system

Abstract

The system according to this embodiment aims to enable elderly people to use digital payments with peace of mind. [Solution] The system according to the embodiment comprises a sensor unit, an analysis unit, a payment unit, and an authentication unit. The sensor unit is a sensor unit for monitoring the behavior of elderly people. The analysis unit analyzes the data collected by the sensor unit. The payment unit performs digital payment based on the analysis results obtained by the analysis unit. The authentication unit performs authentication when payment is made by the payment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when elderly people use digital payment, they are resistant to using smartphones or cards, so there is a problem that it is difficult to use.

[0005] The system according to the embodiment aims to enable elderly people to use digital payment with confidence.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sensor unit, an analysis unit, a payment unit, and an authentication unit. The sensor unit is a sensor unit for monitoring the behavior of elderly people. The analysis unit analyzes the data collected by the sensor unit. The payment unit performs digital payment based on the analysis results obtained by the analysis unit. The authentication unit performs authentication when payment is made by the payment unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable elderly people to use digital payments with peace of mind. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 digital payment keychain system according to an embodiment of the present invention is a system that solves the problem of elderly people being reluctant to carry smartphones or cards. This system monitors the behavior of elderly people and enables them to use digital payments with peace of mind. First, a GPS sensor and an accelerometer are built into the keychain to monitor the behavior of elderly people. These sensors detect what kind of actions the elderly person is taking in real time and collect the data. For example, it detects the movements of elderly people when they are shopping and prepares for digital payment based on that information. Next, the collected data is analyzed by AI. The AI ​​learns the behavior patterns of elderly people and determines when to perform digital payment. For example, when elderly people approach the cash register, the AI ​​automatically prepares for payment. Furthermore, the keychain has a built-in digital payment function and automatically performs payment at the timing determined by the AI. In this case, elderly people do not need to do any special operation; payment is completed simply by having the keychain with them. For example, after scanning items at the cash register, the keychain automatically performs the payment, and the elderly person can take the items home. This system eliminates the need for elderly people to carry smartphones or cards, allowing them to use digital payments with peace of mind. Furthermore, by learning behavioral patterns, AI can enable smoother payments. For example, by learning the frequency and timing of elderly people's shopping and processing payments at the optimal time, waiting times can be reduced. In this way, the present invention monitors the behavior of elderly people and automates digital payments using AI, enabling them to use digital payments with peace of mind. As a result, the digital payment keychain system can monitor the behavior of elderly people and enable them to use digital payments with confidence.

[0029] The digital payment keychain system according to this embodiment comprises a sensor unit, an analysis unit, a payment unit, and an authentication unit. The sensor unit monitors the behavior of elderly people. The sensor unit detects the location information and movements of elderly people using, for example, a GPS sensor or an acceleration sensor. The sensor unit can acquire the location information of elderly people in real time using, for example, a GPS sensor. The sensor unit can also detect the movements of elderly people using an acceleration sensor and collect the data. Furthermore, the sensor unit can monitor the behavior of elderly people in detail by combining multiple sensors. The analysis unit analyzes the data collected by the sensor unit. The analysis unit can learn the behavior patterns of elderly people using, for example, AI, and perform analysis to perform digital payment at the optimal timing. The analysis unit can learn the behavior patterns of elderly people using AI and output behavior patterns using, for example, AI taking the behavior data of elderly people as input. The analysis unit can learn the behavior patterns of elderly people using AI and determine when digital payment should be performed. The payment unit performs digital payment based on the analysis results obtained by the analysis unit. The payment unit can perform digital payment using, for example, QR code (registered trademark) payment or NFC. The payment unit, for example, generates a QR code and performs payment by scanning it at the register. The payment unit can also perform contactless payments using NFC. Furthermore, the payment unit can combine multiple payment methods to provide diverse digital payment options. The authentication unit performs authentication when the payment unit performs payment. The authentication unit can authenticate elderly individuals using, for example, fingerprint authentication or facial recognition. The authentication unit can authenticate the elderly person's fingerprint using a fingerprint sensor, for example. The authentication unit can also authenticate the elderly person's face using a camera. Furthermore, the authentication unit can provide high security by combining multiple authentication methods. As a result, the digital payment keychain system according to this embodiment can monitor the elderly person's behavior and allow them to use digital payments with peace of mind.

[0030] The sensor unit monitors the behavior of elderly individuals. For example, it uses GPS sensors and accelerometers to detect the elderly person's location and movement. Specifically, the GPS sensor acquires the elderly person's current location in real time and periodically updates the location information. This allows the system to constantly know where the elderly person is. The accelerometer also detects the elderly person's movements in detail, monitoring walking speed, falls, and other factors. For example, if an elderly person suddenly falls, the accelerometer can detect the abnormal movement and immediately issue an alert. Furthermore, the sensor unit can be used in combination with other sensors such as barometric pressure sensors and temperature sensors. This allows for the collection of environmental information surrounding the elderly person, enabling comprehensive behavioral monitoring. For example, the barometric pressure sensor can be used to detect whether the elderly person is going up or down stairs, and the temperature sensor can be used to monitor the temperature of the environment in which the elderly person is staying. This allows the sensor unit to gain a detailed understanding of the elderly person's behavior and environment, collecting basic data to provide necessary support.

[0031] The analysis unit analyzes the data collected by the sensor unit. For example, the analysis unit uses AI to learn the behavioral patterns of elderly people and performs analysis to determine the optimal timing for digital payments. Specifically, the AI ​​receives location information and movement data of elderly people as input and analyzes their behavioral patterns based on this data. For example, the AI ​​can learn patterns such as elderly people often being in specific places at specific times of day, and predict the optimal payment timing based on that pattern. Furthermore, the AI ​​can detect changes in the behavior of elderly people based on past data and issue alerts if abnormal behavior occurs. For example, if an elderly person stays in an unusual location for an extended period, the AI ​​can detect this anomaly and notify family members or caregivers. The analysis unit also determines the optimal timing for digital payments based on the results of the behavioral pattern analysis. For example, if an elderly person frequently visits a particular store, the system can be set to prioritize payments at that store. This allows the analysis unit to analyze the behavior of elderly people in detail and maximize the efficiency of digital payments.

