system

Abstract

The system according to this embodiment aims to prevent fraud and improve transparency in the management of the assets and legal acts of the elderly. [Solution] The system according to the embodiment comprises a management unit, a detection unit, a creation unit, and a support unit. The management unit manages assets. The detection unit detects anomalies based on the assets managed by the management unit. The creation unit creates contracts and legal documents based on the anomalies detected by the detection unit. The support unit supports communication with family and professionals based on the documents created by the creation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in the property management of the elderly and the representation of legal acts, prevention of fraud and improvement of transparency have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent fraud and improve transparency in the property management of the elderly and the representation of legal acts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, a detection unit, a creation unit, and a support unit. The management unit manages assets. The detection unit detects anomalies based on the assets managed by the management unit. The creation unit creates contracts and legal documents based on the anomalies detected by the detection unit. The support unit supports communication with family and professionals based on the documents created by the creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can help prevent fraud and improve transparency in managing the assets and acting as an agent for legal acts for the elderly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent adult guardianship service according to an embodiment of the present invention is an AI agent that supports the management of the elderly's assets, legal representation, and daily financial management. This AI agent utilizes LLM (Limited Liability Management) and leverages its expertise to support the elderly in living their lives with peace of mind. Aiming to prevent fraud and improve transparency, it also supports communication with family and professionals. For example, the AI ​​agent adult guardianship service manages the elderly's assets and performs optimal asset management and expenditure management. Next, the AI ​​agent adult guardianship service analyzes regular expenditure patterns and issues warnings if abnormal expenditures occur. Furthermore, the AI ​​agent adult guardianship service creates standard contracts and prepares legal documents. Finally, the AI ​​agent adult guardianship service supports communication with family and professionals and prepares and provides regular reports. This service is designed to solve the challenges of the adult guardianship system in an aging society, addressing issues such as the complexity of the procedures for using the adult guardianship system, the shortage of guardians, the risk of fraud, limitations on flexible asset management, and the difficulty of providing continuous support. As a result, the AI ​​agent adult guardianship service can efficiently manage the elderly's assets, detect anomalies, create legal documents, and provide communication support.

[0029] The AI ​​agent adult guardianship service according to this embodiment comprises a management unit, a detection unit, a creation unit, and a support unit. The management unit manages the assets of the elderly. For example, the management unit manages the assets of the elderly and performs optimal asset management and expenditure management. For example, the management unit proposes asset management methods that take risk diversification into consideration. The management unit can also select highly profitable investment targets. Furthermore, the management unit can set budgets and monitor expenditures. The detection unit detects anomalies based on the assets managed by the management unit. For example, the detection unit analyzes regular expenditure patterns and issues warnings if abnormal expenditures occur. For example, the detection unit analyzes monthly expenditure history and detects abnormal expenditures. The detection unit can also analyze expenditures by category and detect abnormal expenditures. Furthermore, the detection unit can also warn of anomalies by providing alert notifications or email notifications. The creation unit creates contracts and legal documents based on the anomalies detected by the detection unit. For example, the creation unit prepares standard contracts and legal documents. The document creation unit can, for example, create lease agreements and sales contracts. It can also create wills and power of attorney documents. Furthermore, the document creation unit can review the content of legal documents and make necessary revisions. The support unit supports communication with family and professionals based on the documents created by the document creation unit. For example, the support unit supports communication with family and professionals and provides periodic reports. The support unit communicates using methods such as telephone, email, and video calls. The support unit can also create monthly and quarterly reports and provide them to family and professionals. As a result, the AI ​​agent adult guardianship service according to this embodiment can efficiently manage the assets of the elderly, detect anomalies, create legal documents, and provide communication support.

[0030] The Management Department manages the assets of elderly individuals. For example, the Management Department manages the assets of elderly individuals and implements optimal asset management and expenditure management. Specifically, the Management Department centrally manages the assets of elderly individuals, such as bank accounts, investment accounts, and real estate, and understands the current state of their assets. When proposing asset management methods that take risk diversification into consideration, the Management Department uses AI to analyze market trends and risk factors and construct an optimal investment portfolio. For example, by diversifying investments across different asset classes such as stocks, bonds, and real estate investment trusts (REITs), it maximizes returns while reducing risk. In addition, to select highly profitable investment targets, the AI ​​analyzes past investment data and market trends to identify investment targets that are expected to yield high returns in the future. Furthermore, the Management Department sets budgets for elderly individuals' living expenses and medical expenses and monitors expenditures. For example, it manages monthly living expenses and medical expenses to stay within budget and issues alerts if the budget is exceeded. This prevents the elderly individuals' assets from being wasted and ensures long-term asset preservation. Through these functions, the Management Department efficiently and effectively manages the assets of elderly individuals and provides an environment in which they can live with peace of mind.

[0031] The detection unit detects anomalies based on assets managed by the management unit. For example, the detection unit analyzes regular spending patterns and issues warnings if abnormal spending occurs. Specifically, it uses AI to learn from the elderly person's past spending data and model normal spending patterns. For example, it analyzes spending patterns for monthly food, medical expenses, and utility bills, and immediately detects abnormal spending. When analyzing monthly spending history, the AI ​​identifies anomalies based on data from the past few months and issues an alert if spending exceeds the normal range. It can also detect abnormal spending in specific categories by analyzing spending by category. For example, if a large expenditure suddenly occurs in a category that usually consists of small expenditures, the AI ​​will detect the anomaly and issue a warning. Furthermore, the detection unit warns of anomalies by sending alert notifications and email notifications. For example, if abnormal spending is detected, it immediately notifies family members or legal guardians to encourage prompt action. This allows the detection unit to detect fraudulent activity or abnormal spending related to the elderly person's assets early and take appropriate measures.

[0032] The creation unit creates contracts and legal documents based on anomalies detected by the detection unit. For example, the creation unit prepares standard contracts and legal documents. Specifically, it uses AI to automatically input necessary information based on contract and legal document templates, creating documents quickly and accurately. For example, when creating lease agreements or sales agreements, it inputs the contract details and information of the parties involved, generating legally valid documents. When creating wills or power of attorney, it includes content that reflects the wishes of the elderly person and creates documents in a legally valid format. Furthermore, the creation unit can review the content of legal documents and make necessary corrections. For example, it updates the content of documents in accordance with legal revisions or changes in circumstances, reflecting the latest information. As a result, the creation unit can quickly and accurately create legal documents necessary for the management of the elderly person's assets, preventing legal troubles from occurring.

