system

Abstract

We provide the system. [Solution] Means of collecting information from users, A generation means for analyzing the collected information and generating an action plan based on the analysis results, A means for presenting the generated action plan to the user and receiving feedback, Means for adjusting the action plan based on the aforementioned feedback, A system that includes end-of-life planning support.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The preparatory activities for end-of-life arrangements cover a wide range, including creating a will, tidying up one's affairs during one's lifetime, and preparing for funerals. Due to their complexity, users do not know how to start or which procedures to follow, resulting in the problem of anxiety and confusion. Furthermore, there is also a problem that one's last wishes are not properly conveyed, and the psychological and physical burdens left on family members and relatives after death become greater.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that analyzes information collected from users and generates individually optimized action plans for end-of-life planning based on the results. Specifically, the system includes means for collecting information from users, means for analyzing the collected information and generating action plans based on the analysis results, means for presenting the generated plans to users and receiving feedback, and means for adjusting the action plans based on the feedback. This makes it possible to provide optimal end-of-life support to each user and reduce anxiety and burden related to end-of-life planning.

[0006] A "user" is an individual who uses the end-of-life planning support system, or an entity that provides information on their behalf.

[0007] "Information" refers to data provided by users, such as personal data, asset status, and wishes and requests regarding end-of-life planning, which the system uses for analysis.

[0008] "Analysis" refers to the process of evaluating user characteristics and needs based on collected information and using that information to generate action plans.

[0009] An "action plan" is a set of specific steps and guidance designed to support users in their end-of-life planning.

[0010] "Generation method" refers to a process or algorithm for automatically creating an action plan based on the analysis results.

[0011] "Feedback" refers to response information such as evaluations and requests for modifications to plans provided by users.

[0012] "Adjustment" refers to the process of updating or improving action plans based on user feedback.

[0013] An "end-of-life planning support system" refers to a system that integrates a series of processes and functions designed to efficiently and effectively support users' end-of-life planning activities. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The end-of-life support system in this invention is primarily configured as a web application or mobile application accessed via the internet, enabling users to receive necessary end-of-life support through an online platform. The program's processing is described below in natural language.

[0036] First, the user accesses the system's login screen and logs in using their account information. After logging in, the user is shown a page where they answer basic questions related to end-of-life planning, including personal information, asset information, family information, and wishes regarding end-of-life planning. Once the user has finished entering the information into this input form, they press the submit button to send the information to the system.

[0037] The server receives information sent by the user and immediately stores it in a database. Next, it uses generative AI to analyze the user's characteristics based on the stored information. This analysis includes a process of evaluating the user's needs and unique characteristics by utilizing historical data and statistical insights.

[0038] The server, having received the analysis results, generates individually optimized action plans to help users efficiently carry out end-of-life planning. Each action plan includes necessary preparations, procedures, and relevant legal guidance. The generated action plan provides concrete steps for users to proceed with their end-of-life planning.

[0039] After generation, the server sends the action plan to the terminal and displays it on the user's screen. The user can review the action plan on the screen and then begin taking specific actions based on the plan. For example, in the "Writing a Will" step, the user can download recommended templates and receive assistance in creating a document that meets their specific needs.

[0040] After executing an action plan, users can provide feedback and further requests for evaluation through the system's feedback function. This feedback is sent to the server via the terminal, which analyzes the received feedback and adjusts the action plan as needed.

[0041] Through the above process, the end-of-life support system provides detailed support tailored to each user's individual circumstances, reducing the psychological and physical burden on users in end-of-life planning.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user logs into the end-of-life planning support system using their device. After logging in, the initial screen displays a form for entering personal information and wishes regarding end-of-life planning. The user then fills out these forms.

[0045] Step 2:

[0046] The terminal verifies the information entered by the user in real time to check for any errors. If there are no errors, pressing the send button sends the information to the server.

[0047] Step 3:

[0048] The server receives user information sent from the terminal. The received information is immediately stored in the database. Next, the server passes the information to the analysis engine, which begins to evaluate the user's characteristics and needs.

[0049] Step 4:

[0050] The analysis engine uses generated AI based on collected information to analyze the user's background and preferences. This is done to build an optimal action plan for each individual user by referring to similar past data.

[0051] Step 5:

[0052] The server generates a customized action plan based on the analysis results. This plan includes guidance on drafting a will, procedures for organizing assets, and specific steps.

[0053] Step 6:

[0054] The server sends the generated action plan to the terminal. The terminal displays the received action plan appropriately in the user interface and notifies the user of the next action to take.

[0055] Step 7:

[0056] Users review their action plan on their device and then begin concrete end-of-life planning steps according to it. For example, they might download a template for creating a will and consider its contents based on their own wishes.

[0057] Step 8:

[0058] Users can input feedback on the implemented action plan via their device. This feedback includes evaluations and modifications to the action plan.

[0059] Step 9:

[0060] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and sends the adjusted plan back to the device.

[0061] As a result, the end-of-life planning support system provides users with flexible and effective end-of-life support.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Users engaged in end-of-life planning face challenges in organizing necessary information and developing appropriate action plans. Furthermore, there is a lack of efficient support due to the inability to provide plans optimized for individual users.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for collecting information from the user, means for using a generation artificial intelligence model to analyze the collected information, and means for generating an action plan based on the analysis results. This makes it possible to provide the user with an action plan that is individually optimized and to adjust the plan in response to feedback.

[0067] A "user" is an individual or group that utilizes the system, provides information, or receives generated action plans.

[0068] "Means of collecting information" refers to the process of providing functionality to acquire input data from users and store it in a database.

[0069] A "generative artificial intelligence model" refers to a technical method for analyzing collected information and generating action plans suitable for the user.

[0070] An "action plan" is a document that includes specific steps and guidance for users to proceed with end-of-life planning based on the analysis results.

[0071] "Means of receiving feedback" refers to the process of obtaining user evaluations and requests regarding the generated action plan.

[0072] "Means for adjusting action plans" refers to a process that provides the ability to analyze the feedback received and modify or update existing action plans as needed.

[0073] This invention provides a system that allows users to receive efficient and individually optimized end-of-life planning support. It is primarily configured as a web application or mobile application accessed via the internet.

[0074] Users access the system through an interface and authenticate at the login screen. The devices used for this are PCs and smartphones. After logging in, users are asked to enter personal information, asset information, family information, and end-of-life planning wishes. This information is encrypted for privacy reasons and sent to the server.

[0075] The server receives data sent from the user and stores it in a dedicated database. Next, a generative AI model is used to analyze this information. The generative AI model leverages historical data and statistical algorithms to evaluate the user's characteristics and needs. Based on this evaluation, it generates an action plan optimized for the user. The generated plan includes necessary preparations, procedures, and legal guidance.

[0076] For example, if a user requests to "create a will," the action plan will include a download link for a will template and legal advice. Similarly, in the step of "writing a thank-you letter to family," the AI ​​model will provide a letter template tailored to the user. An example of a prompt in this case would be, "Please generate a letter template for a man in his 50s to express his gratitude to his family."

[0077] The device displays the generated action plan to the user. The user can review the plan on the screen and begin taking specific actions. This allows the user to proceed with end-of-life planning in a planned and efficient manner.

[0078] Furthermore, the feedback function allows users to submit evaluations and requests regarding the action plans they have implemented, and the server can adjust the action plans based on this feedback. In this way, the system provides support tailored to the individual needs of each user.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user accesses the system's login screen and enters their authentication information, which includes their email address and password. The terminal sends this information to the server, initiating the authentication process. The server compares this information with the database and, if correct, grants access to the dashboard; otherwise, sends an error message.

[0082] Step 2:

[0083] After navigating to the dashboard, the user accesses a section to enter information necessary for end-of-life planning. Here, they enter personal information, asset information, family information, and their wishes. The device then transfers this entire set of data to the server when the user presses the submit button.

[0084] Step 3:

[0085] The server saves the received data to a database before analysis. After saving, it begins analyzing the user's characteristics using the implemented generative AI model. The input for the analysis is the data provided by the user, and the output is a user profile. This is used to evaluate the user's needs and characteristics.

[0086] Step 4:

[0087] The server generates a customized action plan based on the analysis results obtained from the generated AI model. Based on the input user profile, the action plan includes necessary preparations and legal guidance. This process has the functionality to generate an action plan by prompting the AI ​​model. The generated plan is output in PDF or other formats.

[0088] Step 5:

[0089] The terminal displays the action plan sent from the server on the user's screen. The user can review the plan on the screen and follow the instructions to perform specific steps. For example, the "creating a will" step involves downloading and using a template.

[0090] Step 6:

[0091] After executing an action plan, users input their evaluation using the system's built-in feedback function. This input includes suggestions for improvement and additional requests regarding the action plan. The feedback is sent as data from the terminal to the server, which analyzes it to determine whether changes to the plan are necessary.

[0092] Step 7:

[0093] The server analyzes the feedback and updates the action plan as needed. The updated action plan is then sent back to the terminal and presented to the user. This ensures that support tailored to each user's needs is continuously provided.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] When users engage in end-of-life planning, gathering necessary information, generating an optimal action plan based on that information, and executing it is a complex and time-consuming task. Furthermore, there is the challenge of difficulty in receiving appropriate services and support tailored to individual needs. This increases the psychological and physical burden, leading to problems in the smooth progress of end-of-life planning.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and generating an action plan based on the analysis results, and means for presenting the generated action plan to the user and receiving feedback. This makes it possible for the user to use the virtual space to confirm the optimal action plan and to execute specific procedures such as legal document creation and asset management preparation.

[0099] A "user" is an entity that utilizes the system, inputs information related to end-of-life planning support, and receives action plans.

[0100] "Means of collecting information" refers to an interface for obtaining data necessary for end-of-life planning from users.

[0101] "Generative means for analyzing collected information" refers to a device or program that performs analysis based on input user information and creates an appropriate action plan.

[0102] The "generation means for generating action plans" is a processing device that, based on the results of analysis, formulates specific steps for end-of-life planning optimized for the user.

[0103] A "means of receiving feedback" refers to a device or program that has the functionality to allow users to submit opinions and suggestions for improvement regarding an action plan.

[0104] A "means for adjusting the action plan" is a processing device that reviews and improves the content of the action plan based on the feedback received.

[0105] "Means of providing virtual space" refers to technologies that enable users to select and execute end-of-life planning-related services within a virtually constructed environment.

[0106] "Guidance on legal document preparation" refers to guidelines or advice designed to assist users in preparing legal documents necessary for end-of-life planning.

[0107] "Specific procedures for preparing for asset management" refers to the specific methods and processes necessary for a user to efficiently organize and manage their own assets.

[0108] This invention provides a system that allows users to efficiently carry out end-of-life planning using mobile devices or personal computers. The main components of this system include a user interface, a server, and a generative AI model.

[0109] Users access an application on their device via the internet and enter information related to their end-of-life planning (personal information, asset information, wishes, etc.). This information is sent to a server using Python or Flask.

[0110] The server stores the received information in a secure database and then analyzes the information using a generative AI model. Deep learning frameworks such as TENSORFLOW® and PyTorch are used for the analysis. Based on this analysis, an optimized action plan is generated for the user, providing specific guidance and recommendation services.

[0111] The generated action plan is sent to the terminal and displayed on the user's screen. Here, the user can use the virtual space to execute specific steps related to creating legal documents and organizing assets. The user can also provide feedback on the proposed plan to the system, and the server adjusts the plan as needed based on that feedback.

[0112] For example, a user might select a funeral service and receive guidance on the procedures. In this case, practical advice is provided in a virtual space, allowing the user to take concrete actions based on that advice.

[0113] Examples of prompt statements include the following:

[0114] "Please enter information about your end-of-life planning. The system will suggest the best action plan for you."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] Users access the application on their device via the internet and enter information related to end-of-life planning (personal information, asset information, wishes, etc.). The entered information is sent to the server as a digital form. Here, basic information about the user's end-of-life planning is collected.

[0118] Step 2:

[0119] The server stores the received user information in a secure database. At this stage, the raw data obtained from the user is saved and made available for subsequent processing. The database plays a role in maintaining the security and integrity of the information.

[0120] Step 3:

[0121] The server provides stored user information as input to a generative AI model. The generative AI model (e.g., a model using TensorFlow or PyTorch) analyzes this information to understand the user's characteristics and needs. As a result of the analysis, an individually optimized action plan is generated. This process involves data processing and calculations using historical data and statistical methods.

[0122] Step 4:

[0123] The server sends the generated action plan to the terminal and displays it on the user's screen. This plan includes specific steps and legal guidance. The user can review and proceed with the suggested actions based on this information.

