system

Abstract

The system according to this embodiment aims to prevent fraud and illegal transactions in the asset management of the elderly and to support sound asset management. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a provision unit, a feedback unit, a detection unit, and a notification unit. The collection unit collects data related to the asset management of elderly people. The generation unit generates fraud scenarios based on the data collected by the collection unit. The provision unit provides interactive training modules based on the fraud scenarios generated by the generation unit. The feedback unit provides real-time feedback based on the training modules provided by the provision unit. The detection unit detects suspicious transactions and expenditures based on the data collected by the collection unit. The notification unit notifies of suspicious transactions and expenditures detected by the detection unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, effective measures for preventing fraud and illegal transactions in the asset management of the elderly have not been fully taken, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent fraud and illegal transactions in the asset management of the elderly and support sound asset management.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, a generation unit, a provision unit, a feedback unit, a detection unit, and a notification unit. The collection unit collects data related to the asset management of elderly people. The generation unit generates fraud scenarios based on the data collected by the collection unit. The provision unit provides interactive training modules based on the fraud scenarios generated by the generation unit. The feedback unit provides real-time feedback based on the training modules provided by the provision unit. The detection unit detects suspicious transactions and expenditures based on the data collected by the collection unit. The notification unit notifies of suspicious transactions and expenditures detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can prevent fraud and illegal transactions in the asset management of the elderly and support sound asset management. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 asset management system according to an embodiment of the present invention is an asset management system targeted at the elderly. This asset management system is intended to maintain the assets of the elderly in a sound manner and prevent them from becoming victims of fraud. The aging rate in Japan exceeds 28%, and is projected to reach approximately 35% by 2050. The proportion of financial assets held by the elderly is on the rise, and it is said that those aged 75 and over own approximately 40% of household financial assets. However, the amount of money lost to fraud and unscrupulous business practices targeting the elderly amounts to tens of billions of yen annually. When parents become elderly, they often resist when people talk about money in order to maintain and protect their assets in a sound manner, and there is a need for services that can address this. The asset management system provides fraud prevention training by an AI agent. It autonomously creates fraud scenarios that the elderly may encounter and provides an interactive training module for learning how to deal with them. It is designed in a learning game format so that the elderly can learn in an enjoyable way. Specifically, it consists of the following steps: First, the AI ​​agent collects data on the asset management of the elderly. Next, based on the collected data, the AI ​​agent generates fraud scenarios. Based on generated fraud scenarios, an interactive training module is provided to help seniors learn how to deal with them. The training module is presented in a learning game format, designed to make learning enjoyable for seniors. The AI ​​agent also provides real-time feedback and advice tailored to the senior's level of understanding. This helps seniors become more resistant to fraud. Furthermore, the AI ​​agent has the ability to detect suspicious transactions and expenditures in real time and notify the individual, pre-designated family members, and trusted third parties. This reduces the risk of fraud and excessive spending, enabling early detection and intervention. In this way, the asset management system can maintain the financial health of seniors' assets and prevent fraud. It can also facilitate communication with family members and provide seniors with an environment where they can live with peace of mind.

[0029] The asset management system according to the embodiment comprises a collection unit, a generation unit, a provision unit, a feedback unit, a detection unit, and a notification unit. The collection unit collects data related to the asset management of elderly persons. The collection unit can, for example, collect data related to the financial assets, real estate, and other assets of elderly persons. The collection unit can, for example, collect transaction history of bank accounts and credit card usage history. The collection unit can also collect data related to the living situation and areas of interest of elderly persons. For example, the collection unit can collect data related to the health status and hobbies of elderly persons. The generation unit generates fraud scenarios based on the data collected by the collection unit. The generation unit generates fraud scenarios using, for example, AI. The generation unit can, for example, generate fraud scenarios based on past fraud cases. The generation unit can also estimate the emotions of elderly persons and adjust the way the fraud scenarios are presented based on the estimated emotions. For example, if the elderly person is relaxed, the generation unit will generate fraud scenarios using a gentle presentation. The provision unit provides interactive training modules based on the fraud scenarios generated by the generation unit. The provision unit provides training modules in the form of, for example, a study game. The provider unit can, for example, provide visually stimulating training modules. It can also estimate the emotions of older adults and adjust the presentation of the training modules based on those estimated emotions. For example, if an older adult is feeling anxious, the provider unit will provide the training modules in a simple and easy-to-understand manner. The feedback unit provides real-time feedback based on the training modules provided by the provider unit. The feedback unit adjusts the level of detail of the feedback based on the older adult's level of understanding. For example, if an older adult has a high level of understanding, the feedback unit will provide detailed feedback. The feedback unit can also estimate the emotions of older adults and adjust the presentation of the feedback based on those estimated emotions. For example, if an older adult is relaxed, the feedback unit will provide feedback in a calm manner. The detection unit detects suspicious transactions and expenditures based on the data collected by the collection unit.The detection unit detects suspicious transactions and expenditures, for example, using AI. The detection unit can detect suspicious transactions based on past transaction history, for example. The detection unit can also estimate the emotions of elderly individuals and adjust the detection criteria for suspicious transactions and expenditures based on the estimated emotions. For example, if the elderly individual is stressed, the detection unit will apply stricter detection criteria. The notification unit notifies individuals of suspicious transactions and expenditures detected by the detection unit. The notification unit notifies individuals and pre-designated family members or trusted third parties, for example. The notification unit can notify individuals via email or SMS, for example. The notification unit can also estimate the emotions of elderly individuals and adjust the wording of the notification based on the estimated emotions. For example, if the elderly individual is relaxed, the notification unit will provide a calmer wording. As a result, the asset management system according to the embodiment can maintain the assets of elderly individuals in a healthy state and prevent fraud.

[0030] The data collection unit collects data related to the asset management of elderly individuals. Specifically, the unit can collect data on elderly individuals' financial assets, real estate, and other assets. For example, it collects transaction history from bank accounts and credit card usage history, and manages this data centrally. The unit can also collect data on elderly individuals' living situations and areas of interest. For example, it collects data on elderly individuals' health status and hobbies, and uses this information to understand their lifestyle patterns and behavioral tendencies. To collect this data, the unit collaborates with banks, credit card companies, medical institutions, hobby groups, and other organizations to obtain the necessary data. Furthermore, the unit securely stores the collected data and implements appropriate security measures to protect privacy. For example, it encrypts data and controls access to prevent unauthorized access and data leaks. This allows the unit to efficiently and securely collect the data necessary for the asset management of elderly individuals, improving the overall reliability of the system.

[0031] The generation unit generates fraud scenarios based on data collected by the collection unit. Specifically, the generation unit uses AI to generate fraud scenarios. The AI ​​can learn from past fraud cases and generate new fraud scenarios based on these cases. For example, the AI ​​can extract common patterns and methods from past fraud cases and combine them to create new fraud scenarios. The generation unit can also estimate the emotions of elderly people and adjust the way the fraud scenarios are presented based on the estimated emotions. For example, if an elderly person is relaxed, the generation unit will generate a fraud scenario using calm language; if an elderly person is tense, the generation unit will generate a fraud scenario using language that encourages greater vigilance. By providing these fraud scenarios to elderly people, the generation unit can train them to understand fraud tactics and prevent them from becoming victims of fraud. Furthermore, the generation unit can regularly update the generated fraud scenarios to respond to new fraud tactics. In this way, the generation unit can provide effective training scenarios to protect the assets of elderly people and contribute to preventing fraud.

[0032] The provider unit provides interactive training modules based on fraud scenarios generated by the generator unit. Specifically, the provider unit provides training modules in the form of educational games. For example, it provides visually stimulating training modules to allow seniors to learn while having fun. The provider unit can also estimate the emotions of seniors and adjust the presentation of the training modules based on the estimated emotions. For example, if a senior is tense, it provides training modules in a simple and easy-to-understand manner, and if a senior is relaxed, it provides more detailed information. Through these training modules, the provider unit enables seniors to understand fraud tactics and acquire the skills to prevent becoming a victim of fraud. Furthermore, the provider unit can evaluate the effectiveness of the training modules and improve the content as needed. For example, it can monitor the learning progress and understanding of seniors and adjust the difficulty level and content of the training modules. In this way, the provider unit can provide effective training to seniors and contribute to preventing fraud.

