system

Abstract

The system according to this embodiment aims to securely manage the assets of elderly people and to detect and notify them of suspicious transactions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, a holding unit, and a notification unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects suspicious transactions based on the analysis unit. The holding unit temporarily holds the suspicious transactions detected by the detection unit. The notification unit notifies the person or a designated family member of the transactions held by the holding unit.

Description

Technical Field

[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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, asset management for preventing fraud and malicious business practices targeting the elderly has not been sufficiently carried out, and there is room for improvement. [[ID=3,7]]

[0005] The system according to the embodiment aims to safely manage the assets of the elderly, detect and notify suspicious transactions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a holding unit, and a notification unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects suspicious transactions based on the analysis unit. The holding unit temporarily holds the suspicious transactions detected by the detection unit. The notification unit notifies the person or a designated family member of the transactions held by the holding unit. [Effects of the Invention]

[0007] The system according to this embodiment can securely manage the assets of elderly people and detect and notify them of suspicious transactions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The asset management system according to an embodiment of the present invention is an asset management system primarily targeting the elderly. This asset management system is based on the fact that Japan's aging rate exceeds 28% and is projected to reach approximately 35% by 2050. The proportion of financial assets held by the elderly is on the rise, with those aged 75 and over owning approximately 40% of household financial assets. However, the amount of money lost to scams and fraudulent business practices targeting the elderly amounts to tens of billions of yen annually. For example, large amounts of money are lost to bank transfer fraud. Given this situation, when parents become elderly and try to talk about money to ensure the sound maintenance and preservation of their assets, they often resist, saying things like, "It's not a problem, I can do it myself, leave me alone," making meaningful conversation impossible. As a countermeasure to this, we have devised an autonomous Asset Guardian agent. This agent assesses the safety of daily transactions conducted by the elderly in real time, automatically temporarily suspends any suspicious transactions, and prompts the individual or a designated family member for confirmation. It also analyzes the content of phone calls and door-to-door sales to determine the risk of fraud. Specifically, it consists of the following steps. First, data from multiple financial institutions is centralized, and asset status is monitored in real time. Next, AI is used to detect suspicious transactions and expenditures in real time, and notifications are sent to the individual, pre-designated family members, and trusted third parties. This reduces the risk of fraud and excessive spending, enabling early detection and intervention. Furthermore, an autonomous Asset Guardian agent assesses the safety of daily transactions made by seniors in real time, and automatically puts any suspicious transactions on hold. This agent prompts the individual or designated family members for confirmation, analyzes the content of phone calls and door-to-door sales, and determines the risk of fraud. Generative AI is used to automatically monitor asset transactions, monitoring them in real time and detecting suspicious transactions. Generative AI performs natural language analysis of transaction content to determine the risk of fraud and issues alerts as needed. This system allows for the sound maintenance and preservation of seniors' assets and protects them from fraud and unscrupulous business practices. It also facilitates communication with family members and provides an environment where seniors can live with peace of mind. In summary, the asset management system can soundly maintain and preserve seniors' assets and protect them from fraud and unscrupulous business practices.

[0029] The asset management system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a holding unit, and a notification unit. The collection unit collects data. The collection unit collects, for example, financial transaction data and personal information data. The collection unit can, for example, centrally collect data from multiple financial institutions. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the collection unit is stressed, it can delay the collection timing and collect data when the user is relaxed. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to detect suspicious transactions. The analysis unit can, for example, analyze the content of phone calls and door-to-door sales to determine the risk of fraud. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the analysis unit is tense, it can provide a simple and highly visual presentation. The detection unit detects suspicious transactions by the analysis unit. The detection unit can, for example, perform natural language analysis on transaction details to determine fraud risk. The detection unit can improve detection accuracy by considering the interrelationships between transactions. Furthermore, the detection unit can estimate the user's emotions and adjust detection criteria based on those emotions. For example, if the user is stressed, the detection unit can perform detection with stricter criteria. The holding unit temporarily holds suspicious transactions detected by the detection unit. The holding unit can, for example, automatically hold suspicious transactions. The holding unit can, for example, determine the priority of holding transactions based on their importance. Furthermore, the holding unit can estimate the user's emotions and adjust the holding method based on those emotions. For example, if the user is stressed, the holding unit can simplify the holding method to reduce the user's burden. The notification unit notifies the user or a designated family member of transactions held by the holding unit. The notification unit can, for example, issue alerts as needed. The notification unit can, for example, adjust the level of detail in notifications based on the importance of the transaction.Furthermore, the notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is feeling stressed, the notification unit can provide a simple and easily recognizable notification. This allows the asset management system according to this embodiment to properly maintain and protect the assets of the elderly and safeguard them from fraud and unethical business practices.

[0030] The data collection unit collects data. For example, the data collection unit collects financial transaction data and personal information data. Specifically, the data collection unit can centrally collect transaction data from multiple financial institutions. This makes it possible to comprehensively understand the user's asset situation. The data collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the data collection unit is stressed, it can delay the collection timing and collect data when the user is relaxed. This reduces the burden on the user and enables more accurate data collection. The data collection unit uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine their stress or relaxation state. It also uses facial recognition technology to read emotions from the user's facial expressions and adjust the timing of data collection. Furthermore, the data collection unit can learn the user's behavior patterns and automatically determine the optimal data collection timing. This allows the data collection unit to flexibly collect data according to the user's emotional state, thereby improving the overall accuracy and reliability of the system.

[0031] The analysis department analyzes data collected by the data collection department. For example, the analysis department uses AI to analyze data and detect suspicious transactions. Specifically, the analysis department uses machine learning algorithms to analyze transaction data and detect abnormal patterns and signs of fraud. For example, it learns normal transaction patterns based on past transaction data and identifies suspicious transactions by comparing them with new transaction data. The analysis department can also analyze the content of phone calls and door-to-door sales to determine the risk of fraud. It uses speech recognition technology to transcribe call content into text and natural language processing technology to analyze the content and detect signs of fraud. Furthermore, the analysis department can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visual presentation. This allows the user to easily understand the analysis results and take appropriate action. The analysis department uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine whether they are nervous or relaxed. Furthermore, facial recognition technology is used to read emotions from the user's facial expressions and adjust the way the analysis results are presented. This allows the analysis unit to perform flexible analysis according to the user's emotional state, improving the overall accuracy and reliability of the system.

[0032] The detection unit detects suspicious transactions through the analysis unit. For example, the detection unit can perform natural language analysis on transaction details to determine fraud risk. Specifically, the detection unit transcribes transaction details into text and analyzes them using natural language processing technology to detect signs of fraud. For example, it analyzes keywords and phrases included in transaction details to identify transactions with a high probability of fraud. Furthermore, the detection unit can improve detection accuracy by considering the interrelationships between transactions. For example, it analyzes the relationships between multiple transactions and detects abnormal patterns to determine fraud risk. In addition, the detection unit can estimate the user's emotions and adjust detection criteria based on the estimated emotions. For example, if the user is tense, detection can be performed using stricter criteria. This allows the detection unit to achieve flexible detection in response to the user's emotional state, improving the overall accuracy and reliability of the system. The detection unit uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine their state of tension or relaxation. Furthermore, facial recognition technology is used to read emotions from the user's facial expressions and adjust the detection criteria. This allows the detection unit to achieve flexible detection according to the user's emotional state, improving the overall accuracy and reliability of the system.