[0124] Step 5:

[0125] The user executes the presented action plan and sends feedback to the server via their device as needed. Specific actions include filling out an on-screen feedback form.

[0126] Step 6:

[0127] The server analyzes the received feedback and adjusts the action plan. This involves data re-analysis and plan adaptation based on the feedback. This allows for adjustments that more accurately meet user needs.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] The end-of-life support system of this invention, in addition to the basic elements of collecting and analyzing user information, incorporates an emotion engine to recognize the user's emotional state and provide an action plan that takes this into account. The program's processing is described below in natural language.

[0130] At the start of the system, the user logs in using a terminal. During this process, the user fills out a form containing personal information and their expectations and wishes regarding end-of-life planning. During this information entry and subsequent operations, the terminal transmits emotional data to the emotion engine based on the user's input patterns, speed, and word choices.

[0131] The server receives information sent by the user, stores it in a database, and passes it to the analysis engine to begin analyzing the information. This analysis compares the information with past data to determine the user's characteristics and needs. Simultaneously, the emotion engine also operates to evaluate the user's emotional state. For example, it detects signs of stress displayed during input and whether the user is feeling positive emotions based on the tone of their conversation.

[0132] After the evaluation is complete, the server integrates the analysis results and the emotional assessment results to generate an action plan optimized for the user's emotional state. For example, if the user expresses anxiety about end-of-life planning, the server can include support information and advice to reassure the user in the plan.

[0133] This action plan is presented to the user via their device. The user can review the plan on the screen and take specific actions based on its contents. For example, if the emotion engine determines that "writing a will is causing stress," the system will suggest a simple template or further support options to the user.

[0134] After executing an action plan, users can input feedback into the system. This feedback includes evaluations of the plan and additional requests. This feedback is sent to the server, analyzed again, and the action plan is adjusted as needed to improve the user experience.

[0135] Through this system, users will be able to receive flexible and effective end-of-life support that takes their emotions into consideration.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users log in to the end-of-life planning support system via their device. After logging in, the initial screen displays a form for entering basic information and wishes regarding end-of-life planning. Users fill in the required information on the form and submit it.

[0139] Step 2:

[0140] The terminal verifies the information entered by the user in real time and sends the input data to the emotion engine. It also sends the verified information to the server.

[0141] Step 3:

[0142] The server receives user information sent from the terminal and stores it in the database. The server then passes the information to the analysis engine, which begins the process of analyzing the user's characteristics and needs.

[0143] Step 4:

[0144] The emotion engine evaluates the user's emotional state based on user input data received from the device. For example, it analyzes whether slow input speed or the use of certain words indicates a possible negative emotion.

[0145] Step 5:

[0146] The server integrates analysis results from the analysis engine and emotion evaluation results from the emotion engine to generate an optimized action plan. For example, if the user is experiencing stress, the plan might include simpler tasks or enhanced support.

[0147] Step 6:

[0148] The server sends the generated action plan to the terminal. The terminal displays it in its user interface, presenting the user with specific actions to take next.

[0149] Step 7:

[0150] Users review their action plan on their device and take actions according to the plan. The action plan includes emotionally responsive support information and guidance to provide reassurance.

[0151] Step 8:

[0152] After executing their action plan, users can provide feedback via their device. This feedback can include evaluations of the plan and suggestions for improvement.

[0153] Step 9:

[0154] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and prepares to present it to the user again.

[0155] In this way, the end-of-life support system provides flexible and personalized support that takes the user's emotions into consideration.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] Conventional end-of-life planning support systems often provide uniform action plans without adequately considering the user's emotional state, making it difficult to alleviate the user's mental burden. Furthermore, the lack of sufficient consideration of emotional aspects in plan generation based on user input hinders improvements in the user experience.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and emotional data and generating an action plan based on the analysis results and emotional evaluation, and means for presenting the generated action plan to the user and receiving feedback on a plan optimized for the user's emotional state. This makes it possible to provide a flexible and personalized action plan that corresponds to the user's emotional state.

[0161] A "user" refers to an individual who uses the system to input their own information and receive support related to end-of-life planning.

[0162] "Information gathering means" refers to methods and devices used to obtain personal information and wishes and expectations regarding end-of-life planning from users.

[0163] "Emotional data" refers to numerical data that indicates the emotional state of a user, inferred from their input patterns, speed, word choice, and other factors.

[0164] "Analysis means" refers to methods and devices for determining user characteristics, needs, and emotional states based on collected information and emotional data.

[0165] "Generation means" refers to methods or devices for creating an optimal action plan based on analyzed data.

[0166] An "action plan" refers to a plan that includes specific steps and guidance provided to support users in their end-of-life planning activities.

[0167] "Presentation means" refers to methods or functions for providing the generated action plan to the user visually or audibly.

[0168] "Feedback receiving methods" refer to methods and devices for receiving evaluations and requests from users regarding action plans and using that information to improve the system.

[0169] "Adjustment measures" refer to methods and devices for reviewing action plans based on feedback and modifying them as necessary.

[0170] This invention is a system that supports users in their end-of-life planning, and its core function is to generate and provide action plans that take into account the user's emotional state. This system primarily operates through the collaborative efforts of a terminal and a server.

[0171] Users use a terminal to access the system. The terminal is equipped with a dedicated application or web interface through which they input personal information and their wishes and expectations regarding end-of-life planning. As users input information, the terminal collects emotional data on their input patterns, speed, and word choices. This data is used to infer the user's emotional state.

[0172] The terminal is a device that transmits information and sentiment data collected from the user to the server. Common terminals used here include personal computers and smartphones. The collected data is transmitted to the server via the internet.

[0173] When the server receives information sent by the user, it stores it in a database. The stored information is then passed to the analysis engine, which analyzes the user's characteristics and needs. Based on the analysis results, the emotion engine evaluates the user's emotional state. This process takes into account factors such as stress during input and the positivity of the statements.

[0174] The server integrates the analysis results and sentiment evaluation to generate an optimized action plan for the user. A generative AI model is used in this process. The generative AI model suggests appropriate actions based on the user's situation, using prompts. An example of a prompt is, "Generate a support plan based on the user's emotional state, using the information entered after logging in."

[0175] The generated action plan is presented to the user via the device. The user can review the presented plan and begin taking specific actions based on it. For example, if the emotion engine determines that "writing a will is stressful," the system will suggest a simple template or further support options to the user.

[0176] In this way, coordinated operation between the terminal and the server enables flexible and effective end-of-life support that takes into account the user's emotional state.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] Users log in using the system's dedicated application or web interface. The login process involves entering a user ID and password. This authentication information is sent from the terminal to the server. The server then uses this information to authenticate the user and verify their legitimacy. Upon successful login, the user can proceed to the next step.

[0180] Step 2:

[0181] After logging in, users fill out a form on the interface that includes their personal information and their wishes and expectations regarding end-of-life planning. This form contains information about the user's thoughts and concerns about end-of-life planning. The device collects this input data in real time, capturing input patterns and speed as metadata. This data is sent to an emotion engine and used for an initial assessment of the user's emotional state.

[0182] Step 3:

[0183] The terminal packages the information and metadata collected from the user and sends it to the server. The input received by the server is the raw data itself, and the process involves saving this to a database and simultaneously passing it to the analysis engine. The server compares it with similar historical data and begins computational processing to gain new insights. Once the analyzed data is output, it is ready for the next step.

[0184] Step 4:

[0185] The server's analysis engine analyzes the collected information in detail. This process analyzes user characteristics, needs, and past behavioral history. The output of the analysis provides the core information for a specific action plan. Meanwhile, the emotion engine determines the user's emotional state from the input data. For example, if the input speed is very slow, it may indicate fatigue or anxiety. Based on these analysis results, feedback for the action plan is generated.

[0186] Step 5:

[0187] The server integrates the results from the analysis engine and the emotion engine to generate an optimized action plan for the user. This is where the generative AI model comes into play, automatically creating the optimal plan based on the user's specific situation. Prompt messages are formatted to correspond to the scenario of the generated action plan, such as "Generate a support plan based on the user's emotional state, using the information entered after logging in." The output is an action plan tailored to the user's emotions.

[0188] Step 6:

[0189] The terminal receives the action plan sent from the server, visualizes it, and presents it to the user. Here, the information is presented in a user-friendly format, and additional instructions or actions are taken based on it. The user then tries out this action plan and evaluates its usefulness.

[0190] Step 7:

[0191] Users input feedback into the system regarding the results of implementing their action plans. The collected information includes the ease of executing the plan and any additional support needed. This feedback is then used by the server in the next step, contributing to the overall improvement of the system.

[0192] Step 8:

[0193] The server re-analyzes user feedback and adjusts the action plan as needed. This feedback processing results in improved action plans and enhanced system adaptability, which in turn further increases user satisfaction.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] In modern society, improving individual safety and psychological well-being is a crucial issue. However, conventional security systems struggle to provide safety measures that take into account the user's psychological state, resulting in a lack of flexible support tailored to individual needs.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes a device for collecting information from the user, a device for analyzing the collected information and generating an action plan based on the analysis results, and a device for evaluating the user's psychological state using an emotion analysis engine. This makes it possible to provide security measures and mental support information that take the user's psychological state into consideration.

[0199] A "user" refers to an individual who uses the system to provide information and receive support.

[0200] "Information" refers to data collected from users, including personal information and data related to emotional states.

[0201] "Device" refers to a function incorporated into a system for collecting, analyzing, generating, and presenting information.

[0202] An "action plan" refers to a set of specific actions that a user should take, generated based on analyzed information and the user's psychological state.

[0203] An "emotion analysis engine" refers to a technical means for evaluating a user's emotional state, and has the function of analyzing their psychological state from collected data.

[0204] "Psychological state" refers to data that indicates the user's emotional and moodal state, which the system analyzes and incorporates into its action plan.

[0205] "Security measures" refer to specific actions incorporated into an action plan to ensure user safety.

[0206] "Opinions" refers to feedback and evaluations provided by users regarding the action plan.

[0207] A description of embodiments for carrying out this invention will be given.

[0208] The system's main components consist of a server, terminals, and users, and each element functions in cooperation with the others. The server plays a central role in collecting, analyzing, and generating action plans for information. The terminals serve as the interface between the user and the system, and are used for data input from the user and for providing feedback from the system.

[0209] The server is equipped with advanced data analysis software and an emotion analysis engine to receive and process data sent from users. Based on user input and past data, it analyzes the user's psychological state and generates an optimal action plan. It also develops plans that include information for security measures and psychological support.

[0210] The terminal is a tool for users to input personal information and feedback into the system, and smartphones or other mobile devices are used. User input data is sent to the server in real time, and feedback is received from the server after completion.

[0211] Users can review their action plan through their device and provide feedback if needed. For example, if a user experiences stress or anxiety, the system detects these using an emotion analysis engine and quickly suggests countermeasures.

[0212] For example, in a situation where a user feels uneasy walking alone at night, the device can display real-time security recommendations generated by the server and suggest actions that provide the user with a sense of security. Through this interaction, the user can benefit from improved environmental safety and psychological support.

[0213] An example of a prompt message is, "Please describe the situation that is causing you anxiety. What measures do you think would be most effective?"

[0214] Ultimately, this invention aims to provide flexible security support services based on the user's psychological needs.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The user logs into the system using a terminal. The user enters personal information and data related to their current psychological state. The terminal sends the entered information to the server. In this step, the input consists of personal information and emotion-related data provided by the user, and the output is the data sent to the server.

[0218] Step 2:

[0219] The server stores the received user information in a database and passes the data to the analysis engine. The analysis engine compares past data with the current input data to analyze the user's characteristics and current needs. Here, the input is the data received from the user, and the output is the result of the user characteristic analysis.

[0220] Step 3:

[0221] The server's sentiment analysis engine evaluates the user's psychological state. It performs text analysis on the user's input data and extracts the emotional state. The input for this step is the user's word choices and input patterns, and the output is the user's sentiment evaluation result.

[0222] Step 4:

[0223] The server integrates the analyzed characteristics and sentiment assessments to generate an optimal action plan for the user. This plan includes security recommendations and emotional support information. The input is the results of steps 2 and 3, and the output is the generated action plan.

[0224] Step 5:

[0225] The server sends an action plan to the terminal. The terminal presents the action plan to the user, who then takes the necessary actions based on it. The input is the action plan sent from the server, and the output is the action plan received by the user.

[0226] Step 6:

[0227] The user executes the presented plan and inputs their opinions and evaluations of the results into the terminal. The terminal sends this feedback back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0228] Step 7:

[0229] The server then re-analyzes the feedback and adjusts the action plan as needed. The input for this step is the user feedback, and the output is the adjusted action plan.