[0033] The Support Department assists with communication with families and professionals based on documents created by the Document Creation Department. For example, the Support Department supports communication with families and professionals and provides regular reports. Specifically, it contacts families and professionals via telephone, email, and video calls to report on the elderly person's financial management status and the results of any anomaly detections. For instance, by creating and providing monthly and quarterly reports to families and professionals, the Support Department ensures transparency in financial management and builds trust. Furthermore, the Support Department collects feedback from families and professionals and provides this feedback to the Management, Detection, and Document Creation Departments to improve services. For example, it provides better service by enriching the content of reports and increasing communication methods based on requests from families. In addition, the Support Department can also handle emergencies. For example, if an unusual expenditure is detected, it immediately contacts the family or professional to encourage a swift response. In this way, the Support Department plays a crucial role in the financial management of the elderly person and facilitates smooth communication with families and professionals.

[0034] The management department can manage the assets of elderly individuals and implement optimal asset management and expenditure management. For example, the management department can manage the assets of elderly individuals and implement optimal asset management and expenditure management. For example, the management department can propose asset management methods that take risk diversification into consideration. The management department can also select highly profitable investment targets. Furthermore, the management department can set budgets and monitor expenditures. This optimizes the asset management of elderly individuals. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input elderly individuals' asset data into a generating AI and have the generating AI propose optimal asset management methods.

[0035] The detection unit can analyze regular spending patterns and issue a warning if an abnormal expenditure occurs. For example, the detection unit can analyze regular spending patterns and issue a warning if an abnormal expenditure occurs. For example, the detection unit can analyze monthly spending history and detect abnormal expenditures. The detection unit can also analyze spending by category and detect abnormal expenditures. Furthermore, the detection unit can issue alert notifications or email notifications to warn of anomalies. As a result, abnormal expenditures are detected and warnings are issued. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input elderly people's spending data into a generating AI and have the generating AI perform abnormal expenditure detection.

[0036] The drafting unit can create standard contracts and prepare legal documents. For example, the drafting unit can create standard contracts and prepare legal documents. For example, the drafting unit can create lease agreements and sales agreements. It can also create wills and power of attorney documents. Furthermore, the drafting unit can review the content of legal documents and make necessary revisions. This allows for the efficient creation of contracts and legal documents. Some or all of the above processes in the drafting unit may be performed using AI, for example, or not. For example, the drafting unit can input a contract template into a generation AI and have the generation AI generate the optimal contract.

[0037] The support department can facilitate communication with family and professionals and provide regular reports. For example, the support department can communicate using methods such as telephone, email, and video calls. The support department can also create and provide monthly and quarterly reports to family and professionals. This facilitates smooth communication with family and professionals. Some or all of the above processes performed by the support department may be carried out using AI, or not. For example, the support department can input communication content into a generating AI and have the generating AI create the most suitable report.

[0038] The management department can analyze the past asset management history of elderly individuals and select the optimal asset management method. For example, the management department can propose a similar investment method based on the elderly individual's past successful investment methods. For example, the management department can propose a risk-diversified investment method to avoid the elderly individual's past unsuccessful investment methods. Furthermore, the management department can analyze the elderly individual's past spending patterns and propose an investment method to reduce unnecessary spending. This enables optimal asset management based on past asset management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the elderly individual's past asset management data into a generating AI and have the generating AI select the optimal asset management method.

[0039] The management department can adjust the risk of asset management based on the elderly person's current living situation and health condition. For example, if the elderly person is in good health, the management department may propose a high-risk asset management method. If the elderly person's health is deteriorating, the management department may propose a low-risk asset management method. Furthermore, the management department may propose an asset management method that takes into account the elderly person's necessary expenses, depending on their living situation. This allows for asset management tailored to the elderly person's living situation and health condition. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input data on the elderly person's living situation and health condition into a generating AI and have the generating AI perform the risk adjustment.

[0040] The management department can select the optimal asset management method by considering the geographical location information of elderly individuals. For example, if an elderly person lives in an urban area, the management department can propose an asset management method specific to urban areas. For example, if an elderly person lives in a rural area, the management department can also propose an asset management method specific to rural areas. Furthermore, the management department can propose an asset management method that takes into account the local economic situation based on the elderly person's geographical location information. This enables optimal asset management based on geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the geographical location information of elderly individuals into a generating AI and have the generating AI select the optimal asset management method.

[0041] The management department can analyze the social media activities of elderly individuals and provide relevant investment information. For example, the management department can suggest investment options that elderly individuals have shown interest in on social media. The management department can also suggest investment methods that take into account the opinions of experts that elderly individuals follow on social media. Furthermore, the management department can analyze the social media activities of elderly individuals and provide investment information that they might find interesting. This ensures that investment information is provided based on social media activity. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input elderly individuals' social media data into a generating AI and have the generating AI provide relevant investment information.

[0042] The detection unit can predict the probability of an anomaly occurring by referring to past spending patterns. For example, the detection unit can analyze the past spending patterns of elderly people and predict the probability of an anomaly occurring. The detection unit can also predict the timing of an anomaly occurring based on the past spending patterns of elderly people. Furthermore, the detection unit can also evaluate the risk of an anomaly occurring by referring to the past spending patterns of elderly people. This allows for the prediction of the probability of an anomaly occurring based on past spending patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past spending data of elderly people into a generating AI and have the generating AI perform the prediction of the probability of an anomaly occurring.

[0043] The detection unit can assess the risk of abnormalities based on the health status and living conditions of elderly individuals. For example, if an elderly individual's health status deteriorates, the detection unit will rate the risk of abnormal spending higher. The detection unit can also re-evaluate the risk of abnormal spending if an elderly individual's living conditions change. Furthermore, the detection unit can comprehensively assess the risk of abnormal spending by considering the health status and living conditions of elderly individuals. This allows for an assessment of the risk of abnormalities based on health status and living conditions. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on the health status and living conditions of elderly individuals into a generating AI and have the generating AI perform an abnormality risk assessment.