[0033] The feedback unit provides real-time feedback based on the training modules provided by the service provider. Specifically, the feedback unit adjusts the level of detail of the feedback based on the elderly person's level of understanding. For example, if the elderly person has a high level of understanding, detailed feedback is provided; if their understanding is low, more concise and easy-to-understand feedback is provided. The feedback unit can also estimate the elderly person's emotions and adjust the way the feedback is expressed based on those emotions. For example, if the elderly person is relaxed, feedback is provided in a calm manner; if the elderly person is tense, more encouraging words are used. Through this feedback, the feedback unit helps the elderly person effectively learn the training modules and acquire the skills to prevent becoming a victim of fraud. Furthermore, the feedback unit can periodically review the content of the feedback and improve it as needed. For example, it can monitor the elderly person's learning progress and level of understanding and adjust the content and expression of the feedback. This allows the feedback unit to provide effective feedback to the elderly person and contribute to preventing them from becoming a victim of fraud.

[0034] The detection unit detects suspicious transactions and expenditures based on data collected by the collection unit. Specifically, the detection unit uses AI to detect suspicious transactions and expenditures. The AI ​​learns from past transaction history and can detect unusual transactions that deviate from normal transaction patterns. For example, the AI ​​can detect high-value expenditures, frequent transactions, and transactions in unusual locations, and identify these as suspicious transactions. The detection unit can also estimate the emotions of elderly individuals and adjust the detection criteria for suspicious transactions and expenditures based on these estimated emotions. For example, if an elderly person is stressed, stricter detection criteria are applied; if they are relaxed, normal detection criteria are applied. By detecting these suspicious transactions and expenditures, the detection unit can protect the assets of elderly individuals and prevent fraud. Furthermore, the detection unit can record details of detected suspicious transactions and expenditures for later analysis and countermeasures. For example, it can analyze patterns of detected suspicious transactions to discover new fraud methods and develop countermeasures. In this way, the detection unit can provide an effective detection function to protect the assets of elderly individuals and contribute to preventing fraud.

[0035] The notification unit notifies individuals of suspicious transactions or expenditures detected by the detection unit. Specifically, the notification unit notifies the individual, pre-designated family members, and trusted third parties. For example, it may use email or SMS to quickly disseminate information. The notification unit can also estimate the emotions of elderly individuals and adjust the wording of the notification based on those emotions. For example, if an elderly individual is relaxed, the notification will be delivered in a calm tone; if an elderly individual is stressed, the notification will be delivered in a more attention-grabbing tone. Through these notifications, the notification unit enables elderly individuals and their families to respond quickly to suspicious transactions or expenditures and prevent fraud. Furthermore, the notification unit can periodically review the content and method of notifications and improve them as needed. For example, it can adjust the timing, frequency, and wording of notifications to provide more effective notifications. In this way, the notification unit can provide elderly individuals and their families with timely and appropriate information and contribute to preventing fraud.

[0036] The data collection unit can analyze the past asset management history of elderly individuals and select the optimal data collection method. For example, the data collection unit can propose the optimal data collection method based on the asset management methods used by the elderly individual in the past. For example, the data collection unit can adjust the frequency of data collection based on the elderly individual's past asset management history. The data collection unit can also analyze the elderly individual's past asset management history and optimize the timing of data collection. This enables efficient data collection by selecting the optimal data collection method based on past asset management history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly individual's past asset management history data into AI and have the AI ​​select the optimal data collection method.

[0037] The data collection unit can filter asset management data based on the elderly person's current living situation and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the elderly person's current living situation. For example, the data collection unit can collect only the necessary data based on the elderly person's areas of interest. The data collection unit can also analyze the elderly person's living situation and areas of interest and adjust the scope of data collection. This allows for the collection of highly relevant data by filtering the data based on the elderly person's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's living situation data into AI and have the AI ​​perform the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals when collecting asset management data. For example, the data collection unit can prioritize the collection of region-related data based on the elderly individual's current location. For example, the data collection unit can prioritize the collection of data from nearby financial institutions by considering the geographical location information of elderly individuals. The data collection unit can also collect data related to the regional economic situation based on the geographical location information of elderly individuals. In this way, by considering the geographical location information of elderly individuals, region-related data can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information data of elderly individuals into AI and have the AI ​​perform the collection of highly relevant data.

[0039] The data collection unit can analyze the social media activities of elderly individuals and collect relevant data when collecting asset management data. For example, the data collection unit can analyze the social media activities of elderly individuals and collect data related to financial products of interest. For example, the data collection unit can collect information indicating a high risk of fraud from the social media activities of elderly individuals. The data collection unit can also collect information useful for asset management based on the social media activities of elderly individuals. In this way, by analyzing the social media activities of elderly individuals, data related to financial products of interest can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the social media activity data of elderly individuals into AI and have the AI ​​perform the collection of relevant data.

[0040] The generation unit can adjust the level of detail of a fraud scenario based on past fraud cases when generating fraud scenarios. For example, the generation unit can generate a detailed fraud scenario based on past fraud cases. For example, the generation unit can extract key points from past fraud cases and generate a simple fraud scenario. The generation unit can also analyze past fraud cases and adjust the level of detail of the scenario according to the fraudulent methods. By adjusting the level of detail of the scenario based on past fraud cases, it is possible to generate more realistic fraud scenarios. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past fraud case data into AI and have the AI ​​perform the adjustment of the level of detail of the scenario.

[0041] The generation unit can apply different generation algorithms depending on the type of fraud when generating fraud scenarios. For example, in the case of bank transfer fraud, the generation unit can apply a generation algorithm corresponding to a specific method. For example, in the case of sequential sales, the generation unit can apply a generation algorithm corresponding to multiple methods. Furthermore, in the case of excessive donations, the generation unit can apply a generation algorithm corresponding to a specific situation. By applying different generation algorithms depending on the type of fraud, more effective fraud scenarios can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input fraud type data into AI and have AI perform the application of generation algorithms.

[0042] The generation unit can determine the priority of fraud scenarios based on when the fraud occurred. For example, the generation unit can prioritize generating fraud scenarios based on recent fraud cases. For example, the generation unit can prioritize generating high-priority fraud scenarios based on past fraud cases. The generation unit can also consider when the fraud occurred and generate scenarios that correspond to the latest fraud methods. By prioritizing scenarios based on when the fraud occurred, it is possible to generate scenarios that correspond to the latest fraud methods. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input fraud occurrence data into AI and have the AI ​​perform the determination of scenario priorities.

[0043] The generation unit can adjust the order of fraud scenarios based on their relevance when generating fraud scenarios. For example, the generation unit can prioritize generating highly relevant scenarios based on fraud cases that elderly people have encountered in the past. For example, the generation unit can prioritize generating scenarios of high importance by considering the relevance of the frauds. The generation unit can also adjust the order of scenarios based on their relevance to provide effective training. This allows for effective training by adjusting the order of scenarios based on their relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input fraud relevance data into AI and have the AI ​​perform the adjustment of the scenario order.

[0044] The provider can adjust the level of detail of training modules based on the importance of the fraud scenarios when providing them. For example, the provider can provide detailed training modules for high-importance fraud scenarios, and simple training modules for low-importance fraud scenarios. The provider can also adjust the level of detail of training modules considering the importance of the fraud scenarios. This allows for effective training by adjusting the level of detail of modules based on the importance of the fraud scenarios. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input fraud scenario importance data into AI and have the AI ​​perform the adjustment of module detail.

[0045] The service provider can apply different provisioning algorithms depending on the category of the fraud scenario when providing training modules. For example, in the case of bank transfer fraud, the service provider can provide a training module corresponding to a specific method. For example, in the case of sequential sales, the service provider can provide training modules corresponding to multiple methods. Furthermore, in the case of excessive donations, the service provider can provide a training module corresponding to a specific situation. This allows for effective training by applying different provisioning algorithms depending on the category of the fraud scenario. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input fraud scenario category data into AI and have the AI ​​perform the application of the provisioning algorithm.

[0046] The service provider can prioritize training modules based on when fraud scenarios occur. For example, the service provider can prioritize providing training modules based on recent fraud cases. For example, the service provider can prioritize providing high-priority training modules based on past fraud cases. The service provider can also provide training modules that correspond to the latest fraud methods, taking into account when fraud scenarios occur. This allows the service provider to provide training that corresponds to the latest fraud methods by prioritizing modules based on when fraud scenarios occur. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input fraud scenario occurrence data into AI and have the AI ​​perform the module prioritization.

