the generation unit in a legally valid format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately organized endings based on the user's interests and automatically generated wills, so there is room for improvement.

[0005] The system according to the embodiment aims to organize endings based on the user's interests and automatically generate a will. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a generation unit, and a storage unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and identifies the user's interests. The suggestion unit suggests an ending based on the interests identified by the analysis unit. The generation unit automatically generates a will based on the ending suggested by the suggestion unit. The storage unit stores the will generated by the generation unit in a legally valid format. [Effects of the Invention]

[0007] The system according to the embodiment can organize endings based on the user's interests and automatically generate a will. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An ending planning system according to an embodiment of the present invention collects and analyzes user information, proposes an ending, generates a will, and saves it in a legally valid format. The ending planning system collects and analyzes information such as the user's work, news, emails, and social media to identify the user's interests. The ending planning system then proposes an ending based on the identified interests and automatically generates a will. The generated will is saved in a legally valid format. For example, the ending planning system analyzes the user's behavioral history and interests to identify the user's current interests. The ending planning system then proposes an ending based on the user's current interests. For example, if the user is interested in traveling, the system proposes a travel-related ending plan. Furthermore, if the user is interested in a particular hobby, the system creates a to-do list related to that hobby. Furthermore, the ending planning system provides a function for deleting information the user does not want to keep, in case of sudden death. For example, specific information can be automatically deleted based on the user's designated prohibited behaviors and words. The ending planning system also automatically generates a will and facilitates legal processing. For example, the AI ​​creates a will based on the user's wishes and saves it in a legally valid format. Finally, the end-of-life planning system provides a function that allows users to set contact points after their death and communicate information such as funeral details, assets, wills, and subsequent procedures. For example, the system can notify the contact points set by the user of funeral details and how assets will be distributed. This allows the end-of-life planning system to enable users to decide the end of their lives according to their own wishes, allowing them to live their lives with peace of mind. This allows the end-of-life planning system to enable users to decide the end of their lives according to their own wishes, allowing them to live their lives with peace of mind.

[0029] The ending organizing system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a generation unit, and a storage unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and interests. The collection unit collects information such as the user's work, news, emails, and social media. The collection unit can also collect the user's behavioral history. For example, the collection unit collects news articles and social media posts frequently viewed by the user. The collection unit can also collect the user's interests. For example, the collection unit collects information related to topics in which the user is interested. The analysis unit analyzes the information collected by the collection unit to identify the user's interests. The analysis can be performed using, for example, data mining or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit analyzes the collected information to identify the user's behavioral history and interests. The analysis unit can also analyze the user's past behavioral history to identify the user's current interests. The analysis unit can also analyze survey results to identify the user's interests. The suggestion unit suggests an ending based on the interests identified by the analysis unit. The suggestion is made based on, for example, the user's interests, but is not limited to this example. For example, if the user is interested in traveling, the suggestion unit suggests an ending plan related to travel. Furthermore, if the user is interested in a particular hobby, the suggestion unit can create a to-do list related to the hobby. Furthermore, the suggestion unit can suggest an ending plan based on the user's interests. The generation unit automatically generates a will based on the ending suggested by the suggestion unit. The generation is made based on, for example, the user's intention, but is not limited to this example. For example, the generation unit automatically generates a will based on the user's intention. Furthermore, the generation unit can generate a will that reflects the user's intention. Furthermore, the generation unit can generate a will based on the user's intention. The storage unit saves the will generated by the generation unit in a legally valid format. The storage is made in, for example, a legally valid format, but is not limited to this example.For example, the storage unit stores the generated will in a legally valid format. The storage unit can also store the generated will with a signature. The storage unit can also store the generated will with witnesses. As a result, the ending organization system according to the embodiment can collect and analyze user information, suggest endings, generate a will, and store it in a legally valid format.

[0030] The collection unit can collect information from multiple information sources for the user. The collection unit collects, for example, information such as the user's work, news, emails, and social media. For example, the collection unit collects posts from the user's social media accounts. The collection unit can also collect email content from the user's email accounts. Furthermore, the collection unit can collect news articles from news sites that the user views. This allows the collection unit to collect information from a variety of information sources for the user.

[0031] The analysis unit can analyze the collected information and identify the user's behavioral history and interests. The analysis unit can, for example, analyze the collected information and identify the user's behavioral history and interests. For example, the analysis unit can analyze the user's website browsing history and identify the user's interests. The analysis unit can also analyze the user's purchase history and identify the user's interests. Furthermore, the analysis unit can analyze the content of the user's posts on SNS and identify the user's interests. This allows the analysis unit to identify the user's behavioral history and interests.

[0032] The suggestion unit can suggest an ending plan based on the user's interests. The suggestion unit, for example, suggests an ending plan based on the user's interests. For example, if the user is interested in traveling, the suggestion unit can suggest an ending plan related to traveling. Also, if the user is interested in a particular hobby, the suggestion unit can create a to-do list related to that hobby. Furthermore, the suggestion unit can suggest an ending plan based on the user's interests. This allows the suggestion unit to suggest an ending plan based on the user's interests.

[0033] The generation unit can automatically generate a will based on the user's intentions. The generation unit automatically generates a will based on, for example, the user's intentions. For example, the generation unit generates a will that reflects the user's intentions. The generation unit can also generate a will based on the user's intentions. Furthermore, the generation unit can also generate a will that reflects the user's intentions. In this way, the generation unit can automatically generate a will based on the user's intentions.

[0034] The storage unit can store the generated will in a legally valid format. The storage unit, for example, stores the generated will in a legally valid format. For example, the storage unit stores the generated will with a signature. The storage unit can also store the generated will with witnesses. Furthermore, the storage unit can also store the generated will in a legally valid format. In this way, the storage unit can store the generated will in a legally valid format.