[0044] The detection unit can predict the occurrence of anomalies by considering the geographical distribution of elderly people. For example, if elderly people live in urban areas, the detection unit can predict the risk of anomalies specific to urban areas. For example, if elderly people live in rural areas, the detection unit can also predict the risk of anomalies specific to rural areas. Furthermore, the detection unit can predict the risk of anomalies considering regional characteristics based on the geographical distribution of elderly people. This allows for the prediction of anomalies based on geographical distribution. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical distribution data of elderly people into a generating AI and have the generating AI perform the prediction of anomalies.

[0045] The detection unit can assess the risk of anomalies by referring to relevant legal documents. For example, when an anomaly is detected, the detection unit can refer to relevant legal documents and assess the risk of the anomaly. For example, when an anomaly is detected, the detection unit can also reassess the risk of anomalies based on the legal documents. Furthermore, for example, when an anomaly is detected, the detection unit can refer to legal documents and comprehensively assess the risk of the anomaly. This allows for an assessment of the risk of anomalies based on legal documents. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input relevant legal document data into a generating AI and have the generating AI perform an anomaly risk assessment.

[0046] The creation unit can generate the optimal document by referring to similar documents from the past. For example, the creation unit can generate the optimal document by referring to similar contracts created in the past. For example, the creation unit can also generate the optimal document by extracting necessary information based on past legal documents. Furthermore, the creation unit can analyze similar documents from the past and generate a document using the most appropriate expression. This generates the optimal document based on similar documents from the past. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data on similar documents from the past into a generation AI and have the generation AI perform the generation of the optimal document.

[0047] The creation unit can adjust the content of a document based on the elderly person's current living situation and health condition. For example, the creation unit can create a document containing necessary information according to the elderly person's living situation. The creation unit can also create a document containing appropriate content, taking into account the elderly person's health condition. Furthermore, the creation unit can create an optimal document based on the elderly person's living situation and health condition. This adjusts the content of the document based on the living situation and health condition. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data on the elderly person's living situation and health condition into a generating AI and have the generating AI perform the adjustment of the document content.

[0048] The creation unit can generate optimal documents by taking into account the geographical location information of elderly individuals. For example, if an elderly person lives in an urban area, the creation unit can create a contract specific to urban areas. If an elderly person lives in a rural area, the creation unit can also create a contract specific to rural areas. Furthermore, the creation unit can create a contract that takes regional characteristics into account, based on the geographical location information of the elderly person. This results in the generation of optimal documents based on geographical location information. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the geographical location information of the elderly person into a generation AI and have the generation AI execute the generation of optimal documents.

[0049] The creation unit can analyze the social media activities of elderly individuals and reflect relevant information in the document. For example, the creation unit can reflect information that elderly individuals are interested in on social media. For example, the creation unit can also reflect the opinions of experts that elderly individuals follow on social media. Furthermore, the creation unit can analyze the social media activities of elderly individuals and reflect information that they might be interested in. This ensures that relevant information based on social media activities is reflected in the document. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input social media data of elderly individuals into a generating AI and have the generating AI perform the task of reflecting relevant information in the document.

[0050] The support unit can select the optimal support method by referring to past communication history. For example, the support unit may suggest a similar method based on the communication methods the elderly person preferred in the past. The support unit may also suggest an alternative method to avoid communication methods the elderly person avoided in the past. Furthermore, the support unit may analyze the elderly person's past communication history and select the most effective support method. This ensures that the optimal support method is selected based on past communication history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input past communication history data into a generating AI and have the generating AI select the optimal support method.

[0051] The support unit can adjust the support provided based on the elderly person's current living situation and health condition. For example, the support unit provides necessary information according to the elderly person's living situation. The support unit can also provide appropriate support, taking into account the elderly person's health condition. Furthermore, the support unit can provide optimal support based on the elderly person's living situation and health condition. This adjusts the support provided based on the living situation and health condition. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input data on the elderly person's living situation and health condition into a generating AI and have the generating AI perform the adjustment of the support provided.

[0052] The support unit can select the optimal support method by considering the geographical location information of the elderly person. For example, if the elderly person lives in an urban area, the support unit can propose a support method specific to urban areas. For example, if the elderly person lives in a rural area, the support unit can also propose a support method specific to rural areas. Furthermore, the support unit can propose a support method that takes into account the characteristics of the region based on the elderly person's geographical location information. This ensures that the optimal support method is selected based on geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the elderly person's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0053] The support department can analyze the social media activities of elderly individuals and provide relevant information. For example, the support department can provide information that elderly individuals are interested in on social media. The support department can also provide, for example, the opinions of experts that elderly individuals follow on social media. Furthermore, the support department can analyze the social media activities of elderly individuals and provide information that they might find interesting. This ensures that relevant information is provided based on their social media activities. Some or all of the above-described processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input elderly individuals' social media data into a generating AI and have the generating AI provide relevant information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The management department can propose environmentally conscious asset management methods for the elderly. For example, the management department can select investment targets related to renewable energy. It can also purchase shares of companies that contribute to environmental protection. Furthermore, the management department can propose environmentally conscious investment trusts such as eco-funds. This allows for the elderly to manage their assets in an environmentally conscious manner.

[0056] The detection unit can consider seasonal spending trends when analyzing the spending patterns of elderly individuals. For example, the detection unit can predict an increase in heating costs during winter and avoid detecting it as an abnormal expenditure. It can also consider an increase in cooling costs during summer and avoid detecting it as an abnormal expenditure. Furthermore, it can consider special spending during the year-end and New Year period and avoid detecting it as an abnormal expenditure. This enables anomaly detection based on seasonal spending trends.

[0057] The drafting department can adjust the content of contracts and legal documents to take into account the cultural background of elderly individuals. For example, if an elderly person practices a particular religion, the department can create documents that include content that respects that religion. Furthermore, if an elderly person has specific cultural customs, the department can create documents that respect those customs. In addition, if an elderly person speaks a particular language, the department can create documents in that language. This ensures that the content of documents is tailored to the cultural background.

[0058] The support department can tailor the content of communication based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in music, the support department can provide music-related topics. Similarly, if an elderly person is interested in gardening, the support department can provide gardening-related information. Furthermore, if an elderly person is interested in travel, the support department can provide travel-related topics. This allows for communication based on hobbies and interests.