[0033] The holding unit temporarily holds suspicious transactions detected by the detection unit. The holding unit can, for example, automatically hold suspicious transactions. Specifically, the holding unit automatically identifies detected suspicious transactions and immediately processes them for holding. This makes it possible to respond quickly before fraudulent transactions are executed. The holding unit can, for example, determine the priority of holding based on the importance of the transaction. For example, high-value transactions or transactions involving important assets are given priority for holding and detailed verification. The holding unit can also estimate the user's emotions and adjust the holding method based on the estimated user emotions. For example, if the user is nervous, the holding method can be simplified to reduce the user's burden. This allows the user to use the system with peace of mind. The holding unit estimates the user's emotions using AI. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine whether they are nervous or relaxed. It also uses facial recognition technology to read emotions from the user's facial expressions and adjust the holding method. This allows the hold function to perform flexible hold processing according to the user's emotional state, improving the overall accuracy and reliability of the system.

[0034] The notification unit notifies the user or a designated family member of transactions that have been put on hold by the holding unit. The notification unit can, for example, issue alerts as needed. Specifically, the notification unit notifies the user of detailed information about the put-on transaction and requests confirmation. For example, it sends a notification containing information such as the transaction details, amount, and date and time, allowing the user to verify the legitimacy of the transaction. The notification unit can, for example, adjust the level of detail of the notification based on the importance of the transaction. For example, for high-value transactions or transactions involving important assets, it provides detailed information to allow the user to respond quickly. The notification unit can also estimate the user's emotions and adjust the way the notification is presented based on the estimated emotions of the user. For example, if the user is stressed, it can provide a simple and highly visible presentation. This allows the user to easily understand the notification and take appropriate action. The notification unit uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and typing speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine whether they are stressed or relaxed. Furthermore, facial recognition technology is used to read the user's emotions from their facial expressions and adjust the way notifications are displayed. This allows the notification unit to provide flexible notifications that respond to the user's emotional state, improving the overall accuracy and reliability of the system.

[0035] The analysis department can analyze the content of telephone or door-to-door sales to determine the risk of fraud. For example, the analysis department can analyze the content of telephone conversations or the details of products sold through door-to-door sales. For example, the analysis department can determine the risk of fraud by comparing it with past fraud cases or by using a risk score calculation method. In this way, the risk of fraud can be determined by analyzing the content of telephone or door-to-door sales. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input the content of a telephone conversation into a generative AI, which can then analyze the conversation content to determine the risk of fraud.

[0036] The notification unit can issue alerts as needed. For example, it can issue alerts when a certain risk score is exceeded or when a specific transaction occurs. The notification unit can issue alerts via email or telephone, for example. This allows for a quick response by issuing alerts as needed. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the risk score into the AI, which can then determine whether or not to issue an alert.

[0037] The data collection unit can centralize data from multiple financial institutions. For example, the data collection unit can collect and centralize data from banks, securities companies, insurance companies, etc. The data collection unit can obtain and centralize data from each financial institution using APIs, for example. This allows for unified management of asset status by centralizing data from multiple financial institutions. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from each financial institution into AI, and the AI ​​can centralize the data.

[0038] The detection unit can perform natural language analysis on the transaction details to determine the risk of fraud. The detection unit can analyze the transaction details using natural language analysis techniques such as morphological analysis, grammatical analysis, and semantic analysis. The detection unit can determine the risk of fraud by comparing the transaction details with past fraud cases or by using a risk score calculation method. In this way, the risk of fraud can be determined by performing natural language analysis on the transaction details. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the detection unit can input the transaction details into a generative AI, which can then perform natural language analysis to determine the risk of fraud.

[0039] The holding unit can automatically temporarily hold suspicious transactions. For example, it can automatically hold transactions if a certain risk score is exceeded or if a specific transaction occurs. The holding unit can, for example, set the length of the holding period and the processing method during the holding period. This allows for risk reduction by automatically temporarily holding suspicious transactions. Some or all of the above-described processing in the holding unit may be performed using AI, or not. For example, the holding unit can input the risk score into the AI, which can then automatically determine whether to temporarily hold the transaction.

[0040] The data collection unit can analyze the user's past transaction history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the user's frequently executed past transactions. For example, the data collection unit can propose an efficient data collection method based on the user's past transaction history. For example, the data collection unit can analyze the user's transaction patterns and select the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past transaction 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 user's transaction history data into AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant data by considering the user's current lifestyle. For example, the data collection unit can filter and collect necessary data based on the user's areas of interest. For example, the data collection unit can analyze the user's lifestyle and areas of interest and collect the most relevant data. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user lifestyle data into AI, which can then filter and collect highly relevant data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can filter and collect necessary data by considering the user's geographical location information. For example, the data collection unit can analyze the user's location information and collect the most suitable data. This allows for the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into AI, which can then filter and collect highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's social media activity. For example, the data collection unit can analyze the user's social media activity and filter and collect the necessary data. For example, the data collection unit can collect the optimal data by considering the user's social media activity. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then filter and collect highly relevant data.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of each transaction. For example, the analysis unit can determine the importance of a transaction based on criteria such as transaction amount, transaction frequency, and trading partner. For example, the analysis unit can perform a detailed analysis on high-importance transactions. For example, it can perform a simplified analysis on low-importance transactions. The analysis unit can select the optimal analysis method based on the importance of each transaction. This allows for efficient analysis by adjusting the level of detail based on the importance of each transaction. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input transaction data into AI, which can then determine the importance of each transaction and adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the transaction category during analysis. For example, the analysis unit can select the optimal analysis algorithm based on transaction categories such as purchase, sale, and remittance. For example, the analysis unit can apply a dedicated analysis algorithm to financial transactions. For example, the analysis unit can apply a different analysis algorithm to purchase transactions. For example, the analysis unit can select the optimal analysis algorithm based on the transaction category. This enables highly accurate analysis by applying different analysis algorithms depending on the transaction category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input transaction data into AI, which can determine the transaction category and apply the optimal analysis algorithm.

[0046] The analysis department can determine the priority of analysis based on the submission date of transactions. For example, the analysis department can determine the submission date of transactions based on criteria such as the transaction occurrence date or submission deadline. For example, the analysis department can prioritize the analysis of recently submitted transactions. For example, the analysis department can postpone the analysis of older transactions. For example, the analysis department can determine the optimal analysis order based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of transactions. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input transaction data into AI, which can determine the submission date of transactions and determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of transactions during the analysis process. The analysis unit can determine the relevance of transactions based on criteria such as the content of the transaction, the trading partner, and the purpose of the transaction. The analysis unit can, for example, prioritize the analysis of highly relevant transactions. The analysis unit can, for example, postpone the analysis of less relevant transactions. The analysis unit can, for example, determine the optimal order of analysis based on the relevance of transactions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of transactions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input transaction data into AI, which can determine the relevance of transactions and adjust the order of analysis.