[0032] The payment unit performs digital payments based on the analysis results obtained by the analysis unit. The payment unit can perform digital payments using, for example, QR code payments or NFC. Specifically, with QR code payments, payment is completed by scanning the QR code generated by the payment unit at the register. Elderly people can easily make payments simply by having a keychain with them. With contactless payments using NFC, payment is completed simply by holding the keychain over the payment terminal. This allows elderly people to make payments smoothly without having to take out their wallets. Furthermore, the payment unit can offer a variety of digital payment options by combining multiple payment methods. For example, supporting both QR code payments and NFC payments can improve user convenience. The payment unit can also record payment history for later review. This allows elderly people and their families to easily check what payments have been made. Furthermore, the payment unit can automatically perform payments under specific conditions based on instructions from the analysis unit. For example, it can be set up so that payments are automatically made when an elderly person visits a specific store. This allows the payment unit to maximize convenience for elderly people and provide an environment where they can use digital payments with peace of mind.

[0033] The authentication unit performs authentication when the payment unit makes a payment. The authentication unit can authenticate elderly people, for example, using fingerprint authentication or facial recognition. Specifically, it authenticates the fingerprint of an elderly person using a fingerprint sensor to confirm that they are a legitimate user. Fingerprint authentication can be easily performed by the elderly person simply by having them hold a keychain, thus enhancing security. When using facial recognition, it authenticates the elderly person's face using a camera to confirm that they are a legitimate user. Facial recognition is contactless, making it superior in terms of hygiene. Furthermore, the authentication unit can provide high security by combining multiple authentication methods. For example, by combining both fingerprint authentication and facial recognition, dual authentication can be performed to prevent fraudulent use. The authentication unit can also encrypt and store authentication data to prevent unauthorized access from outside. In this way, the authentication unit can provide high security for safe digital payments by the elderly. Furthermore, the authentication unit can enable elderly people to use digital payments without stress by speeding up the authentication process. For example, the speed of fingerprint authentication and facial recognition can be optimized to minimize the time required for authentication. In this way, the authentication unit can provide an environment in which elderly people can use digital payments with peace of mind.

[0034] The sensor unit includes a GPS sensor and an accelerometer. The sensor unit can, for example, acquire the location information of an elderly person in real time using the GPS sensor. The sensor unit can, for example, detect the movement of an elderly person using the accelerometer and collect the data. The sensor unit can, for example, combine the GPS sensor and the accelerometer to monitor the behavior of an elderly person in detail. This allows for more accurate monitoring of the elderly person's behavior. Some or all of the above-described processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input data acquired from the GPS sensor and the accelerometer into the AI, which can then analyze the elderly person's behavior.

[0035] The payment unit performs digital payments using QR code payments and NFC. For example, the payment unit can generate a QR code and perform a payment by scanning it at the register. The payment unit can also perform contactless payments using NFC. For example, the payment unit can provide a variety of digital payment methods by combining QR code payments and NFC. This allows for the provision of diverse digital payment methods. Some or all of the above-described processes in the payment unit may be performed using AI or not. For example, the payment unit can have AI perform the procedures for QR code payments and digital payments using NFC.

[0036] The authentication unit performs authentication using fingerprint authentication and facial authentication. For example, the authentication unit can authenticate the fingerprints of elderly people using a fingerprint sensor. For example, the authentication unit can also authenticate the faces of elderly people using a camera. For example, the authentication unit can provide high security by combining fingerprint authentication and facial authentication. This provides high security. Some or all of the above-described processes in the authentication unit may be performed using AI or not. For example, the authentication unit can have AI perform the fingerprint authentication and facial authentication procedures.

[0037] The analysis unit learns the behavioral patterns of elderly people and performs digital payments at the optimal timing. For example, the analysis unit uses AI to learn the behavioral data of elderly people and analyze their behavioral patterns. For example, the analysis unit uses AI to take the behavioral data of elderly people as input and outputs behavioral patterns. The analysis unit can learn the behavioral patterns of elderly people using AI and determine when digital payments should be made. This enables smooth payments tailored to the behavior of elderly people. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the behavioral data of elderly people into a generative AI, and the generative AI can analyze the behavioral patterns.

[0038] The sensor unit analyzes the elderly person's past behavioral history and selects the optimal sensor placement. The sensor unit, for example, places sensors in locations frequently visited by the elderly person to efficiently collect data. The sensor unit can dynamically change the sensor placement based on the elderly person's behavioral patterns. The sensor unit can analyze the elderly person's travel routes and place sensors at important points, for example, to efficiently collect data. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input the elderly person's past behavioral history into the AI, which can then select the optimal sensor placement.

[0039] The sensor unit filters the collected behavioral data based on the elderly person's current health status and activity level. For example, if the elderly person is tired, the sensor unit limits the data collected to reduce the burden. For example, if the elderly person is actively moving, the sensor unit can collect detailed data. For example, if the elderly person is in poor health, the sensor unit can minimize the data collected. This allows for the collection of necessary data while reducing the burden on the elderly person. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input data on the elderly person's health status and activity level into the AI, which can then perform the filtering.

[0040] The sensor unit prioritizes collecting highly relevant data when collecting behavioral data, taking into account the geographical location information of the elderly person. For example, if the elderly person is in a specific location, the sensor unit prioritizes collecting data related to that location. For example, if the elderly person is on the move, the sensor unit can prioritize collecting data related to their travel route. For example, if the elderly person is at home, the sensor unit can prioritize collecting data related to their activities within the home. This allows for the efficient collection of highly relevant data. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input the elderly person's geographical location information into the AI, which can then prioritize collecting highly relevant data.

[0041] The sensor unit analyzes the social media activities of elderly individuals and collects relevant data when collecting behavioral data. For example, the sensor unit collects relevant data based on information shared by elderly individuals on social media. For example, the sensor unit can analyze the content of elderly individuals' social media posts and associate it with behavioral data. For example, the sensor unit can adjust the timing of data collection by considering the time of day when elderly individuals are active on social media. This enables data collection based on social media activity. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input elderly individuals' social media activity data into the AI, which can then collect relevant data.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit performs a detailed analysis on important behavioral data. For example, the analysis unit can perform a simplified analysis on less important behavioral data. For example, the analysis unit can dynamically allocate analysis resources according to the importance of the behavioral data. This enables analysis according to importance. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the behavioral data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the category of the behavioral data during analysis. For example, the analysis unit applies a health analysis algorithm to health-related behavioral data. For example, the analysis unit can apply a mobility analysis algorithm to mobility-related behavioral data. For example, the analysis unit can apply a social media analysis algorithm to social media-related behavioral data. This enables analysis according to category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the categories of behavioral data into the AI, and the AI ​​can apply different analysis algorithms.