[0059] The management department can select the optimal asset management method by considering the geographical location information of elderly individuals. For example, if an elderly person lives in an urban area, the management department can propose an asset management method specific to urban areas. If an elderly person lives in a rural area, the management department can also propose an asset management method specific to rural areas. Furthermore, the management department can propose an asset management method that takes into account the local economic conditions based on the elderly person's geographical location information. This enables optimal asset management based on geographical location information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The management department manages the assets of elderly individuals. For example, the management department manages the assets of elderly individuals and implements optimal asset management and expenditure management. The management department can propose asset management methods that take risk diversification into consideration and select highly profitable investment destinations. Furthermore, the management department sets budgets and monitors expenditures. Step 2: The detection unit detects anomalies based on the assets managed by the management unit. The detection unit analyzes regular spending patterns and issues a warning if an anomaly occurs. The detection unit can analyze monthly spending history and spending by category to detect anomalies. Furthermore, the detection unit warns of anomalies by sending alert notifications and email notifications. Step 3: The drafting unit creates contracts and legal documents based on the anomalies detected by the detection unit. The drafting unit prepares standard contracts and legal documents. The drafting unit can create lease agreements, sales agreements, wills, and power of attorney documents. Furthermore, the drafting unit reviews the content of legal documents and makes any necessary revisions. Step 4: The support department assists with communication with family and professionals based on the documents created by the creation department. The support department assists with communication with family and professionals and prepares and provides regular reports. The support department communicates using telephone, email, and video calls. The support department can also prepare and provide monthly and quarterly reports to family and professionals.

[0062] (Example of form 2) The AI ​​agent adult guardianship service according to an embodiment of the present invention is an AI agent that supports the management of the elderly's assets, legal representation, and daily financial management. This AI agent utilizes LLM (Limited Liability Management) and leverages its expertise to support the elderly in living their lives with peace of mind. Aiming to prevent fraud and improve transparency, it also supports communication with family and professionals. For example, the AI ​​agent adult guardianship service manages the elderly's assets and performs optimal asset management and expenditure management. Next, the AI ​​agent adult guardianship service analyzes regular expenditure patterns and issues warnings if abnormal expenditures occur. Furthermore, the AI ​​agent adult guardianship service creates standard contracts and prepares legal documents. Finally, the AI ​​agent adult guardianship service supports communication with family and professionals and prepares and provides regular reports. This service is designed to solve the challenges of the adult guardianship system in an aging society, addressing issues such as the complexity of the procedures for using the adult guardianship system, the shortage of guardians, the risk of fraud, limitations on flexible asset management, and the difficulty of providing continuous support. As a result, the AI ​​agent adult guardianship service can efficiently manage the elderly's assets, detect anomalies, create legal documents, and provide communication support.

[0063] The AI ​​agent adult guardianship service according to this embodiment comprises a management unit, a detection unit, a creation unit, and a support unit. The management unit manages the assets of the elderly. For example, the management unit manages the assets of the elderly and performs optimal asset management and expenditure management. For example, the management unit proposes asset management methods that take risk diversification into consideration. The management unit can also select highly profitable investment targets. Furthermore, the management unit can set budgets and monitor expenditures. The detection unit detects anomalies based on the assets managed by the management unit. For example, the detection unit analyzes regular expenditure patterns and issues warnings if abnormal expenditures occur. For example, the detection unit analyzes monthly expenditure history and detects abnormal expenditures. The detection unit can also analyze expenditures by category and detect abnormal expenditures. Furthermore, the detection unit can also warn of anomalies by providing alert notifications or email notifications. The creation unit creates contracts and legal documents based on the anomalies detected by the detection unit. For example, the creation unit prepares standard contracts and legal documents. The document creation unit can, for example, create lease agreements and sales contracts. It can also create wills and power of attorney documents. Furthermore, the document creation unit can review the content of legal documents and make necessary revisions. The support unit supports communication with family and professionals based on the documents created by the document creation unit. For example, the support unit supports communication with family and professionals and provides periodic reports. The support unit communicates using methods such as telephone, email, and video calls. The support unit can also create monthly and quarterly reports and provide them to family and professionals. As a result, the AI ​​agent adult guardianship service according to this embodiment can efficiently manage the assets of the elderly, detect anomalies, create legal documents, and provide communication support.

[0064] The Management Department manages the assets of elderly individuals. For example, the Management Department manages the assets of elderly individuals and implements optimal asset management and expenditure management. Specifically, the Management Department centrally manages the assets of elderly individuals, such as bank accounts, investment accounts, and real estate, and understands the current state of their assets. When proposing asset management methods that take risk diversification into consideration, the Management Department uses AI to analyze market trends and risk factors and construct an optimal investment portfolio. For example, by diversifying investments across different asset classes such as stocks, bonds, and real estate investment trusts (REITs), it maximizes returns while reducing risk. In addition, to select highly profitable investment targets, the AI ​​analyzes past investment data and market trends to identify investment targets that are expected to yield high returns in the future. Furthermore, the Management Department sets budgets for elderly individuals' living expenses and medical expenses and monitors expenditures. For example, it manages monthly living expenses and medical expenses to stay within budget and issues alerts if the budget is exceeded. This prevents the elderly individuals' assets from being wasted and ensures long-term asset preservation. Through these functions, the Management Department efficiently and effectively manages the assets of elderly individuals and provides an environment in which they can live with peace of mind.

[0065] The detection unit detects anomalies based on assets managed by the management unit. For example, the detection unit analyzes regular spending patterns and issues warnings if abnormal spending occurs. Specifically, it uses AI to learn from the elderly person's past spending data and model normal spending patterns. For example, it analyzes spending patterns for monthly food, medical expenses, and utility bills, and immediately detects abnormal spending. When analyzing monthly spending history, the AI ​​identifies anomalies based on data from the past few months and issues an alert if spending exceeds the normal range. It can also detect abnormal spending in specific categories by analyzing spending by category. For example, if a large expenditure suddenly occurs in a category that usually consists of small expenditures, the AI ​​will detect the anomaly and issue a warning. Furthermore, the detection unit warns of anomalies by sending alert notifications and email notifications. For example, if abnormal spending is detected, it immediately notifies family members or legal guardians to encourage prompt action. This allows the detection unit to detect fraudulent activity or abnormal spending related to the elderly person's assets early and take appropriate measures.