[0048] The detection unit can improve detection accuracy by considering the interrelationships between transactions during detection. The detection unit can select the optimal detection method based on the interrelationships between transactions, such as consecutive transactions or related transactions. The detection unit can analyze the interrelationships between transactions and prioritize the detection of highly relevant transactions. The detection unit can improve detection accuracy by considering the interrelationships between transactions. As a result, detection accuracy is improved by considering the interrelationships between 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 transaction data into AI, which can determine the interrelationships between transactions and improve detection accuracy.

[0049] The detection unit can perform detection while considering the attribute information of the transaction submitter. The detection unit can select the optimal detection method based on the submitter's attribute information, such as age, gender, and occupation. The detection unit can prioritize the detection of highly relevant transactions based on the submitter's attribute information. The detection unit can improve the accuracy of detection by considering the submitter's attribute information. As a result, the accuracy of detection is improved by considering the attribute information of the transaction submitter. 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 submitter's attribute information into AI, and the AI ​​can perform detection based on the attribute information.

[0050] The detection unit can perform detection while considering the geographical distribution of transactions. The detection unit can select the optimal detection method based on geographical distribution, such as the number of transactions by region or the number of transactions by country. The detection unit can, for example, prioritize the detection of highly relevant transactions based on the geographical distribution of transactions. The detection unit can improve the accuracy of detection by considering the geographical distribution of transactions. As a result, the accuracy of detection is improved by considering the geographical distribution 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 transaction data into AI, which can determine the geographical distribution of transactions and perform detection.

[0051] The detection unit can improve detection accuracy by referring to relevant literature on transactions during detection. The detection unit can select the optimal detection method based on relevant literature such as past research papers and industry reports. The detection unit can prioritize the detection of highly relevant transactions based on relevant literature on transactions. The detection unit can improve detection accuracy by referring to relevant literature on transactions. As a result, detection accuracy is improved by referring to relevant literature on 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 transaction data into AI, and the AI ​​can perform detection by referring to relevant literature.

[0052] The holding unit can determine the priority of holding transactions based on their importance when they are being held. The holding unit can determine the priority of holding transactions based on criteria such as the importance of the transaction or the risk score. For example, the holding unit can prioritize holding transactions with high importance. For example, the holding unit can postpone holding transactions with low importance. For example, the holding unit can determine the optimal holding order based on the importance of the transactions. This allows important transactions to be prioritized for holding by determining the priority of holding transactions based on their importance. Some or all of the above processing in the holding unit may be performed using AI, for example, or without using AI. For example, the holding unit can input transaction data into AI, which can determine the importance of the transactions and determine the priority of holding transactions.

[0053] The holding unit can apply different holding algorithms depending on the transaction category when holding a transaction. The holding unit can select the optimal holding method based on holding algorithms such as risk-based holding and random holding. The holding unit can apply a dedicated holding algorithm to financial transactions, for example. The holding unit can apply a different holding algorithm to purchase transactions, for example. The holding unit can select the optimal holding algorithm based on the transaction category, for example. This enables highly accurate holding by applying different holding algorithms depending on the transaction category. Some or all of the above processing in the holding unit may be performed using AI, for example, or without AI. For example, the holding unit can input transaction data into AI, which can determine the transaction category and apply the optimal holding algorithm.

[0054] The holding unit can determine the priority of holding transactions based on the submission date of the transactions. The holding unit can determine the submission date of a transaction based on criteria such as the transaction occurrence date or submission deadline. The holding unit can, for example, prioritize holding recently submitted transactions. The holding unit can, for example, postpone older transactions. The holding unit can, for example, determine the optimal holding order based on the submission date. This enables efficient holding by determining the priority of holding transactions based on the submission date. Some or all of the above processing in the holding unit may be performed using AI, for example, or not using AI. For example, the holding unit can input transaction data into AI, which can determine the submission date of the transactions and determine the priority of holding.

[0055] The holding unit can adjust the order of holding transactions based on their relevance when they are being held. The holding unit can determine the relevance of transactions based on criteria such as the content of the transaction, the trading partner, and the purpose of the transaction. The holding unit can, for example, prioritize holding transactions that are highly relevant. The holding unit can, for example, postpone holding transactions that are less relevant. The holding unit can, for example, determine the optimal holding order based on the relevance of the transactions. This allows for efficient holding by adjusting the order of holding transactions based on their relevance. Some or all of the above processing in the holding unit may be performed using AI, for example, or not using AI. For example, the holding unit can input transaction data into AI, which can determine the relevance of the transactions and adjust the order of holding.

[0056] The notification unit can adjust the level of detail of notifications based on the importance of the transaction. For example, the notification unit can adjust the level of detail of notifications based on criteria such as the importance of the transaction or the risk score. For example, the notification unit can provide detailed notifications for high-importance transactions. For example, the notification unit can provide simplified notifications for low-importance transactions. For example, the notification unit can select the optimal notification method based on the importance of the transaction. This allows for detailed notifications to be provided for important transactions by adjusting the level of detail of notifications based on the importance of the transaction. 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 transaction data into AI, which can determine the importance of the transaction and adjust the level of detail of the notification.

[0057] The notification unit can apply different notification algorithms depending on the transaction category at the time of notification. The notification unit can select the optimal notification method based on notification algorithms such as risk-based notification and random notification. The notification unit can apply a dedicated notification algorithm to financial transactions, for example. The notification unit can apply a different notification algorithm to purchase transactions, for example. The notification unit can select the optimal notification algorithm based on the transaction category, for example. This enables highly accurate notifications by applying different notification algorithms depending on the transaction category. 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 transaction data into AI, which can determine the transaction category and apply the optimal notification algorithm.

[0058] The notification unit can determine the priority of notifications based on the timing of transaction submissions. The notification unit can determine the timing of transaction submissions based on criteria such as the transaction occurrence date or submission deadline. The notification unit can, for example, prioritize notifications for recently submitted transactions. The notification unit can, for example, postpone notifications for older transactions. The notification unit can, for example, determine the optimal notification order based on the submission timing. This enables efficient notifications by determining the priority of notifications based on the timing of transaction submissions. 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 transaction data into AI, which can determine the timing of transaction submissions and determine the priority of notifications.

[0059] The notification unit can adjust the order of notifications based on the relevance of the transactions. The notification unit can determine the relevance of transactions based on criteria such as the content of the transaction, the trading partner, and the purpose of the transaction. The notification unit can, for example, prioritize notifications for highly relevant transactions. The notification unit can, for example, postpone notifications for less relevant transactions. The notification unit can, for example, determine the optimal order of notifications based on the relevance of the transactions. This enables efficient notifications by adjusting the order of notifications based on the relevance of the transactions. 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 transaction data into AI, which can determine the relevance of the transactions and adjust the order of 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 an education section. This section provides educational content to improve financial literacy among seniors. For example, it could offer video tutorials on fraud tactics and prevention. It could also provide online courses on basic financial product knowledge and trading risks. Furthermore, it could regularly host webinars, providing lectures and Q&A sessions led by experts. This would allow seniors to improve their financial literacy and manage their assets more securely.