[0044] The analysis unit determines the priority of analysis based on the timing of behavioral data collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent behavioral data. For example, the analysis unit can prioritize the most recent data while referring to past behavioral data. For example, the analysis unit can dynamically adjust the priority of analysis according to the timing of behavioral data collection. This enables analysis according to the collection timing. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the timing of behavioral data collection into the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the behavioral data during the analysis. For example, the analysis unit prioritizes the analysis of behavioral data with high relevance. For example, the analysis unit can postpone the analysis of behavioral data with low relevance. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the behavioral data. This enables analysis according to relevance. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the behavioral data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The settlement unit adjusts the level of detail in the settlement process based on the importance of the product. For example, the settlement unit performs detailed settlement procedures for important products, and simplified settlement procedures for less important products. The settlement unit can dynamically allocate settlement resources according to the importance of the product, enabling settlements tailored to the product's importance. Some or all of the above processes in the settlement unit may be performed using AI, or not. For example, the settlement unit can input the importance of the product into the AI, which can then adjust the level of detail in the settlement process.

[0047] The payment unit applies different payment algorithms depending on the product category at the time of payment. For example, the payment unit applies a payment algorithm specifically for food products. For example, the payment unit can apply a payment algorithm specifically for clothing products. For example, the payment unit can apply a payment algorithm specifically for electronic devices to electronic devices. This enables payment according to the category. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit can input the product category into the AI, and the AI ​​can apply different payment algorithms.

[0048] The settlement unit determines the payment priority based on the product submission date at the time of settlement. For example, the settlement unit prioritizes the most recent products. For example, the settlement unit can prioritize the most recent products while referring to past products. For example, the settlement unit can dynamically adjust the payment priority according to the product submission date. This allows settlement to be performed with priority according to the product submission date. Some or all of the above processing in the settlement unit may be performed using AI or not. For example, the settlement unit can input the product submission date into the AI, and the AI ​​can determine the payment priority.

[0049] The payment unit adjusts the payment order based on the relevance of the products during payment. For example, the payment unit may prioritize payment for products with high relevance. For example, the payment unit may postpone payment for products with low relevance. For example, the payment unit may dynamically adjust the payment order according to the relevance of the products. This allows payments to be made in an order that corresponds to the relevance of the products. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit may input the relevance of the products into the AI, and the AI ​​may adjust the payment order.

[0050] The authentication unit selects the optimal authentication method by referring to the elderly person's past authentication history during authentication. For example, the authentication unit may prioritize suggesting authentication methods that the elderly person has used in the past. For example, the authentication unit can select the authentication method with the highest success rate from the elderly person's authentication history. For example, the authentication unit can analyze the elderly person's authentication history and dynamically select the optimal authentication method. This allows the system to provide the optimal authentication method based on past authentication history. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's past authentication history into an AI, which can then select the optimal authentication method.

[0051] The authentication unit customizes the authentication method based on the elderly person's current health condition during authentication. For example, if the elderly person is tired, the authentication unit can provide a simplified authentication method. For example, if the elderly person is healthy, the authentication unit can provide a detailed authentication method. The authentication unit can dynamically customize the authentication method according to the elderly person's health condition. This allows for the provision of an authentication method tailored to the elderly person's health condition. Some or all of the above-described processes in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's health condition into the AI, which can then customize the authentication method.

[0052] The authentication unit selects the optimal authentication method during authentication, taking into account the elderly person's geographical location. For example, if the elderly person is at home, the authentication unit can provide a simple authentication method. For example, if the elderly person is in a public place, the authentication unit can provide a more detailed authentication method. The authentication unit can dynamically select an authentication method according to the elderly person's geographical location, for example. This allows for the provision of an optimal authentication method based on geographical location information. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's geographical location information into the AI, which can then select the optimal authentication method.

[0053] The authentication unit analyzes the social media activity of elderly individuals during authentication and proposes an authentication method. For example, the authentication unit proposes the optimal authentication method based on information shared by elderly individuals on social media. For example, the authentication unit can analyze the content of elderly individuals' social media posts and associate it with an authentication method. For example, the authentication unit can propose an authentication method considering the time of day when elderly individuals are active on social media. This allows for the provision of an authentication method based on social media activity. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly individual's social media activity data into an AI, which can then propose the optimal authentication method.

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

[0055] The sensor unit can analyze ambient sounds to supplement details of an elderly person's behavior when collecting behavioral data. For example, when an elderly person is shopping, the sensor unit can detect sounds such as the cash register or in-store announcements to understand details of their behavior. The sensor unit can also estimate what kind of products an elderly person is purchasing based on ambient sounds. The sensor unit can also analyze what kind of conversation an elderly person is having based on ambient sounds to supplement details of their behavior. This improves the accuracy of behavioral data and enables more precise analysis. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input ambient sound data into the AI, which can then supplement details of the behavior.

[0056] The analysis unit can adjust the analysis results by taking weather information into account when analyzing the behavioral data of elderly people. For example, it can perform the analysis considering that the frequency of going out decreases on rainy days. The analysis unit can adjust the behavioral patterns of elderly people based on weather information to obtain more accurate analysis results. The analysis unit can also improve the accuracy of predictions by predicting the behavior of elderly people based on weather information. This makes it possible to perform analysis that takes the influence of weather into account, and to grasp behavioral patterns more accurately. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather information into AI, and the AI ​​can adjust the analysis results.

[0057] The payment department can analyze the purchase history of elderly people and provide them with the most suitable coupons and discount information. For example, it can provide coupons for products that elderly people frequently purchase. The payment department can, for example, provide discount information for specific products based on the elderly person's purchase history. The payment department can, for example, analyze the elderly person's purchase history and provide promotional information for related products. This improves the elderly person's shopping experience and allows them to shop more economically. Some or all of the above processes in the payment department may be performed using AI or not. For example, the payment department can input the elderly person's purchase history into AI, which can then provide them with the most suitable coupons and discount information.

[0058] The authentication unit can add voice authentication when authenticating elderly individuals. For example, authentication can be performed by the elderly person speaking a specific phrase. The authentication unit can provide high security by combining voice authentication and fingerprint authentication, for example. The authentication unit can improve authentication accuracy by combining voice authentication and facial recognition, for example. This allows for high security by combining multiple authentication methods. Some or all of the above-described processes in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's voice data into the AI, which can then perform voice authentication.

[0059] The analysis unit can detect anomalies when analyzing the behavioral data of elderly individuals by comparing it with past data. For example, it can issue an alert if it detects behavior that differs from the normal behavioral pattern. The analysis unit can, for example, detect abnormal behavior by comparing it with past data and notify family members or caregivers. The analysis unit can, for example, detect changes in health status by comparing it with past data and notify medical institutions. This allows for the early detection of abnormal behavior and changes in health status, enabling appropriate responses. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input past behavioral data into AI, which can then detect anomalies.