[0035] The suggestion unit can create a to-do list based on the user's interests. The suggestion unit, for example, creates a to-do list based on the user's interests. For example, if the user is interested in traveling, the suggestion unit can create a to-do list related to travel. Also, if the user is interested in a particular hobby, the suggestion unit can create a to-do list related to the hobby. Furthermore, the suggestion unit can create a to-do list based on the user's interests. In this way, the suggestion unit can create a to-do list based on the user's interests.

[0036] The suggestion unit can delete specific information based on the inappropriate behaviors and words set by the user so as to be able to respond to sudden deaths. The suggestion unit deletes specific information based on the inappropriate behaviors and words set by the user so as to be able to respond to sudden deaths, for example. For example, the suggestion unit automatically deletes specific information based on the inappropriate behaviors and words set by the user. The suggestion unit can also delete specific information based on the inappropriate behaviors and words set by the user. Furthermore, the suggestion unit can delete specific information based on the inappropriate behaviors and words set by the user. In this way, the suggestion unit can delete specific information based on the inappropriate behaviors and words set by the user so as to be able to respond to sudden deaths.

[0037] The suggestion unit can set a contact to be contacted after death and inform them of the funeral method, assets, will, and subsequent procedures. The suggestion unit, for example, sets a contact to be contacted after death and informs them of the funeral method, assets, will, and subsequent procedures. For example, the suggestion unit notifies the contacts set by the user of the funeral details and how the assets will be distributed. The suggestion unit can also notify the contacts set by the user of the funeral method and the contents of the will. Furthermore, the suggestion unit can notify the contacts set by the user of the funeral method and how the assets will be distributed. In this way, the suggestion unit can set a contact to be contacted after death and inform them of the funeral method, assets, will, and subsequent procedures.

[0038] The collection unit can analyze the user's past information collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past information collection history and selects the optimal collection method. For example, the collection unit prioritizes collection of information sources that the user frequently accessed in the past. The collection unit can also analyze trends in articles that the user liked to read in the past and collect similar information. Furthermore, the collection unit can exclude information sources that the user avoided in the past and collect information only from preferred information sources. In this way, the collection unit can analyze the user's past information collection history and select the optimal collection method.

[0039] The collection unit can filter information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit filters information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit collects only information related to topics in which the user is currently interested. The collection unit can also prioritize collection of highly relevant information depending on the user's living situation (work, home, etc.). Furthermore, the collection unit can filter appropriate information based on the user's current activity (traveling, at work, etc.). This allows the collection unit to filter information based on the user's current living situation and areas of interest.

[0040] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0041] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in their current location, the collection unit prioritizes collecting news and event information related to that area. Also, when the user is traveling, the collection unit can prioritize collecting tourist information and restaurant information related to the travel destination. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0042] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect the content of posts from accounts the user follows on social media. The collection unit can also collect information related to posts that the user has "liked" or shared on social media. Furthermore, the collection unit can analyze the user's social media activity history and collect related information. This allows the collection unit to analyze the user's social media activities and collect related information.

[0043] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the type of information to be collected based on feedback provided by the user in the past. The collection unit can also preferentially collect information sources that the user has previously preferred. Furthermore, the collection unit can exclude information sources that the user has previously avoided and collect information only from preferred information sources. In this way, the collection unit can customize the collection method by reflecting the user's past feedback.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm dedicated to news to news information. The analysis unit can also apply an analysis algorithm dedicated to email to email information. Furthermore, the analysis unit can apply an analysis algorithm dedicated to SNS to SNS information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of information.

[0046] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and identify areas for improvement in the analysis. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results.

[0047] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit determines the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the analysis priority of information that was submitted earlier. Furthermore, the analysis unit can adjust the priority of analysis depending on the time of submission. This allows the analysis unit to determine the priority of analysis based on the time of submission of information.

[0048] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone the order of analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of information. This allows the analysis unit to adjust the order of analysis based on the relevance of information.

[0049] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0050] The proposal unit can adjust the level of detail of the proposal based on the importance of the ending plan when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the ending plan when making the proposal. For example, the proposal unit makes a detailed proposal for an ending plan with a high level of importance. The proposal unit can also make a concise proposal for an ending plan with a low level of importance. Furthermore, the proposal unit can also determine the priority of the proposal according to the importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the ending plan.

[0051] The suggestion unit can apply different suggestion algorithms depending on the category of the ending plan when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the ending plan when making a suggestion. For example, the suggestion unit applies a suggestion algorithm dedicated to travel to an ending plan related to travel. The suggestion unit can also apply a suggestion algorithm dedicated to hobbies to an ending plan related to hobbies. Furthermore, the suggestion unit can also apply a suggestion algorithm dedicated to asset management to an ending plan related to asset management. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the category of the ending plan.

[0052] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and identify areas for improvement in the suggestion. This allows the suggestion unit to improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0053] The proposal unit can determine the priority of the proposal based on the submission time of the ending plan at the time of proposal. For example, the proposal unit determines the priority of the proposal based on the submission time of the ending plan at the time of proposal. For example, the proposal unit preferentially proposes ending plans that are due to be submitted soon. The proposal unit can also lower the priority of proposals for ending plans that are due to be submitted further away. Furthermore, the proposal unit can adjust the priority of the proposal depending on the submission time. This allows the proposal unit to determine the priority of the proposal based on the submission time of the ending plan.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the ending plans when making proposals. For example, the proposal unit adjusts the order of proposals based on the relevance of the ending plans when making proposals. For example, the proposal unit preferentially proposes ending plans with high relevance. The proposal unit can also postpone the order of proposals for ending plans with low relevance. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the ending plans. This allows the proposal unit to adjust the order of proposals based on the relevance of the ending plans.

[0055] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terminology in the proposal according to the user's level of expertise.

[0056] The generation unit can analyze the user's past intentions and select the optimal generation method when generating a will. For example, the generation unit analyzes the user's past intentions and selects the optimal generation method when generating a will. For example, the generation unit selects the optimal will format based on the user's past intentions. The generation unit can also analyze the user's past intentions and customize the content of the will. Furthermore, the generation unit can adjust the will generation method by referring to the user's past intentions. This allows the generation unit to analyze the user's past intentions and select the optimal generation method.