[0062] The asset management system can also include a health monitoring unit. This unit monitors the health status of elderly individuals in real time and notifies them if any abnormalities are detected. For example, it can measure vital signs such as heart rate, blood pressure, and body temperature, and notify the individual or their family if abnormal values ​​are detected. Furthermore, the unit can provide regular health checklists to support elderly individuals in self-managing their health. In addition, the unit can collaborate with medical institutions and encourage them to seek medical attention when necessary. This allows for constant monitoring of the elderly individual's health status and enables early intervention.

[0063] The asset management system can also include a communication section. This section provides functions to facilitate communication between the elderly and their families and friends. For example, it could offer video call and chat functions, making it easy for the elderly to stay in touch with family and friends. It could also provide a regular messaging function to help prevent the elderly from becoming isolated. Furthermore, it could offer a shared calendar function with family and friends, making it easier to coordinate and share schedules. This allows the elderly to maintain social connections and live with peace of mind.

[0064] The asset management system can also include an entertainment section. This section provides content that seniors can enjoy. For example, it could offer movies, music, games, and other content to help seniors relax and have fun. It could also suggest content tailored to their hobbies and interests, helping seniors discover new interests. Furthermore, it could offer online events and community activities, providing seniors with opportunities to interact with others. This allows seniors to spend their time more fulfilling and improve their quality of life.

[0065] The asset management system can also include a reminder function. This reminder function helps seniors remember important appointments and tasks. For example, it can set reminders for medication times, medical appointments, and payment deadlines. It can also provide notifications via voice and text messages to help seniors remember important actions. Furthermore, the reminder function can collaborate with family members and caregivers to share important appointments and tasks. This allows seniors to remember important things in their daily lives and live with peace of mind.

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

[0067] Step 1: The collection unit collects data. The collection unit collects data such as financial transaction data and personal information data. The collection unit can centrally collect data from multiple financial institutions. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Step 2: The analysis department analyzes the data collected by the collection department. The analysis department uses AI to analyze the data and detect suspicious transactions. The analysis department can analyze the content of phone calls and door-to-door sales to determine the risk of fraud. The analysis department can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. Step 3: The detection unit detects suspicious transactions through the analysis unit. The detection unit can perform natural language analysis on the transaction details and determine the risk of fraud. The detection unit can improve the accuracy of detection by considering the interrelationships between transactions. In addition, the detection unit can estimate the user's sentiment and adjust the detection criteria based on the estimated user sentiment. Step 4: The holding unit temporarily holds suspicious transactions detected by the detection unit. The holding unit can automatically hold suspicious transactions. The holding unit can determine the priority of holding transactions based on their importance. The holding unit can also estimate the user's sentiment and adjust the holding method based on the estimated user sentiment. Step 5: The notification unit notifies the user or a designated family member of the transaction that has been put on hold by the holding unit. The notification unit can issue alerts as needed. The notification unit can adjust the level of detail of the notification based on the importance of the transaction. The notification unit can also estimate the user's sentiment and adjust the way the notification is expressed based on the estimated user sentiment.

[0068] (Example of form 2) The asset management system according to an embodiment of the present invention is an asset management system primarily targeting the elderly. This asset management system is based on the fact that Japan's aging rate exceeds 28% and is projected to reach approximately 35% by 2050. The proportion of financial assets held by the elderly is on the rise, with those aged 75 and over owning approximately 40% of household financial assets. However, the amount of money lost to scams and fraudulent business practices targeting the elderly amounts to tens of billions of yen annually. For example, large amounts of money are lost to bank transfer fraud. Given this situation, when parents become elderly and try to talk about money to ensure the sound maintenance and preservation of their assets, they often resist, saying things like, "It's not a problem, I can do it myself, leave me alone," making meaningful conversation impossible. As a countermeasure to this, we have devised an autonomous Asset Guardian agent. This agent assesses the safety of daily transactions conducted by the elderly in real time, automatically temporarily suspends any suspicious transactions, and prompts the individual or a designated family member for confirmation. It also analyzes the content of phone calls and door-to-door sales to determine the risk of fraud. Specifically, it consists of the following steps. First, data from multiple financial institutions is centralized, and asset status is monitored in real time. Next, AI is used to detect suspicious transactions and expenditures in real time, and notifications are sent to the individual, pre-designated family members, and trusted third parties. This reduces the risk of fraud and excessive spending, enabling early detection and intervention. Furthermore, an autonomous Asset Guardian agent assesses the safety of daily transactions made by seniors in real time, and automatically puts any suspicious transactions on hold. This agent prompts the individual or designated family members for confirmation, analyzes the content of phone calls and door-to-door sales, and determines the risk of fraud. Generative AI is used to automatically monitor asset transactions, monitoring them in real time and detecting suspicious transactions. Generative AI performs natural language analysis of transaction content to determine the risk of fraud and issues alerts as needed. This system allows for the sound maintenance and preservation of seniors' assets and protects them from fraud and unscrupulous business practices. It also facilitates communication with family members and provides an environment where seniors can live with peace of mind. In summary, the asset management system can soundly maintain and preserve seniors' assets and protect them from fraud and unscrupulous business practices.

[0069] The asset management system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a holding unit, and a notification unit. The collection unit collects data. The collection unit collects, for example, financial transaction data and personal information data. The collection unit can, for example, centrally collect data from multiple financial institutions. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the collection unit is stressed, it can delay the collection timing and collect data when the user is relaxed. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to detect suspicious transactions. The analysis unit can, for example, analyze the content of phone calls and door-to-door sales to determine the risk of fraud. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the analysis unit is tense, it can provide a simple and highly visual presentation. The detection unit detects suspicious transactions by the analysis unit. The detection unit can, for example, perform natural language analysis on transaction details to determine fraud risk. The detection unit can improve detection accuracy by considering the interrelationships between transactions. Furthermore, the detection unit can estimate the user's emotions and adjust detection criteria based on those emotions. For example, if the user is stressed, the detection unit can perform detection with stricter criteria. The holding unit temporarily holds suspicious transactions detected by the detection unit. The holding unit can, for example, automatically hold suspicious transactions. The holding unit can, for example, determine the priority of holding transactions based on their importance. Furthermore, the holding unit can estimate the user's emotions and adjust the holding method based on those emotions. For example, if the user is stressed, the holding unit can simplify the holding method to reduce the user's burden. The notification unit notifies the user or a designated family member of transactions held by the holding unit. The notification unit can, for example, issue alerts as needed. The notification unit can, for example, adjust the level of detail in notifications based on the importance of the transaction.Furthermore, the notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is feeling stressed, the notification unit can provide a simple and easily recognizable notification. This allows the asset management system according to this embodiment to properly maintain and protect the assets of the elderly and safeguard them from fraud and unethical business practices.

[0070] The data collection unit collects data. For example, the data collection unit collects financial transaction data and personal information data. Specifically, the data collection unit can centrally collect transaction data from multiple financial institutions. This makes it possible to comprehensively understand the user's asset situation. The data collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the data collection unit is stressed, it can delay the collection timing and collect data when the user is relaxed. This reduces the burden on the user and enables more accurate data collection. The data collection unit uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine their stress or relaxation state. It also uses facial recognition technology to read emotions from the user's facial expressions and adjust the timing of data collection. Furthermore, the data collection unit can learn the user's behavior patterns and automatically determine the optimal data collection timing. This allows the data collection unit to flexibly collect data according to the user's emotional state, thereby improving the overall accuracy and reliability of the system.