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

[0061] Step 1: The sensor unit monitors the elderly person's behavior. The sensor unit detects the elderly person's location and movement using, for example, a GPS sensor or an accelerometer. The sensor unit can acquire the elderly person's location information in real time using, for example, a GPS sensor. The sensor unit can also detect the elderly person's movement using an accelerometer and collect that data. Furthermore, the sensor unit can combine multiple sensors to monitor the elderly person's behavior in detail. Step 2: The analysis unit analyzes the data collected by the sensor unit. The analysis unit, for example, uses AI to learn the behavioral patterns of elderly people and performs analysis to determine the optimal timing for digital payment. For example, the AI ​​takes the behavioral data of elderly people as input and outputs behavioral patterns. The analysis unit can learn the behavioral patterns of elderly people using AI and determine when digital payment should be made. Step 3: The payment unit performs digital payment based on the analysis results obtained by the analysis unit. The payment unit can perform digital payment using, for example, QR code payment or NFC. For example, the payment unit can generate a QR code and perform payment by scanning it at the register. The payment unit can also perform contactless payment using NFC. Furthermore, the payment unit can provide a variety of digital payment options by combining multiple payment methods. Step 4: The authentication unit performs authentication when the payment unit makes a payment. The authentication unit can authenticate elderly people, for example, using fingerprint authentication or facial recognition. For example, the authentication unit can authenticate the fingerprint of an elderly person using a fingerprint sensor. The authentication unit can also authenticate the face of an elderly person using a camera. Furthermore, the authentication unit can provide high security by combining multiple authentication methods.

[0062] (Example of form 2) The digital payment keychain system according to an embodiment of the present invention is a system that solves the problem of elderly people being reluctant to carry smartphones or cards. This system monitors the behavior of elderly people and enables them to use digital payments with peace of mind. First, a GPS sensor and an accelerometer are built into the keychain to monitor the behavior of elderly people. These sensors detect what kind of actions the elderly person is taking in real time and collect the data. For example, it detects the movements of elderly people when they are shopping and prepares for digital payment based on that information. Next, the collected data is analyzed by AI. The AI ​​learns the behavior patterns of elderly people and determines when to perform digital payment. For example, when elderly people approach the cash register, the AI ​​automatically prepares for payment. Furthermore, the keychain has a built-in digital payment function and automatically performs payment at the timing determined by the AI. In this case, elderly people do not need to do any special operation; payment is completed simply by having the keychain with them. For example, after scanning items at the cash register, the keychain automatically performs the payment, and the elderly person can take the items home. This system eliminates the need for elderly people to carry smartphones or cards, allowing them to use digital payments with peace of mind. Furthermore, by learning behavioral patterns, AI can enable smoother payments. For example, by learning the frequency and timing of elderly people's shopping and processing payments at the optimal time, waiting times can be reduced. In this way, the present invention monitors the behavior of elderly people and automates digital payments using AI, enabling them to use digital payments with peace of mind. As a result, the digital payment keychain system can monitor the behavior of elderly people and enable them to use digital payments with confidence.

[0063] The digital payment keychain system according to this embodiment comprises a sensor unit, an analysis unit, a payment unit, and an authentication unit. The sensor unit monitors the behavior of elderly people. The sensor unit detects the location information and movements of elderly people using, for example, a GPS sensor or an acceleration sensor. The sensor unit can acquire the location information of elderly people in real time using, for example, a GPS sensor. The sensor unit can also detect the movements of elderly people using an acceleration sensor and collect the data. Furthermore, the sensor unit can monitor the behavior of elderly people in detail by combining multiple sensors. The analysis unit analyzes the data collected by the sensor unit. The analysis unit can learn the behavior patterns of elderly people using, for example, AI, and perform analysis to perform digital payment at the optimal timing. The analysis unit can learn the behavior patterns of elderly people using AI and determine when digital payment should be performed. The payment unit performs digital payment based on the analysis results obtained by the analysis unit. The payment unit can perform digital payment using, for example, QR code payment or NFC. The payment unit, for example, generates a QR code and performs payment by scanning it at the register. The payment unit can also perform contactless payments using NFC. Furthermore, the payment unit can combine multiple payment methods to provide diverse digital payment options. The authentication unit performs authentication when the payment unit performs payment. The authentication unit can authenticate elderly individuals using, for example, fingerprint authentication or facial recognition. The authentication unit can authenticate the elderly person's fingerprint using a fingerprint sensor, for example. The authentication unit can also authenticate the elderly person's face using a camera. Furthermore, the authentication unit can provide high security by combining multiple authentication methods. As a result, the digital payment keychain system according to this embodiment can monitor the elderly person's behavior and allow them to use digital payments with peace of mind.

[0064] The sensor unit monitors the behavior of elderly individuals. For example, it uses GPS sensors and accelerometers to detect the elderly person's location and movement. Specifically, the GPS sensor acquires the elderly person's current location in real time and periodically updates the location information. This allows the system to constantly know where the elderly person is. The accelerometer also detects the elderly person's movements in detail, monitoring walking speed, falls, and other factors. For example, if an elderly person suddenly falls, the accelerometer can detect the abnormal movement and immediately issue an alert. Furthermore, the sensor unit can be used in combination with other sensors such as barometric pressure sensors and temperature sensors. This allows for the collection of environmental information surrounding the elderly person, enabling comprehensive behavioral monitoring. For example, the barometric pressure sensor can be used to detect whether the elderly person is going up or down stairs, and the temperature sensor can be used to monitor the temperature of the environment in which the elderly person is staying. This allows the sensor unit to gain a detailed understanding of the elderly person's behavior and environment, collecting basic data to provide necessary support.

[0065] The analysis unit analyzes the data collected by the sensor unit. For example, the analysis unit uses AI to learn the behavioral patterns of elderly people and performs analysis to determine the optimal timing for digital payments. Specifically, the AI ​​receives location information and movement data of elderly people as input and analyzes their behavioral patterns based on this data. For example, the AI ​​can learn patterns such as elderly people often being in specific places at specific times of day, and predict the optimal payment timing based on that pattern. Furthermore, the AI ​​can detect changes in the behavior of elderly people based on past data and issue alerts if abnormal behavior occurs. For example, if an elderly person stays in an unusual location for an extended period, the AI ​​can detect this anomaly and notify family members or caregivers. The analysis unit also determines the optimal timing for digital payments based on the results of the behavioral pattern analysis. For example, if an elderly person frequently visits a particular store, the system can be set to prioritize payments at that store. This allows the analysis unit to analyze the behavior of elderly people in detail and maximize the efficiency of digital payments.