[0047] The service provider can adjust the order of training modules based on the relevance of the fraud scenarios when providing them. For example, the service provider can prioritize providing highly relevant training modules based on fraud cases that elderly people have encountered in the past. For example, the service provider can prioritize providing high-priority training modules by considering the relevance of the fraud scenarios. The service provider can also adjust the order of training modules based on the relevance of the fraud scenarios to provide effective training. This allows for effective training by adjusting the order of modules based on the relevance of the fraud scenarios. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the relevance of fraud scenarios into AI and have the AI ​​perform the adjustment of the module order.

[0048] The feedback unit can adjust the level of detail in the feedback based on the elderly person's level of understanding when providing feedback. For example, if the elderly person has a high level of understanding, the feedback unit can provide detailed feedback. If the elderly person has a low level of understanding, the feedback unit can provide simple, concise feedback. The feedback unit can also adjust the level of detail in the feedback, taking into account the elderly person's level of understanding. This allows for more effective feedback to be provided by adjusting the level of detail in the feedback based on the elderly person's level of understanding. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the elderly person's level of understanding into AI and have the AI ​​perform the adjustment of the level of detail in the feedback.

[0049] The feedback unit can provide optimal feedback by referring to the elderly person's past training history when providing feedback. For example, the feedback unit can provide optimal feedback based on the elderly person's past training history. For example, the feedback unit can provide feedback that highlights areas of high understanding from the elderly person's past training history. The feedback unit can also analyze the elderly person's past training history and provide effective feedback. In this way, optimal feedback can be provided by referring to the elderly person's past training history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the elderly person's past training history data into AI and have the AI ​​perform the task of providing optimal feedback.

[0050] The feedback unit can customize the content of feedback based on the elderly person's living situation when providing feedback. For example, the feedback unit considers the elderly person's current living situation and provides highly relevant feedback. For example, the feedback unit can provide only the necessary feedback based on the elderly person's living situation. The feedback unit can also analyze the elderly person's living situation and customize the content of the feedback. By customizing the content of feedback based on the elderly person's living situation, more relevant feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input elderly person's living situation data into AI and have the AI ​​customize the content of the feedback.

[0051] The feedback unit can analyze the social media activity of elderly individuals and provide relevant feedback when providing feedback. For example, the feedback unit can analyze the social media activity of elderly individuals and provide feedback related to financial products of interest. For example, the feedback unit can provide feedback related to information with a high risk of fraud based on the social media activity of elderly individuals. The feedback unit can also provide feedback useful for asset management based on the social media activity of elderly individuals. In this way, by analyzing the social media activity of elderly individuals, feedback related to financial products of interest can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the social media activity data of elderly individuals into AI and have the AI ​​perform the provision of relevant feedback.

[0052] The detection unit can optimize its detection algorithm by referring to past transaction history when detecting suspicious transactions or expenditures. For example, the detection unit can apply the optimal detection algorithm based on the past transaction history of an elderly person. For example, the detection unit can optimize an algorithm to detect abnormal transactions from the past transaction history of an elderly person. The detection unit can also analyze the past transaction history of an elderly person and apply an effective detection algorithm. This allows the optimal detection algorithm to be applied by referring to past transaction history. 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 past transaction history data of an elderly person into AI and have the AI ​​perform the optimization of the detection algorithm.

[0053] The detection unit can apply different detection algorithms depending on the type of transaction when it detects suspicious transactions or expenditures. For example, in the case of bank transfer transactions, the detection unit can apply a detection algorithm that corresponds to a specific method. For example, in the case of credit card transactions, the detection unit can apply an algorithm that detects abnormal expenditures. Furthermore, in the case of cash transactions, the detection unit can apply a detection algorithm that corresponds to a specific situation. By applying different detection algorithms depending on the type of transaction, more effective detection becomes possible. 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 transaction type data into the AI ​​and have the AI ​​execute the application of the detection algorithm.

[0054] The detection unit can determine the detection priority based on the timing of the transaction when it detects suspicious transactions or expenditures. For example, the detection unit may prioritize detection based on recently occurring suspicious transactions. For example, the detection unit may prioritize detection of high-priority transactions based on past suspicious transactions. The detection unit may also prioritize detection of the most recent suspicious transactions, taking into account the timing of the transaction. In this way, by determining the detection priority based on the timing of the transaction, the most recent suspicious transactions can be detected preferentially. 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 may input transaction timing data into AI and have the AI ​​perform the determination of detection priorities.

[0055] The detection unit can adjust the detection order based on the relevance of transactions when detecting suspicious transactions or expenditures. For example, the detection unit can prioritize detecting highly relevant transactions based on suspicious transactions that elderly people have encountered in the past. For example, the detection unit can prioritize detecting transactions of high importance by considering the relevance of suspicious transactions. The detection unit can also adjust the detection order based on the relevance of suspicious transactions to perform effective detection. This makes effective detection possible by adjusting the detection order based on the relevance of transactions. 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 data on the relevance of suspicious transactions into AI and have AI perform the adjustment of the detection order.

[0056] The notification unit can adjust the level of detail of notifications based on the elderly person's level of understanding when providing notifications. For example, if the elderly person has a high level of understanding, the notification unit can provide a detailed notification. For example, if the elderly person has a low level of understanding, the notification unit can provide a simple and concise notification. The notification unit can also adjust the level of detail of notifications, taking into account the elderly person's level of understanding. By adjusting the level of detail of notifications based on the elderly person's level of understanding, more effective notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the elderly person's level of understanding into AI and have AI perform the adjustment of the level of detail of notifications.

[0057] The notification unit can provide the most suitable notification by referring to the elderly person's past notification history when providing a notification. For example, the notification unit can provide the most suitable notification based on the elderly person's past notification history. For example, the notification unit can provide a notification that highlights the parts of the elderly person's past notification history that they understand well. The notification unit can also analyze the elderly person's past notification history and provide an effective notification. In this way, the most suitable notification can be provided by referring to the elderly person's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input the elderly person's past notification history data into AI and have the AI ​​perform the task of providing the most suitable notification.

[0058] The notification unit can customize the content of notifications based on the elderly person's living situation when providing them. For example, the notification unit can consider the elderly person's current living situation and provide highly relevant notifications. For example, the notification unit can provide only the necessary notifications based on the elderly person's living situation. The notification unit can also analyze the elderly person's living situation and customize the content of notifications. By customizing the content of notifications based on the elderly person's living situation, more relevant notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input elderly person's living situation data into AI and have the AI ​​customize the content of notifications.

[0059] The notification unit can analyze the social media activity of elderly individuals and provide relevant notifications when providing notifications. For example, the notification unit can analyze the social media activity of elderly individuals and provide notifications related to financial products of interest. For example, the notification unit can provide notifications related to information with a high risk of fraud based on the social media activity of elderly individuals. The notification unit can also provide notifications that are useful for asset management based on the social media activity of elderly individuals. In this way, by analyzing the social media activity of elderly individuals, notifications related to financial products of interest can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the social media activity data of elderly individuals into AI and have the AI ​​perform the provision of relevant notifications.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The asset management system can also include a health management department that monitors the health status of elderly individuals. This department collects health data from the elderly and provides asset management advice based on their health status. For example, if an elderly person's health is deteriorating, it can provide advice on increasing asset liquidity. Furthermore, the health management department can adjust asset management priorities according to the elderly person's health status. This enables asset management tailored to the health condition of the elderly person.

[0062] The asset management system can also include a social activity management department that monitors the social activities of elderly individuals. This department collects data on the elderly's social activities and provides asset management advice based on those activities. For example, if an elderly person is actively engaged in social activities, the system can suggest asset management strategies. Furthermore, the social activity management department can adjust asset management priorities according to the elderly person's social activities. This enables asset management tailored to the elderly person's social activities.

[0063] The asset management system can also include a hobby management section that monitors the hobbies and interests of elderly individuals. This section collects data on the hobbies and interests of elderly individuals and provides asset management advice based on those interests. For example, if an elderly person enjoys traveling, the system can suggest ways to manage travel funds. Furthermore, the hobby management section can adjust asset management priorities according to the elderly person's hobbies and interests. This enables asset management tailored to the hobbies and interests of elderly individuals.

[0064] The asset management system may also include a family relationship management unit that monitors the family relationships of elderly individuals. This unit collects data on the elderly individual's family relationships and provides asset management advice based on those relationships. For example, if an elderly individual has good relationships with their family, the unit can suggest ways to manage their assets jointly with their family. Furthermore, the family relationship management unit can adjust asset management priorities according to the elderly individual's family relationships. This enables asset management tailored to the elderly individual's family circumstances.