[0071] The analysis department analyzes data collected by the data collection department. For example, the analysis department uses AI to analyze data and detect suspicious transactions. Specifically, the analysis department uses machine learning algorithms to analyze transaction data and detect abnormal patterns and signs of fraud. For example, it learns normal transaction patterns based on past transaction data and identifies suspicious transactions by comparing them with new transaction data. The analysis department can also analyze the content of phone calls and door-to-door sales to determine the risk of fraud. It uses speech recognition technology to transcribe call content into text and natural language processing technology to analyze the content and detect signs of fraud. Furthermore, the analysis department can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visual presentation. This allows the user to easily understand the analysis results and take appropriate action. The analysis department uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine whether they are nervous or relaxed. Furthermore, facial recognition technology is used to read emotions from the user's facial expressions and adjust the way the analysis results are presented. This allows the analysis unit to perform flexible analysis according to the user's emotional state, improving the overall accuracy and reliability of the system.

[0072] The detection unit detects suspicious transactions through the analysis unit. For example, the detection unit can perform natural language analysis on transaction details to determine fraud risk. Specifically, the detection unit transcribes transaction details into text and analyzes them using natural language processing technology to detect signs of fraud. For example, it analyzes keywords and phrases included in transaction details to identify transactions with a high probability of fraud. Furthermore, the detection unit can improve detection accuracy by considering the interrelationships between transactions. For example, it analyzes the relationships between multiple transactions and detects abnormal patterns to determine fraud risk. In addition, the detection unit can estimate the user's emotions and adjust detection criteria based on the estimated emotions. For example, if the user is tense, detection can be performed using stricter criteria. This allows the detection unit to achieve flexible detection in response to the user's emotional state, improving the overall accuracy and reliability of the system. The detection unit uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine their state of tension or relaxation. Furthermore, facial recognition technology is used to read emotions from the user's facial expressions and adjust the detection criteria. This allows the detection unit to achieve flexible detection according to the user's emotional state, improving the overall accuracy and reliability of the system.

[0073] The holding unit temporarily holds suspicious transactions detected by the detection unit. The holding unit can, for example, automatically hold suspicious transactions. Specifically, the holding unit automatically identifies detected suspicious transactions and immediately processes them for holding. This makes it possible to respond quickly before fraudulent transactions are executed. The holding unit can, for example, determine the priority of holding based on the importance of the transaction. For example, high-value transactions or transactions involving important assets are given priority for holding and detailed verification. The holding unit can also estimate the user's emotions and adjust the holding method based on the estimated user emotions. For example, if the user is nervous, the holding method can be simplified to reduce the user's burden. This allows the user to use the system with peace of mind. The holding unit estimates the user's emotions using AI. Specifically, it analyzes data such as the user's voice tone, facial expressions, and input speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine whether they are nervous or relaxed. It also uses facial recognition technology to read emotions from the user's facial expressions and adjust the holding method. This allows the hold function to perform flexible hold processing according to the user's emotional state, improving the overall accuracy and reliability of the system.

[0074] The notification unit notifies the user or a designated family member of transactions that have been put on hold by the holding unit. The notification unit can, for example, issue alerts as needed. Specifically, the notification unit notifies the user of detailed information about the put-on transaction and requests confirmation. For example, it sends a notification containing information such as the transaction details, amount, and date and time, allowing the user to verify the legitimacy of the transaction. The notification unit can, for example, adjust the level of detail of the notification based on the importance of the transaction. For example, for high-value transactions or transactions involving important assets, it provides detailed information to allow the user to respond quickly. The notification unit can also estimate the user's emotions and adjust the way the notification is presented based on the estimated emotions of the user. For example, if the user is stressed, it can provide a simple and highly visible presentation. This allows the user to easily understand the notification and take appropriate action. The notification unit uses AI to estimate the user's emotions. Specifically, it analyzes data such as the user's voice tone, facial expressions, and typing speed to estimate the user's emotional state. For example, it uses speech recognition technology to detect changes in the user's voice tone and speaking style to determine whether they are stressed or relaxed. Furthermore, facial recognition technology is used to read the user's emotions from their facial expressions and adjust the way notifications are displayed. This allows the notification unit to provide flexible notifications that respond to the user's emotional state, improving the overall accuracy and reliability of the system.

[0075] The analysis department can analyze the content of telephone or door-to-door sales to determine the risk of fraud. For example, the analysis department can analyze the content of telephone conversations or the details of products sold through door-to-door sales. For example, the analysis department can determine the risk of fraud by comparing it with past fraud cases or by using a risk score calculation method. In this way, the risk of fraud can be determined by analyzing the content of telephone or door-to-door sales. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input the content of a telephone conversation into a generative AI, which can then analyze the conversation content to determine the risk of fraud.

[0076] The notification unit can issue alerts as needed. For example, it can issue alerts when a certain risk score is exceeded or when a specific transaction occurs. The notification unit can issue alerts via email or telephone, for example. This allows for a quick response by issuing alerts as needed. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the risk score into the AI, which can then determine whether or not to issue an alert.

[0077] The data collection unit can centralize data from multiple financial institutions. For example, the data collection unit can collect and centralize data from banks, securities companies, insurance companies, etc. The data collection unit can obtain and centralize data from each financial institution using APIs, for example. This allows for unified management of asset status by centralizing data from multiple financial institutions. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from each financial institution into AI, and the AI ​​can centralize the data.

[0078] The detection unit can perform natural language analysis on the transaction details to determine the risk of fraud. The detection unit can analyze the transaction details using natural language analysis techniques such as morphological analysis, grammatical analysis, and semantic analysis. The detection unit can determine the risk of fraud by comparing the transaction details with past fraud cases or by using a risk score calculation method. In this way, the risk of fraud can be determined by performing natural language analysis on the transaction details. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the detection unit can input the transaction details into a generative AI, which can then perform natural language analysis to determine the risk of fraud.

[0079] The holding unit can automatically temporarily hold suspicious transactions. For example, it can automatically hold transactions if a certain risk score is exceeded or if a specific transaction occurs. The holding unit can, for example, set the length of the holding period and the processing method during the holding period. This allows for risk reduction by automatically temporarily holding suspicious transactions. Some or all of the above-described processing in the holding unit may be performed using AI, or not. For example, the holding unit can input the risk score into the AI, which can then automatically determine whether to temporarily hold the transaction.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The data collection unit can estimate the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can advance the collection timing to collect data efficiently. For example, if the user is in a hurry, the data collection unit can adjust the collection timing to reduce the user's burden. In this way, the user's burden can be reduced by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the collection timing.

[0081] The data collection unit can analyze the user's past transaction history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the user's frequently executed past transactions. For example, the data collection unit can propose an efficient data collection method based on the user's past transaction history. For example, the data collection unit can analyze the user's transaction patterns and select the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past transaction 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 user's transaction history data into AI, which can then select the optimal data collection method.