[0230] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0231] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0236] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0237] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0238] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0239] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0240] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0241] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0242] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0243] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0244] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0245] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0246] The end-of-life support system in this invention is primarily configured as a web application or mobile application accessed via the internet, enabling users to receive necessary end-of-life support through an online platform. The program's processing is described below in natural language.

[0247] First, the user accesses the system's login screen and logs in using their account information. After logging in, the user is shown a page where they answer basic questions related to end-of-life planning, including personal information, asset information, family information, and wishes regarding end-of-life planning. Once the user has finished entering the information into this input form, they press the submit button to send the information to the system.

[0248] The server receives information sent by the user and immediately stores it in a database. Next, it uses generative AI to analyze the user's characteristics based on the stored information. This analysis includes a process of evaluating the user's needs and unique characteristics by utilizing historical data and statistical insights.

[0249] The server, having received the analysis results, generates individually optimized action plans to help users efficiently carry out end-of-life planning. Each action plan includes necessary preparations, procedures, and relevant legal guidance. The generated action plan provides concrete steps for users to proceed with their end-of-life planning.

[0250] After generation, the server sends the action plan to the terminal and displays it on the user's screen. The user can review the action plan on the screen and then begin taking specific actions based on the plan. For example, in the "Writing a Will" step, the user can download recommended templates and receive assistance in creating a document that meets their specific needs.

[0251] After executing an action plan, users can provide feedback and further requests for evaluation through the system's feedback function. This feedback is sent to the server via the terminal, which analyzes the received feedback and adjusts the action plan as needed.

[0252] Through the above process, the end-of-life support system provides detailed support tailored to each user's individual circumstances, reducing the psychological and physical burden on users in end-of-life planning.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The user logs into the end-of-life planning support system using their device. After logging in, the initial screen displays a form for entering personal information and wishes regarding end-of-life planning. The user then fills out these forms.

[0256] Step 2:

[0257] The terminal verifies the information entered by the user in real time to check for any errors. If there are no errors, pressing the send button sends the information to the server.

[0258] Step 3:

[0259] The server receives user information sent from the terminal. The received information is immediately stored in the database. Next, the server passes the information to the analysis engine, which begins to evaluate the user's characteristics and needs.

[0260] Step 4:

[0261] The analysis engine uses generated AI based on collected information to analyze the user's background and preferences. This is done to build an optimal action plan for each individual user by referring to similar past data.

[0262] Step 5:

[0263] The server generates a customized action plan based on the analysis results. This plan includes guidance on drafting a will, procedures for organizing assets, and specific steps.

[0264] Step 6:

[0265] The server sends the generated action plan to the terminal. The terminal displays the received action plan appropriately in the user interface and notifies the user of the next action to take.

[0266] Step 7:

[0267] Users review their action plan on their device and then begin concrete end-of-life planning steps according to it. For example, they might download a template for creating a will and consider its contents based on their own wishes.

[0268] Step 8:

[0269] Users can input feedback on the implemented action plan via their device. This feedback includes evaluations and modifications to the action plan.

[0270] Step 9:

[0271] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and sends the adjusted plan back to the device.

[0272] As a result, the end-of-life planning support system provides users with flexible and effective end-of-life support.

[0273] (Example 1)

[0274] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0275] Users engaged in end-of-life planning face challenges in organizing necessary information and developing appropriate action plans. Furthermore, there is a lack of efficient support due to the inability to provide plans optimized for individual users.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0277] In this invention, the server includes means for collecting information from users, means for using a generation artificial intelligence model to analyze the collected information, and means for generating an action plan based on the analysis result. Thereby, it becomes possible to provide an action plan optimized individually for the user and to adjust the plan according to feedback.

[0278] A "user" is an individual or group that uses the system, provides information, or receives the generated action plan.

[0279] The "means for collecting information" is a process that provides a function for acquiring input data from a user and storing it in a database.

[0280] The "generation artificial intelligence model" refers to a technical method for analyzing the collected information and generating an action plan suitable for the user.

[0281] An "action plan" is a plan document that includes specific steps and guidance for the user to proceed with the end-of-life process based on the analysis result.

[0282] The "means for receiving feedback" is a process for acquiring the user's evaluation and requests for the generated action plan.

[0283] The "means for adjusting the action plan" is a process that provides a function for analyzing the received feedback and modifying or updating the existing action plan as necessary.

[0284] This invention provides a system in which a user can receive efficient and individually optimized end-of-life support. It is mainly configured as a web application or a mobile application via the Internet.

[0285] The user accesses the system through the interface and performs authentication on the login screen. The terminals used at this time are personal computers and smartphones. After logging in, the user is required to input personal information, asset information, kinship information, desired items for end-of-life preparation, etc. These pieces of information are encrypted considering privacy and sent to the server.

[0286] The server receives the data sent from the user and stores it in a dedicated database. Next, the generative AI model is used to analyze this information. The generative AI model utilizes past data and statistical algorithms to evaluate the user's characteristics and needs. Based on this evaluation, an action plan optimized for the user is generated. The generated plan includes necessary preparations, procedures, and legal guidance.

[0287] As a specific example, when the user wishes to "create a will", the action plan provides a template download link for the will and legal advice. Also, in the step of "writing a letter of gratitude to the family", the generative AI model provides a template for a letter suitable for the user. An example of the prompt sentence at this time is "Please generate a template for a letter for a 50-year-old man to convey his gratitude to his family".

[0288] The terminal is responsible for displaying the generated action plan to the user. The user can view the plan on the screen and start specific actions. In this way, the user can proceed with end-of-life preparation in a planned and efficient manner.

[0289] Furthermore, through the feedback function, the user can send evaluations and requests regarding the executed action plan, and based on this feedback, the server can adjust the action plan. In this way, the system provides support according to the needs of each user.

[0290] The flow of the specific process in Example 1 will be described using FIG. 11.

[0291] Step 1:

[0292] The user accesses the system's login screen and enters their authentication information, which includes their email address and password. The terminal sends this information to the server, initiating the authentication process. The server compares this information with the database and, if correct, grants access to the dashboard; otherwise, sends an error message.

[0293] Step 2:

[0294] After navigating to the dashboard, the user accesses a section to enter information necessary for end-of-life planning. Here, they enter personal information, asset information, family information, and their wishes. The device then transfers this entire set of data to the server when the user presses the submit button.

[0295] Step 3:

[0296] The server saves the received data to a database before analysis. After saving, it begins analyzing the user's characteristics using the implemented generative AI model. The input for the analysis is the data provided by the user, and the output is a user profile. This is used to evaluate the user's needs and characteristics.

[0297] Step 4:

[0298] The server generates a customized action plan based on the analysis results obtained from the generated AI model. Based on the input user profile, the action plan includes necessary preparations and legal guidance. This process has the functionality to generate an action plan by prompting the AI ​​model. The generated plan is output in PDF or other formats.

[0299] Step 5:

[0300] The terminal displays the action plan sent from the server on the user's screen. The user can check the plan on the screen and execute specific steps according to the guidance. For example, in the "Will creation" step, actions such as downloading and using a template occur.

[0301] Step 6:

[0302] After the user executes the action plan, they use the feedback function built into the system to input an evaluation. This input includes points for improvement and additional requirements regarding the action plan. The feedback is sent from the terminal to the server as data, and the server analyzes it to determine whether changes to the plan are necessary.

[0303] Step 7:

[0304] The server analyzes the results of the feedback and updates the action plan as necessary. The updated action plan is sent to the terminal again and presented to the user. This sustains the support tailored to each user's needs.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] When a user conducts estate settlement, collecting the necessary information, generating and executing an optimal action plan based on it is a complex and time-consuming task. Furthermore, there is an issue that it is difficult to receive appropriate services and support according to individual needs. This leads to an increase in psychological and physical burdens, causing problems with the smooth progress of estate settlement.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and generating an action plan based on the analysis results, and means for presenting the generated action plan to the user and receiving feedback. This makes it possible for the user to use the virtual space to confirm the optimal action plan and to execute specific procedures such as legal document creation and asset management preparation.

[0310] A "user" is an entity that utilizes the system, inputs information related to end-of-life planning support, and receives action plans.

[0311] "Means of collecting information" refers to an interface for obtaining data necessary for end-of-life planning from users.

[0312] "Generative means for analyzing collected information" refers to a device or program that performs analysis based on input user information and creates an appropriate action plan.

[0313] The "generation means for generating action plans" is a processing device that, based on the results of analysis, formulates specific steps for end-of-life planning optimized for the user.

[0314] A "means of receiving feedback" refers to a device or program that has the functionality to allow users to submit opinions and suggestions for improvement regarding an action plan.

[0315] A "means for adjusting the action plan" is a processing device that reviews and improves the content of the action plan based on the feedback received.

[0316] "Means of providing virtual space" refers to technologies that enable users to select and execute end-of-life planning-related services within a virtually constructed environment.

[0317] "Guidance on legal document preparation" refers to guidelines or advice designed to assist users in preparing legal documents necessary for end-of-life planning.

[0318] "Specific procedures for preparing for asset management" refers to the specific methods and processes necessary for a user to efficiently organize and manage their own assets.

[0319] This invention provides a system that allows users to efficiently carry out end-of-life planning using mobile devices or personal computers. The main components of this system include a user interface, a server, and a generative AI model.

[0320] Users access an application on their device via the internet and enter information related to their end-of-life planning (personal information, asset information, wishes, etc.). This information is sent to a server using Python or Flask.

[0321] The server stores the received information in a secure database and then analyzes the information using a generative AI model. Deep learning frameworks such as TensorFlow and PyTorch are used for the analysis. Based on this analysis, an optimized action plan is generated for the user, providing specific guidance and recommendation services.

[0322] The generated action plan is sent to the terminal and displayed on the user's screen. Here, the user can use the virtual space to execute specific steps related to creating legal documents and organizing assets. The user can also provide feedback on the proposed plan to the system, and the server adjusts the plan as needed based on that feedback.

[0323] For example, a user might select a funeral service and receive guidance on the procedures. In this case, practical advice is provided in a virtual space, allowing the user to take concrete actions based on that advice.

[0324] Examples of prompt statements include the following:

[0325] "Please enter information about your end-of-life planning. The system will suggest the best action plan for you."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] Users access the application on their device via the internet and enter information related to end-of-life planning (personal information, asset information, wishes, etc.). The entered information is sent to the server as a digital form. Here, basic information about the user's end-of-life planning is collected.

[0329] Step 2:

[0330] The server stores the received user information in a secure database. At this stage, the raw data obtained from the user is saved and made available for subsequent processing. The database plays a role in maintaining the security and integrity of the information.

[0331] Step 3:

[0332] The server provides stored user information as input to a generative AI model. The generative AI model (e.g., a model using TensorFlow or PyTorch) analyzes this information to understand the user's characteristics and needs. As a result of the analysis, an individually optimized action plan is generated. This process involves data processing and calculations using historical data and statistical methods.

[0333] Step 4:

[0334] The server sends the generated action plan to the terminal and displays it on the user's screen. This plan includes specific steps and legal guidance. The user can review and proceed with the suggested actions based on this information.

[0335] Step 5:

[0336] The user executes the presented action plan and sends feedback to the server via their device as needed. Specific actions include filling out an on-screen feedback form.

[0337] Step 6:

[0338] The server analyzes the received feedback and adjusts the action plan. This involves data re-analysis and plan adaptation based on the feedback. This allows for adjustments that more accurately meet user needs.

[0339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0340] The end-of-life support system of this invention, in addition to the basic elements of collecting and analyzing user information, incorporates an emotion engine to recognize the user's emotional state and provide an action plan that takes this into account. The program's processing is described below in natural language.

[0341] At the start of the system, the user logs in using a terminal. During this process, the user fills out a form containing personal information and their expectations and wishes regarding end-of-life planning. During this information entry and subsequent operations, the terminal transmits emotional data to the emotion engine based on the user's input patterns, speed, and word choices.

[0342] The server receives information sent by the user, stores it in a database, and passes it to the analysis engine to begin analyzing the information. This analysis compares the information with past data to determine the user's characteristics and needs. Simultaneously, the emotion engine also operates to evaluate the user's emotional state. For example, it detects signs of stress displayed during input and whether the user is feeling positive emotions based on the tone of their conversation.

[0343] After the evaluation is complete, the server integrates the analysis results and the emotional assessment results to generate an action plan optimized for the user's emotional state. For example, if the user expresses anxiety about end-of-life planning, the server can include support information and advice to reassure the user in the plan.