[0057] The generation unit can customize the generation means based on the user's current living situation when generating a will. For example, the generation unit customizes the generation means based on the user's current living situation when generating a will. For example, if the user has a family, the generation unit can generate a will that includes content related to the family. Also, if the user is single, the generation unit can generate a will that includes content related to personal assets. Furthermore, the generation unit can customize the content of the will according to the user's living situation. This allows the generation unit to customize the generation means based on the user's current living situation.

[0058] The generation unit can improve the generation method by reflecting user feedback when generating a will. For example, the generation unit improves the generation method by reflecting user feedback when generating a will. For example, the generation unit improves the will generation method based on feedback provided by the user. The generation unit can also adjust the content of the will by reflecting user feedback. Furthermore, the generation unit can analyze user feedback and optimize the will generation method. This allows the generation unit to improve the generation method by reflecting user feedback.

[0059] The generation unit can select the optimal generation method in consideration of the user's geographical location information when generating a will. For example, the generation unit selects the optimal generation method in consideration of the user's geographical location information when generating a will. For example, if the user lives in a specific area, the generation unit generates a will based on the laws of that area. Also, if the user is traveling, the generation unit can generate a will based on the laws of the destination. Furthermore, the generation unit can customize the content of the will according to the user's geographical location information. This allows the generation unit to select the optimal generation method in consideration of the user's geographical location information.

[0060] The generation unit can analyze the user's social media activity and suggest a generation method when generating a will. For example, the generation unit analyzes the user's social media activity and suggests a generation method when generating a will. For example, the generation unit can suggest the content of the will based on information shared by the user on social media. The generation unit can also analyze the user's social media activity history and suggest the optimal will format. Furthermore, the generation unit can suggest the content of the will by referring to the activity of the user's friends on social media. In this way, the generation unit can analyze the user's social media activity and suggest the optimal generation method.

[0061] The generation unit can customize the generation method by reflecting the user's past feedback when generating a will. For example, the generation unit customizes the generation method by reflecting the user's past feedback when generating a will. For example, the generation unit customizes the will generation method based on feedback provided by the user in the past. The generation unit can also adjust the content of the will by reflecting the user's past feedback. Furthermore, the generation unit can analyze the user's past feedback and optimize the will generation method. This allows the generation unit to customize the generation method by reflecting the user's past feedback.

[0062] The storage unit can optimize a storage algorithm by referring to past storage data when storing data. For example, the storage unit optimizes a storage algorithm by referring to past storage data when storing data. For example, the storage unit selects an optimal storage algorithm based on past storage data. The storage unit can also analyze past storage data and improve a storage method. Furthermore, the storage unit can optimize a storage algorithm by referring to past storage data. In this way, the storage unit can optimize a storage algorithm by referring to past storage data.

[0063] The storage unit may update the stored data by reflecting user feedback when storing the data. For example, the storage unit may update the stored data by reflecting user feedback when storing the data. For example, the storage unit may update the stored data based on feedback provided by the user. The storage unit may also improve the storage method by reflecting user feedback. Furthermore, the storage unit may analyze user feedback and optimize the stored data. In this way, the storage unit may update the stored data by reflecting user feedback.

[0064] The storage unit can weight the stored data based on the time of submission of the will when storing the data. For example, the storage unit weights the stored data based on the time of submission of the will when storing the data. For example, the storage unit prioritizes storing wills that were submitted recently. The storage unit can also lower the storage priority for wills that were submitted more recently. Furthermore, the storage unit can adjust the weighting of the stored data depending on the time of submission. This allows the storage unit to weight the stored data based on the time of submission of the will.

[0065] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. For example, the storage unit integrates information from different data sources to enrich the stored data when storing the data. For example, the storage unit integrates information from different data sources to enrich the stored data. The storage unit can also analyze information from different data sources to optimize the stored data. Furthermore, the storage unit can update the stored data by referring to information from different data sources. In this way, the storage unit can integrate information from different data sources to enrich the stored data.

[0066] The storage unit can customize the storage method by referring to the user's previously saved data when saving. For example, the storage unit customizes the storage method by referring to the user's previously saved data when saving. For example, the storage unit customizes the storage method based on the user's previously saved data. The storage unit can also analyze the user's previously saved data and improve the storage method. Furthermore, the storage unit can optimize the storage method by referring to the user's previously saved data. This allows the storage unit to customize the storage method by referring to the user's previously saved data.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The ending organization system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, blood pressure, sleep patterns, etc.) and provides it to the analysis unit. The analysis unit can analyze the collected health data and evaluate the user's health condition. For example, the analysis unit may analyze the user's heart rate fluctuations and estimate stress levels. The analysis unit may also analyze the user's sleep patterns and evaluate sleep quality. Furthermore, the analysis unit may adjust the proposed ending plan based on the user's health condition. This allows the ending organization system to propose an ending plan that takes the user's health condition into consideration.

[0069] The ending planning system can further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes the user's social media accounts and contact list to identify the user's social connections. For example, the social network analysis unit identifies people with whom the user frequently interacts and evaluates those relationships. The social network analysis unit can also analyze the user's relationships with friends and family and reflect these in the ending plan. Furthermore, the social network analysis unit can automatically set notification destinations for the ending plan based on the user's social network. This allows the ending planning system to propose an ending plan that takes the user's social network into consideration.

[0070] The ending planning system can further include a hobby analysis unit that customizes the ending plan based on the user's hobbies and interests. The hobby analysis unit collects information related to the user's hobbies and interests and provides it to the analysis unit. For example, the hobby analysis unit collects information about hobby groups and events in which the user participates. The hobby analysis unit can also collect information about websites the user visits and magazines they subscribe to. Furthermore, the hobby analysis unit can identify the user's hobbies and interests based on the collected information and reflect them in the ending plan. This allows the ending planning system to propose an ending plan that matches the user's hobbies and interests.