[0082] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant data by considering the user's current lifestyle. For example, the data collection unit can filter and collect necessary data based on the user's areas of interest. For example, the data collection unit can analyze the user's lifestyle and areas of interest and collect the most relevant data. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user lifestyle data into AI, which can then filter and collect highly relevant data.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. The data collection unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the data collection unit can postpone collecting less important data and prioritize collecting more important data. For example, if the user is relaxed, the data collection unit can collect all data equally. For example, if the user is in a hurry, the data collection unit can prioritize collecting more important data. In this way, important data can be collected preferentially by determining the priority of data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of data to collect.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can filter and collect necessary data by considering the user's geographical location information. For example, the data collection unit can analyze the user's location information and collect the most suitable data. This allows for the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into AI, which can then filter and collect highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's social media activity. For example, the data collection unit can analyze the user's social media activity and filter and collect the necessary data. For example, the data collection unit can collect the optimal data by considering the user's social media activity. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then filter and collect highly relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. For example, if the user is relaxed, the analysis unit can provide a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a presentation that gets straight to the point. By adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of its analysis based on the importance of each transaction. For example, the analysis unit can determine the importance of a transaction based on criteria such as transaction amount, transaction frequency, and trading partner. For example, the analysis unit can perform a detailed analysis on high-importance transactions. For example, it can perform a simplified analysis on low-importance transactions. The analysis unit can select the optimal analysis method based on the importance of each transaction. This allows for efficient analysis by adjusting the level of detail based on the importance of each transaction. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input transaction data into AI, which can then determine the importance of each transaction and adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the transaction category during analysis. For example, the analysis unit can select the optimal analysis algorithm based on transaction categories such as purchase, sale, and remittance. For example, the analysis unit can apply a dedicated analysis algorithm to financial transactions. For example, the analysis unit can apply a different analysis algorithm to purchase transactions. For example, the analysis unit can select the optimal analysis algorithm based on the transaction category. This enables highly accurate analysis by applying different analysis algorithms depending on the transaction category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input transaction data into AI, which can determine the transaction category and apply the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. For example, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the length of the analysis.

[0090] The analysis department can determine the priority of analysis based on the submission date of transactions. For example, the analysis department can determine the submission date of transactions based on criteria such as the transaction occurrence date or submission deadline. For example, the analysis department can prioritize the analysis of recently submitted transactions. For example, the analysis department can postpone the analysis of older transactions. For example, the analysis department can determine the optimal analysis order based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of transactions. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input transaction data into AI, which can determine the submission date of transactions and determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of transactions during the analysis process. The analysis unit can determine the relevance of transactions based on criteria such as the content of the transaction, the trading partner, and the purpose of the transaction. The analysis unit can, for example, prioritize the analysis of highly relevant transactions. The analysis unit can, for example, postpone the analysis of less relevant transactions. The analysis unit can, for example, determine the optimal order of analysis based on the relevance of transactions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of transactions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input transaction data into AI, which can determine the relevance of transactions and adjust the order of analysis.

[0092] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. The detection unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, the detection unit can perform detection with strict criteria when the user is tense. For example, the detection unit can perform detection with flexible criteria when the user is relaxed. For example, the detection unit can perform detection quickly when the user is in a hurry. By adjusting the detection criteria based on the user's emotions, appropriate detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the detection criteria.

[0093] The detection unit can improve detection accuracy by considering the interrelationships between transactions during detection. The detection unit can select the optimal detection method based on the interrelationships between transactions, such as consecutive transactions or related transactions. The detection unit can analyze the interrelationships between transactions and prioritize the detection of highly relevant transactions. The detection unit can improve detection accuracy by considering the interrelationships between transactions. As a result, detection accuracy is improved by considering the interrelationships between 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 transaction data into AI, which can determine the interrelationships between transactions and improve detection accuracy.

[0094] The detection unit can perform detection while considering the attribute information of the transaction submitter. The detection unit can select the optimal detection method based on the submitter's attribute information, such as age, gender, and occupation. The detection unit can prioritize the detection of highly relevant transactions based on the submitter's attribute information. The detection unit can improve the accuracy of detection by considering the submitter's attribute information. As a result, the accuracy of detection is improved by considering the attribute information of the transaction submitter. 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 submitter's attribute information into AI, and the AI ​​can perform detection based on the attribute information.

[0095] The detection unit can estimate the user's emotions and adjust the order in which the detection results are displayed based on the estimated emotions. The detection unit can estimate the user's emotions using techniques such as facial recognition, speech analysis, and text analysis. For example, if the user is tense, the detection unit can prioritize displaying results of high importance. For example, if the user is relaxed, the detection unit can display all results equally. For example, if the user is in a hurry, the detection unit can prioritize displaying results of high importance. This allows the system to provide results that are easy for the user to understand by adjusting the order in which the detection results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI or not using AI. For example, the detection unit can input the user's facial expression data into the generative AI, which will estimate the emotions and adjust the order in which the detection results are displayed.

[0096] The detection unit can perform detection while considering the geographical distribution of transactions. The detection unit can select the optimal detection method based on geographical distribution, such as the number of transactions by region or the number of transactions by country. The detection unit can, for example, prioritize the detection of highly relevant transactions based on the geographical distribution of transactions. The detection unit can improve the accuracy of detection by considering the geographical distribution of transactions. As a result, the accuracy of detection is improved by considering the geographical distribution 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 transaction data into AI, which can determine the geographical distribution of transactions and perform detection.

[0097] The detection unit can improve detection accuracy by referring to relevant literature on transactions during detection. The detection unit can select the optimal detection method based on relevant literature such as past research papers and industry reports. The detection unit can prioritize the detection of highly relevant transactions based on relevant literature on transactions. The detection unit can improve detection accuracy by referring to relevant literature on transactions. As a result, detection accuracy is improved by referring to relevant literature on 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 transaction data into AI, and the AI ​​can perform detection by referring to relevant literature.

[0098] The hold function can estimate the user's emotions and adjust the hold method based on the estimated emotions. The hold function can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is nervous, the hold function can simplify the hold method to reduce the user's burden. For example, if the user is relaxed, the hold function can provide a detailed hold method. For example, if the user is in a hurry, the hold function can perform a hold quickly. In this way, by adjusting the hold method based on the user's emotions, an appropriate hold method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the hold function may be performed using AI, for example, or without AI. For example, the hold function can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the hold method.

[0099] The holding unit can determine the priority of holding transactions based on their importance when they are being held. The holding unit can determine the priority of holding transactions based on criteria such as the importance of the transaction or the risk score. For example, the holding unit can prioritize holding transactions with high importance. For example, the holding unit can postpone holding transactions with low importance. For example, the holding unit can determine the optimal holding order based on the importance of the transactions. This allows important transactions to be prioritized for holding by determining the priority of holding transactions based on their importance. Some or all of the above processing in the holding unit may be performed using AI, for example, or without using AI. For example, the holding unit can input transaction data into AI, which can determine the importance of the transactions and determine the priority of holding transactions.

[0100] The holding unit can apply different holding algorithms depending on the transaction category when holding a transaction. The holding unit can select the optimal holding method based on holding algorithms such as risk-based holding and random holding. The holding unit can apply a dedicated holding algorithm to financial transactions, for example. The holding unit can apply a different holding algorithm to purchase transactions, for example. The holding unit can select the optimal holding algorithm based on the transaction category, for example. This enables highly accurate holding by applying different holding algorithms depending on the transaction category. Some or all of the above processing in the holding unit may be performed using AI, for example, or without AI. For example, the holding unit can input transaction data into AI, which can determine the transaction category and apply the optimal holding algorithm.