[0344] This action plan is presented to the user via their device. The user can review the plan on the screen and take specific actions based on its contents. For example, if the emotion engine determines that "writing a will is causing stress," the system will suggest a simple template or further support options to the user.

[0345] After executing an action plan, users can input feedback into the system. This feedback includes evaluations of the plan and additional requests. This feedback is sent to the server, analyzed again, and the action plan is adjusted as needed to improve the user experience.

[0346] Through this system, users will be able to receive flexible and effective end-of-life support that takes their emotions into consideration.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] Users log in to the end-of-life planning support system via their device. After logging in, the initial screen displays a form for entering basic information and wishes regarding end-of-life planning. Users fill in the required information on the form and submit it.

[0350] Step 2:

[0351] The terminal verifies the information entered by the user in real time and sends the input data to the emotion engine. It also sends the verified information to the server.

[0352] Step 3:

[0353] The server receives user information sent from the terminal and stores it in the database. The server then passes the information to the analysis engine, which begins the process of analyzing the user's characteristics and needs.

[0354] Step 4:

[0355] The emotion engine evaluates the user's emotional state based on user input data received from the device. For example, it analyzes whether slow input speed or the use of certain words indicates a possible negative emotion.

[0356] Step 5:

[0357] The server integrates analysis results from the analysis engine and emotion evaluation results from the emotion engine to generate an optimized action plan. For example, if the user is experiencing stress, the plan might include simpler tasks or enhanced support.

[0358] Step 6:

[0359] The server sends the generated action plan to the terminal. The terminal displays it in its user interface, presenting the user with specific actions to take next.

[0360] Step 7:

[0361] Users review their action plan on their device and take actions according to the plan. The action plan includes emotionally responsive support information and guidance to provide reassurance.

[0362] Step 8:

[0363] After executing their action plan, users can provide feedback via their device. This feedback can include evaluations of the plan and suggestions for improvement.

[0364] Step 9:

[0365] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and prepares to present it to the user again.

[0366] In this way, the end-of-life support system provides flexible and personalized support that takes the user's emotions into consideration.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] Conventional end-of-life planning support systems often provide uniform action plans without adequately considering the user's emotional state, making it difficult to alleviate the user's mental burden. Furthermore, the lack of sufficient consideration of emotional aspects in plan generation based on user input hinders improvements in the user experience.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and emotional data and generating an action plan based on the analysis results and emotional evaluation, and means for presenting the generated action plan to the user and receiving feedback on a plan optimized for the user's emotional state. This makes it possible to provide a flexible and personalized action plan that corresponds to the user's emotional state.

[0372] A "user" refers to an individual who uses the system to input their own information and receive support related to end-of-life planning.

[0373] "Information gathering means" refers to methods and devices used to obtain personal information and wishes and expectations regarding end-of-life planning from users.

[0374] "Emotional data" refers to numerical data that indicates the emotional state of a user, inferred from their input patterns, speed, word choice, and other factors.

[0375] "Analysis means" refers to methods and devices for determining user characteristics, needs, and emotional states based on collected information and emotional data.

[0376] "Generation means" refers to methods or devices for creating an optimal action plan based on analyzed data.

[0377] An "action plan" refers to a plan that includes specific steps and guidance provided to support users in their end-of-life planning activities.

[0378] "Presentation means" refers to methods or functions for providing the generated action plan to the user visually or audibly.

[0379] "Feedback receiving methods" refer to methods and devices for receiving evaluations and requests from users regarding action plans and using that information to improve the system.

[0380] "Adjustment measures" refer to methods and devices for reviewing action plans based on feedback and modifying them as necessary.

[0381] This invention is a system that supports users in their end-of-life planning, and its core function is to generate and provide action plans that take into account the user's emotional state. This system primarily operates through the collaborative efforts of a terminal and a server.

[0382] Users use a terminal to access the system. The terminal is equipped with a dedicated application or web interface through which they input personal information and their wishes and expectations regarding end-of-life planning. As users input information, the terminal collects emotional data on their input patterns, speed, and word choices. This data is used to infer the user's emotional state.

[0383] The terminal is a device that transmits information and sentiment data collected from the user to the server. Common terminals used here include personal computers and smartphones. The collected data is transmitted to the server via the internet.

[0384] When the server receives information sent by the user, it stores it in a database. The stored information is then passed to the analysis engine, which analyzes the user's characteristics and needs. Based on the analysis results, the emotion engine evaluates the user's emotional state. This process takes into account factors such as stress during input and the positivity of the statements.

[0385] The server integrates the analysis results and sentiment evaluation to generate an optimized action plan for the user. A generative AI model is used in this process. The generative AI model suggests appropriate actions based on the user's situation, using prompts. An example of a prompt is, "Generate a support plan based on the user's emotional state, using the information entered after logging in."

[0386] The generated action plan is presented to the user via the device. The user can review the presented plan and begin taking specific actions based on it. For example, if the emotion engine determines that "writing a will is stressful," the system will suggest a simple template or further support options to the user.

[0387] In this way, coordinated operation between the terminal and the server enables flexible and effective end-of-life support that takes into account the user's emotional state.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] Users log in using the system's dedicated application or web interface. The login process involves entering a user ID and password. This authentication information is sent from the terminal to the server. The server then uses this information to authenticate the user and verify their legitimacy. Upon successful login, the user can proceed to the next step.

[0391] Step 2:

[0392] After logging in, users fill out a form on the interface that includes their personal information and their wishes and expectations regarding end-of-life planning. This form contains information about the user's thoughts and concerns about end-of-life planning. The device collects this input data in real time, capturing input patterns and speed as metadata. This data is sent to an emotion engine and used for an initial assessment of the user's emotional state.

[0393] Step 3:

[0394] The terminal packages the information and metadata collected from the user and sends it to the server. The input received by the server is the raw data itself, and the process involves saving this to a database and simultaneously passing it to the analysis engine. The server compares it with similar historical data and begins computational processing to gain new insights. Once the analyzed data is output, it is ready for the next step.

[0395] Step 4:

[0396] The server's analysis engine analyzes the collected information in detail. This process analyzes user characteristics, needs, and past behavioral history. The output of the analysis provides the core information for a specific action plan. Meanwhile, the emotion engine determines the user's emotional state from the input data. For example, if the input speed is very slow, it may indicate fatigue or anxiety. Based on these analysis results, feedback for the action plan is generated.

[0397] Step 5:

[0398] The server integrates the results from the analysis engine and the emotion engine to generate an optimized action plan for the user. This is where the generative AI model comes into play, automatically creating the optimal plan based on the user's specific situation. Prompt messages are formatted to correspond to the scenario of the generated action plan, such as "Generate a support plan based on the user's emotional state, using the information entered after logging in." The output is an action plan tailored to the user's emotions.

[0399] Step 6:

[0400] The terminal receives the action plan sent from the server, visualizes it, and presents it to the user. Here, the information is presented in a user-friendly format, and additional instructions or actions are taken based on it. The user then tries out this action plan and evaluates its usefulness.

[0401] Step 7:

[0402] Users input feedback into the system regarding the results of implementing their action plans. The collected information includes the ease of executing the plan and any additional support needed. This feedback is then used by the server in the next step, contributing to the overall improvement of the system.

[0403] Step 8:

[0404] The server re-analyzes user feedback and adjusts the action plan as needed. This feedback processing results in improved action plans and enhanced system adaptability, which in turn further increases user satisfaction.

[0405] (Application Example 2)

[0406] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0407] In modern society, improving individual safety and psychological well-being is a crucial issue. However, conventional security systems struggle to provide safety measures that take into account the user's psychological state, resulting in a lack of flexible support tailored to individual needs.

[0408] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0409] In this invention, the server includes a device for collecting information from the user, a device for analyzing the collected information and generating an action plan based on the analysis results, and a device for evaluating the user's psychological state using an emotion analysis engine. This makes it possible to provide security measures and mental support information that take the user's psychological state into consideration.

[0410] A "user" refers to an individual who uses the system to provide information and receive support.

[0411] "Information" refers to data collected from users, including personal information and data related to emotional states.

[0412] "Device" refers to a function incorporated into a system for collecting, analyzing, generating, and presenting information.

[0413] An "action plan" refers to a set of specific actions that a user should take, generated based on analyzed information and the user's psychological state.

[0414] An "emotion analysis engine" refers to a technical means for evaluating a user's emotional state, and has the function of analyzing their psychological state from collected data.

[0415] "Psychological state" refers to data that indicates the user's emotional and moodal state, which the system analyzes and incorporates into its action plan.

[0416] "Security measures" refer to specific actions incorporated into an action plan to ensure user safety.

[0417] "Opinions" refers to feedback and evaluations provided by users regarding the action plan.

[0418] A description of embodiments for carrying out this invention will be given.

[0419] The system's main components consist of a server, terminals, and users, and each element functions in cooperation with the others. The server plays a central role in collecting, analyzing, and generating action plans for information. The terminals serve as the interface between the user and the system, and are used for data input from the user and for providing feedback from the system.

[0420] The server is equipped with advanced data analysis software and an emotion analysis engine to receive and process data sent from users. Based on user input and past data, it analyzes the user's psychological state and generates an optimal action plan. It also develops plans that include information for security measures and psychological support.

[0421] The terminal is a tool for users to input personal information and feedback into the system, and smartphones or other mobile devices are used. User input data is sent to the server in real time, and feedback is received from the server after completion.

[0422] Users can review their action plan through their device and provide feedback if needed. For example, if a user experiences stress or anxiety, the system detects these using an emotion analysis engine and quickly suggests countermeasures.

[0423] For example, in a situation where a user feels uneasy walking alone at night, the device can display real-time security recommendations generated by the server and suggest actions that provide the user with a sense of security. Through this interaction, the user can benefit from improved environmental safety and psychological support.

[0424] An example of a prompt message is, "Please describe the situation that is causing you anxiety. What measures do you think would be most effective?"

[0425] Ultimately, this invention aims to provide flexible security support services based on the user's psychological needs.

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] The user logs into the system using a terminal. The user enters personal information and data related to their current psychological state. The terminal sends the entered information to the server. In this step, the input consists of personal information and emotion-related data provided by the user, and the output is the data sent to the server.

[0429] Step 2:

[0430] The server stores the received user information in a database and passes the data to the analysis engine. The analysis engine compares past data with the current input data to analyze the user's characteristics and current needs. Here, the input is the data received from the user, and the output is the result of the user characteristic analysis.

[0431] Step 3:

[0432] The server's sentiment analysis engine evaluates the user's psychological state. It performs text analysis on the user's input data and extracts the emotional state. The input for this step is the user's word choices and input patterns, and the output is the user's sentiment evaluation result.

[0433] Step 4:

[0434] The server integrates the analyzed characteristics and sentiment assessments to generate an optimal action plan for the user. This plan includes security recommendations and emotional support information. The input is the results of steps 2 and 3, and the output is the generated action plan.

[0435] Step 5:

[0436] The server sends an action plan to the terminal. The terminal presents the action plan to the user, who then takes the necessary actions based on it. The input is the action plan sent from the server, and the output is the action plan received by the user.

[0437] Step 6:

[0438] The user executes the presented plan and inputs their opinions and evaluations of the results into the terminal. The terminal sends this feedback back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0439] Step 7:

[0440] The server then re-analyzes the feedback and adjusts the action plan as needed. The input for this step is the user feedback, and the output is the adjusted action plan.

[0441] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0442] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0447] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0449] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0451] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0452] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0453] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0454] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0456] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0457] The end-of-life support system in this invention is primarily configured as a web application or mobile application accessed via the internet, enabling users to receive necessary end-of-life support through an online platform. The program's processing is described below in natural language.

[0458] First, the user accesses the system's login screen and logs in using their account information. After logging in, the user is shown a page where they answer basic questions related to end-of-life planning, including personal information, asset information, family information, and wishes regarding end-of-life planning. Once the user has finished entering the information into this input form, they press the submit button to send the information to the system.

[0459] The server receives information sent by the user and immediately stores it in a database. Next, it uses generative AI to analyze the user's characteristics based on the stored information. This analysis includes a process of evaluating the user's needs and unique characteristics by utilizing historical data and statistical insights.

[0460] The server, having received the analysis results, generates individually optimized action plans to help users efficiently carry out end-of-life planning. Each action plan includes necessary preparations, procedures, and relevant legal guidance. The generated action plan provides concrete steps for users to proceed with their end-of-life planning.

[0461] After generation, the server sends the action plan to the terminal and displays it on the user's screen. The user can review the action plan on the screen and then begin taking specific actions based on the plan. For example, in the "Writing a Will" step, the user can download recommended templates and receive assistance in creating a document that meets their specific needs.