[0071] The ending planning system may further include a property management unit that manages the user's property information. The property management unit collects the user's property information (e.g., real estate, bank accounts, stocks, etc.) and provides it to the analysis unit. The analysis unit can analyze the collected property information and evaluate the user's property situation. For example, the analysis unit may evaluate the value of the user's property and reflect this in the will. The analysis unit may also analyze the balance of the user's bank account and make property distribution proposals. Furthermore, the analysis unit may adjust the proposed ending plan based on the user's property information. This allows the ending planning system to propose an ending plan that takes into account the user's property situation.

[0072] The ending organizing system may further include a life event management unit that manages the user's life events. The life event management unit collects the user's life events (e.g., marriage, childbirth, moving, etc.) and provides them to the analysis unit. The analysis unit can analyze the collected life event information and evaluate the user's life stage. For example, if the user gets married, the analysis unit can propose an ending plan related to the marriage. Also, if the user gives birth, the analysis unit can propose an ending plan related to the child. Furthermore, the analysis unit can adjust the proposed ending plan based on the user's life events. This allows the ending organizing system to propose an ending plan that corresponds to the user's life events.

[0073] The ending arrangement system may further include a cultural analysis unit that proposes an ending plan taking into account the user's cultural background. The cultural analysis unit collects information related to the user's cultural background (e.g., religion, traditions, customs, etc.) and provides it to the analysis unit. The analysis unit can analyze the collected cultural background information and evaluate the user's cultural background. For example, if the user believes in a particular religion, the analysis unit can propose an ending plan based on that religion. The analysis unit can also customize the ending plan based on the user's traditions and customs. Furthermore, the analysis unit can adjust the proposed ending plan based on the user's cultural background. This allows the ending arrangement system to propose an ending plan that takes into account the user's cultural background.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection unit collects user information. This information includes personal information, behavioral history, and interests. For example, the collection unit collects information about the user's work, news, emails, social media, etc., and collects news articles and social media posts that the user frequently views. It also collects information related to topics that interest the user. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's interests. The analysis is performed using data mining and machine learning algorithms to identify the user's behavioral history and interests. It can also analyze past behavioral history and survey results to identify current interests. Step 3: The suggestion unit suggests endings based on the interests identified by the analysis unit. For example, if the user is interested in travel, the suggestion unit suggests ending plans related to travel. If the user is interested in a particular hobby, the suggestion unit creates a to-do list related to that hobby. Step 4: The generator automatically generates a will based on the endings proposed by the suggester. The generation is performed based on the user's wishes, and a will that reflects the user's wishes is generated. Step 5: The storage unit stores the will generated by the generation unit in a legally valid format. The storage is performed in a legally valid format, and can also be stored with signatures and witnesses.

[0076] (Example 2) An ending planning system according to an embodiment of the present invention collects and analyzes user information, proposes an ending, generates a will, and saves it in a legally valid format. The ending planning system collects and analyzes information such as the user's work, news, emails, and social media to identify the user's interests. The ending planning system then proposes an ending based on the identified interests and automatically generates a will. The generated will is saved in a legally valid format. For example, the ending planning system analyzes the user's behavioral history and interests to identify the user's current interests. The ending planning system then proposes an ending based on the user's current interests. For example, if the user is interested in traveling, the system proposes a travel-related ending plan. Furthermore, if the user is interested in a particular hobby, the system creates a to-do list related to that hobby. Furthermore, the ending planning system provides a function for deleting information the user does not want to keep, in case of sudden death. For example, specific information can be automatically deleted based on the user's designated prohibited behaviors and words. The ending planning system also automatically generates a will and facilitates legal processing. For example, the AI ​​creates a will based on the user's wishes and saves it in a legally valid format. Finally, the end-of-life planning system provides a function that allows users to set contact points after their death and communicate information such as funeral details, assets, wills, and subsequent procedures. For example, the system can notify the contact points set by the user of funeral details and how assets will be distributed. This allows the end-of-life planning system to enable users to decide the end of their lives according to their own wishes, allowing them to live their lives with peace of mind. This allows the end-of-life planning system to enable users to decide the end of their lives according to their own wishes, allowing them to live their lives with peace of mind.

[0077] The ending organizing system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a generation unit, and a storage unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and interests. The collection unit collects information such as the user's work, news, emails, and social media. The collection unit can also collect the user's behavioral history. For example, the collection unit collects news articles and social media posts frequently viewed by the user. The collection unit can also collect the user's interests. For example, the collection unit collects information related to topics in which the user is interested. The analysis unit analyzes the information collected by the collection unit to identify the user's interests. The analysis can be performed using, for example, data mining or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit analyzes the collected information to identify the user's behavioral history and interests. The analysis unit can also analyze the user's past behavioral history to identify the user's current interests. The analysis unit can also analyze survey results to identify the user's interests. The suggestion unit suggests an ending based on the interests identified by the analysis unit. The suggestion is made based on, for example, the user's interests, but is not limited to this example. For example, if the user is interested in traveling, the suggestion unit suggests an ending plan related to travel. Furthermore, if the user is interested in a particular hobby, the suggestion unit can create a to-do list related to the hobby. Furthermore, the suggestion unit can suggest an ending plan based on the user's interests. The generation unit automatically generates a will based on the ending suggested by the suggestion unit. The generation is made based on, for example, the user's intention, but is not limited to this example. For example, the generation unit automatically generates a will based on the user's intention. Furthermore, the generation unit can generate a will that reflects the user's intention. Furthermore, the generation unit can generate a will based on the user's intention. The storage unit saves the will generated by the generation unit in a legally valid format. The storage is made in, for example, a legally valid format, but is not limited to this example.For example, the storage unit stores the generated will in a legally valid format. The storage unit can also store the generated will with a signature. The storage unit can also store the generated will with witnesses. As a result, the ending organization system according to the embodiment can collect and analyze user information, suggest endings, generate a will, and store it in a legally valid format.