[0065] The asset management system can also include a living environment management department that monitors the living environment of elderly individuals. This department collects data on the elderly person's living environment and provides asset management advice based on that environment. For example, if an elderly person lives in a safe area, the department can suggest methods for preserving their assets. Furthermore, the living environment management department can adjust asset management priorities according to the elderly person's living environment. This enables asset management tailored to the elderly person's living situation.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data related to the asset management of elderly individuals. For example, it collects data on elderly individuals' financial assets, real estate, and other assets, bank account transaction history and credit card usage history, and data on their living situation and areas of interest (such as health status and hobbies). Step 2: The generation unit generates fraud scenarios based on the data collected by the collection unit. For example, it uses AI to generate fraud scenarios based on past fraud cases and adjusts the expression method by estimating the emotions of elderly people. Step 3: The provider unit provides interactive training modules based on the fraud scenarios generated by the generator unit. For example, it provides educational game-style or visually stimulating training modules, and estimates the emotions of the elderly and adjusts the way they are expressed. Step 4: The feedback unit provides real-time feedback based on the training modules provided by the delivery unit. For example, it adjusts the level of detail of the feedback based on the elderly person's level of understanding and adjusts the expression method by estimating the elderly person's emotions. Step 5: The detection unit detects suspicious transactions and expenditures based on the data collected by the collection unit. For example, it uses AI to detect suspicious transactions based on past transaction history and adjusts the detection criteria by estimating the emotions of elderly people. Step 6: The notification unit notifies the user of any suspicious transactions or expenditures detected by the detection unit. For example, it notifies the user and pre-designated family members or trusted third parties via email or SMS, and adjusts the wording based on the user's emotional state.

[0068] (Example of form 2) The asset management system according to an embodiment of the present invention is an asset management system targeted at the elderly. This asset management system is intended to maintain the assets of the elderly in a sound manner and prevent them from becoming victims of fraud. The aging rate in Japan exceeds 28%, and is projected to reach approximately 35% by 2050. The proportion of financial assets held by the elderly is on the rise, and it is said that those aged 75 and over own approximately 40% of household financial assets. However, the amount of money lost to fraud and unscrupulous business practices targeting the elderly amounts to tens of billions of yen annually. When parents become elderly, they often resist when people talk about money in order to maintain and protect their assets in a sound manner, and there is a need for services that can address this. The asset management system provides fraud prevention training by an AI agent. It autonomously creates fraud scenarios that the elderly may encounter and provides an interactive training module for learning how to deal with them. It is designed in a learning game format so that the elderly can learn in an enjoyable way. Specifically, it consists of the following steps: First, the AI ​​agent collects data on the asset management of the elderly. Next, based on the collected data, the AI ​​agent generates fraud scenarios. Based on generated fraud scenarios, an interactive training module is provided to help seniors learn how to deal with them. The training module is presented in a learning game format, designed to make learning enjoyable for seniors. The AI ​​agent also provides real-time feedback and advice tailored to the senior's level of understanding. This helps seniors become more resistant to fraud. Furthermore, the AI ​​agent has the ability to detect suspicious transactions and expenditures in real time and notify the individual, pre-designated family members, and trusted third parties. This reduces the risk of fraud and excessive spending, enabling early detection and intervention. In this way, the asset management system can maintain the financial health of seniors' assets and prevent fraud. It can also facilitate communication with family members and provide seniors with an environment where they can live with peace of mind.

[0069] The asset management system according to the embodiment comprises a collection unit, a generation unit, a provision unit, a feedback unit, a detection unit, and a notification unit. The collection unit collects data related to the asset management of elderly persons. The collection unit can, for example, collect data related to the financial assets, real estate, and other assets of elderly persons. The collection unit can, for example, collect transaction history of bank accounts and credit card usage history. The collection unit can also collect data related to the living situation and areas of interest of elderly persons. For example, the collection unit can collect data related to the health status and hobbies of elderly persons. The generation unit generates fraud scenarios based on the data collected by the collection unit. The generation unit generates fraud scenarios using, for example, AI. The generation unit can, for example, generate fraud scenarios based on past fraud cases. The generation unit can also estimate the emotions of elderly persons and adjust the way the fraud scenarios are presented based on the estimated emotions. For example, if the elderly person is relaxed, the generation unit will generate fraud scenarios using a gentle presentation. The provision unit provides interactive training modules based on the fraud scenarios generated by the generation unit. The provision unit provides training modules in the form of, for example, a study game. The provider unit can, for example, provide visually stimulating training modules. It can also estimate the emotions of older adults and adjust the presentation of the training modules based on those estimated emotions. For example, if an older adult is feeling anxious, the provider unit will provide the training modules in a simple and easy-to-understand manner. The feedback unit provides real-time feedback based on the training modules provided by the provider unit. The feedback unit adjusts the level of detail of the feedback based on the older adult's level of understanding. For example, if an older adult has a high level of understanding, the feedback unit will provide detailed feedback. The feedback unit can also estimate the emotions of older adults and adjust the presentation of the feedback based on those estimated emotions. For example, if an older adult is relaxed, the feedback unit will provide feedback in a calm manner. The detection unit detects suspicious transactions and expenditures based on the data collected by the collection unit.The detection unit detects suspicious transactions and expenditures, for example, using AI. The detection unit can detect suspicious transactions based on past transaction history, for example. The detection unit can also estimate the emotions of elderly individuals and adjust the detection criteria for suspicious transactions and expenditures based on the estimated emotions. For example, if the elderly individual is stressed, the detection unit will apply stricter detection criteria. The notification unit notifies individuals of suspicious transactions and expenditures detected by the detection unit. The notification unit notifies individuals and pre-designated family members or trusted third parties, for example. The notification unit can notify individuals via email or SMS, for example. The notification unit can also estimate the emotions of elderly individuals and adjust the wording of the notification based on the estimated emotions. For example, if the elderly individual is relaxed, the notification unit will provide a calmer wording. As a result, the asset management system according to the embodiment can maintain the assets of elderly individuals in a healthy state and prevent fraud.

[0070] The data collection unit collects data related to the asset management of elderly individuals. Specifically, the unit can collect data on elderly individuals' financial assets, real estate, and other assets. For example, it collects transaction history from bank accounts and credit card usage history, and manages this data centrally. The unit can also collect data on elderly individuals' living situations and areas of interest. For example, it collects data on elderly individuals' health status and hobbies, and uses this information to understand their lifestyle patterns and behavioral tendencies. To collect this data, the unit collaborates with banks, credit card companies, medical institutions, hobby groups, and other organizations to obtain the necessary data. Furthermore, the unit securely stores the collected data and implements appropriate security measures to protect privacy. For example, it encrypts data and controls access to prevent unauthorized access and data leaks. This allows the unit to efficiently and securely collect the data necessary for the asset management of elderly individuals, improving the overall reliability of the system.

[0071] The generation unit generates fraud scenarios based on data collected by the collection unit. Specifically, the generation unit uses AI to generate fraud scenarios. The AI ​​can learn from past fraud cases and generate new fraud scenarios based on these cases. For example, the AI ​​can extract common patterns and methods from past fraud cases and combine them to create new fraud scenarios. The generation unit can also estimate the emotions of elderly people and adjust the way the fraud scenarios are presented based on the estimated emotions. For example, if an elderly person is relaxed, the generation unit will generate a fraud scenario using calm language; if an elderly person is tense, the generation unit will generate a fraud scenario using language that encourages greater vigilance. By providing these fraud scenarios to elderly people, the generation unit can train them to understand fraud tactics and prevent them from becoming victims of fraud. Furthermore, the generation unit can regularly update the generated fraud scenarios to respond to new fraud tactics. In this way, the generation unit can provide effective training scenarios to protect the assets of elderly people and contribute to preventing fraud.

[0072] The provider unit provides interactive training modules based on fraud scenarios generated by the generator unit. Specifically, the provider unit provides training modules in the form of educational games. For example, it provides visually stimulating training modules to allow seniors to learn while having fun. The provider unit can also estimate the emotions of seniors and adjust the presentation of the training modules based on the estimated emotions. For example, if a senior is tense, it provides training modules in a simple and easy-to-understand manner, and if a senior is relaxed, it provides more detailed information. Through these training modules, the provider unit enables seniors to understand fraud tactics and acquire the skills to prevent becoming a victim of fraud. Furthermore, the provider unit can evaluate the effectiveness of the training modules and improve the content as needed. For example, it can monitor the learning progress and understanding of seniors and adjust the difficulty level and content of the training modules. In this way, the provider unit can provide effective training to seniors and contribute to preventing fraud.