[0066] The creation unit creates contracts and legal documents based on anomalies detected by the detection unit. For example, the creation unit prepares standard contracts and legal documents. Specifically, it uses AI to automatically input necessary information based on contract and legal document templates, creating documents quickly and accurately. For example, when creating lease agreements or sales agreements, it inputs the contract details and information of the parties involved, generating legally valid documents. When creating wills or power of attorney, it includes content that reflects the wishes of the elderly person and creates documents in a legally valid format. Furthermore, the creation unit can review the content of legal documents and make necessary corrections. For example, it updates the content of documents in accordance with legal revisions or changes in circumstances, reflecting the latest information. As a result, the creation unit can quickly and accurately create legal documents necessary for the management of the elderly person's assets, preventing legal troubles from occurring.

[0067] The Support Department assists with communication with families and professionals based on documents created by the Document Creation Department. For example, the Support Department supports communication with families and professionals and provides regular reports. Specifically, it contacts families and professionals via telephone, email, and video calls to report on the elderly person's financial management status and the results of any anomaly detections. For instance, by creating and providing monthly and quarterly reports to families and professionals, the Support Department ensures transparency in financial management and builds trust. Furthermore, the Support Department collects feedback from families and professionals and provides this feedback to the Management, Detection, and Document Creation Departments to improve services. For example, it provides better service by enriching the content of reports and increasing communication methods based on requests from families. In addition, the Support Department can also handle emergencies. For example, if an unusual expenditure is detected, it immediately contacts the family or professional to encourage a swift response. In this way, the Support Department plays a crucial role in the financial management of the elderly person and facilitates smooth communication with families and professionals.

[0068] The management department can manage the assets of elderly individuals and implement optimal asset management and expenditure management. For example, the management department can manage the assets of elderly individuals and implement optimal asset management and expenditure management. For example, the management department can propose asset management methods that take risk diversification into consideration. The management department can also select highly profitable investment targets. Furthermore, the management department can set budgets and monitor expenditures. This optimizes the asset management of elderly individuals. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input elderly individuals' asset data into a generating AI and have the generating AI propose optimal asset management methods.

[0069] The detection unit can analyze regular spending patterns and issue a warning if an abnormal expenditure occurs. For example, the detection unit can analyze regular spending patterns and issue a warning if an abnormal expenditure occurs. For example, the detection unit can analyze monthly spending history and detect abnormal expenditures. The detection unit can also analyze spending by category and detect abnormal expenditures. Furthermore, the detection unit can issue alert notifications or email notifications to warn of anomalies. As a result, abnormal expenditures are detected and warnings are issued. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input elderly people's spending data into a generating AI and have the generating AI perform abnormal expenditure detection.

[0070] The drafting unit can create standard contracts and prepare legal documents. For example, the drafting unit can create standard contracts and prepare legal documents. For example, the drafting unit can create lease agreements and sales agreements. It can also create wills and power of attorney documents. Furthermore, the drafting unit can review the content of legal documents and make necessary revisions. This allows for the efficient creation of contracts and legal documents. Some or all of the above processes in the drafting unit may be performed using AI, for example, or not. For example, the drafting unit can input a contract template into a generation AI and have the generation AI generate the optimal contract.

[0071] The support department can facilitate communication with family and professionals and provide regular reports. For example, the support department can communicate using methods such as telephone, email, and video calls. The support department can also create and provide monthly and quarterly reports to family and professionals. This facilitates smooth communication with family and professionals. Some or all of the above processes performed by the support department may be carried out using AI, or not. For example, the support department can input communication content into a generating AI and have the generating AI create the most suitable report.

[0072] The management department can estimate the emotions of elderly individuals and adjust their asset management methods based on these estimated emotions. For example, if an elderly individual is feeling anxious, the management department can suggest low-risk investment methods. If an elderly individual is feeling secure, the management department can also suggest high-risk but high-return investment methods. Furthermore, if an elderly individual is feeling stressed, the management department can suggest ways to simplify expenditure management and reduce stress. This allows for asset management tailored to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI or not. For example, the management department can input elderly individuals' emotional data into a generative AI and have the generative AI adjust asset management methods based on their emotions.

[0073] The management department can analyze the past asset management history of elderly individuals and select the optimal asset management method. For example, the management department can propose a similar investment method based on the elderly individual's past successful investment methods. For example, the management department can propose a risk-diversified investment method to avoid the elderly individual's past unsuccessful investment methods. Furthermore, the management department can analyze the elderly individual's past spending patterns and propose an investment method to reduce unnecessary spending. This enables optimal asset management based on past asset management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the elderly individual's past asset management data into a generating AI and have the generating AI select the optimal asset management method.

[0074] The management department can adjust the risk of asset management based on the elderly person's current living situation and health condition. For example, if the elderly person is in good health, the management department may propose a high-risk asset management method. If the elderly person's health is deteriorating, the management department may propose a low-risk asset management method. Furthermore, the management department may propose an asset management method that takes into account the elderly person's necessary expenses, depending on their living situation. This allows for asset management tailored to the elderly person's living situation and health condition. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input data on the elderly person's living situation and health condition into a generating AI and have the generating AI perform the risk adjustment.

[0075] The management department can estimate the emotions of elderly individuals and determine priorities for asset management based on those estimated emotions. For example, if an elderly individual is feeling anxious, the management department may prioritize urgent expenditures. If an elderly individual is feeling secure, the management department may also prioritize long-term asset management. Furthermore, if an elderly individual is feeling stressed, the management department may also prioritize expenditures to alleviate stress. This determines asset management priorities that correspond to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input elderly individuals' emotion data into a generative AI and have the generative AI perform the determination of emotion-based asset management priorities.

[0076] The management department can select the optimal asset management method by considering the geographical location information of elderly individuals. For example, if an elderly person lives in an urban area, the management department can propose an asset management method specific to urban areas. For example, if an elderly person lives in a rural area, the management department can also propose an asset management method specific to rural areas. Furthermore, the management department can propose an asset management method that takes into account the local economic situation based on the elderly person's geographical location information. This enables optimal asset management based on geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the geographical location information of elderly individuals into a generating AI and have the generating AI select the optimal asset management method.