[0066] The payment unit performs digital payments based on the analysis results obtained by the analysis unit. The payment unit can perform digital payments using, for example, QR code payments or NFC. Specifically, with QR code payments, payment is completed by scanning the QR code generated by the payment unit at the register. Elderly people can easily make payments simply by having a keychain with them. With contactless payments using NFC, payment is completed simply by holding the keychain over the payment terminal. This allows elderly people to make payments smoothly without having to take out their wallets. Furthermore, the payment unit can offer a variety of digital payment options by combining multiple payment methods. For example, supporting both QR code payments and NFC payments can improve user convenience. The payment unit can also record payment history for later review. This allows elderly people and their families to easily check what payments have been made. Furthermore, the payment unit can automatically perform payments under specific conditions based on instructions from the analysis unit. For example, it can be set up so that payments are automatically made when an elderly person visits a specific store. This allows the payment unit to maximize convenience for elderly people and provide an environment where they can use digital payments with peace of mind.

[0067] The authentication unit performs authentication when the payment unit makes a payment. The authentication unit can authenticate elderly people, for example, using fingerprint authentication or facial recognition. Specifically, it authenticates the fingerprint of an elderly person using a fingerprint sensor to confirm that they are a legitimate user. Fingerprint authentication can be easily performed by the elderly person simply by having them hold a keychain, thus enhancing security. When using facial recognition, it authenticates the elderly person's face using a camera to confirm that they are a legitimate user. Facial recognition is contactless, making it superior in terms of hygiene. Furthermore, the authentication unit can provide high security by combining multiple authentication methods. For example, by combining both fingerprint authentication and facial recognition, dual authentication can be performed to prevent fraudulent use. The authentication unit can also encrypt and store authentication data to prevent unauthorized access from outside. In this way, the authentication unit can provide high security for safe digital payments by the elderly. Furthermore, the authentication unit can enable elderly people to use digital payments without stress by speeding up the authentication process. For example, the speed of fingerprint authentication and facial recognition can be optimized to minimize the time required for authentication. In this way, the authentication unit can provide an environment in which elderly people can use digital payments with peace of mind.

[0068] The sensor unit includes a GPS sensor and an accelerometer. The sensor unit can, for example, acquire the location information of an elderly person in real time using the GPS sensor. The sensor unit can, for example, detect the movement of an elderly person using the accelerometer and collect the data. The sensor unit can, for example, combine the GPS sensor and the accelerometer to monitor the behavior of an elderly person in detail. This allows for more accurate monitoring of the elderly person's behavior. Some or all of the above-described processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input data acquired from the GPS sensor and the accelerometer into the AI, which can then analyze the elderly person's behavior.

[0069] The payment unit performs digital payments using QR code payments and NFC. For example, the payment unit can generate a QR code and perform a payment by scanning it at the register. The payment unit can also perform contactless payments using NFC. For example, the payment unit can provide a variety of digital payment methods by combining QR code payments and NFC. This allows for the provision of diverse digital payment methods. Some or all of the above-described processes in the payment unit may be performed using AI or not. For example, the payment unit can have AI perform the procedures for QR code payments and digital payments using NFC.

[0070] The authentication unit performs authentication using fingerprint authentication and facial authentication. For example, the authentication unit can authenticate the fingerprints of elderly people using a fingerprint sensor. For example, the authentication unit can also authenticate the faces of elderly people using a camera. For example, the authentication unit can provide high security by combining fingerprint authentication and facial authentication. This provides high security. Some or all of the above-described processes in the authentication unit may be performed using AI or not. For example, the authentication unit can have AI perform the fingerprint authentication and facial authentication procedures.

[0071] The analysis unit learns the behavioral patterns of elderly people and performs digital payments at the optimal timing. For example, the analysis unit uses AI to learn the behavioral data of elderly people and analyze their behavioral patterns. For example, the analysis unit uses AI to take the behavioral data of elderly people as input and outputs behavioral patterns. The analysis unit can learn the behavioral patterns of elderly people using AI and determine when digital payments should be made. This enables smooth payments tailored to the behavior of elderly people. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the behavioral data of elderly people into a generative AI, and the generative AI can analyze the behavioral patterns.

[0072] The sensor unit estimates the emotions of elderly individuals and adjusts the frequency of collecting behavioral data based on the estimated emotions. For example, if an elderly person is stressed, the sensor unit reduces the collection frequency to lessen the burden. For example, if an elderly person is relaxed, the sensor unit can increase the collection frequency to obtain more detailed data. For example, if an elderly person is in a hurry, the sensor unit can adjust the collection frequency appropriately to obtain only the necessary data. This allows for the collection of necessary data while reducing the burden on the elderly person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input the elderly person's emotion data into a generative AI, which can then estimate the emotion.

[0073] The sensor unit analyzes the elderly person's past behavioral history and selects the optimal sensor placement. The sensor unit, for example, places sensors in locations frequently visited by the elderly person to efficiently collect data. The sensor unit can dynamically change the sensor placement based on the elderly person's behavioral patterns. The sensor unit can analyze the elderly person's travel routes and place sensors at important points, for example, to efficiently collect data. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input the elderly person's past behavioral history into the AI, which can then select the optimal sensor placement.

[0074] The sensor unit filters the collected behavioral data based on the elderly person's current health status and activity level. For example, if the elderly person is tired, the sensor unit limits the data collected to reduce the burden. For example, if the elderly person is actively moving, the sensor unit can collect detailed data. For example, if the elderly person is in poor health, the sensor unit can minimize the data collected. This allows for the collection of necessary data while reducing the burden on the elderly person. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input data on the elderly person's health status and activity level into the AI, which can then perform the filtering.

[0075] The sensor unit estimates the emotions of elderly individuals and determines the priority of data to collect based on the estimated emotions. For example, if an elderly individual is stressed, the sensor unit prioritizes collecting only important data. For example, if an elderly individual is relaxed, the sensor unit can prioritize collecting detailed data. For example, if an elderly individual is in a hurry, the sensor unit can prioritize collecting only the minimum necessary data. This enables data collection tailored to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input the elderly individual's emotion data into a generative AI, which can then estimate the emotion.

[0076] The sensor unit prioritizes collecting highly relevant data when collecting behavioral data, taking into account the geographical location information of the elderly person. For example, if the elderly person is in a specific location, the sensor unit prioritizes collecting data related to that location. For example, if the elderly person is on the move, the sensor unit can prioritize collecting data related to their travel route. For example, if the elderly person is at home, the sensor unit can prioritize collecting data related to their activities within the home. This allows for the efficient collection of highly relevant data. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input the elderly person's geographical location information into the AI, which can then prioritize collecting highly relevant data.