[0073] The feedback unit provides real-time feedback based on the training modules provided by the service provider. Specifically, the feedback unit adjusts the level of detail of the feedback based on the elderly person's level of understanding. For example, if the elderly person has a high level of understanding, detailed feedback is provided; if their understanding is low, more concise and easy-to-understand feedback is provided. The feedback unit can also estimate the elderly person's emotions and adjust the way the feedback is expressed based on those emotions. For example, if the elderly person is relaxed, feedback is provided in a calm manner; if the elderly person is tense, more encouraging words are used. Through this feedback, the feedback unit helps the elderly person effectively learn the training modules and acquire the skills to prevent becoming a victim of fraud. Furthermore, the feedback unit can periodically review the content of the feedback and improve it as needed. For example, it can monitor the elderly person's learning progress and level of understanding and adjust the content and expression of the feedback. This allows the feedback unit to provide effective feedback to the elderly person and contribute to preventing them from becoming a victim of fraud.

[0074] The detection unit detects suspicious transactions and expenditures based on data collected by the collection unit. Specifically, the detection unit uses AI to detect suspicious transactions and expenditures. The AI ​​learns from past transaction history and can detect unusual transactions that deviate from normal transaction patterns. For example, the AI ​​can detect high-value expenditures, frequent transactions, and transactions in unusual locations, and identify these as suspicious transactions. The detection unit can also estimate the emotions of elderly individuals and adjust the detection criteria for suspicious transactions and expenditures based on these estimated emotions. For example, if an elderly person is stressed, stricter detection criteria are applied; if they are relaxed, normal detection criteria are applied. By detecting these suspicious transactions and expenditures, the detection unit can protect the assets of elderly individuals and prevent fraud. Furthermore, the detection unit can record details of detected suspicious transactions and expenditures for later analysis and countermeasures. For example, it can analyze patterns of detected suspicious transactions to discover new fraud methods and develop countermeasures. In this way, the detection unit can provide an effective detection function to protect the assets of elderly individuals and contribute to preventing fraud.

[0075] The notification unit notifies individuals of suspicious transactions or expenditures detected by the detection unit. Specifically, the notification unit notifies the individual, pre-designated family members, and trusted third parties. For example, it may use email or SMS to quickly disseminate information. The notification unit can also estimate the emotions of elderly individuals and adjust the wording of the notification based on those emotions. For example, if an elderly individual is relaxed, the notification will be delivered in a calm tone; if an elderly individual is stressed, the notification will be delivered in a more attention-grabbing tone. Through these notifications, the notification unit enables elderly individuals and their families to respond quickly to suspicious transactions or expenditures and prevent fraud. Furthermore, the notification unit can periodically review the content and method of notifications and improve them as needed. For example, it can adjust the timing, frequency, and wording of notifications to provide more effective notifications. In this way, the notification unit can provide elderly individuals and their families with timely and appropriate information and contribute to preventing fraud.

[0076] The data collection unit can estimate the emotions of elderly individuals and adjust the timing of asset management data collection based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit can delay the collection timing to collect data when the individual is relaxed. If an elderly individual is relaxed, the data collection unit can collect data immediately to obtain the latest information. The data collection unit can also adjust the collection timing to collect data when an elderly individual is busy, allowing for data collection during their free time. This enables more appropriate data collection by adjusting the timing of data collection according to the emotions of the elderly individual. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the emotional data of elderly individuals into AI and have the AI ​​perform the adjustment of collection timing based on emotions.

[0077] The data collection unit can analyze the past asset management history of elderly individuals and select the optimal data collection method. For example, the data collection unit can propose the optimal data collection method based on the asset management methods used by the elderly individual in the past. For example, the data collection unit can adjust the frequency of data collection based on the elderly individual's past asset management history. The data collection unit can also analyze the elderly individual's past asset management history and optimize the timing of data collection. This enables efficient data collection by selecting the optimal data collection method based on past asset management history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly individual's past asset management history data into AI and have the AI ​​select the optimal data collection method.

[0078] The data collection unit can filter asset management data based on the elderly person's current living situation and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the elderly person's current living situation. For example, the data collection unit can collect only the necessary data based on the elderly person's areas of interest. The data collection unit can also analyze the elderly person's living situation and areas of interest and adjust the scope of data collection. This allows for the collection of highly relevant data by filtering the data based on the elderly person's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's living situation data into AI and have the AI ​​perform the filtering.

[0079] The data collection unit can estimate the emotions of elderly individuals and determine the priority of data to collect based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit will postpone collecting less important data. If an elderly individual is relaxed, the data collection unit can prioritize collecting more important data. Furthermore, if an elderly individual is busy, the data collection unit can prioritize collecting only the minimum necessary data. In this way, by prioritizing data according to the emotions of elderly individuals, important data can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the emotional data of elderly individuals into AI and have the AI ​​perform the data prioritization.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals when collecting asset management data. For example, the data collection unit can prioritize the collection of region-related data based on the elderly individual's current location. For example, the data collection unit can prioritize the collection of data from nearby financial institutions by considering the geographical location information of elderly individuals. The data collection unit can also collect data related to the regional economic situation based on the geographical location information of elderly individuals. In this way, by considering the geographical location information of elderly individuals, region-related data can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information data of elderly individuals into AI and have the AI ​​perform the collection of highly relevant data.

[0081] The data collection unit can analyze the social media activities of elderly individuals and collect relevant data when collecting asset management data. For example, the data collection unit can analyze the social media activities of elderly individuals and collect data related to financial products of interest. For example, the data collection unit can collect information indicating a high risk of fraud from the social media activities of elderly individuals. The data collection unit can also collect information useful for asset management based on the social media activities of elderly individuals. In this way, by analyzing the social media activities of elderly individuals, data related to financial products of interest can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the social media activity data of elderly individuals into AI and have the AI ​​perform the collection of relevant data.

[0082] The generation unit can estimate the emotions of elderly individuals and adjust the presentation of fraud scenarios based on these estimated emotions. For example, if an elderly individual is relaxed, the generation unit can generate fraud scenarios using a calm presentation. If an elderly individual is tense, the generation unit can generate fraud scenarios using a simple and easy-to-understand presentation. Furthermore, if an elderly individual is excited, the generation unit can generate fraud scenarios using a visually stimulating presentation. This allows for more effective training by adjusting the presentation of fraud scenarios according to the emotions of elderly individuals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input elderly individuals' emotional data into an AI and have the AI ​​adjust the presentation of fraud scenarios.

[0083] The generation unit can adjust the level of detail of a fraud scenario based on past fraud cases when generating fraud scenarios. For example, the generation unit can generate a detailed fraud scenario based on past fraud cases. For example, the generation unit can extract key points from past fraud cases and generate a simple fraud scenario. The generation unit can also analyze past fraud cases and adjust the level of detail of the scenario according to the fraudulent methods. By adjusting the level of detail of the scenario based on past fraud cases, it is possible to generate more realistic fraud scenarios. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past fraud case data into AI and have the AI ​​perform the adjustment of the level of detail of the scenario.

[0084] The generation unit can apply different generation algorithms depending on the type of fraud when generating fraud scenarios. For example, in the case of bank transfer fraud, the generation unit can apply a generation algorithm corresponding to a specific method. For example, in the case of sequential sales, the generation unit can apply a generation algorithm corresponding to multiple methods. Furthermore, in the case of excessive donations, the generation unit can apply a generation algorithm corresponding to a specific situation. By applying different generation algorithms depending on the type of fraud, more effective fraud scenarios can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input fraud type data into AI and have AI perform the application of generation algorithms.

[0085] The generation unit can estimate the emotions of elderly individuals and adjust the length of the fraud scenario based on the estimated emotions. For example, if an elderly individual is relaxed, the generation unit can generate a detailed fraud scenario. If an elderly individual is tense, the generation unit can generate a short, concise fraud scenario. Furthermore, if an elderly individual is excited, the generation unit can generate a visually stimulating fraud scenario. This allows for more effective training by adjusting the length of the fraud scenario according to the emotions of the elderly individual. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the emotional data of elderly individuals into AI and have the AI ​​adjust the length of the fraud scenario.