[0077] The management department can analyze the social media activities of elderly individuals and provide relevant investment information. For example, the management department can suggest investment options that elderly individuals have shown interest in on social media. The management department can also suggest investment methods that take into account the opinions of experts that elderly individuals follow on social media. Furthermore, the management department can analyze the social media activities of elderly individuals and provide investment information that they might find interesting. This ensures that investment information is provided based on social media activity. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input elderly individuals' social media data into a generating AI and have the generating AI provide relevant investment information.

[0078] The detection unit can estimate the emotions of elderly individuals and adjust the anomaly detection criteria based on the estimated emotions. For example, if an elderly individual is feeling anxious, the detection unit may tighten the anomaly detection criteria. For example, if an elderly individual is feeling at ease, the detection unit may also loosen the anomaly detection criteria. Furthermore, if an elderly individual is feeling stressed, the detection unit may adjust the anomaly detection criteria to reduce stress. This adjusts the anomaly detection criteria according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform the adjustment of the anomaly detection criteria based on emotions.

[0079] The detection unit can predict the probability of an anomaly occurring by referring to past spending patterns. For example, the detection unit can analyze the past spending patterns of elderly people and predict the probability of an anomaly occurring. The detection unit can also predict the timing of an anomaly occurring based on the past spending patterns of elderly people. Furthermore, the detection unit can also evaluate the risk of an anomaly occurring by referring to the past spending patterns of elderly people. This allows for the prediction of the probability of an anomaly occurring based on past spending patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past spending data of elderly people into a generating AI and have the generating AI perform the prediction of the probability of an anomaly occurring.

[0080] The detection unit can assess the risk of abnormalities based on the health status and living conditions of elderly individuals. For example, if an elderly individual's health status deteriorates, the detection unit will rate the risk of abnormal spending higher. The detection unit can also re-evaluate the risk of abnormal spending if an elderly individual's living conditions change. Furthermore, the detection unit can comprehensively assess the risk of abnormal spending by considering the health status and living conditions of elderly individuals. This allows for an assessment of the risk of abnormalities based on health status and living conditions. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on the health status and living conditions of elderly individuals into a generating AI and have the generating AI perform an abnormality risk assessment.

[0081] The detection unit can estimate the emotions of elderly individuals and adjust the order in which anomaly detection results are displayed based on the estimated emotions of the elderly individuals. For example, if an elderly individual is feeling anxious, the detection unit can prioritize displaying important anomaly detection results. For example, if an elderly individual is feeling at ease, the detection unit can also display detailed anomaly detection results. Furthermore, if an elderly individual is feeling stressed, the detection unit can prioritize displaying anomaly detection results that help reduce stress. This adjusts the display order of anomaly detection results according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI adjust the display order of anomaly detection results based on emotions.

[0082] The detection unit can predict the occurrence of anomalies by considering the geographical distribution of elderly people. For example, if elderly people live in urban areas, the detection unit can predict the risk of anomalies specific to urban areas. For example, if elderly people live in rural areas, the detection unit can also predict the risk of anomalies specific to rural areas. Furthermore, the detection unit can predict the risk of anomalies considering regional characteristics based on the geographical distribution of elderly people. This allows for the prediction of anomalies based on geographical distribution. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical distribution data of elderly people into a generating AI and have the generating AI perform the prediction of anomalies.

[0083] The detection unit can assess the risk of anomalies by referring to relevant legal documents. For example, when an anomaly is detected, the detection unit can refer to relevant legal documents and assess the risk of the anomaly. For example, when an anomaly is detected, the detection unit can also reassess the risk of anomalies based on the legal documents. Furthermore, for example, when an anomaly is detected, the detection unit can refer to legal documents and comprehensively assess the risk of the anomaly. This allows for an assessment of the risk of anomalies based on legal documents. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input relevant legal document data into a generating AI and have the generating AI perform an anomaly risk assessment.

[0084] The creation unit can estimate the emotions of elderly individuals and adjust the wording of contracts and legal documents based on the estimated emotions. For example, if an elderly person is feeling anxious, the creation unit will use concise and easy-to-understand language. If an elderly person is feeling secure, the creation unit may also use language that includes detailed information. Furthermore, if an elderly person is feeling stressed, the creation unit may also use language that helps reduce stress. This adjusts the language to suit the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input elderly individuals' emotion data into a generative AI and have the generative AI perform the adjustment of language based on emotions.

[0085] The creation unit can generate the optimal document by referring to similar documents from the past. For example, the creation unit can generate the optimal document by referring to similar contracts created in the past. For example, the creation unit can also generate the optimal document by extracting necessary information based on past legal documents. Furthermore, the creation unit can analyze similar documents from the past and generate a document using the most appropriate expression. This generates the optimal document based on similar documents from the past. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data on similar documents from the past into a generation AI and have the generation AI perform the generation of the optimal document.

[0086] The creation unit can adjust the content of a document based on the elderly person's current living situation and health condition. For example, the creation unit can create a document containing necessary information according to the elderly person's living situation. The creation unit can also create a document containing appropriate content, taking into account the elderly person's health condition. Furthermore, the creation unit can create an optimal document based on the elderly person's living situation and health condition. This adjusts the content of the document based on the living situation and health condition. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data on the elderly person's living situation and health condition into a generating AI and have the generating AI perform the adjustment of the document content.

[0087] The creation unit can generate optimal documents by taking into account the geographical location information of elderly individuals. For example, if an elderly person lives in an urban area, the creation unit can create a contract specific to urban areas. If an elderly person lives in a rural area, the creation unit can also create a contract specific to rural areas. Furthermore, the creation unit can create a contract that takes regional characteristics into account, based on the geographical location information of the elderly person. This results in the generation of optimal documents based on geographical location information. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the geographical location information of the elderly person into a generation AI and have the generation AI execute the generation of optimal documents.

[0088] The creation unit can analyze the social media activities of elderly individuals and reflect relevant information in the document. For example, the creation unit can reflect information that elderly individuals are interested in on social media. For example, the creation unit can also reflect the opinions of experts that elderly individuals follow on social media. Furthermore, the creation unit can analyze the social media activities of elderly individuals and reflect information that they might be interested in. This ensures that relevant information based on social media activities is reflected in the document. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input social media data of elderly individuals into a generating AI and have the generating AI perform the task of reflecting relevant information in the document.