[0101] The hold function can estimate the user's emotions and adjust the hold duration based on the estimated emotions. The hold function can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, the hold function can set a short hold time if the user is in a hurry. For example, the hold function can set a longer hold time if the user is relaxed. For example, the hold function can set an appropriate hold time if the user is excited. This allows the system to provide the user with an appropriate hold time by adjusting the hold duration based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the hold function may be performed using AI or not using AI. For example, the hold function can input the user's facial expression data into the generative AI, which can then estimate the emotions and adjust the hold duration.

[0102] The holding unit can determine the priority of holding transactions based on the submission date of the transactions. The holding unit can determine the submission date of a transaction based on criteria such as the transaction occurrence date or submission deadline. The holding unit can, for example, prioritize holding recently submitted transactions. The holding unit can, for example, postpone older transactions. The holding unit can, for example, determine the optimal holding order based on the submission date. This enables efficient holding by determining the priority of holding transactions based on the submission date. Some or all of the above processing in the holding unit may be performed using AI, for example, or not using AI. For example, the holding unit can input transaction data into AI, which can determine the submission date of the transactions and determine the priority of holding.

[0103] The holding unit can adjust the order of holding transactions based on their relevance when they are being held. The holding unit can determine the relevance of transactions based on criteria such as the content of the transaction, the trading partner, and the purpose of the transaction. The holding unit can, for example, prioritize holding transactions that are highly relevant. The holding unit can, for example, postpone holding transactions that are less relevant. The holding unit can, for example, determine the optimal holding order based on the relevance of the transactions. This allows for efficient holding by adjusting the order of holding transactions based on their relevance. Some or all of the above processing in the holding unit may be performed using AI, for example, or not using AI. For example, the holding unit can input transaction data into AI, which can determine the relevance of the transactions and adjust the order of holding.

[0104] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. The notification unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is tense, the notification unit can provide a simple and easily visible notification. If the user is relaxed, the notification unit can provide a notification that includes detailed information. If the user is in a hurry, the notification unit can provide a notification that gets straight to the point. This allows for easy-to-understand notifications by adjusting the notification presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input user facial data into a generative AI, which can then estimate the emotions and adjust the notification presentation.

[0105] The notification unit can adjust the level of detail of notifications based on the importance of the transaction. For example, the notification unit can adjust the level of detail of notifications based on criteria such as the importance of the transaction or the risk score. For example, the notification unit can provide detailed notifications for high-importance transactions. For example, the notification unit can provide simplified notifications for low-importance transactions. For example, the notification unit can select the optimal notification method based on the importance of the transaction. This allows for detailed notifications to be provided for important transactions by adjusting the level of detail of notifications based on the importance of the transaction. 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 transaction data into AI, which can determine the importance of the transaction and adjust the level of detail of the notification.

[0106] The notification unit can apply different notification algorithms depending on the transaction category at the time of notification. The notification unit can select the optimal notification method based on notification algorithms such as risk-based notification and random notification. The notification unit can apply a dedicated notification algorithm to financial transactions, for example. The notification unit can apply a different notification algorithm to purchase transactions, for example. The notification unit can select the optimal notification algorithm based on the transaction category, for example. This enables highly accurate notifications by applying different notification algorithms depending on the transaction category. 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 transaction data into AI, which can determine the transaction category and apply the optimal notification algorithm.

[0107] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated emotions. The notification unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the notification unit can send a short, to-the-point notification. For example, if the user is relaxed, the notification unit can send a longer notification with detailed explanations. For example, if the user is excited, the notification unit can send a notification with visually stimulating effects. By adjusting the length of the notification based on the user's emotions, the notification unit can provide the user with an appropriate notification. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the user's facial expression data into the generative AI, which can estimate the emotions and adjust the length of the notification.

[0108] The notification unit can determine the priority of notifications based on the timing of transaction submissions. The notification unit can determine the timing of transaction submissions based on criteria such as the transaction occurrence date or submission deadline. The notification unit can, for example, prioritize notifications for recently submitted transactions. The notification unit can, for example, postpone notifications for older transactions. The notification unit can, for example, determine the optimal notification order based on the submission timing. This enables efficient notifications by determining the priority of notifications based on the timing of transaction submissions. 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 transaction data into AI, which can determine the timing of transaction submissions and determine the priority of notifications.

[0109] The notification unit can adjust the order of notifications based on the relevance of the transactions. The notification unit can determine the relevance of transactions based on criteria such as the content of the transaction, the trading partner, and the purpose of the transaction. The notification unit can, for example, prioritize notifications for highly relevant transactions. The notification unit can, for example, postpone notifications for less relevant transactions. The notification unit can, for example, determine the optimal order of notifications based on the relevance of the transactions. This enables efficient notifications by adjusting the order of notifications based on the relevance of the transactions. 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 transaction data into AI, which can determine the relevance of the transactions and adjust the order of notifications.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The asset management system can also include an education section. This section provides educational content to improve financial literacy among seniors. For example, it could offer video tutorials on fraud tactics and prevention. It could also provide online courses on basic financial product knowledge and trading risks. Furthermore, it could regularly host webinars, providing lectures and Q&A sessions led by experts. This would allow seniors to improve their financial literacy and manage their assets more securely.

[0112] The asset management system can also include a health monitoring unit. This unit monitors the health status of elderly individuals in real time and notifies them if any abnormalities are detected. For example, it can measure vital signs such as heart rate, blood pressure, and body temperature, and notify the individual or their family if abnormal values ​​are detected. Furthermore, the unit can provide regular health checklists to support elderly individuals in self-managing their health. In addition, the unit can collaborate with medical institutions and encourage them to seek medical attention when necessary. This allows for constant monitoring of the elderly individual's health status and enables early intervention.

[0113] The asset management system can also include a communication section. This section provides functions to facilitate communication between the elderly and their families and friends. For example, it could offer video call and chat functions, making it easy for the elderly to stay in touch with family and friends. It could also provide a regular messaging function to help prevent the elderly from becoming isolated. Furthermore, it could offer a shared calendar function with family and friends, making it easier to coordinate and share schedules. This allows the elderly to maintain social connections and live with peace of mind.

[0114] The asset management system can also include an entertainment section. This section provides content that seniors can enjoy. For example, it could offer movies, music, games, and other content to help seniors relax and have fun. It could also suggest content tailored to their hobbies and interests, helping seniors discover new interests. Furthermore, it could offer online events and community activities, providing seniors with opportunities to interact with others. This allows seniors to spend their time more fulfilling and improve their quality of life.

[0115] The asset management system can also include a reminder function. This reminder function helps seniors remember important appointments and tasks. For example, it can set reminders for medication times, medical appointments, and payment deadlines. It can also provide notifications via voice and text messages to help seniors remember important actions. Furthermore, the reminder function can collaborate with family members and caregivers to share important appointments and tasks. This allows seniors to remember important things in their daily lives and live with peace of mind.