[0077] The sensor unit analyzes the social media activities of elderly individuals and collects relevant data when collecting behavioral data. For example, the sensor unit collects relevant data based on information shared by elderly individuals on social media. For example, the sensor unit can analyze the content of elderly individuals' social media posts and associate it with behavioral data. For example, the sensor unit can adjust the timing of data collection by considering the time of day when elderly individuals are active on social media. This enables data collection based on social media activity. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input elderly individuals' social media activity data into the AI, which can then collect relevant data.

[0078] The analysis unit estimates the emotions of elderly individuals and adjusts the analysis algorithm based on the estimated emotions. For example, if an elderly individual is stressed, the analysis unit simplifies the analysis algorithm to reduce its burden. For example, if an elderly individual is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. For example, if an elderly individual is in a hurry, the analysis unit can apply an algorithm that performs a rapid analysis. This enables analysis that is tailored to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the elderly individual's emotion data into a generative AI, which can then estimate the emotion and adjust the analysis algorithm.

[0079] The analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit performs a detailed analysis on important behavioral data. For example, the analysis unit can perform a simplified analysis on less important behavioral data. For example, the analysis unit can dynamically allocate analysis resources according to the importance of the behavioral data. This enables analysis according to importance. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the behavioral data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit applies different analysis algorithms depending on the category of the behavioral data during analysis. For example, the analysis unit applies a health analysis algorithm to health-related behavioral data. For example, the analysis unit can apply a mobility analysis algorithm to mobility-related behavioral data. For example, the analysis unit can apply a social media analysis algorithm to social media-related behavioral data. This enables analysis according to category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the categories of behavioral data into the AI, and the AI ​​can apply different analysis algorithms.

[0081] The analysis unit estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions of the elderly individuals. For example, if an elderly individual is tense, the analysis unit provides a simple and highly visible display method. For example, if an elderly individual is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if an elderly individual is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for the provision of a display method that is appropriate to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the emotional data of elderly individuals into a generative AI, which can then estimate the emotions and adjust the display method.

[0082] The analysis unit determines the priority of analysis based on the timing of behavioral data collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent behavioral data. For example, the analysis unit can prioritize the most recent data while referring to past behavioral data. For example, the analysis unit can dynamically adjust the priority of analysis according to the timing of behavioral data collection. This enables analysis according to the collection timing. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the timing of behavioral data collection into the AI, and the AI ​​can determine the priority of analysis.

[0083] The analysis unit adjusts the order of analysis based on the relevance of the behavioral data during the analysis. For example, the analysis unit prioritizes the analysis of behavioral data with high relevance. For example, the analysis unit can postpone the analysis of behavioral data with low relevance. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the behavioral data. This enables analysis according to relevance. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the behavioral data into the AI, and the AI ​​can adjust the order of analysis.

[0084] The payment unit estimates the emotions of elderly individuals and adjusts the timing of payments based on the estimated emotions. For example, if an elderly individual is stressed, the payment unit may delay the payment to reduce their burden. For example, if an elderly individual is relaxed, the payment unit may process the payment quickly. For example, if an elderly individual is in a hurry, the payment unit may process the payment immediately. This allows for payment timing tailored to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the payment unit may be performed using AI or not. For example, the payment unit can input the emotional data of elderly individuals into a generative AI, which can then estimate the emotions and adjust the timing of the payment.

[0085] The settlement unit adjusts the level of detail in the settlement process based on the importance of the product. For example, the settlement unit performs detailed settlement procedures for important products, and simplified settlement procedures for less important products. The settlement unit can dynamically allocate settlement resources according to the importance of the product, enabling settlements tailored to the product's importance. Some or all of the above processes in the settlement unit may be performed using AI, or not. For example, the settlement unit can input the importance of the product into the AI, which can then adjust the level of detail in the settlement process.

[0086] The payment unit applies different payment algorithms depending on the product category at the time of payment. For example, the payment unit applies a payment algorithm specifically for food products. For example, the payment unit can apply a payment algorithm specifically for clothing products. For example, the payment unit can apply a payment algorithm specifically for electronic devices to electronic devices. This enables payment according to the category. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit can input the product category into the AI, and the AI ​​can apply different payment algorithms.

[0087] The settlement unit estimates the emotions of elderly individuals and determines settlement priorities based on the estimated emotions. For example, if an elderly individual is stressed, the settlement unit prioritizes important settlements. If an elderly individual is relaxed, the settlement unit prioritizes detailed settlements. If an elderly individual is in a hurry, the settlement unit prioritizes quick settlements. This allows settlements to be prioritized according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settlement unit may be performed using AI or not. For example, the settlement unit can input elderly individual emotion data into a generative AI, which can estimate emotions and determine settlement priorities.

[0088] The settlement unit determines the payment priority based on the product submission date at the time of settlement. For example, the settlement unit prioritizes the most recent products. For example, the settlement unit can prioritize the most recent products while referring to past products. For example, the settlement unit can dynamically adjust the payment priority according to the product submission date. This allows settlement to be performed with priority according to the product submission date. Some or all of the above processing in the settlement unit may be performed using AI or not. For example, the settlement unit can input the product submission date into the AI, and the AI ​​can determine the payment priority.

[0089] The payment unit adjusts the payment order based on the relevance of the products during payment. For example, the payment unit may prioritize payment for products with high relevance. For example, the payment unit may postpone payment for products with low relevance. For example, the payment unit may dynamically adjust the payment order according to the relevance of the products. This allows payments to be made in an order that corresponds to the relevance of the products. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit may input the relevance of the products into the AI, and the AI ​​may adjust the payment order.

[0090] The authentication unit estimates the emotions of the elderly person and adjusts the authentication method based on the estimated emotions. For example, if the elderly person is stressed, the authentication unit can provide a simplified authentication method. For example, if the elderly person is relaxed, the authentication unit can provide a detailed authentication method. For example, if the elderly person is in a hurry, the authentication unit can provide a rapid authentication method. This allows for the provision of authentication methods that are appropriate to the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's emotion data into a generative AI, which can estimate the emotion and adjust the authentication method.

[0091] The authentication unit selects the optimal authentication method by referring to the elderly person's past authentication history during authentication. For example, the authentication unit may prioritize suggesting authentication methods that the elderly person has used in the past. For example, the authentication unit can select the authentication method with the highest success rate from the elderly person's authentication history. For example, the authentication unit can analyze the elderly person's authentication history and dynamically select the optimal authentication method. This allows the system to provide the optimal authentication method based on past authentication history. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's past authentication history into an AI, which can then select the optimal authentication method.

[0092] The authentication unit customizes the authentication method based on the elderly person's current health condition during authentication. For example, if the elderly person is tired, the authentication unit can provide a simplified authentication method. For example, if the elderly person is healthy, the authentication unit can provide a detailed authentication method. The authentication unit can dynamically customize the authentication method according to the elderly person's health condition. This allows for the provision of an authentication method tailored to the elderly person's health condition. Some or all of the above-described processes in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's health condition into the AI, which can then customize the authentication method.