[0462] After executing an action plan, users can provide feedback and further requests for evaluation through the system's feedback function. This feedback is sent to the server via the terminal, which analyzes the received feedback and adjusts the action plan as needed.

[0463] Through the above process, the end-of-life support system provides detailed support tailored to each user's individual circumstances, reducing the psychological and physical burden on users in end-of-life planning.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The user logs into the end-of-life planning support system using their device. After logging in, the initial screen displays a form for entering personal information and wishes regarding end-of-life planning. The user then fills out these forms.

[0467] Step 2:

[0468] The terminal verifies the information entered by the user in real time to check for any errors. If there are no errors, pressing the send button sends the information to the server.

[0469] Step 3:

[0470] The server receives user information sent from the terminal. The received information is immediately stored in the database. Next, the server passes the information to the analysis engine, which begins to evaluate the user's characteristics and needs.

[0471] Step 4:

[0472] The analysis engine uses generated AI based on collected information to analyze the user's background and preferences. This is done to build an optimal action plan for each individual user by referring to similar past data.

[0473] Step 5:

[0474] The server generates a customized action plan based on the analysis results. This plan includes guidance on drafting a will, procedures for organizing assets, and specific steps.

[0475] Step 6:

[0476] The server sends the generated action plan to the terminal. The terminal displays the received action plan appropriately in the user interface and notifies the user of the next action to take.

[0477] Step 7:

[0478] Users review their action plan on their device and then begin concrete end-of-life planning steps according to it. For example, they might download a template for creating a will and consider its contents based on their own wishes.

[0479] Step 8:

[0480] Users can input feedback on the implemented action plan via their device. This feedback includes evaluations and modifications to the action plan.

[0481] Step 9:

[0482] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and sends the adjusted plan back to the device.

[0483] As a result, the end-of-life planning support system provides users with flexible and effective end-of-life support.

[0484] (Example 1)

[0485] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0486] Users engaged in end-of-life planning face challenges in organizing necessary information and developing appropriate action plans. Furthermore, there is a lack of efficient support due to the inability to provide plans optimized for individual users.

[0487] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0488] In this invention, the server includes means for collecting information from the user, means for using a generation artificial intelligence model to analyze the collected information, and means for generating an action plan based on the analysis results. This makes it possible to provide the user with an action plan that is individually optimized and to adjust the plan in response to feedback.

[0489] A "user" is an individual or group that utilizes the system, provides information, or receives generated action plans.

[0490] "Means of collecting information" refers to the process of providing functionality to acquire input data from users and store it in a database.

[0491] A "generative artificial intelligence model" refers to a technical method for analyzing collected information and generating action plans suitable for the user.

[0492] An "action plan" is a document that includes specific steps and guidance for users to proceed with end-of-life planning based on the analysis results.

[0493] "Means of receiving feedback" refers to the process of obtaining user evaluations and requests regarding the generated action plan.

[0494] "Means for adjusting action plans" refers to a process that provides the ability to analyze the feedback received and modify or update existing action plans as needed.

[0495] This invention provides a system that allows users to receive efficient and individually optimized end-of-life planning support. It is primarily configured as a web application or mobile application accessed via the internet.

[0496] Users access the system through an interface and authenticate at the login screen. The devices used for this are PCs and smartphones. After logging in, users are asked to enter personal information, asset information, family information, and end-of-life planning wishes. This information is encrypted for privacy reasons and sent to the server.

[0497] The server receives data sent from the user and stores it in a dedicated database. Next, a generative AI model is used to analyze this information. The generative AI model leverages historical data and statistical algorithms to evaluate the user's characteristics and needs. Based on this evaluation, it generates an action plan optimized for the user. The generated plan includes necessary preparations, procedures, and legal guidance.

[0498] For example, if a user requests to "create a will," the action plan will include a download link for a will template and legal advice. Similarly, in the step of "writing a thank-you letter to family," the AI ​​model will provide a letter template tailored to the user. An example of a prompt in this case would be, "Please generate a letter template for a man in his 50s to express his gratitude to his family."

[0499] The device displays the generated action plan to the user. The user can review the plan on the screen and begin taking specific actions. This allows the user to proceed with end-of-life planning in a planned and efficient manner.

[0500] Furthermore, the feedback function allows users to submit evaluations and requests regarding the action plans they have implemented, and the server can adjust the action plans based on this feedback. In this way, the system provides support tailored to the individual needs of each user.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The user accesses the system's login screen and enters their authentication information, which includes their email address and password. The terminal sends this information to the server, initiating the authentication process. The server compares this information with the database and, if correct, grants access to the dashboard; otherwise, sends an error message.

[0504] Step 2:

[0505] After navigating to the dashboard, the user accesses a section to enter information necessary for end-of-life planning. Here, they enter personal information, asset information, family information, and their wishes. The device then transfers this entire set of data to the server when the user presses the submit button.

[0506] Step 3:

[0507] The server saves the received data to a database before analysis. After saving, it begins analyzing the user's characteristics using the implemented generative AI model. The input for the analysis is the data provided by the user, and the output is a user profile. This is used to evaluate the user's needs and characteristics.

[0508] Step 4:

[0509] The server generates a customized action plan based on the analysis results obtained from the generated AI model. Based on the input user profile, the action plan includes necessary preparations and legal guidance. This process has the functionality to generate an action plan by prompting the AI ​​model. The generated plan is output in PDF or other formats.

[0510] Step 5:

[0511] The terminal displays the action plan sent from the server on the user's screen. The user can review the plan on the screen and follow the instructions to perform specific steps. For example, the "creating a will" step involves downloading and using a template.

[0512] Step 6:

[0513] After executing an action plan, users input their evaluation using the system's built-in feedback function. This input includes suggestions for improvement and additional requests regarding the action plan. The feedback is sent as data from the terminal to the server, which analyzes it to determine whether changes to the plan are necessary.

[0514] Step 7:

[0515] The server analyzes the feedback and updates the action plan as needed. The updated action plan is then sent back to the terminal and presented to the user. This ensures that support tailored to each user's needs is continuously provided.

[0516] (Application Example 1)

[0517] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] When users engage in end-of-life planning, gathering necessary information, generating an optimal action plan based on that information, and executing it is a complex and time-consuming task. Furthermore, there is the challenge of difficulty in receiving appropriate services and support tailored to individual needs. This increases the psychological and physical burden, leading to problems in the smooth progress of end-of-life planning.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0520] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and generating an action plan based on the analysis results, and means for presenting the generated action plan to the user and receiving feedback. This makes it possible for the user to use the virtual space to confirm the optimal action plan and to execute specific procedures such as legal document creation and asset management preparation.

[0521] A "user" is an entity that utilizes the system, inputs information related to end-of-life planning support, and receives action plans.

[0522] "Means of collecting information" refers to an interface for obtaining data necessary for end-of-life planning from users.

[0523] "Generative means for analyzing collected information" refers to a device or program that performs analysis based on input user information and creates an appropriate action plan.

[0524] The "generation means for generating action plans" is a processing device that, based on the results of analysis, formulates specific steps for end-of-life planning optimized for the user.

[0525] A "means of receiving feedback" refers to a device or program that has the functionality to allow users to submit opinions and suggestions for improvement regarding an action plan.

[0526] A "means for adjusting the action plan" is a processing device that reviews and improves the content of the action plan based on the feedback received.

[0527] "Means of providing virtual space" refers to technologies that enable users to select and execute end-of-life planning-related services within a virtually constructed environment.

[0528] "Guidance on legal document preparation" refers to guidelines or advice designed to assist users in preparing legal documents necessary for end-of-life planning.

[0529] "Specific procedures for preparing for asset management" refers to the specific methods and processes necessary for a user to efficiently organize and manage their own assets.

[0530] This invention provides a system that allows users to efficiently carry out end-of-life planning using mobile devices or personal computers. The main components of this system include a user interface, a server, and a generative AI model.

[0531] Users access an application on their device via the internet and enter information related to their end-of-life planning (personal information, asset information, wishes, etc.). This information is sent to a server using Python or Flask.

[0532] The server stores the received information in a secure database and then analyzes the information using a generative AI model. Deep learning frameworks such as TensorFlow and PyTorch are used for the analysis. Based on this analysis, an optimized action plan is generated for the user, providing specific guidance and recommendation services.

[0533] The generated action plan is sent to the terminal and displayed on the user's screen. Here, the user can use the virtual space to execute specific steps related to creating legal documents and organizing assets. The user can also provide feedback on the proposed plan to the system, and the server adjusts the plan as needed based on that feedback.

[0534] For example, a user might select a funeral service and receive guidance on the procedures. In this case, practical advice is provided in a virtual space, allowing the user to take concrete actions based on that advice.

[0535] Examples of prompt statements include the following:

[0536] "Please enter information about your end-of-life planning. The system will suggest the best action plan for you."

[0537] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0538] Step 1:

[0539] Users access the application on their device via the internet and enter information related to end-of-life planning (personal information, asset information, wishes, etc.). The entered information is sent to the server as a digital form. Here, basic information about the user's end-of-life planning is collected.

[0540] Step 2:

[0541] The server stores the received user information in a secure database. At this stage, the raw data obtained from the user is saved and made available for subsequent processing. The database plays a role in maintaining the security and integrity of the information.

[0542] Step 3:

[0543] The server provides stored user information as input to a generative AI model. The generative AI model (e.g., a model using TensorFlow or PyTorch) analyzes this information to understand the user's characteristics and needs. As a result of the analysis, an individually optimized action plan is generated. This process involves data processing and calculations using historical data and statistical methods.

[0544] Step 4:

[0545] The server sends the generated action plan to the terminal and displays it on the user's screen. This plan includes specific steps and legal guidance. The user can review and proceed with the suggested actions based on this information.

[0546] Step 5:

[0547] The user executes the presented action plan and sends feedback to the server via their device as needed. Specific actions include filling out an on-screen feedback form.

[0548] Step 6:

[0549] The server analyzes the received feedback and adjusts the action plan. This involves data re-analysis and plan adaptation based on the feedback. This allows for adjustments that more accurately meet user needs.

[0550] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0551] The end-of-life support system of this invention, in addition to the basic elements of collecting and analyzing user information, incorporates an emotion engine to recognize the user's emotional state and provide an action plan that takes this into account. The program's processing is described below in natural language.

[0552] At the start of the system, the user logs in using a terminal. During this process, the user fills out a form containing personal information and their expectations and wishes regarding end-of-life planning. During this information entry and subsequent operations, the terminal transmits emotional data to the emotion engine based on the user's input patterns, speed, and word choices.

[0553] The server receives information sent by the user, stores it in a database, and passes it to the analysis engine to begin analyzing the information. This analysis compares the information with past data to determine the user's characteristics and needs. Simultaneously, the emotion engine also operates to evaluate the user's emotional state. For example, it detects signs of stress displayed during input and whether the user is feeling positive emotions based on the tone of their conversation.

[0554] After the evaluation is complete, the server integrates the analysis results and the emotional assessment results to generate an action plan optimized for the user's emotional state. For example, if the user expresses anxiety about end-of-life planning, the server can include support information and advice to reassure the user in the plan.

[0555] This action plan is presented to the user via their device. The user can review the plan on the screen and take specific actions based on its contents. For example, if the emotion engine determines that "writing a will is causing stress," the system will suggest a simple template or further support options to the user.

[0556] After executing an action plan, users can input feedback into the system. This feedback includes evaluations of the plan and additional requests. This feedback is sent to the server, analyzed again, and the action plan is adjusted as needed to improve the user experience.

[0557] Through this system, users will be able to receive flexible and effective end-of-life support that takes their emotions into consideration.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] Users log in to the end-of-life planning support system via their device. After logging in, the initial screen displays a form for entering basic information and wishes regarding end-of-life planning. Users fill in the required information on the form and submit it.

[0561] Step 2:

[0562] The terminal verifies the information entered by the user in real time and sends the input data to the emotion engine. It also sends the verified information to the server.

[0563] Step 3:

[0564] The server receives user information sent from the terminal and stores it in the database. The server then passes the information to the analysis engine, which begins the process of analyzing the user's characteristics and needs.

[0565] Step 4:

[0566] The emotion engine evaluates the user's emotional state based on user input data received from the device. For example, it analyzes whether slow input speed or the use of certain words indicates a possible negative emotion.

[0567] Step 5:

[0568] The server integrates analysis results from the analysis engine and emotion evaluation results from the emotion engine to generate an optimized action plan. For example, if the user is experiencing stress, the plan might include simpler tasks or enhanced support.