[0116] The asset management system can also be equipped with an emotion analysis unit. This unit analyzes the user's emotions in real time and provides appropriate responses. For example, it can estimate emotions from the user's facial expressions, voice, and text, and provide relaxing content if the user is feeling stressed or anxious. It can also suggest more enjoyable content if the user is happy. Furthermore, the emotion analysis unit can adjust the timing of notifications and reminders based on the user's emotions, reducing the user's burden. This enables appropriate responses tailored to the user's emotions, supporting a more comfortable life.

[0117] The asset management system can also be equipped with an emotional feedback unit. This unit adjusts the system's operation based on the user's emotions. For example, if the user is stressed, the emotional feedback unit can simplify system operation, reducing the user's burden. Conversely, if the user is relaxed, the emotional feedback unit can provide detailed information, allowing the user to understand the system more deeply. Furthermore, the emotional feedback unit can adjust the content of notifications and reminders according to the user's emotions, providing the user with the most relevant information. This enables flexible responses based on the user's emotions, supporting a more comfortable system experience.

[0118] The asset management system can also be equipped with an emotion prediction unit. This unit predicts future emotions based on the user's past behavior and emotional data. For example, it can predict what emotions a user will feel in a specific situation and prepare appropriate responses in advance. It can also predict situations where a user is likely to feel stressed and provide relaxing content beforehand. Furthermore, it can predict changes in the user's emotions and provide notifications and reminders at the appropriate time. This enables proactive responses to the user's emotions, supporting a more comfortable life.

[0119] The asset management system can also include an emotional history section. This section records and analyzes the user's emotional history. For example, it can record what emotions the user has experienced in the past and analyze emotional patterns. Furthermore, it can visualize changes in the user's emotions using graphs and charts, allowing the user to understand their own emotional tendencies. In addition, based on the user's emotional history, the emotional history section can suggest appropriate countermeasures. This enables users to understand their own emotional tendencies and manage their emotions better.

[0120] The asset management system can also include an emotional support section. This emotional support section provides support tailored to the user's emotions. For example, if the user is feeling stressed, it can offer relaxing music or meditation guides. If the user is feeling anxious, it can provide reassuring messages and support information. Furthermore, if the user is happy, it can suggest more enjoyable activities and content. This ensures that appropriate support is provided according to the user's emotions, contributing to a more comfortable life.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects data. The collection unit collects data such as financial transaction data and personal information data. The collection unit can centrally collect data from multiple financial institutions. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Step 2: The analysis department analyzes the data collected by the collection department. The analysis department uses AI to analyze the data and detect suspicious transactions. The analysis department can analyze the content of phone calls and door-to-door sales to determine the risk of fraud. The analysis department can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. Step 3: The detection unit detects suspicious transactions through the analysis unit. The detection unit can perform natural language analysis on the transaction details and determine the risk of fraud. The detection unit can improve the accuracy of detection by considering the interrelationships between transactions. In addition, the detection unit can estimate the user's sentiment and adjust the detection criteria based on the estimated user sentiment. Step 4: The holding unit temporarily holds suspicious transactions detected by the detection unit. The holding unit can automatically hold suspicious transactions. The holding unit can determine the priority of holding transactions based on their importance. The holding unit can also estimate the user's sentiment and adjust the holding method based on the estimated user sentiment. Step 5: The notification unit notifies the user or a designated family member of the transaction that has been put on hold by the holding unit. The notification unit can issue alerts as needed. The notification unit can adjust the level of detail of the notification based on the importance of the transaction. The notification unit can also estimate the user's sentiment and adjust the way the notification is expressed based on the estimated user sentiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, holding unit, and notification unit, is implemented 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 financial transaction data and personal information data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI to detect suspicious transactions. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs natural language analysis of the transaction content to determine the risk of fraud. The holding unit is implemented by the control unit 46A of the smart device 14 and automatically holds suspicious transactions temporarily. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the person or a designated family member of the held transactions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, holding unit, and notification unit, is implemented 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 financial transaction data and personal information data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI to detect suspicious transactions. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs natural language analysis of the transaction content to determine the risk of fraud. The holding unit is implemented by the control unit 46A of the smart glasses 214 and automatically holds suspicious transactions temporarily. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the person or a designated family member of the held transactions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, holding unit, and notification unit, is implemented 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 financial transaction data and personal information data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI to detect suspicious transactions. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs natural language analysis of the transaction content to determine the risk of fraud. The holding unit is implemented by the control unit 46A of the headset terminal 314 and automatically holds suspicious transactions temporarily. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the person or a designated family member of the held transactions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, holding unit, and notification unit, is implemented 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 financial transaction data and personal information data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI to detect suspicious transactions. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs natural language analysis of the transaction content to determine the risk of fraud. The holding unit is implemented by the control unit 46A of the robot 414 and automatically holds suspicious transactions temporarily. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the person or a designated family member of the held transactions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The aforementioned analysis unit detects suspicious transactions, and A holding unit that temporarily suspends suspicious transactions detected by the detection unit, The system includes a notification unit that notifies the person or a designated family member of the transaction that has been put on hold by the holding unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We analyze the content of phone calls or door-to-door sales to determine the risk of fraud. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Issue alerts as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Centralize data from multiple financial institutions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is We perform natural language analysis on transaction details to determine the risk of fraud. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned retaining portion is Suspicious transactions are automatically put on hold. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past transaction history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the transaction category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the transactions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is When detecting transactions, the accuracy of the detection is improved by considering the interrelationships between them. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is During detection, the attribute information of the transaction submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's sentiment and adjusts the order in which the detection results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is During detection, the geographical distribution of transactions is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is During detection, we improve detection accuracy by referring to relevant literature related to the transaction. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned retaining portion is It estimates the user's emotions and adjusts the hold method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned retaining portion is When a transaction is put on hold, the priority of the hold is determined based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned retaining portion is When a transaction is put on hold, a different hold algorithm is applied depending on the transaction category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned retaining portion is It estimates the user's emotions and adjusts the hold time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned retaining portion is When a transaction is put on hold, the priority of the hold is determined based on when the transaction was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned retaining portion is When holding transactions, the order of holding will be adjusted based on the relevance of the transactions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions 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 32) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the transaction. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the transaction category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and adjusts the length of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When notifying, we will prioritize notifications based on when the transaction was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the transactions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The aforementioned analysis unit detects suspicious transactions, and A holding unit that temporarily suspends suspicious transactions detected by the detection unit, The system includes a notification unit that notifies the person or a designated family member of the transaction that has been put on hold by the holding unit. A system characterized by the following features.

2. The aforementioned analysis unit is We analyze the content of phone calls or door-to-door sales to determine the risk of fraud. The system according to feature 1.

3. The aforementioned notification unit, Issue alerts as needed. The system according to feature 1.

4. The aforementioned collection unit is Centralize data from multiple financial institutions. The system according to feature 1.

5. The detection unit is We perform natural language analysis on transaction details to determine the risk of fraud. The system according to feature 1.

6. The aforementioned retaining portion is Suspicious transactions are automatically put on hold. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past transaction history and select the optimal data collection method. The system according to feature 1.

Images