[0086] The generation unit can determine the priority of fraud scenarios based on when the fraud occurred. For example, the generation unit can prioritize generating fraud scenarios based on recent fraud cases. For example, the generation unit can prioritize generating high-priority fraud scenarios based on past fraud cases. The generation unit can also consider when the fraud occurred and generate scenarios that correspond to the latest fraud methods. By prioritizing scenarios based on when the fraud occurred, it is possible to generate scenarios that correspond to the latest fraud methods. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input fraud occurrence data into AI and have the AI ​​perform the determination of scenario priorities.

[0087] The generation unit can adjust the order of fraud scenarios based on their relevance when generating fraud scenarios. For example, the generation unit can prioritize generating highly relevant scenarios based on fraud cases that elderly people have encountered in the past. For example, the generation unit can prioritize generating scenarios of high importance by considering the relevance of the frauds. The generation unit can also adjust the order of scenarios based on their relevance to provide effective training. This allows for effective training by adjusting the order of scenarios based on their relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input fraud relevance data into AI and have the AI ​​perform the adjustment of the scenario order.

[0088] The service provider can estimate the emotions of elderly individuals and adjust the presentation of the training modules based on the estimated emotions. For example, if an elderly individual is relaxed, the service provider can provide the training modules in a calm presentation. If an elderly individual is tense, the service provider can provide the training modules in a simple and easy-to-understand presentation. Furthermore, if an elderly individual is excited, the service provider can provide the training modules in a visually stimulating presentation. By adjusting the presentation of the training modules according to the emotions of the elderly individual, more effective training becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the emotional data of elderly individuals into AI and have the AI ​​adjust the presentation of the training modules.

[0089] The provider can adjust the level of detail of training modules based on the importance of the fraud scenarios when providing them. For example, the provider can provide detailed training modules for high-importance fraud scenarios, and simple training modules for low-importance fraud scenarios. The provider can also adjust the level of detail of training modules considering the importance of the fraud scenarios. This allows for effective training by adjusting the level of detail of modules based on the importance of the fraud scenarios. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input fraud scenario importance data into AI and have the AI ​​perform the adjustment of module detail.

[0090] The service provider can apply different provisioning algorithms depending on the category of the fraud scenario when providing training modules. For example, in the case of bank transfer fraud, the service provider can provide a training module corresponding to a specific method. For example, in the case of sequential sales, the service provider can provide training modules corresponding to multiple methods. Furthermore, in the case of excessive donations, the service provider can provide a training module corresponding to a specific situation. This allows for effective training by applying different provisioning algorithms depending on the category of the fraud scenario. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input fraud scenario category data into AI and have the AI ​​perform the application of the provisioning algorithm.

[0091] The service provider can estimate the emotions of elderly individuals and adjust the length of training modules based on the estimated emotions. For example, if an elderly individual is relaxed, the service provider can provide a detailed training module. If an elderly individual is tense, the service provider can provide a short, concise training module. Furthermore, if an elderly individual is agitated, the service provider can provide a visually stimulating training module. By adjusting the length of the training modules according to the emotions of the elderly individual, more effective training becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the emotional data of elderly individuals into AI and have the AI ​​adjust the length of the training modules.

[0092] The service provider can prioritize training modules based on when fraud scenarios occur. For example, the service provider can prioritize providing training modules based on recent fraud cases. For example, the service provider can prioritize providing high-priority training modules based on past fraud cases. The service provider can also provide training modules that correspond to the latest fraud methods, taking into account when fraud scenarios occur. This allows the service provider to provide training that corresponds to the latest fraud methods by prioritizing modules based on when fraud scenarios occur. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input fraud scenario occurrence data into AI and have the AI ​​perform the module prioritization.

[0093] The service provider can adjust the order of training modules based on the relevance of the fraud scenarios when providing them. For example, the service provider can prioritize providing highly relevant training modules based on fraud cases that elderly people have encountered in the past. For example, the service provider can prioritize providing high-priority training modules by considering the relevance of the fraud scenarios. The service provider can also adjust the order of training modules based on the relevance of the fraud scenarios to provide effective training. This allows for effective training by adjusting the order of modules based on the relevance of the fraud scenarios. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the relevance of fraud scenarios into AI and have the AI ​​perform the adjustment of the module order.

[0094] The feedback unit can estimate the emotions of elderly individuals and adjust the way feedback is expressed based on those estimated emotions. For example, if an elderly individual is relaxed, the feedback unit will provide feedback in a calm manner. If an elderly individual is tense, the feedback unit can provide feedback in a simple and easy-to-understand manner. Furthermore, if an elderly individual is excited, the feedback unit can provide feedback in a visually stimulating manner. By adjusting the way feedback is expressed according to the emotions of the elderly individual, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the emotional data of elderly individuals into AI and have the AI ​​adjust the way feedback is expressed.

[0095] The feedback unit can adjust the level of detail in the feedback based on the elderly person's level of understanding when providing feedback. For example, if the elderly person has a high level of understanding, the feedback unit can provide detailed feedback. If the elderly person has a low level of understanding, the feedback unit can provide simple, concise feedback. The feedback unit can also adjust the level of detail in the feedback, taking into account the elderly person's level of understanding. This allows for more effective feedback to be provided by adjusting the level of detail in the feedback based on the elderly person's level of understanding. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the elderly person's level of understanding into AI and have the AI ​​perform the adjustment of the level of detail in the feedback.

[0096] The feedback unit can provide optimal feedback by referring to the elderly person's past training history when providing feedback. For example, the feedback unit can provide optimal feedback based on the elderly person's past training history. For example, the feedback unit can provide feedback that highlights areas of high understanding from the elderly person's past training history. The feedback unit can also analyze the elderly person's past training history and provide effective feedback. In this way, optimal feedback can be provided by referring to the elderly person's past training history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the elderly person's past training history data into AI and have the AI ​​perform the task of providing optimal feedback.

[0097] The feedback unit can estimate the emotions of the elderly person and determine the priority of feedback based on the estimated emotions. For example, if the elderly person is relaxed, the feedback unit will prioritize providing high-importance feedback. If the elderly person is stressed, the feedback unit may postpone providing low-importance feedback. Also, if the elderly person is agitated, the feedback unit may prioritize providing visually stimulating feedback. In this way, important feedback can be prioritized by determining the priority of feedback according to the elderly person's emotions. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the elderly person's emotional data into AI and have the AI ​​perform the task of determining the priority of feedback.

[0098] The feedback unit can customize the content of feedback based on the elderly person's living situation when providing feedback. For example, the feedback unit considers the elderly person's current living situation and provides highly relevant feedback. For example, the feedback unit can provide only the necessary feedback based on the elderly person's living situation. The feedback unit can also analyze the elderly person's living situation and customize the content of the feedback. By customizing the content of feedback based on the elderly person's living situation, more relevant feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input elderly person's living situation data into AI and have the AI ​​customize the content of the feedback.

[0099] The feedback unit can analyze the social media activity of elderly individuals and provide relevant feedback when providing feedback. For example, the feedback unit can analyze the social media activity of elderly individuals and provide feedback related to financial products of interest. For example, the feedback unit can provide feedback related to information with a high risk of fraud based on the social media activity of elderly individuals. The feedback unit can also provide feedback useful for asset management based on the social media activity of elderly individuals. In this way, by analyzing the social media activity of elderly individuals, feedback related to financial products of interest can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the social media activity data of elderly individuals into AI and have the AI ​​perform the provision of relevant feedback.

[0100] The detection unit can estimate the emotions of elderly individuals and adjust the detection criteria for suspicious transactions and expenditures based on the estimated emotions. For example, if an elderly individual is relaxed, the detection unit can apply normal detection criteria. If an elderly individual is tense, the detection unit can apply strict detection criteria. Furthermore, if an elderly individual is agitated, the detection unit can apply flexible detection criteria. This allows for more effective detection by adjusting the detection criteria for suspicious transactions and expenditures according to the emotions of elderly individuals. 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 AI and have the AI ​​perform the adjustment of the detection criteria.

[0101] The detection unit can optimize its detection algorithm by referring to past transaction history when detecting suspicious transactions or expenditures. For example, the detection unit can apply the optimal detection algorithm based on the past transaction history of an elderly person. For example, the detection unit can optimize an algorithm to detect abnormal transactions from the past transaction history of an elderly person. The detection unit can also analyze the past transaction history of an elderly person and apply an effective detection algorithm. This allows the optimal detection algorithm to be applied by referring to past transaction history. 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 past transaction history data of an elderly person into AI and have the AI ​​perform the optimization of the detection algorithm.