[0078] The collection unit can collect information from multiple information sources for the user. The collection unit collects, for example, information such as the user's work, news, emails, and social media. For example, the collection unit collects posts from the user's social media accounts. The collection unit can also collect email content from the user's email accounts. Furthermore, the collection unit can collect news articles from news sites that the user views. This allows the collection unit to collect information from a variety of information sources for the user.

[0079] The analysis unit can analyze the collected information and identify the user's behavioral history and interests. The analysis unit can, for example, analyze the collected information and identify the user's behavioral history and interests. For example, the analysis unit can analyze the user's website browsing history and identify the user's interests. The analysis unit can also analyze the user's purchase history and identify the user's interests. Furthermore, the analysis unit can analyze the content of the user's posts on SNS and identify the user's interests. This allows the analysis unit to identify the user's behavioral history and interests.

[0080] The suggestion unit can suggest an ending plan based on the user's interests. The suggestion unit, for example, suggests an ending plan based on the user's interests. For example, if the user is interested in traveling, the suggestion unit can suggest an ending plan related to traveling. Also, if the user is interested in a particular hobby, the suggestion unit can create a to-do list related to that hobby. Furthermore, the suggestion unit can suggest an ending plan based on the user's interests. This allows the suggestion unit to suggest an ending plan based on the user's interests.

[0081] The generation unit can automatically generate a will based on the user's intentions. The generation unit automatically generates a will based on, for example, the user's intentions. For example, the generation unit generates a will that reflects the user's intentions. The generation unit can also generate a will based on the user's intentions. Furthermore, the generation unit can also generate a will that reflects the user's intentions. In this way, the generation unit can automatically generate a will based on the user's intentions.

[0082] The storage unit can store the generated will in a legally valid format. The storage unit, for example, stores the generated will in a legally valid format. For example, the storage unit stores the generated will with a signature. The storage unit can also store the generated will with witnesses. Furthermore, the storage unit can also store the generated will in a legally valid format. In this way, the storage unit can store the generated will in a legally valid format.

[0083] The suggestion unit can create a to-do list based on the user's interests. The suggestion unit, for example, creates a to-do list based on the user's interests. For example, if the user is interested in traveling, the suggestion unit can create a to-do list related to travel. Also, if the user is interested in a particular hobby, the suggestion unit can create a to-do list related to the hobby. Furthermore, the suggestion unit can create a to-do list based on the user's interests. In this way, the suggestion unit can create a to-do list based on the user's interests.

[0084] The suggestion unit can delete specific information based on the inappropriate behaviors and words set by the user so as to be able to respond to sudden deaths. The suggestion unit deletes specific information based on the inappropriate behaviors and words set by the user so as to be able to respond to sudden deaths, for example. For example, the suggestion unit automatically deletes specific information based on the inappropriate behaviors and words set by the user. The suggestion unit can also delete specific information based on the inappropriate behaviors and words set by the user. Furthermore, the suggestion unit can delete specific information based on the inappropriate behaviors and words set by the user. In this way, the suggestion unit can delete specific information based on the inappropriate behaviors and words set by the user so as to be able to respond to sudden deaths.

[0085] The suggestion unit can set a contact to be contacted after death and inform them of the funeral method, assets, will, and subsequent procedures. The suggestion unit, for example, sets a contact to be contacted after death and informs them of the funeral method, assets, will, and subsequent procedures. For example, the suggestion unit notifies the contacts set by the user of the funeral details and how the assets will be distributed. The suggestion unit can also notify the contacts set by the user of the funeral method and the contents of the will. Furthermore, the suggestion unit can notify the contacts set by the user of the funeral method and how the assets will be distributed. In this way, the suggestion unit can set a contact to be contacted after death and inform them of the funeral method, assets, will, and subsequent procedures.

[0086] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the user is relaxed. Furthermore, if the user is excited, the collection unit can collect information in real time and immediately reflect the information. Furthermore, if the user is tired, the collection unit can temporarily stop information collection and resume it after a rest. This allows the collection unit to adjust the timing of information collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The collection unit can analyze the user's past information collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past information collection history and selects the optimal collection method. For example, the collection unit prioritizes collection of information sources that the user frequently accessed in the past. The collection unit can also analyze trends in articles that the user liked to read in the past and collect similar information. Furthermore, the collection unit can exclude information sources that the user avoided in the past and collect information only from preferred information sources. In this way, the collection unit can analyze the user's past information collection history and select the optimal collection method.

[0088] The collection unit can filter information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit filters information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit collects only information related to topics in which the user is currently interested. The collection unit can also prioritize collection of highly relevant information depending on the user's living situation (work, home, etc.). Furthermore, the collection unit can filter appropriate information based on the user's current activity (traveling, at work, etc.). This allows the collection unit to filter information based on the user's current living situation and areas of interest.

[0089] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0090] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing information. Furthermore, if the user is excited, the collection unit can prioritize collecting stimulating information. Furthermore, if the user is tired, the collection unit can prioritize collecting relaxing information. In this way, the collection unit can prioritize collecting information to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in their current location, the collection unit prioritizes collecting news and event information related to that area. Also, when the user is traveling, the collection unit can prioritize collecting tourist information and restaurant information related to the travel destination. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0092] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect the content of posts from accounts the user follows on social media. The collection unit can also collect information related to posts that the user has "liked" or shared on social media. Furthermore, the collection unit can analyze the user's social media activity history and collect related information. This allows the collection unit to analyze the user's social media activities and collect related information.

[0093] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the type of information to be collected based on feedback provided by the user in the past. The collection unit can also preferentially collect information sources that the user has previously preferred. Furthermore, the collection unit can exclude information sources that the user has previously avoided and collect information only from preferred information sources. In this way, the collection unit can customize the collection method by reflecting the user's past feedback.