[0093] The authentication unit estimates the emotions of elderly individuals and determines authentication priorities based on the estimated emotions. For example, if an elderly individual is stressed, the authentication unit prioritizes important authentication. For example, if an elderly individual is relaxed, the authentication unit prioritizes detailed authentication. For example, if an elderly individual is in a hurry, the authentication unit prioritizes rapid authentication. This allows authentication to be performed with priorities according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly individual's emotion data into a generative AI, which can estimate the emotions and determine authentication priorities.

[0094] The authentication unit selects the optimal authentication method during authentication, taking into account the elderly person's geographical location. For example, if the elderly person is at home, the authentication unit can provide a simple authentication method. For example, if the elderly person is in a public place, the authentication unit can provide a more detailed authentication method. The authentication unit can dynamically select an authentication method according to the elderly person's geographical location, for example. This allows for the provision of an optimal authentication method based on geographical location information. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's geographical location information into the AI, which can then select the optimal authentication method.

[0095] The authentication unit analyzes the social media activity of elderly individuals during authentication and proposes an authentication method. For example, the authentication unit proposes the optimal authentication method based on information shared by elderly individuals on social media. For example, the authentication unit can analyze the content of elderly individuals' social media posts and associate it with an authentication method. For example, the authentication unit can propose an authentication method considering the time of day when elderly individuals are active on social media. This allows for the provision of an authentication method based on social media activity. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly individual's social media activity data into an AI, which can then propose the optimal authentication method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The sensor unit can analyze ambient sounds to supplement details of an elderly person's behavior when collecting behavioral data. For example, when an elderly person is shopping, the sensor unit can detect sounds such as the cash register or in-store announcements to understand details of their behavior. The sensor unit can also estimate what kind of products an elderly person is purchasing based on ambient sounds. The sensor unit can also analyze what kind of conversation an elderly person is having based on ambient sounds to supplement details of their behavior. This improves the accuracy of behavioral data and enables more precise analysis. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input ambient sound data into the AI, which can then supplement details of the behavior.

[0098] The analysis unit can adjust the analysis results by taking weather information into account when analyzing the behavioral data of elderly people. For example, it can perform the analysis considering that the frequency of going out decreases on rainy days. The analysis unit can adjust the behavioral patterns of elderly people based on weather information to obtain more accurate analysis results. The analysis unit can also improve the accuracy of predictions by predicting the behavior of elderly people based on weather information. This makes it possible to perform analysis that takes the influence of weather into account, and to grasp behavioral patterns more accurately. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather information into AI, and the AI ​​can adjust the analysis results.

[0099] The payment department can analyze the purchase history of elderly people and provide them with the most suitable coupons and discount information. For example, it can provide coupons for products that elderly people frequently purchase. The payment department can, for example, provide discount information for specific products based on the elderly person's purchase history. The payment department can, for example, analyze the elderly person's purchase history and provide promotional information for related products. This improves the elderly person's shopping experience and allows them to shop more economically. Some or all of the above processes in the payment department may be performed using AI or not. For example, the payment department can input the elderly person's purchase history into AI, which can then provide them with the most suitable coupons and discount information.

[0100] The authentication unit can add voice authentication when authenticating elderly individuals. For example, authentication can be performed by the elderly person speaking a specific phrase. The authentication unit can provide high security by combining voice authentication and fingerprint authentication, for example. The authentication unit can improve authentication accuracy by combining voice authentication and facial recognition, for example. This allows for high security by combining multiple authentication methods. Some or all of the above-described processes in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's voice data into the AI, which can then perform voice authentication.

[0101] The analysis unit can detect anomalies when analyzing the behavioral data of elderly individuals by comparing it with past data. For example, it can issue an alert if it detects behavior that differs from the normal behavioral pattern. The analysis unit can, for example, detect abnormal behavior by comparing it with past data and notify family members or caregivers. The analysis unit can, for example, detect changes in health status by comparing it with past data and notify medical institutions. This allows for the early detection of abnormal behavior and changes in health status, enabling appropriate responses. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input past behavioral data into AI, which can then detect anomalies.

[0102] The sensor unit estimates the emotions of elderly individuals and adjusts the frequency of collecting behavioral data based on the estimated emotions. For example, if an elderly person is stressed, the sensor unit reduces the collection frequency to lessen the burden. For example, if an elderly person is relaxed, the sensor unit can increase the collection frequency to obtain more detailed data. For example, if an elderly person is in a hurry, the sensor unit can adjust the collection frequency appropriately to obtain only the necessary data. This allows for the collection of necessary data while reducing the burden on the elderly person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input the elderly person's emotion data into a generative AI, which can then estimate the emotion.

[0103] The analysis unit estimates the emotions of elderly individuals and adjusts the analysis algorithm based on the estimated emotions. For example, if an elderly individual is stressed, the analysis unit simplifies the analysis algorithm to reduce its burden. For example, if an elderly individual is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. For example, if an elderly individual is in a hurry, the analysis unit can apply an algorithm that performs a rapid analysis. This enables analysis that is tailored to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the elderly individual's emotion data into a generative AI, which can then estimate the emotion and adjust the analysis algorithm.

[0104] The payment unit estimates the emotions of elderly individuals and adjusts the timing of payments based on the estimated emotions. For example, if an elderly individual is stressed, the payment unit may delay the payment to reduce their burden. For example, if an elderly individual is relaxed, the payment unit may process the payment quickly. For example, if an elderly individual is in a hurry, the payment unit may process the payment immediately. This allows for payment timing tailored to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the payment unit may be performed using AI or not. For example, the payment unit can input the emotional data of elderly individuals into a generative AI, which can then estimate the emotions and adjust the timing of the payment.

[0105] The authentication unit estimates the emotions of the elderly person and adjusts the authentication method based on the estimated emotions. For example, if the elderly person is stressed, the authentication unit can provide a simplified authentication method. For example, if the elderly person is relaxed, the authentication unit can provide a detailed authentication method. For example, if the elderly person is in a hurry, the authentication unit can provide a rapid authentication method. This allows for the provision of authentication methods that are appropriate to the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the authentication unit may be performed using AI or not. For example, the authentication unit can input the elderly person's emotion data into a generative AI, which can estimate the emotion and adjust the authentication method.