[0089] The support unit can estimate the emotions of elderly individuals and adjust its communication methods based on those estimated emotions. For example, if an elderly individual is feeling anxious, the support unit will communicate using gentle language. If an elderly individual is feeling secure, the support unit can also provide detailed information. Furthermore, if an elderly individual is feeling stressed, the support unit can use communication methods to reduce stress. This adjusts the communication method according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI or not. For example, the support unit can input the elderly individual's emotion data into the generative AI and have the generative AI perform the adjustment of the communication method based on the emotion.

[0090] The support unit can select the optimal support method by referring to past communication history. For example, the support unit may suggest a similar method based on the communication methods the elderly person preferred in the past. The support unit may also suggest an alternative method to avoid communication methods the elderly person avoided in the past. Furthermore, the support unit may analyze the elderly person's past communication history and select the most effective support method. This ensures that the optimal support method is selected based on past communication history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input past communication history data into a generating AI and have the generating AI select the optimal support method.

[0091] The support unit can adjust the support provided based on the elderly person's current living situation and health condition. For example, the support unit provides necessary information according to the elderly person's living situation. The support unit can also provide appropriate support, taking into account the elderly person's health condition. Furthermore, the support unit can provide optimal support based on the elderly person's living situation and health condition. This adjusts the support provided based on the living situation and health condition. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input data on the elderly person's living situation and health condition into a generating AI and have the generating AI perform the adjustment of the support provided.

[0092] The support unit can estimate the emotions of elderly individuals and determine communication priorities based on those estimated emotions. For example, if an elderly individual is feeling anxious, the support unit can prioritize providing urgent information. If an elderly individual is feeling secure, the support unit can also prioritize providing long-term information. Furthermore, if an elderly individual is feeling stressed, the support unit can prioritize providing information to alleviate that stress. This determines communication priorities that correspond to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI or not. For example, the support unit can input elderly individuals' emotional data into a generative AI and have the generative AI determine communication priorities based on those emotions.

[0093] The support unit can select the optimal support method by considering the geographical location information of the elderly person. For example, if the elderly person lives in an urban area, the support unit can propose a support method specific to urban areas. For example, if the elderly person lives in a rural area, the support unit can also propose a support method specific to rural areas. Furthermore, the support unit can propose a support method that takes into account the characteristics of the region based on the elderly person's geographical location information. This ensures that the optimal support method is selected based on geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the elderly person's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0094] The support department can analyze the social media activities of elderly individuals and provide relevant information. For example, the support department can provide information that elderly individuals are interested in on social media. The support department can also provide, for example, the opinions of experts that elderly individuals follow on social media. Furthermore, the support department can analyze the social media activities of elderly individuals and provide information that they might find interesting. This ensures that relevant information is provided based on their social media activities. Some or all of the above-described processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input elderly individuals' social media data into a generating AI and have the generating AI provide relevant information.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The management department can propose environmentally conscious asset management methods for the elderly. For example, the management department can select investment targets related to renewable energy. It can also purchase shares of companies that contribute to environmental protection. Furthermore, the management department can propose environmentally conscious investment trusts such as eco-funds. This allows for the elderly to manage their assets in an environmentally conscious manner.

[0097] The detection unit can consider seasonal spending trends when analyzing the spending patterns of elderly individuals. For example, the detection unit can predict an increase in heating costs during winter and avoid detecting it as an abnormal expenditure. It can also consider an increase in cooling costs during summer and avoid detecting it as an abnormal expenditure. Furthermore, it can consider special spending during the year-end and New Year period and avoid detecting it as an abnormal expenditure. This enables anomaly detection based on seasonal spending trends.

[0098] The drafting department can adjust the content of contracts and legal documents to take into account the cultural background of elderly individuals. For example, if an elderly person practices a particular religion, the department can create documents that include content that respects that religion. Furthermore, if an elderly person has specific cultural customs, the department can create documents that respect those customs. In addition, if an elderly person speaks a particular language, the department can create documents in that language. This ensures that the content of documents is tailored to the cultural background.

[0099] The support department can tailor the content of communication based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in music, the support department can provide music-related topics. Similarly, if an elderly person is interested in gardening, the support department can provide gardening-related information. Furthermore, if an elderly person is interested in travel, the support department can provide travel-related topics. This allows for communication based on hobbies and interests.

[0100] The management department can estimate the emotions of elderly individuals and adjust the risk of asset management based on those estimates. For example, if an elderly individual is feeling anxious, the management department can suggest low-risk asset management methods. If an elderly individual is feeling secure, for example, the management department can suggest high-risk but high-return asset management methods. Furthermore, if an elderly individual is feeling stressed, for example, the management department can suggest ways to simplify spending management and reduce stress. This allows for asset management that is tailored to the emotions of elderly individuals.

[0101] The detection unit can estimate the emotions of elderly individuals and adjust the anomaly detection criteria based on the estimated emotions. For example, if an elderly person is feeling anxious, the detection unit will tighten the anomaly detection criteria. Conversely, if an elderly person is feeling at ease, the detection unit can also loosen the anomaly detection criteria. Furthermore, if an elderly person is feeling stressed, the detection unit can adjust the anomaly detection criteria to reduce stress. In this way, the anomaly detection criteria are adjusted according to the emotions of the elderly person.

[0102] The drafting department can estimate the emotions of elderly individuals and adjust the wording of contracts and legal documents based on these estimates. For example, if an elderly person is feeling anxious, the department will use concise and easy-to-understand language. If an elderly person is feeling secure, the department may use language that includes detailed information. Furthermore, if an elderly person is feeling stressed, the department may use language that helps reduce stress. This ensures that the language is tailored to the emotions of the elderly person.

[0103] The support department can estimate the emotions of elderly individuals and adjust its communication methods based on those estimates. For example, if an elderly person is feeling anxious, the support department will use gentle language in its communication. If an elderly person is feeling secure, the support department can also provide detailed information. Furthermore, if an elderly person is feeling stressed, the support department can use communication methods designed to reduce stress. This ensures that communication methods are tailored to the emotions of the elderly individual.

[0104] The support department can estimate the emotions of elderly individuals and determine communication priorities based on those estimates. For example, if an elderly person is feeling anxious, the support department will prioritize providing urgent information. If an elderly person is feeling secure, the support department may also prioritize providing long-term information. Furthermore, if an elderly person is feeling stressed, the support department may prioritize providing information to alleviate that stress. This ensures that communication priorities are tailored to the elderly person's emotions.