[0094] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information.

[0096] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm dedicated to news to news information. The analysis unit can also apply an analysis algorithm dedicated to email to email information. Furthermore, the analysis unit can apply an analysis algorithm dedicated to SNS to SNS information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of information.

[0097] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and identify areas for improvement in the analysis. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results.

[0098] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and to-the-point analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit determines the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the analysis priority of information that was submitted earlier. Furthermore, the analysis unit can adjust the priority of analysis depending on the time of submission. This allows the analysis unit to determine the priority of analysis based on the time of submission of information.

[0100] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone the order of analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of information. This allows the analysis unit to adjust the order of analysis based on the relevance of information.

[0101] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0102] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually stimulating suggestions when the user is excited. This allows the suggestion unit to adjust the way in which suggestions are expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The proposal unit can adjust the level of detail of the proposal based on the importance of the ending plan when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the ending plan when making the proposal. For example, the proposal unit makes a detailed proposal for an ending plan with a high level of importance. The proposal unit can also make a concise proposal for an ending plan with a low level of importance. Furthermore, the proposal unit can also determine the priority of the proposal according to the importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the ending plan.

[0104] The suggestion unit can apply different suggestion algorithms depending on the category of the ending plan when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the ending plan when making a suggestion. For example, the suggestion unit applies a suggestion algorithm dedicated to travel to an ending plan related to travel. The suggestion unit can also apply a suggestion algorithm dedicated to hobbies to an ending plan related to hobbies. Furthermore, the suggestion unit can also apply a suggestion algorithm dedicated to asset management to an ending plan related to asset management. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the category of the ending plan.

[0105] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and identify areas for improvement in the suggestion. This allows the suggestion unit to improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0106] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. Also, if the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the user is excited, the suggestion unit can make a visually stimulating suggestion. This allows the suggestion unit to adjust the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The proposal unit can determine the priority of the proposal based on the submission time of the ending plan at the time of proposal. For example, the proposal unit determines the priority of the proposal based on the submission time of the ending plan at the time of proposal. For example, the proposal unit preferentially proposes ending plans that are due to be submitted soon. The proposal unit can also lower the priority of proposals for ending plans that are due to be submitted further away. Furthermore, the proposal unit can adjust the priority of the proposal depending on the submission time. This allows the proposal unit to determine the priority of the proposal based on the submission time of the ending plan.

[0108] The proposal unit can adjust the order of proposals based on the relevance of the ending plans when making proposals. For example, the proposal unit adjusts the order of proposals based on the relevance of the ending plans when making proposals. For example, the proposal unit preferentially proposes ending plans with high relevance. The proposal unit can also postpone the order of proposals for ending plans with low relevance. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the ending plans. This allows the proposal unit to adjust the order of proposals based on the relevance of the ending plans.

[0109] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terminology in the proposal according to the user's level of expertise.

[0110] The generation unit can estimate the user's emotions and adjust the will generation method based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the will generation method based on the estimated user emotions. For example, the generation unit generates a detailed will when the user is relaxed. The generation unit can also generate a concise will that focuses on the main points when the user is in a hurry. Furthermore, the generation unit can generate a visually stimulating will when the user is excited. This allows the generation unit to adjust the will generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The generation unit can analyze the user's past intentions and select the optimal generation method when generating a will. For example, the generation unit analyzes the user's past intentions and selects the optimal generation method when generating a will. For example, the generation unit selects the optimal will format based on the user's past intentions. The generation unit can also analyze the user's past intentions and customize the content of the will. Furthermore, the generation unit can adjust the will generation method by referring to the user's past intentions. This allows the generation unit to analyze the user's past intentions and select the optimal generation method.

[0112] The generation unit can customize the generation means based on the user's current living situation when generating a will. For example, the generation unit customizes the generation means based on the user's current living situation when generating a will. For example, if the user has a family, the generation unit can generate a will that includes content related to the family. Also, if the user is single, the generation unit can generate a will that includes content related to personal assets. Furthermore, the generation unit can customize the content of the will according to the user's living situation. This allows the generation unit to customize the generation means based on the user's current living situation.

[0113] The generation unit can improve the generation method by reflecting user feedback when generating a will. For example, the generation unit improves the generation method by reflecting user feedback when generating a will. For example, the generation unit improves the will generation method based on feedback provided by the user. The generation unit can also adjust the content of the will by reflecting user feedback. Furthermore, the generation unit can analyze user feedback and optimize the will generation method. This allows the generation unit to improve the generation method by reflecting user feedback.

[0114] The generation unit can estimate the user's emotions and determine the priority of wills based on the estimated user emotions. The generation unit can, for example, estimate the user's emotions and determine the priority of wills based on the estimated user emotions. For example, the generation unit can prioritize generating a detailed will when the user is relaxed. The generation unit can also prioritize generating a concise will when the user is in a hurry. Furthermore, the generation unit can prioritize generating a visually stimulating will when the user is excited. This allows the generation unit to prioritize wills based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The generation unit can select the optimal generation method in consideration of the user's geographical location information when generating a will. For example, the generation unit selects the optimal generation method in consideration of the user's geographical location information when generating a will. For example, if the user lives in a specific area, the generation unit generates a will based on the laws of that area. Also, if the user is traveling, the generation unit can generate a will based on the laws of the destination. Furthermore, the generation unit can customize the content of the will according to the user's geographical location information. This allows the generation unit to select the optimal generation method in consideration of the user's geographical location information.

[0116] The generation unit can analyze the user's social media activity and suggest a generation method when generating a will. For example, the generation unit analyzes the user's social media activity and suggests a generation method when generating a will. For example, the generation unit can suggest the content of the will based on information shared by the user on social media. The generation unit can also analyze the user's social media activity history and suggest the optimal will format. Furthermore, the generation unit can suggest the content of the will by referring to the activity of the user's friends on social media. In this way, the generation unit can analyze the user's social media activity and suggest the optimal generation method.