[0106] The analysis unit estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions of the elderly individuals. For example, if an elderly individual is tense, the analysis unit provides a simple and highly visible display method. For example, if an elderly individual is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if an elderly individual is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for the provision of a display method that is appropriate to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the emotional data of elderly individuals into a generative AI, which can then estimate the emotions and adjust the display method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The sensor unit monitors the elderly person's behavior. The sensor unit detects the elderly person's location and movement using, for example, a GPS sensor or an accelerometer. The sensor unit can acquire the elderly person's location information in real time using, for example, a GPS sensor. The sensor unit can also detect the elderly person's movement using an accelerometer and collect that data. Furthermore, the sensor unit can combine multiple sensors to monitor the elderly person's behavior in detail. Step 2: The analysis unit analyzes the data collected by the sensor unit. The analysis unit, for example, uses AI to learn the behavioral patterns of elderly people and performs analysis to determine the optimal timing for digital payment. For example, the AI ​​takes the behavioral data of elderly people as input and outputs behavioral patterns. The analysis unit can learn the behavioral patterns of elderly people using AI and determine when digital payment should be made. Step 3: The payment unit performs digital payment based on the analysis results obtained by the analysis unit. The payment unit can perform digital payment using, for example, QR code payment or NFC. For example, the payment unit can generate a QR code and perform payment by scanning it at the register. The payment unit can also perform contactless payment using NFC. Furthermore, the payment unit can provide a variety of digital payment options by combining multiple payment methods. Step 4: The authentication unit performs authentication when the payment unit makes a payment. The authentication unit can authenticate elderly people, for example, using fingerprint authentication or facial recognition. For example, the authentication unit can authenticate the fingerprint of an elderly person using a fingerprint sensor. The authentication unit can also authenticate the face of an elderly person using a camera. Furthermore, the authentication unit can provide high security by combining multiple authentication methods.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the sensor unit, analysis unit, payment unit, and authentication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sensor unit monitors the behavior of the elderly person using the GPS sensor and acceleration sensor of the smart device 14. The analysis unit performs behavioral pattern analysis using AI by the identification processing unit 290 of the data processing unit 12. The payment unit performs digital payment using the NFC and QR code generation functions of the smart device 14. The authentication unit authenticates the elderly person using the fingerprint sensor and camera of the smart device 14. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the sensor unit, analysis unit, payment unit, and authentication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor unit monitors the behavior of the elderly person using the GPS sensor and acceleration sensor of the smart glasses 214. The analysis unit analyzes behavioral patterns using AI with the identification processing unit 290 of the data processing unit 12. The payment unit performs digital payment using the NFC and QR code generation functions of the smart glasses 214. The authentication unit authenticates the elderly person using the fingerprint sensor and camera of the smart glasses 214. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 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.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the 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.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the sensor unit, analysis unit, payment unit, and authentication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor unit monitors the behavior of the elderly person using the GPS sensor and acceleration sensor of the headset terminal 314. The analysis unit performs behavioral pattern analysis using AI with the identification processing unit 290 of the data processing unit 12. The payment unit performs digital payment using the NFC and QR code generation functions of the headset terminal 314. The authentication unit authenticates the elderly person using the fingerprint sensor and camera of the headset terminal 314. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the sensor unit, analysis unit, payment unit, and authentication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the sensor unit monitors the behavior of the elderly person using the GPS sensor and acceleration sensor of the robot 414. The analysis unit performs behavioral pattern analysis using AI by the identification processing unit 290 of the data processing unit 12. The payment unit performs digital payment using the NFC and QR code generation functions of the robot 414. The authentication unit authenticates the elderly person using the fingerprint sensor and camera of the robot 414. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A sensor unit for monitoring the behavior of elderly people, An analysis unit that analyzes the data collected by the sensor unit, A settlement unit that performs digital settlement based on the analysis results obtained by the analysis unit, The settlement unit includes an authentication unit that performs authentication when settlement is performed by the settlement unit. A system characterized by the following features. (Note 2) The aforementioned sensor unit is Equipped with a GPS sensor and an accelerometer. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned settlement unit, Digital payments are made using QR code payments and NFC. The system described in Appendix 1, characterized by the features described herein. (Note 4) The authentication unit, Authentication is performed using fingerprint and facial recognition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Learn the behavioral patterns of the elderly and perform digital payments at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sensor unit is We estimate the emotions of older adults and adjust the frequency of behavioral data collection based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor unit is Analyze the past behavioral history of elderly individuals to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sensor unit is When collecting behavioral data, filtering is performed based on the current health status and activity level of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sensor unit is We estimate the emotions of older adults and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sensor unit is When collecting behavioral data, prioritize the collection of highly relevant data by considering the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sensor unit is When collecting behavioral data, analyze the social media activity of older adults and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the analysis algorithm based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions of the elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned settlement unit, The system estimates the emotions of elderly people and adjusts the timing of payments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned settlement unit, When making a payment, reduce the level of detail in the payment based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned settlement unit, When payment is processed, different payment algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned settlement unit, The system estimates the emotions of elderly individuals and determines payment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned settlement unit, At the time of payment, the payment priority is determined based on when the products were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned settlement unit, During checkout, the order of payments will be adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 24) The authentication unit, The system estimates the emotions of elderly individuals and adjusts the authentication method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The authentication unit, During authentication, the system selects the most suitable authentication method by referring to the elderly person's past authentication history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The authentication unit, During authentication, the authentication method is customized based on the elderly person's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The authentication unit, The system estimates the emotions of elderly individuals and determines authentication priorities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The authentication unit, During authentication, the optimal authentication method is selected considering the geographical location information of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 29) The authentication unit, During authentication, the social media activity of elderly individuals is analyzed to propose authentication methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 sensor unit for monitoring the behavior of elderly people, An analysis unit that analyzes the data collected by the sensor unit, A settlement unit that performs digital settlement based on the analysis results obtained by the analysis unit, The settlement unit includes an authentication unit that performs authentication when settlement is performed by the settlement unit. A system characterized by the following features.

2. The aforementioned sensor unit is Equipped with a GPS sensor and an accelerometer. The system according to feature 1.

3. The authentication unit, Authentication is performed using fingerprint and facial recognition. The system according to feature 1.

4. The aforementioned analysis unit, Learn the behavioral patterns of the elderly and perform digital payments at the optimal time. The system according to feature 1.

5. The aforementioned sensor unit is We estimate the emotions of older adults and adjust the frequency of behavioral data collection based on the estimated emotions of older adults. The system according to feature 1.

6. The aforementioned sensor unit is Analyze the past behavioral history of elderly individuals to select the optimal sensor placement. The system according to feature 1.

7. The aforementioned sensor unit is When collecting behavioral data, filtering is performed based on the current health status and activity level of older adults. The system according to feature 1.

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