[0102] The detection unit can apply different detection algorithms depending on the type of transaction when it detects suspicious transactions or expenditures. For example, in the case of bank transfer transactions, the detection unit can apply a detection algorithm that corresponds to a specific method. For example, in the case of credit card transactions, the detection unit can apply an algorithm that detects abnormal expenditures. Furthermore, in the case of cash transactions, the detection unit can apply a detection algorithm that corresponds to a specific situation. By applying different detection algorithms depending on the type of transaction, more effective detection becomes possible. 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 transaction type data into the AI ​​and have the AI ​​execute the application of the detection algorithm.

[0103] The detection unit can estimate the emotions of elderly individuals and prioritize suspicious transactions and expenditures based on the estimated emotions. For example, if an elderly individual is relaxed, the detection unit will prioritize detecting high-priority suspicious transactions. If an elderly individual is stressed, the detection unit may postpone detecting low-priority suspicious transactions. Furthermore, if an elderly individual is agitated, the detection unit may prioritize detecting visually stimulating suspicious transactions. This allows for the priority detection of important transactions by prioritizing suspicious transactions and expenditures according to the emotions of elderly individuals. 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 AI and have the AI ​​perform the task of prioritizing suspicious transactions and expenditures.

[0104] The detection unit can determine the detection priority based on the timing of the transaction when it detects suspicious transactions or expenditures. For example, the detection unit may prioritize detection based on recently occurring suspicious transactions. For example, the detection unit may prioritize detection of high-priority transactions based on past suspicious transactions. The detection unit may also prioritize detection of the most recent suspicious transactions, taking into account the timing of the transaction. In this way, by determining the detection priority based on the timing of the transaction, the most recent suspicious transactions can be detected preferentially. 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 may input transaction timing data into AI and have the AI ​​perform the determination of detection priorities.

[0105] The detection unit can adjust the detection order based on the relevance of transactions when detecting suspicious transactions or expenditures. For example, the detection unit can prioritize detecting highly relevant transactions based on suspicious transactions that elderly people have encountered in the past. For example, the detection unit can prioritize detecting transactions of high importance by considering the relevance of suspicious transactions. The detection unit can also adjust the detection order based on the relevance of suspicious transactions to perform effective detection. This makes effective detection possible by adjusting the detection order based on the relevance of transactions. 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 data on the relevance of suspicious transactions into AI and have AI perform the adjustment of the detection order.

[0106] The notification unit can estimate the emotions of elderly individuals and adjust the way notifications are presented based on these estimated emotions. For example, if an elderly individual is relaxed, the notification unit can provide a notification in a calm manner. If an elderly individual is tense, the notification unit can provide a notification in a simple and easy-to-understand manner. Furthermore, if an elderly individual is excited, the notification unit can provide a notification in a visually stimulating manner. By adjusting the way notifications are presented according to the emotions of elderly individuals, more effective notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the emotional data of elderly individuals into AI and have the AI ​​adjust the way notifications are presented.

[0107] The notification unit can adjust the level of detail of notifications based on the elderly person's level of understanding when providing notifications. For example, if the elderly person has a high level of understanding, the notification unit can provide a detailed notification. For example, if the elderly person has a low level of understanding, the notification unit can provide a simple and concise notification. The notification unit can also adjust the level of detail of notifications, taking into account the elderly person's level of understanding. By adjusting the level of detail of notifications based on the elderly person's level of understanding, more effective notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the elderly person's level of understanding into AI and have AI perform the adjustment of the level of detail of notifications.

[0108] The notification unit can provide the most suitable notification by referring to the elderly person's past notification history when providing a notification. For example, the notification unit can provide the most suitable notification based on the elderly person's past notification history. For example, the notification unit can provide a notification that highlights the parts of the elderly person's past notification history that they understand well. The notification unit can also analyze the elderly person's past notification history and provide an effective notification. In this way, the most suitable notification can be provided by referring to the elderly person's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input the elderly person's past notification history data into AI and have the AI ​​perform the task of providing the most suitable notification.

[0109] The notification unit can estimate the emotions of elderly individuals and determine the priority of notifications based on the estimated emotions. For example, if an elderly individual is relaxed, the notification unit will prioritize providing high-priority notifications. If an elderly individual is stressed, the notification unit may postpone less important notifications. Furthermore, if an elderly individual is agitated, the notification unit may prioritize providing visually stimulating notifications. This ensures that important notifications are prioritized by determining notification priorities according to the elderly individual's emotions. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input elderly individuals' emotional data into an AI and have the AI ​​determine the notification priorities.

[0110] The notification unit can customize the content of notifications based on the elderly person's living situation when providing them. For example, the notification unit can consider the elderly person's current living situation and provide highly relevant notifications. For example, the notification unit can provide only the necessary notifications based on the elderly person's living situation. The notification unit can also analyze the elderly person's living situation and customize the content of notifications. By customizing the content of notifications based on the elderly person's living situation, more relevant notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input elderly person's living situation data into AI and have the AI ​​customize the content of notifications.

[0111] The notification unit can analyze the social media activity of elderly individuals and provide relevant notifications when providing notifications. For example, the notification unit can analyze the social media activity of elderly individuals and provide notifications related to financial products of interest. For example, the notification unit can provide notifications related to information with a high risk of fraud based on the social media activity of elderly individuals. The notification unit can also provide notifications that are useful for asset management based on the social media activity of elderly individuals. In this way, by analyzing the social media activity of elderly individuals, notifications related to financial products of interest can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the social media activity data of elderly individuals into AI and have the AI ​​perform the provision of relevant notifications.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The asset management system can also include a health management department that monitors the health status of elderly individuals. This department collects health data from the elderly and provides asset management advice based on their health status. For example, if an elderly person's health is deteriorating, it can provide advice on increasing asset liquidity. Furthermore, the health management department can adjust asset management priorities according to the elderly person's health status. This enables asset management tailored to the health condition of the elderly person.

[0114] The asset management system can also include a social activity management department that monitors the social activities of elderly individuals. This department collects data on the elderly's social activities and provides asset management advice based on those activities. For example, if an elderly person is actively engaged in social activities, the system can suggest asset management strategies. Furthermore, the social activity management department can adjust asset management priorities according to the elderly person's social activities. This enables asset management tailored to the elderly person's social activities.

[0115] The asset management system can also include a hobby management section that monitors the hobbies and interests of elderly individuals. This section collects data on the hobbies and interests of elderly individuals and provides asset management advice based on those interests. For example, if an elderly person enjoys traveling, the system can suggest ways to manage travel funds. Furthermore, the hobby management section can adjust asset management priorities according to the elderly person's hobbies and interests. This enables asset management tailored to the hobbies and interests of elderly individuals.

[0116] The asset management system may also include a family relationship management unit that monitors the family relationships of elderly individuals. This unit collects data on the elderly individual's family relationships and provides asset management advice based on those relationships. For example, if an elderly individual has good relationships with their family, the unit can suggest ways to manage their assets jointly with their family. Furthermore, the family relationship management unit can adjust asset management priorities according to the elderly individual's family relationships. This enables asset management tailored to the elderly individual's family circumstances.

[0117] The asset management system can also include a living environment management department that monitors the living environment of elderly individuals. This department collects data on the elderly person's living environment and provides asset management advice based on that environment. For example, if an elderly person lives in a safe area, the department can suggest methods for preserving their assets. Furthermore, the living environment management department can adjust asset management priorities according to the elderly person's living environment. This enables asset management tailored to the elderly person's living situation.

[0118] The asset management system may also include an emotion management unit that estimates the emotions of elderly individuals and provides asset management advice based on those estimates. The emotion management unit collects emotional data from elderly individuals and provides asset management advice based on those emotions. For example, if an elderly individual is experiencing stress, it can suggest low-risk investment methods. Furthermore, the emotion management unit can adjust asset management priorities according to the elderly individual's emotions. This enables asset management tailored to the emotional needs of elderly individuals.

[0119] The asset management system may also include a risk assessment unit that estimates the emotions of elderly individuals and performs a risk assessment of asset management based on those estimated emotions. The risk assessment unit collects emotional data from elderly individuals and performs a risk assessment of asset management based on those emotions. For example, if an elderly individual is relaxed, the system may suggest a high-risk asset management method. Furthermore, the risk assessment unit can adjust the risk assessment criteria according to the elderly individual's emotions. This enables risk assessment tailored to the emotions of elderly individuals.