[0117] The generation unit can customize the generation method by reflecting the user's past feedback when generating a will. For example, the generation unit customizes the generation method by reflecting the user's past feedback when generating a will. For example, the generation unit customizes the will generation method based on feedback provided by the user in the past. The generation unit can also adjust the content of the will by reflecting the user's past feedback. Furthermore, the generation unit can analyze the user's past feedback and optimize the will generation method. This allows the generation unit to customize the generation method by reflecting the user's past feedback.

[0118] The storage unit can estimate the user's emotion and adjust the storage method based on the estimated user's emotion. For example, the storage unit can estimate the user's emotion and adjust the storage method based on the estimated user's emotion. For example, the storage unit can provide a detailed storage method when the user is relaxed. The storage unit can also provide a concise storage method when the user is in a hurry. Furthermore, the storage unit can provide a visually stimulating storage method when the user is excited. This allows the storage unit to adjust the storage method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The storage unit can optimize a storage algorithm by referring to past storage data when storing data. For example, the storage unit optimizes a storage algorithm by referring to past storage data when storing data. For example, the storage unit selects an optimal storage algorithm based on past storage data. The storage unit can also analyze past storage data and improve a storage method. Furthermore, the storage unit can optimize a storage algorithm by referring to past storage data. In this way, the storage unit can optimize a storage algorithm by referring to past storage data.

[0120] The storage unit may update the stored data by reflecting user feedback when storing the data. For example, the storage unit may update the stored data by reflecting user feedback when storing the data. For example, the storage unit may update the stored data based on feedback provided by the user. The storage unit may also improve the storage method by reflecting user feedback. Furthermore, the storage unit may analyze user feedback and optimize the stored data. In this way, the storage unit may update the stored data by reflecting user feedback.

[0121] The storage unit can estimate the user's emotion and adjust the frequency of saving based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion and adjusts the frequency of saving based on the estimated user's emotion. For example, the storage unit saves frequently when the user is relaxed. The storage unit can also reduce the frequency of saving when the user is in a hurry. Furthermore, the storage unit can adjust the frequency of saving when the user is excited. In this way, the storage unit can adjust the frequency of saving based on the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0122] The storage unit can weight the stored data based on the time of submission of the will when storing the data. For example, the storage unit weights the stored data based on the time of submission of the will when storing the data. For example, the storage unit prioritizes storing wills that were submitted recently. The storage unit can also lower the storage priority for wills that were submitted more recently. Furthermore, the storage unit can adjust the weighting of the stored data depending on the time of submission. This allows the storage unit to weight the stored data based on the time of submission of the will.

[0123] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. For example, the storage unit integrates information from different data sources to enrich the stored data when storing the data. For example, the storage unit integrates information from different data sources to enrich the stored data. The storage unit can also analyze information from different data sources to optimize the stored data. Furthermore, the storage unit can update the stored data by referring to information from different data sources. In this way, the storage unit can integrate information from different data sources to enrich the stored data.

[0124] The storage unit can customize the storage method by referring to the user's previously saved data when saving. For example, the storage unit customizes the storage method by referring to the user's previously saved data when saving. For example, the storage unit customizes the storage method based on the user's previously saved data. The storage unit can also analyze the user's previously saved data and improve the storage method. Furthermore, the storage unit can optimize the storage method by referring to the user's previously saved data. This allows the storage unit to customize the storage method by referring to the user's previously saved data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and storage unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify the user's interests. The suggestion unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, suggests an ending based on the identified interests. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, automatically generates a will based on the suggested ending. The storage unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, saves the generated will in a legally valid format. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and storage unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify the user's interests. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests an ending based on the identified interests. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates a will based on the suggested ending. The storage unit, realized, for example, by the specific processing unit 290 of the data processing device 12, saves the generated will in a legally valid format. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and storage unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected information to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify the user's interests. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests an ending based on the identified interests. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates a will based on the suggested ending. The storage unit, realized, for example, by the specific processing unit 290 of the data processing device 12, saves the generated will in a legally valid format. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and storage unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify the user's interests. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests an ending based on the identified interests. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates a will based on the suggested ending. The storage unit, realized, for example, by the specific processing unit 290 of the data processing device 12, saves the generated will in a legally valid format.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The ending organization system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, blood pressure, sleep patterns, etc.) and provides it to the analysis unit. The analysis unit can analyze the collected health data and evaluate the user's health condition. For example, the analysis unit may analyze the user's heart rate fluctuations and estimate stress levels. The analysis unit may also analyze the user's sleep patterns and evaluate sleep quality. Furthermore, the analysis unit may adjust the proposed ending plan based on the user's health condition. This allows the ending organization system to propose an ending plan that takes the user's health condition into consideration.

[0127] The ending planning system can further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes the user's social media accounts and contact list to identify the user's social connections. For example, the social network analysis unit identifies people with whom the user frequently interacts and evaluates those relationships. The social network analysis unit can also analyze the user's relationships with friends and family and reflect these in the ending plan. Furthermore, the social network analysis unit can automatically set notification destinations for the ending plan based on the user's social network. This allows the ending planning system to propose an ending plan that takes the user's social network into consideration.

[0128] The ending arrangement system can further include an emotion analysis unit that estimates the user's emotions and adjusts the proposed ending plan based on the estimated emotions. The emotion analysis unit analyzes the user's text messages and voice data to estimate emotions. For example, the emotion analysis unit estimates emotions such as joy, sadness, and anger from the content of the user's messages. The emotion analysis unit can also estimate emotions from the user's tone of voice and speaking style. Furthermore, the emotion analysis unit can adjust the proposed ending plan based on the estimated emotions. This allows the ending arrangement system to propose an ending plan that takes the user's emotions into consideration.