[0569] Step 6:

[0570] The server sends the generated action plan to the terminal. The terminal displays it in its user interface, presenting the user with specific actions to take next.

[0571] Step 7:

[0572] Users review their action plan on their device and take actions according to the plan. The action plan includes emotionally responsive support information and guidance to provide reassurance.

[0573] Step 8:

[0574] After executing their action plan, users can provide feedback via their device. This feedback can include evaluations of the plan and suggestions for improvement.

[0575] Step 9:

[0576] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and prepares to present it to the user again.

[0577] In this way, the end-of-life support system provides flexible and personalized support that takes the user's emotions into consideration.

[0578] (Example 2)

[0579] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] Conventional end-of-life planning support systems often provide uniform action plans without adequately considering the user's emotional state, making it difficult to alleviate the user's mental burden. Furthermore, the lack of sufficient consideration of emotional aspects in plan generation based on user input hinders improvements in the user experience.

[0581] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0582] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and emotional data and generating an action plan based on the analysis results and emotional evaluation, and means for presenting the generated action plan to the user and receiving feedback on a plan optimized for the user's emotional state. This makes it possible to provide a flexible and personalized action plan that corresponds to the user's emotional state.

[0583] A "user" refers to an individual who uses the system to input their own information and receive support related to end-of-life planning.

[0584] "Information gathering means" refers to methods and devices used to obtain personal information and wishes and expectations regarding end-of-life planning from users.

[0585] "Emotional data" refers to numerical data that indicates the emotional state of a user, inferred from their input patterns, speed, word choice, and other factors.

[0586] "Analysis means" refers to methods and devices for determining user characteristics, needs, and emotional states based on collected information and emotional data.

[0587] "Generation means" refers to methods or devices for creating an optimal action plan based on analyzed data.

[0588] An "action plan" refers to a plan that includes specific steps and guidance provided to support users in their end-of-life planning activities.

[0589] "Presentation means" refers to methods or functions for providing the generated action plan to the user visually or audibly.

[0590] "Feedback receiving methods" refer to methods and devices for receiving evaluations and requests from users regarding action plans and using that information to improve the system.

[0591] "Adjustment measures" refer to methods and devices for reviewing action plans based on feedback and modifying them as necessary.

[0592] This invention is a system that supports users in their end-of-life planning, and its core function is to generate and provide action plans that take into account the user's emotional state. This system primarily operates through the collaborative efforts of a terminal and a server.

[0593] Users use a terminal to access the system. The terminal is equipped with a dedicated application or web interface through which they input personal information and their wishes and expectations regarding end-of-life planning. As users input information, the terminal collects emotional data on their input patterns, speed, and word choices. This data is used to infer the user's emotional state.

[0594] The terminal is a device that transmits information and sentiment data collected from the user to the server. Common terminals used here include personal computers and smartphones. The collected data is transmitted to the server via the internet.

[0595] When the server receives information sent by the user, it stores it in a database. The stored information is then passed to the analysis engine, which analyzes the user's characteristics and needs. Based on the analysis results, the emotion engine evaluates the user's emotional state. This process takes into account factors such as stress during input and the positivity of the statements.

[0596] The server integrates the analysis results and sentiment evaluation to generate an optimized action plan for the user. A generative AI model is used in this process. The generative AI model suggests appropriate actions based on the user's situation, using prompts. An example of a prompt is, "Generate a support plan based on the user's emotional state, using the information entered after logging in."

[0597] The generated action plan is presented to the user via the device. The user can review the presented plan and begin taking specific actions based on it. For example, if the emotion engine determines that "writing a will is stressful," the system will suggest a simple template or further support options to the user.

[0598] In this way, coordinated operation between the terminal and the server enables flexible and effective end-of-life support that takes into account the user's emotional state.

[0599] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0600] Step 1:

[0601] Users log in using the system's dedicated application or web interface. The login process involves entering a user ID and password. This authentication information is sent from the terminal to the server. The server then uses this information to authenticate the user and verify their legitimacy. Upon successful login, the user can proceed to the next step.

[0602] Step 2:

[0603] After logging in, users fill out a form on the interface that includes their personal information and their wishes and expectations regarding end-of-life planning. This form contains information about the user's thoughts and concerns about end-of-life planning. The device collects this input data in real time, capturing input patterns and speed as metadata. This data is sent to an emotion engine and used for an initial assessment of the user's emotional state.

[0604] Step 3:

[0605] The terminal packages the information and metadata collected from the user and sends it to the server. The input received by the server is the raw data itself, and the process involves saving this to a database and simultaneously passing it to the analysis engine. The server compares it with similar historical data and begins computational processing to gain new insights. Once the analyzed data is output, it is ready for the next step.

[0606] Step 4:

[0607] The server's analysis engine analyzes the collected information in detail. This process analyzes user characteristics, needs, and past behavioral history. The output of the analysis provides the core information for a specific action plan. Meanwhile, the emotion engine determines the user's emotional state from the input data. For example, if the input speed is very slow, it may indicate fatigue or anxiety. Based on these analysis results, feedback for the action plan is generated.

[0608] Step 5:

[0609] The server integrates the results from the analysis engine and the emotion engine to generate an optimized action plan for the user. This is where the generative AI model comes into play, automatically creating the optimal plan based on the user's specific situation. Prompt messages are formatted to correspond to the scenario of the generated action plan, such as "Generate a support plan based on the user's emotional state, using the information entered after logging in." The output is an action plan tailored to the user's emotions.

[0610] Step 6:

[0611] The terminal receives the action plan sent from the server, visualizes it, and presents it to the user. Here, the information is presented in a user-friendly format, and additional instructions or actions are taken based on it. The user then tries out this action plan and evaluates its usefulness.

[0612] Step 7:

[0613] Users input feedback into the system regarding the results of implementing their action plans. The collected information includes the ease of executing the plan and any additional support needed. This feedback is then used by the server in the next step, contributing to the overall improvement of the system.

[0614] Step 8:

[0615] The server re-analyzes user feedback and adjusts the action plan as needed. This feedback processing results in improved action plans and enhanced system adaptability, which in turn further increases user satisfaction.

[0616] (Application Example 2)

[0617] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0618] In modern society, improving individual safety and psychological well-being is a crucial issue. However, conventional security systems struggle to provide safety measures that take into account the user's psychological state, resulting in a lack of flexible support tailored to individual needs.

[0619] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0620] In this invention, the server includes a device for collecting information from the user, a device for analyzing the collected information and generating an action plan based on the analysis results, and a device for evaluating the user's psychological state using an emotion analysis engine. This makes it possible to provide security measures and mental support information that take the user's psychological state into consideration.

[0621] A "user" refers to an individual who uses the system to provide information and receive support.

[0622] "Information" refers to data collected from users, including personal information and data related to emotional states.

[0623] "Device" refers to a function incorporated into a system for collecting, analyzing, generating, and presenting information.

[0624] An "action plan" refers to a set of specific actions that a user should take, generated based on analyzed information and the user's psychological state.

[0625] An "emotion analysis engine" refers to a technical means for evaluating a user's emotional state, and has the function of analyzing their psychological state from collected data.

[0626] "Psychological state" refers to data that indicates the user's emotional and moodal state, which the system analyzes and incorporates into its action plan.

[0627] "Security measures" refer to specific actions incorporated into an action plan to ensure user safety.

[0628] "Opinions" refers to feedback and evaluations provided by users regarding the action plan.

[0629] A description of embodiments for carrying out this invention will be given.

[0630] The system's main components consist of a server, terminals, and users, and each element functions in cooperation with the others. The server plays a central role in collecting, analyzing, and generating action plans for information. The terminals serve as the interface between the user and the system, and are used for data input from the user and for providing feedback from the system.

[0631] The server is equipped with advanced data analysis software and an emotion analysis engine to receive and process data sent from users. Based on user input and past data, it analyzes the user's psychological state and generates an optimal action plan. It also develops plans that include information for security measures and psychological support.

[0632] The terminal is a tool for users to input personal information and feedback into the system, and smartphones or other mobile devices are used. User input data is sent to the server in real time, and feedback is received from the server after completion.

[0633] Users can review their action plan through their device and provide feedback if needed. For example, if a user experiences stress or anxiety, the system detects these using an emotion analysis engine and quickly suggests countermeasures.

[0634] For example, in a situation where a user feels uneasy walking alone at night, the device can display real-time security recommendations generated by the server and suggest actions that provide the user with a sense of security. Through this interaction, the user can benefit from improved environmental safety and psychological support.

[0635] An example of a prompt message is, "Please describe the situation that is causing you anxiety. What measures do you think would be most effective?"

[0636] Ultimately, this invention aims to provide flexible security support services based on the user's psychological needs.

[0637] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0638] Step 1:

[0639] The user logs into the system using a terminal. The user enters personal information and data related to their current psychological state. The terminal sends the entered information to the server. In this step, the input consists of personal information and emotion-related data provided by the user, and the output is the data sent to the server.

[0640] Step 2:

[0641] The server stores the received user information in a database and passes the data to the analysis engine. The analysis engine compares past data with the current input data to analyze the user's characteristics and current needs. Here, the input is the data received from the user, and the output is the result of the user characteristic analysis.

[0642] Step 3:

[0643] The server's sentiment analysis engine evaluates the user's psychological state. It performs text analysis on the user's input data and extracts the emotional state. The input for this step is the user's word choices and input patterns, and the output is the user's sentiment evaluation result.

[0644] Step 4:

[0645] The server integrates the analyzed characteristics and sentiment assessments to generate an optimal action plan for the user. This plan includes security recommendations and emotional support information. The input is the results of steps 2 and 3, and the output is the generated action plan.

[0646] Step 5:

[0647] The server sends an action plan to the terminal. The terminal presents the action plan to the user, who then takes the necessary actions based on it. The input is the action plan sent from the server, and the output is the action plan received by the user.

[0648] Step 6:

[0649] The user executes the presented plan and inputs their opinions and evaluations of the results into the terminal. The terminal sends this feedback back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0650] Step 7:

[0651] The server then re-analyzes the feedback and adjusts the action plan as needed. The input for this step is the user feedback, and the output is the adjusted action plan.

[0652] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0653] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0654] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0655] [Fourth Embodiment]

[0656] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0657] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0658] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0659] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0660] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0661] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0662] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0663] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0664] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0665] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0666] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0667] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0668] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] The end-of-life support system in this invention is primarily configured as a web application or mobile application accessed via the internet, enabling users to receive necessary end-of-life support through an online platform. The program's processing is described below in natural language.

[0670] First, the user accesses the system's login screen and logs in using their account information. After logging in, the user is shown a page where they answer basic questions related to end-of-life planning, including personal information, asset information, family information, and wishes regarding end-of-life planning. Once the user has finished entering the information into this input form, they press the submit button to send the information to the system.

[0671] The server receives information sent by the user and immediately stores it in a database. Next, it uses generative AI to analyze the user's characteristics based on the stored information. This analysis includes a process of evaluating the user's needs and unique characteristics by utilizing historical data and statistical insights.

[0672] The server, having received the analysis results, generates individually optimized action plans to help users efficiently carry out end-of-life planning. Each action plan includes necessary preparations, procedures, and relevant legal guidance. The generated action plan provides concrete steps for users to proceed with their end-of-life planning.

[0673] After generation, the server sends the action plan to the terminal and displays it on the user's screen. The user can review the action plan on the screen and then begin taking specific actions based on the plan. For example, in the "Writing a Will" step, the user can download recommended templates and receive assistance in creating a document that meets their specific needs.

[0674] After executing an action plan, users can provide feedback and further requests for evaluation through the system's feedback function. This feedback is sent to the server via the terminal, which analyzes the received feedback and adjusts the action plan as needed.

[0675] Through the above process, the end-of-life support system provides detailed support tailored to each user's individual circumstances, reducing the psychological and physical burden on users in end-of-life planning.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The user logs into the end-of-life planning support system using their device. After logging in, the initial screen displays a form for entering personal information and wishes regarding end-of-life planning. The user then fills out these forms.

[0679] Step 2:

[0680] The terminal verifies the information entered by the user in real time to check for any errors. If there are no errors, pressing the send button sends the information to the server.

[0681] Step 3:

[0682] The server receives user information sent from the terminal. The received information is immediately stored in the database. Next, the server passes the information to the analysis engine, which begins to evaluate the user's characteristics and needs.

[0683] Step 4:

[0684] The analysis engine uses generated AI based on collected information to analyze the user's background and preferences. This is done to build an optimal action plan for each individual user by referring to similar past data.

[0685] Step 5:

[0686] The server generates a customized action plan based on the analysis results. This plan includes guidance on drafting a will, procedures for organizing assets, and specific steps.