[0120] The asset management system may further include a goal-setting unit that estimates the emotions of elderly individuals and sets asset management goals based on those estimated emotions. The goal-setting unit collects emotional data from elderly individuals and sets asset management goals based on those emotions. For example, if an elderly individual is agitated, an aggressive asset management goal can be set. The goal-setting unit can also adjust the criteria for goal setting according to the elderly individual's emotions. This enables goal setting that is tailored to the elderly individual's emotions.

[0121] The asset management system may further include a feedback management unit that estimates the emotions of elderly individuals and provides asset management feedback based on those estimated emotions. The feedback management unit collects emotional data from elderly individuals and provides asset management feedback based on those emotions. For example, if an elderly individual is relaxed, detailed feedback can be provided. The feedback management unit can also adjust the content of the feedback according to the elderly individual's emotions. This enables feedback tailored to the elderly individual's emotional state.

[0122] The asset management system may also include a notification management unit that estimates the emotions of elderly individuals and provides asset management notifications based on those estimated emotions. The notification management unit collects emotional data from elderly individuals and provides asset management notifications based on those emotions. For example, if an elderly individual is feeling anxious, a simple and easy-to-understand notification can be provided. Furthermore, the notification management unit can adjust the content of notifications according to the elderly individual's emotions. This enables notifications tailored to the elderly individual's emotional state.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects data related to the asset management of elderly individuals. For example, it collects data on elderly individuals' financial assets, real estate, and other assets, bank account transaction history and credit card usage history, and data on their living situation and areas of interest (such as health status and hobbies). Step 2: The generation unit generates fraud scenarios based on the data collected by the collection unit. For example, it uses AI to generate fraud scenarios based on past fraud cases and adjusts the expression method by estimating the emotions of elderly people. Step 3: The provider unit provides interactive training modules based on the fraud scenarios generated by the generator unit. For example, it provides educational game-style or visually stimulating training modules, and estimates the emotions of the elderly and adjusts the way they are expressed. Step 4: The feedback unit provides real-time feedback based on the training modules provided by the delivery unit. For example, it adjusts the level of detail of the feedback based on the elderly person's level of understanding and adjusts the expression method by estimating the elderly person's emotions. Step 5: The detection unit detects suspicious transactions and expenditures based on the data collected by the collection unit. For example, it uses AI to detect suspicious transactions based on past transaction history and adjusts the detection criteria by estimating the emotions of elderly people. Step 6: The notification unit notifies the user of any suspicious transactions or expenditures detected by the detection unit. For example, it notifies the user and pre-designated family members or trusted third parties via email or SMS, and adjusts the wording based on the user's emotional state.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, detection unit, and notification unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects bank account transaction history and credit card usage history. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates fraud scenarios based on the collected data. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides an interactive training module based on the generated fraud scenario. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 and provides real-time feedback based on the training module. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects suspicious transactions or expenditures based on the collected data. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies the person and pre-designated family members or trusted third parties of the detected suspicious transactions or expenditures. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, detection unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects bank account transaction history and credit card usage history. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates fraud scenarios based on the collected data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an interactive training module based on the generated fraud scenario. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time feedback based on the training module. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects suspicious transactions or expenditures based on the collected data. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the person and pre-designated family members or trusted third parties of the detected suspicious transactions or expenditures. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[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 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.

[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 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.

[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 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.

[0160] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, detection unit, and notification unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects bank account transaction history and credit card usage history. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates fraud scenarios based on the collected data. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides an interactive training module based on the generated fraud scenario. The feedback unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides real-time feedback based on the training module. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects suspicious transactions or expenditures based on the collected data. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 and notifies the person and pre-designated family members or trusted third parties of the detected suspicious transactions or expenditures. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, detection unit, and notification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects bank account transaction history and credit card usage history. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates fraud scenarios based on the collected data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides an interactive training module based on the generated fraud scenario. The feedback unit is implemented, for example, by the control unit 46A of the robot 414 and provides real-time feedback based on the training module. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects suspicious transactions or expenditures based on the collected data. The notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies the person and pre-designated family members or trusted third parties of the detected suspicious transactions or expenditures. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A data collection department that collects data on asset management for the elderly, A generation unit generates fraud scenarios based on the data collected by the collection unit, A providing unit that provides an interactive training module based on the fraud scenario generated by the generation unit, A feedback unit provides real-time feedback based on the training module provided by the aforementioned provision unit, A detection unit that detects suspicious transactions or expenditures based on the data collected by the aforementioned collection unit, The system includes a notification unit that notifies the user of suspicious transactions or expenditures detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the emotions of elderly individuals and adjust the timing of asset management data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the past asset management history of elderly individuals and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting asset management data, filtering is performed based on the elderly person's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the emotions of older adults and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting asset management data, the collection of highly relevant data will be prioritized, taking into account the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting asset management data, analyze the social media activity of elderly individuals and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is The system estimates the emotions of elderly people and adjusts the way fraud scenarios are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating fraud scenarios, adjust the level of detail in the scenarios based on past fraud cases. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating fraud scenarios, different generation algorithms are applied depending on the type of fraud. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates the emotions of elderly people and adjusts the length of the fraud scenario based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating fraud scenarios, prioritize the scenarios based on when the fraud occurred. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating fraud scenarios, adjust the order of the scenarios based on the relevance of the fraud. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates the emotions of older adults and adjusts the presentation of the training modules based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing training modules, adjust the level of detail in the modules based on the importance of the fraud scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing training modules, different delivery algorithms are applied depending on the category of the fraud scenario. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the emotions of older adults and adjusts the length of the training modules based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing training modules, prioritize modules based on when the fraud scenarios occurred. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing training modules, the order of the modules will be adjusted based on the relevance of the fraud scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is The system estimates the emotions of older adults and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the older person's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, we refer to the elderly person's past training history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is The system estimates the emotions of older adults and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the living situation of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, analyze the social media activity of older adults and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The detection unit is We estimate the emotions of older adults and adjust the detection criteria for suspicious transactions and expenditures based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The detection unit is When suspicious transactions or expenditures are detected, the detection algorithm is optimized by referring to past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The detection unit is When suspicious transactions or expenditures are detected, different detection algorithms are applied depending on the type of transaction. The system described in Appendix 1, characterized by the features described herein. (Note 29) The detection unit is The system estimates the emotions of elderly individuals and prioritizes suspicious transactions and expenditures based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The detection unit is When suspicious transactions or expenditures are detected, the detection priority is determined based on when the transaction occurred. The system described in Appendix 1, characterized by the features described herein. (Note 31) The detection unit is When suspicious transactions or expenditures are detected, the order of detection is adjusted based on the relevance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, The system estimates the emotions of elderly individuals and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When providing notifications, the level of detail in the notifications will be adjusted based on the elderly person's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When providing notifications, the system refers to the elderly person's past notification history to provide the most appropriate notifications. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, The system estimates the emotions of older adults and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When providing notifications, customize the content of the notifications based on the elderly person's living situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, When providing notifications, we analyze the social media activity of older adults and provide relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection department that collects data on asset management for the elderly, A generation unit generates fraud scenarios based on the data collected by the collection unit, A providing unit that provides an interactive training module based on the fraud scenario generated by the generation unit, A feedback unit provides real-time feedback based on the training module provided by the aforementioned provision unit, A detection unit that detects suspicious transactions or expenditures based on the data collected by the aforementioned collection unit, The system includes a notification unit that notifies the user of suspicious transactions or expenditures detected by the detection unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the emotions of elderly individuals and adjust the timing of asset management data collection based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the past asset management history of elderly individuals and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting asset management data, filtering is performed based on the elderly person's current living situation and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is We estimate the emotions of older adults and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting asset management data, the collection of highly relevant data will be prioritized, taking into account the geographical location information of elderly individuals. The system according to feature 1.

7. The aforementioned collection unit is When collecting asset management data, analyze the social media activity of elderly individuals and collect relevant data. The system according to feature 1.

8. The generating unit is The system estimates the emotions of elderly people and adjusts the way fraud scenarios are presented based on those estimated emotions. The system according to feature 1.

9. The generating unit is When generating fraud scenarios, adjust the level of detail in the scenarios based on past fraud cases. The system according to feature 1.

10. The generating unit is When generating fraud scenarios, different generation algorithms are applied depending on the type of fraud. The system according to feature 1.

Images