[0105] The management department can select the optimal asset management method by considering the geographical location information of elderly individuals. For example, if an elderly person lives in an urban area, the management department can propose an asset management method specific to urban areas. If an elderly person lives in a rural area, the management department can also propose an asset management method specific to rural areas. Furthermore, the management department can propose an asset management method that takes into account the local economic conditions based on the elderly person's geographical location information. This enables optimal asset management based on geographical location information.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The management department manages the assets of elderly individuals. For example, the management department manages the assets of elderly individuals and implements optimal asset management and expenditure management. The management department can propose asset management methods that take risk diversification into consideration and select highly profitable investment destinations. Furthermore, the management department sets budgets and monitors expenditures. Step 2: The detection unit detects anomalies based on the assets managed by the management unit. The detection unit analyzes regular spending patterns and issues a warning if an anomaly occurs. The detection unit can analyze monthly spending history and spending by category to detect anomalies. Furthermore, the detection unit warns of anomalies by sending alert notifications and email notifications. Step 3: The drafting unit creates contracts and legal documents based on the anomalies detected by the detection unit. The drafting unit prepares standard contracts and legal documents. The drafting unit can create lease agreements, sales agreements, wills, and power of attorney documents. Furthermore, the drafting unit reviews the content of legal documents and makes any necessary revisions. Step 4: The support department assists with communication with family and professionals based on the documents created by the creation department. The support department assists with communication with family and professionals and prepares and provides regular reports. The support department communicates using telephone, email, and video calls. The support department can also prepare and provide monthly and quarterly reports to family and professionals.

[0108] 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.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the management unit, detection unit, creation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart device 14 and manages the assets of the elderly and performs optimal asset management and expenditure management. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes regular expenditure patterns and issues a warning if abnormal expenditure occurs. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates standard contracts and prepares legal documents. The support unit is implemented by the control unit 46A of the smart device 14 and supports communication with family and professionals and creates and provides periodic reports. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] 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.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] 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.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the management unit, detection unit, creation unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart glasses 214 and manages the elderly person's assets and performs optimal asset management and expenditure management. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes regular expenditure patterns and issues a warning if an abnormal expenditure occurs. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates standard contracts and prepares legal documents. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports communication with family and professionals and creates and provides regular reports. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] 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.

[0135] 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.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the management unit, detection unit, creation unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the headset terminal 314 and manages the assets of the elderly and performs optimal asset management and expenditure management. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes regular expenditure patterns and issues a warning if abnormal expenditure occurs. The creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates standard contracts and prepares legal documents. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports communication with family and professionals and creates and provides periodic reports. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] 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.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] 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.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the management unit, detection unit, creation unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the robot 414 and manages the assets of the elderly and performs optimal asset management and expenditure management. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes regular expenditure patterns and issues a warning if an abnormal expenditure occurs. The creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates standard contracts and prepares legal documents. The support unit is implemented by the control unit 46A of the robot 414 and supports communication with family and professionals and creates and provides periodic reports. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0161] 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.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] 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.

[0164] 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.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] 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."

[0167] 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.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] 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.

[0179] (Note 1) The management department that manages assets, A detection unit that detects abnormalities based on the property managed by the aforementioned management unit, A creation unit that creates contracts and legal documents based on the anomalies detected by the aforementioned detection unit, The system includes a support unit that supports communication with family members and professionals based on the documents created by the creation unit. A system characterized by the following features. (Note 2) The aforementioned management department, Manage the assets of the elderly and provide optimal asset management and expenditure control. The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit, It analyzes regular spending patterns and issues warnings if unusual spending occurs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, We create standard contracts and prepare legal documents. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is We support communication with family and professionals and provide regular reports. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Estimate the emotions of elderly people and adjust their financial management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Analyze the past asset management history of elderly individuals to select the optimal asset management method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, Adjusting investment risk based on the current living situation and health status of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, The system estimates the emotions of elderly individuals and determines priorities for asset management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, Select the optimal asset management method by taking into account the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, We analyze the social media activity of senior citizens and provide relevant asset management information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit, The system estimates the emotions of elderly individuals and adjusts the criteria for detecting anomalies based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit, Predicting the probability of anomalies occurring by referring to past spending patterns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit, Assess the risk of abnormalities based on the health status and lifestyle of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit, The system estimates the emotions of elderly individuals and adjusts the order in which anomaly detection results are displayed based on the estimated emotions of the elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit, Predicting the occurrence of abnormalities by considering the geographical distribution of the elderly The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit, Assess the risk of anomalies by referring to relevant legal literature. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned creation unit, We estimate the emotions of elderly people and adjust the wording of contracts and legal documents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, Generate the optimal document by referring to similar documents from the past. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, The content of the document should be adjusted based on the current living situation and health status of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, Generate optimal documents considering the geographical location information of elderly users. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, Analyze the social media activities of older adults and reflect the relevant information in a document. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is The system estimates the emotions of elderly individuals and adjusts communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is We will select the most suitable support method by referring to past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is Support services are adjusted based on the current living situation and health condition of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is The system estimates the emotions of elderly individuals and prioritizes communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is Select the optimal support method by considering the geographical location of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is Analyze the social media activities of older adults and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The management department that manages assets, A detection unit that detects abnormalities based on the property managed by the aforementioned management unit, A creation unit that creates contracts and legal documents based on the anomalies detected by the aforementioned detection unit, The system includes a support unit that supports communication with family members and professionals based on the documents created by the creation unit. A system characterized by the following features.

2. The aforementioned management department, Manage the assets of the elderly and provide optimal asset management and expenditure control. The system according to feature 1.

3. The detection unit, It analyzes regular spending patterns and issues warnings if unusual spending occurs. The system according to feature 1.

4. The aforementioned creation unit, We create standard contracts and prepare legal documents. The system according to feature 1.

5. The aforementioned support unit is We support communication with family and professionals and provide regular reports. The system according to feature 1.

6. The aforementioned management department, Estimate the emotions of elderly people and adjust their financial management methods based on those estimated emotions. The system according to feature 1.

7. The aforementioned management department, Analyze the past asset management history of elderly individuals to select the optimal asset management method. The system according to feature 1.

8. The aforementioned management department, Adjusting investment risk based on the current living situation and health status of elderly individuals. The system according to feature 1.

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