[0687] Step 6:

[0688] The server sends the generated action plan to the terminal. The terminal displays the received action plan appropriately in the user interface and notifies the user of the next action to take.

[0689] Step 7:

[0690] Users review their action plan on their device and then begin concrete end-of-life planning steps according to it. For example, they might download a template for creating a will and consider its contents based on their own wishes.

[0691] Step 8:

[0692] Users can input feedback on the implemented action plan via their device. This feedback includes evaluations and modifications to the action plan.

[0693] Step 9:

[0694] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and sends the adjusted plan back to the device.

[0695] As a result, the end-of-life planning support system provides users with flexible and effective end-of-life support.

[0696] (Example 1)

[0697] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] Users engaged in end-of-life planning face challenges in organizing necessary information and developing appropriate action plans. Furthermore, there is a lack of efficient support due to the inability to provide plans optimized for individual users.

[0699] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0700] In this invention, the server includes means for collecting information from the user, means for using a generation artificial intelligence model to analyze the collected information, and means for generating an action plan based on the analysis results. This makes it possible to provide the user with an action plan that is individually optimized and to adjust the plan in response to feedback.

[0701] A "user" is an individual or group that utilizes the system, provides information, or receives generated action plans.

[0702] "Means of collecting information" refers to the process of providing functionality to acquire input data from users and store it in a database.

[0703] A "generative artificial intelligence model" refers to a technical method for analyzing collected information and generating action plans suitable for the user.

[0704] An "action plan" is a document that includes specific steps and guidance for users to proceed with end-of-life planning based on the analysis results.

[0705] "Means of receiving feedback" refers to the process of obtaining user evaluations and requests regarding the generated action plan.

[0706] "Means for adjusting action plans" refers to a process that provides the ability to analyze the feedback received and modify or update existing action plans as needed.

[0707] This invention provides a system that allows users to receive efficient and individually optimized end-of-life planning support. It is primarily configured as a web application or mobile application accessed via the internet.

[0708] Users access the system through an interface and authenticate at the login screen. The devices used for this are PCs and smartphones. After logging in, users are asked to enter personal information, asset information, family information, and end-of-life planning wishes. This information is encrypted for privacy reasons and sent to the server.

[0709] The server receives data sent from the user and stores it in a dedicated database. Next, a generative AI model is used to analyze this information. The generative AI model leverages historical data and statistical algorithms to evaluate the user's characteristics and needs. Based on this evaluation, it generates an action plan optimized for the user. The generated plan includes necessary preparations, procedures, and legal guidance.

[0710] For example, if a user requests to "create a will," the action plan will include a download link for a will template and legal advice. Similarly, in the step of "writing a thank-you letter to family," the AI ​​model will provide a letter template tailored to the user. An example of a prompt in this case would be, "Please generate a letter template for a man in his 50s to express his gratitude to his family."

[0711] The device displays the generated action plan to the user. The user can review the plan on the screen and begin taking specific actions. This allows the user to proceed with end-of-life planning in a planned and efficient manner.

[0712] Furthermore, the feedback function allows users to submit evaluations and requests regarding the action plans they have implemented, and the server can adjust the action plans based on this feedback. In this way, the system provides support tailored to the individual needs of each user.

[0713] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0714] Step 1:

[0715] The user accesses the system's login screen and enters their authentication information, which includes their email address and password. The terminal sends this information to the server, initiating the authentication process. The server compares this information with the database and, if correct, grants access to the dashboard; otherwise, sends an error message.

[0716] Step 2:

[0717] After navigating to the dashboard, the user accesses a section to enter information necessary for end-of-life planning. Here, they enter personal information, asset information, family information, and their wishes. The device then transfers this entire set of data to the server when the user presses the submit button.

[0718] Step 3:

[0719] The server saves the received data to a database before analysis. After saving, it begins analyzing the user's characteristics using the implemented generative AI model. The input for the analysis is the data provided by the user, and the output is a user profile. This is used to evaluate the user's needs and characteristics.

[0720] Step 4:

[0721] The server generates a customized action plan based on the analysis results obtained from the generated AI model. Based on the input user profile, the action plan includes necessary preparations and legal guidance. This process has the functionality to generate an action plan by prompting the AI ​​model. The generated plan is output in PDF or other formats.

[0722] Step 5:

[0723] The terminal displays the action plan sent from the server on the user's screen. The user can review the plan on the screen and follow the instructions to perform specific steps. For example, the "creating a will" step involves downloading and using a template.

[0724] Step 6:

[0725] After executing an action plan, users input their evaluation using the system's built-in feedback function. This input includes suggestions for improvement and additional requests regarding the action plan. The feedback is sent as data from the terminal to the server, which analyzes it to determine whether changes to the plan are necessary.

[0726] Step 7:

[0727] The server analyzes the feedback and updates the action plan as needed. The updated action plan is then sent back to the terminal and presented to the user. This ensures that support tailored to each user's needs is continuously provided.

[0728] (Application Example 1)

[0729] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] When users engage in end-of-life planning, gathering necessary information, generating an optimal action plan based on that information, and executing it is a complex and time-consuming task. Furthermore, there is the challenge of difficulty in receiving appropriate services and support tailored to individual needs. This increases the psychological and physical burden, leading to problems in the smooth progress of end-of-life planning.

[0731] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0732] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and generating an action plan based on the analysis results, and means for presenting the generated action plan to the user and receiving feedback. This makes it possible for the user to use the virtual space to confirm the optimal action plan and to execute specific procedures such as legal document creation and asset management preparation.

[0733] A "user" is an entity that utilizes the system, inputs information related to end-of-life planning support, and receives action plans.

[0734] "Means of collecting information" refers to an interface for obtaining data necessary for end-of-life planning from users.

[0735] "Generative means for analyzing collected information" refers to a device or program that performs analysis based on input user information and creates an appropriate action plan.

[0736] The "generation means for generating action plans" is a processing device that, based on the results of analysis, formulates specific steps for end-of-life planning optimized for the user.

[0737] A "means of receiving feedback" refers to a device or program that has the functionality to allow users to submit opinions and suggestions for improvement regarding an action plan.

[0738] A "means for adjusting the action plan" is a processing device that reviews and improves the content of the action plan based on the feedback received.

[0739] "Means of providing virtual space" refers to technologies that enable users to select and execute end-of-life planning-related services within a virtually constructed environment.

[0740] "Guidance on legal document preparation" refers to guidelines or advice designed to assist users in preparing legal documents necessary for end-of-life planning.

[0741] "Specific procedures for preparing for asset management" refers to the specific methods and processes necessary for a user to efficiently organize and manage their own assets.

[0742] This invention provides a system that allows users to efficiently carry out end-of-life planning using mobile devices or personal computers. The main components of this system include a user interface, a server, and a generative AI model.

[0743] Users access an application on their device via the internet and enter information related to their end-of-life planning (personal information, asset information, wishes, etc.). This information is sent to a server using Python or Flask.

[0744] The server stores the received information in a secure database and then analyzes the information using a generative AI model. Deep learning frameworks such as TensorFlow and PyTorch are used for the analysis. Based on this analysis, an optimized action plan is generated for the user, providing specific guidance and recommendation services.

[0745] The generated action plan is sent to the terminal and displayed on the user's screen. Here, the user can use the virtual space to execute specific steps related to creating legal documents and organizing assets. The user can also provide feedback on the proposed plan to the system, and the server adjusts the plan as needed based on that feedback.

[0746] For example, a user might select a funeral service and receive guidance on the procedures. In this case, practical advice is provided in a virtual space, allowing the user to take concrete actions based on that advice.

[0747] Examples of prompt statements include the following:

[0748] "Please enter information about your end-of-life planning. The system will suggest the best action plan for you."

[0749] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0750] Step 1:

[0751] Users access the application on their device via the internet and enter information related to end-of-life planning (personal information, asset information, wishes, etc.). The entered information is sent to the server as a digital form. Here, basic information about the user's end-of-life planning is collected.

[0752] Step 2:

[0753] The server stores the received user information in a secure database. At this stage, the raw data obtained from the user is saved and made available for subsequent processing. The database plays a role in maintaining the security and integrity of the information.

[0754] Step 3:

[0755] The server provides stored user information as input to a generative AI model. The generative AI model (e.g., a model using TensorFlow or PyTorch) analyzes this information to understand the user's characteristics and needs. As a result of the analysis, an individually optimized action plan is generated. This process involves data processing and calculations using historical data and statistical methods.

[0756] Step 4:

[0757] The server sends the generated action plan to the terminal and displays it on the user's screen. This plan includes specific steps and legal guidance. The user can review and proceed with the suggested actions based on this information.

[0758] Step 5:

[0759] The user executes the presented action plan and sends feedback to the server via their device as needed. Specific actions include filling out an on-screen feedback form.

[0760] Step 6:

[0761] The server analyzes the received feedback and adjusts the action plan. This involves data re-analysis and plan adaptation based on the feedback. This allows for adjustments that more accurately meet user needs.

[0762] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0763] The end-of-life support system of this invention, in addition to the basic elements of collecting and analyzing user information, incorporates an emotion engine to recognize the user's emotional state and provide an action plan that takes this into account. The program's processing is described below in natural language.

[0764] At the start of the system, the user logs in using a terminal. During this process, the user fills out a form containing personal information and their expectations and wishes regarding end-of-life planning. During this information entry and subsequent operations, the terminal transmits emotional data to the emotion engine based on the user's input patterns, speed, and word choices.

[0765] The server receives information sent by the user, stores it in a database, and passes it to the analysis engine to begin analyzing the information. This analysis compares the information with past data to determine the user's characteristics and needs. Simultaneously, the emotion engine also operates to evaluate the user's emotional state. For example, it detects signs of stress displayed during input and whether the user is feeling positive emotions based on the tone of their conversation.

[0766] After the evaluation is complete, the server integrates the analysis results and the emotional assessment results to generate an action plan optimized for the user's emotional state. For example, if the user expresses anxiety about end-of-life planning, the server can include support information and advice to reassure the user in the plan.

[0767] This action plan is presented to the user via their device. The user can review the plan on the screen and take specific actions based on its contents. For example, if the emotion engine determines that "writing a will is causing stress," the system will suggest a simple template or further support options to the user.

[0768] After executing an action plan, users can input feedback into the system. This feedback includes evaluations of the plan and additional requests. This feedback is sent to the server, analyzed again, and the action plan is adjusted as needed to improve the user experience.

[0769] Through this system, users will be able to receive flexible and effective end-of-life support that takes their emotions into consideration.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] Users log in to the end-of-life planning support system via their device. After logging in, the initial screen displays a form for entering basic information and wishes regarding end-of-life planning. Users fill in the required information on the form and submit it.

[0773] Step 2:

[0774] The terminal verifies the information entered by the user in real time and sends the input data to the emotion engine. It also sends the verified information to the server.

[0775] Step 3:

[0776] The server receives user information sent from the terminal and stores it in the database. The server then passes the information to the analysis engine, which begins the process of analyzing the user's characteristics and needs.

[0777] Step 4:

[0778] The emotion engine evaluates the user's emotional state based on user input data received from the device. For example, it analyzes whether slow input speed or the use of certain words indicates a possible negative emotion.

[0779] Step 5:

[0780] The server integrates analysis results from the analysis engine and emotion evaluation results from the emotion engine to generate an optimized action plan. For example, if the user is experiencing stress, the plan might include simpler tasks or enhanced support.

[0781] Step 6:

[0782] The server sends the generated action plan to the terminal. The terminal displays it in its user interface, presenting the user with specific actions to take next.

[0783] Step 7:

[0784] Users review their action plan on their device and take actions according to the plan. The action plan includes emotionally responsive support information and guidance to provide reassurance.

[0785] Step 8:

[0786] After executing their action plan, users can provide feedback via their device. This feedback can include evaluations of the plan and suggestions for improvement.

[0787] Step 9:

[0788] The device sends user feedback to the server. The server analyzes the feedback, adjusts the action plan as needed, and prepares to present it to the user again.

[0789] In this way, the end-of-life support system provides flexible and personalized support that takes the user's emotions into consideration.

[0790] (Example 2)

[0791] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0792] Conventional end-of-life planning support systems often provide uniform action plans without adequately considering the user's emotional state, making it difficult to alleviate the user's mental burden. Furthermore, the lack of sufficient consideration of emotional aspects in plan generation based on user input hinders improvements in the user experience.