[0129] The ending planning system can further include a hobby analysis unit that customizes the ending plan based on the user's hobbies and interests. The hobby analysis unit collects information related to the user's hobbies and interests and provides it to the analysis unit. For example, the hobby analysis unit collects information about hobby groups and events in which the user participates. The hobby analysis unit can also collect information about websites the user visits and magazines they subscribe to. Furthermore, the hobby analysis unit can identify the user's hobbies and interests based on the collected information and reflect them in the ending plan. This allows the ending planning system to propose an ending plan that matches the user's hobbies and interests.

[0130] The ending arrangement system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the content of the will based on the estimated emotions. The emotion adjustment unit analyzes the user's emotions and adjusts the content of the will to match the user's emotions. For example, if the user is sad, the emotion adjustment unit generates a will that includes words of comfort. Also, if the user is grateful, the emotion adjustment unit can generate a will that includes words of gratitude. Furthermore, the emotion adjustment unit can adjust the tone and expression of the will based on the user's emotions. In this way, the ending arrangement system can generate a will that takes the user's emotions into consideration.

[0131] The ending planning system may further include a property management unit that manages the user's property information. The property management unit collects the user's property information (e.g., real estate, bank accounts, stocks, etc.) and provides it to the analysis unit. The analysis unit can analyze the collected property information and evaluate the user's property situation. For example, the analysis unit may evaluate the value of the user's property and reflect this in the will. The analysis unit may also analyze the balance of the user's bank account and make property distribution proposals. Furthermore, the analysis unit may adjust the proposed ending plan based on the user's property information. This allows the ending planning system to propose an ending plan that takes into account the user's property situation.

[0132] The ending organization system can further include an emotion notification unit that estimates the user's emotion and adjusts the notification method of the ending plan based on the estimated emotion. The emotion notification unit analyzes the user's emotion and adjusts the notification method to match the user's emotion. For example, the emotion notification unit can provide a detailed notification if the user is relaxed. The emotion notification unit can also provide a concise notification if the user is in a hurry. Furthermore, the emotion notification unit can provide a visually stimulating notification if the user is excited. This allows the ending organization system to provide a notification method that takes into consideration the user's emotion.

[0133] The ending organizing system may further include a life event management unit that manages the user's life events. The life event management unit collects the user's life events (e.g., marriage, childbirth, moving, etc.) and provides them to the analysis unit. The analysis unit can analyze the collected life event information and evaluate the user's life stage. For example, if the user gets married, the analysis unit can propose an ending plan related to the marriage. Also, if the user gives birth, the analysis unit can propose an ending plan related to the child. Furthermore, the analysis unit can adjust the proposed ending plan based on the user's life events. This allows the ending organizing system to propose an ending plan that corresponds to the user's life events.

[0134] The ending organization system can further include an emotion prioritization unit that estimates the user's emotions and determines the priority of ending plans based on the estimated emotions. The emotion prioritization unit analyzes the user's emotions and adjusts the priority of ending plans to match the user's emotions. For example, if the user is feeling stressed, the emotion prioritization unit can prioritize suggesting relaxing ending plans. Also, if the user is excited, the emotion prioritization unit can prioritize suggesting stimulating ending plans. Furthermore, the emotion prioritization unit can adjust the priority of ending plans based on the user's emotions. This allows the ending organization system to prioritize suggesting ending plans that take the user's emotions into consideration.

[0135] The ending arrangement system may further include a cultural analysis unit that proposes an ending plan taking into account the user's cultural background. The cultural analysis unit collects information related to the user's cultural background (e.g., religion, traditions, customs, etc.) and provides it to the analysis unit. The analysis unit can analyze the collected cultural background information and evaluate the user's cultural background. For example, if the user believes in a particular religion, the analysis unit can propose an ending plan based on that religion. The analysis unit can also customize the ending plan based on the user's traditions and customs. Furthermore, the analysis unit can adjust the proposed ending plan based on the user's cultural background. This allows the ending arrangement system to propose an ending plan that takes into account the user's cultural background.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The collection unit collects user information. This information includes personal information, behavioral history, and interests. For example, the collection unit collects information about the user's work, news, emails, social media, etc., and collects news articles and social media posts that the user frequently views. It also collects information related to topics that interest the user. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's interests. The analysis is performed using data mining and machine learning algorithms to identify the user's behavioral history and interests. It can also analyze past behavioral history and survey results to identify current interests. Step 3: The suggestion unit suggests endings based on the interests identified by the analysis unit. For example, if the user is interested in travel, the suggestion unit suggests ending plans related to travel. If the user is interested in a particular hobby, the suggestion unit creates a to-do list related to that hobby. Step 4: The generator automatically generates a will based on the endings proposed by the suggester. The generation is performed based on the user's wishes, and a will that reflects the user's wishes is generated. Step 5: The storage unit stores the will generated by the generation unit in a legally valid format. The storage is performed in a legally valid format, and can also be stored with signatures and witnesses.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0140] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0142] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0143] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0168] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0178] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0180] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0181] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0182] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0193] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0194] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0195] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0199] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0200] 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.

[0201] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0202] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0203] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0204] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0206] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0207] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0208] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0209] [Explanation of symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and identifies user interests; a suggestion unit that suggests an ending based on the interests identified by the analysis unit; a generation unit that automatically generates a will based on the ending proposed by the proposal unit; a storage unit that stores the will generated by the generation unit in a legally valid format. A system characterized by:

2. The collecting unit Collect information from multiple sources of information for users 2. The system of claim 1.

3. The analysis unit Analyzing the collected information to identify user behavior and interests 2. The system of claim 1.

4. The proposal unit Propose ending plans based on the user's interests 2. The system of claim 1.

5. The generation unit Automatically generate a will based on the user's wishes 2. The system of claim 1.

6. The storage unit Save the generated will in a legally valid format 2. The system of claim 1.

7. The proposal unit Create to-do lists based on user interests 2. The system of claim 1.

8. The proposal unit To deal with sudden deaths, specific information is deleted based on prohibited actions and words set by the user.

2. The system of claim 1.

Images