[0793] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0794] In this invention, the server includes means for collecting information from the user, means for analyzing the collected information and emotional data and generating an action plan based on the analysis results and emotional evaluation, and means for presenting the generated action plan to the user and receiving feedback on a plan optimized for the user's emotional state. This makes it possible to provide a flexible and personalized action plan that corresponds to the user's emotional state.

[0795] A "user" refers to an individual who uses the system to input their own information and receive support related to end-of-life planning.

[0796] "Information gathering means" refers to methods and devices used to obtain personal information and wishes and expectations regarding end-of-life planning from users.

[0797] "Emotional data" refers to numerical data that indicates the emotional state of a user, inferred from their input patterns, speed, word choice, and other factors.

[0798] "Analysis means" refers to methods and devices for determining user characteristics, needs, and emotional states based on collected information and emotional data.

[0799] "Generation means" refers to methods or devices for creating an optimal action plan based on analyzed data.

[0800] An "action plan" refers to a plan that includes specific steps and guidance provided to support users in their end-of-life planning activities.

[0801] "Presentation means" refers to methods or functions for providing the generated action plan to the user visually or audibly.

[0802] "Feedback receiving methods" refer to methods and devices for receiving evaluations and requests from users regarding action plans and using that information to improve the system.

[0803] "Adjustment measures" refer to methods and devices for reviewing action plans based on feedback and modifying them as necessary.

[0804] This invention is a system that supports users in their end-of-life planning, and its core function is to generate and provide action plans that take into account the user's emotional state. This system primarily operates through the collaborative efforts of a terminal and a server.

[0805] Users use a terminal to access the system. The terminal is equipped with a dedicated application or web interface through which they input personal information and their wishes and expectations regarding end-of-life planning. As users input information, the terminal collects emotional data on their input patterns, speed, and word choices. This data is used to infer the user's emotional state.

[0806] The terminal is a device that transmits information and sentiment data collected from the user to the server. Common terminals used here include personal computers and smartphones. The collected data is transmitted to the server via the internet.

[0807] When the server receives information sent by the user, it stores it in a database. The stored information is then passed to the analysis engine, which analyzes the user's characteristics and needs. Based on the analysis results, the emotion engine evaluates the user's emotional state. This process takes into account factors such as stress during input and the positivity of the statements.

[0808] The server integrates the analysis results and sentiment evaluation to generate an optimized action plan for the user. A generative AI model is used in this process. The generative AI model suggests appropriate actions based on the user's situation, using prompts. An example of a prompt is, "Generate a support plan based on the user's emotional state, using the information entered after logging in."

[0809] The generated action plan is presented to the user via the device. The user can review the presented plan and begin taking specific actions based on it. For example, if the emotion engine determines that "writing a will is stressful," the system will suggest a simple template or further support options to the user.

[0810] In this way, coordinated operation between the terminal and the server enables flexible and effective end-of-life support that takes into account the user's emotional state.

[0811] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0812] Step 1:

[0813] Users log in using the system's dedicated application or web interface. The login process involves entering a user ID and password. This authentication information is sent from the terminal to the server. The server then uses this information to authenticate the user and verify their legitimacy. Upon successful login, the user can proceed to the next step.

[0814] Step 2:

[0815] After logging in, users fill out a form on the interface that includes their personal information and their wishes and expectations regarding end-of-life planning. This form contains information about the user's thoughts and concerns about end-of-life planning. The device collects this input data in real time, capturing input patterns and speed as metadata. This data is sent to an emotion engine and used for an initial assessment of the user's emotional state.

[0816] Step 3:

[0817] The terminal packages the information and metadata collected from the user and sends it to the server. The input received by the server is the raw data itself, and the process involves saving this to a database and simultaneously passing it to the analysis engine. The server compares it with similar historical data and begins computational processing to gain new insights. Once the analyzed data is output, it is ready for the next step.

[0818] Step 4:

[0819] The server's analysis engine analyzes the collected information in detail. This process analyzes user characteristics, needs, and past behavioral history. The output of the analysis provides the core information for a specific action plan. Meanwhile, the emotion engine determines the user's emotional state from the input data. For example, if the input speed is very slow, it may indicate fatigue or anxiety. Based on these analysis results, feedback for the action plan is generated.

[0820] Step 5:

[0821] The server integrates the results from the analysis engine and the emotion engine to generate an optimized action plan for the user. This is where the generative AI model comes into play, automatically creating the optimal plan based on the user's specific situation. Prompt messages are formatted to correspond to the scenario of the generated action plan, such as "Generate a support plan based on the user's emotional state, using the information entered after logging in." The output is an action plan tailored to the user's emotions.

[0822] Step 6:

[0823] The terminal receives the action plan sent from the server, visualizes it, and presents it to the user. Here, the information is presented in a user-friendly format, and additional instructions or actions are taken based on it. The user then tries out this action plan and evaluates its usefulness.

[0824] Step 7:

[0825] Users input feedback into the system regarding the results of implementing their action plans. The collected information includes the ease of executing the plan and any additional support needed. This feedback is then used by the server in the next step, contributing to the overall improvement of the system.

[0826] Step 8:

[0827] The server re-analyzes user feedback and adjusts the action plan as needed. This feedback processing results in improved action plans and enhanced system adaptability, which in turn further increases user satisfaction.

[0828] (Application Example 2)

[0829] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0830] In modern society, improving individual safety and psychological well-being is a crucial issue. However, conventional security systems struggle to provide safety measures that take into account the user's psychological state, resulting in a lack of flexible support tailored to individual needs.

[0831] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0832] In this invention, the server includes a device for collecting information from the user, a device for analyzing the collected information and generating an action plan based on the analysis results, and a device for evaluating the user's psychological state using an emotion analysis engine. This makes it possible to provide security measures and mental support information that take the user's psychological state into consideration.

[0833] A "user" refers to an individual who uses the system to provide information and receive support.

[0834] "Information" refers to data collected from users, including personal information and data related to emotional states.

[0835] "Device" refers to a function incorporated into a system for collecting, analyzing, generating, and presenting information.

[0836] An "action plan" refers to a set of specific actions that a user should take, generated based on analyzed information and the user's psychological state.

[0837] An "emotion analysis engine" refers to a technical means for evaluating a user's emotional state, and has the function of analyzing their psychological state from collected data.

[0838] "Psychological state" refers to data that indicates the user's emotional and moodal state, which the system analyzes and incorporates into its action plan.

[0839] "Security measures" refer to specific actions incorporated into an action plan to ensure user safety.

[0840] "Opinions" refers to feedback and evaluations provided by users regarding the action plan.

[0841] A description of embodiments for carrying out this invention will be given.

[0842] The system's main components consist of a server, terminals, and users, and each element functions in cooperation with the others. The server plays a central role in collecting, analyzing, and generating action plans for information. The terminals serve as the interface between the user and the system, and are used for data input from the user and for providing feedback from the system.

[0843] The server is equipped with advanced data analysis software and an emotion analysis engine to receive and process data sent from users. Based on user input and past data, it analyzes the user's psychological state and generates an optimal action plan. It also develops plans that include information for security measures and psychological support.

[0844] The terminal is a tool for users to input personal information and feedback into the system, and smartphones or other mobile devices are used. User input data is sent to the server in real time, and feedback is received from the server after completion.

[0845] Users can review their action plan through their device and provide feedback if needed. For example, if a user experiences stress or anxiety, the system detects these using an emotion analysis engine and quickly suggests countermeasures.

[0846] For example, in a situation where a user feels uneasy walking alone at night, the device can display real-time security recommendations generated by the server and suggest actions that provide the user with a sense of security. Through this interaction, the user can benefit from improved environmental safety and psychological support.

[0847] An example of a prompt message is, "Please describe the situation that is causing you anxiety. What measures do you think would be most effective?"

[0848] Ultimately, this invention aims to provide flexible security support services based on the user's psychological needs.

[0849] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0850] Step 1:

[0851] The user logs into the system using a terminal. The user enters personal information and data related to their current psychological state. The terminal sends the entered information to the server. In this step, the input consists of personal information and emotion-related data provided by the user, and the output is the data sent to the server.

[0852] Step 2:

[0853] The server stores the received user information in a database and passes the data to the analysis engine. The analysis engine compares past data with the current input data to analyze the user's characteristics and current needs. Here, the input is the data received from the user, and the output is the result of the user characteristic analysis.

[0854] Step 3:

[0855] The server's sentiment analysis engine evaluates the user's psychological state. It performs text analysis on the user's input data and extracts the emotional state. The input for this step is the user's word choices and input patterns, and the output is the user's sentiment evaluation result.

[0856] Step 4:

[0857] The server integrates the analyzed characteristics and sentiment assessments to generate an optimal action plan for the user. This plan includes security recommendations and emotional support information. The input is the results of steps 2 and 3, and the output is the generated action plan.

[0858] Step 5:

[0859] The server sends an action plan to the terminal. The terminal presents the action plan to the user, who then takes the necessary actions based on it. The input is the action plan sent from the server, and the output is the action plan received by the user.

[0860] Step 6:

[0861] The user executes the presented plan and inputs their opinions and evaluations of the results into the terminal. The terminal sends this feedback back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0862] Step 7:

[0863] The server then re-analyzes the feedback and adjusts the action plan as needed. The input for this step is the user feedback, and the output is the adjusted action plan.

[0864] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0865] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0866] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0867] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0868] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0869] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0870] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0871] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0872] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0873] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0874] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0875] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0876] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0877] 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.

[0878] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0879] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0880] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0881] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0882] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0883] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0884] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0885] The following is further disclosed regarding the embodiments described above.

[0886] (Claim 1)

[0887] Means of collecting information from users,

[0888] A generation means for analyzing the collected information and generating an action plan based on the analysis results,

[0889] A means for presenting the generated action plan to the user and receiving feedback,

[0890] Means for adjusting the action plan based on the aforementioned feedback,

[0891] A system that includes end-of-life planning support.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the generated action plan includes guidance on will preparation.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the generated action plan includes specific steps related to pre-death arrangements.

[0896] "Example 1"

[0897] (Claim 1)

[0898] Means of collecting information from users,

[0899] A means of using a generative artificial intelligence model to analyze the collected information,

[0900] Means for generating an action plan based on the aforementioned analysis results,

[0901] A means for presenting the generated action plan to the user and receiving feedback,

[0902] Means for analyzing the aforementioned feedback and adjusting the action plan as necessary,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, wherein the generated action plan includes guidance regarding the creation of a will.

[0906] (Claim 3)

[0907] The system according to claim 1, wherein the generated action plan includes specific procedures for organizing personal information.

[0908] "Application Example 1"

[0909] (Claim 1)

[0910] Means of collecting information from users,

[0911] A generation means for analyzing the collected information and generating an action plan based on the analysis results,

[0912] A means for presenting the generated action plan to the user and receiving feedback,

[0913] Means for adjusting the action plan based on the aforementioned feedback,

[0914] A means of providing a virtual space and facilitating users to select other services and carry out suggested action plans,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the generated action plan includes guidance on the preparation of legal documents.

[0918] (Claim 3)

[0919] The system according to claim 1, which provides specific steps relating to asset management preparations to the generated action plan.

[0920] "Example 2 of combining an emotion engine"

[0921] (Claim 1)

[0922] Means of collecting information from users,

[0923] A generation means that analyzes the collected information and emotional data, and generates an action plan based on the analysis results and emotional evaluation,

[0924] A means for presenting the generated action plan to the user and receiving feedback on the plan optimized for the user's emotional state,

[0925] Means for adjusting the action plan based on the aforementioned feedback,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the generated action plan includes guidance and emotional assessment regarding will preparation.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the generated action plan includes specific steps related to pre-death arrangements and corresponding emotional response guidelines.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A device that collects information from users,

[0934] A device that analyzes the collected information and generates an action plan based on the analysis results,

[0935] A device that uses an emotion analysis engine to evaluate the user's psychological state,

[0936] A device that generates an action plan, including security measures, based on an assessed psychological state,

[0937] A device that presents the generated action plan to the user and receives their feedback,

[0938] A device for adjusting the action plan based on the aforementioned opinion,

[0939] A support system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, which includes mental support information in the generated action plan.

[0942] (Claim 3)

[0943] The system according to claim 1, wherein the generated action plan includes recommendations regarding safety measures. [Explanation of Symbols]

[0944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting information from users, A generation means for analyzing the collected information and generating an action plan based on the analysis results, A means for presenting the generated action plan to the user and receiving feedback, Means for adjusting the action plan based on the aforementioned feedback, A system that includes end-of-life planning support.

2. The system according to claim 1, wherein the generated action plan includes guidance regarding the creation of a will.

3. The system according to claim 1, in which the generated action plan includes specific steps related to pre-death arrangements.

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