system

Abstract

The system according to this embodiment aims to allow individuals to have complete control over their own data and to utilize that data as an asset. [Solution] The system according to the embodiment comprises a collection unit, a management unit, a negotiation unit, an analysis unit, and a monitoring unit. The collection unit collects data from the user's online activities and IoT devices. The management unit centrally manages the data collected by the collection unit. The negotiation unit negotiates the optimal recipients and conditions for receiving compensation in exchange for providing the data managed by the management unit. The analysis unit analyzes the market data provided by the negotiation unit. The monitoring unit monitors data usage in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for an individual to completely control their own data and utilize the data as an asset.

[0005] The system according to the embodiment aims to enable an individual to completely control their own data and utilize the data as an asset.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a management unit, a negotiation unit, an analysis unit, and a monitoring unit. The collection unit collects data from users' online activities and IoT devices. The management unit centrally manages the data collected by the collection unit. The negotiation unit negotiates the optimal recipients and conditions for receiving compensation in exchange for providing the data managed by the management unit. The analysis unit analyzes the market data provided by the negotiation unit. The monitoring unit monitors data usage in real time. [Effects of the Invention]

[0007] The system according to this embodiment allows individuals to have complete control over their own data and utilize that data as an asset. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that enables individuals to have complete control over their own data and utilize it as an asset. This system collects and centrally manages data from the user's online activities and IoT devices. Next, the AI ​​negotiates the optimal recipient and conditions for receiving compensation in exchange for providing data. Furthermore, it monitors data usage in real time and analyzes user data to provide insights such as health management and optimization of consumer behavior. This allows individuals to enjoy the value of their data while protecting their privacy, and enables companies to acquire high-quality data in a legitimate manner. For example, the collection unit collects data from the user's online activities and IoT devices. The management unit centrally manages the data collected by the collection unit. The negotiation unit negotiates the optimal recipient and conditions for receiving compensation in exchange for providing the data managed by the management unit. The analysis unit analyzes the market data from the negotiation unit. The monitoring unit monitors data usage in real time. This allows individuals to enjoy the value of their data while protecting their privacy, and enables companies to acquire high-quality data in a legitimate manner. This allows AI agent systems to enable individuals to have complete control over their own data and utilize it as an asset.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a management unit, a negotiation unit, an analysis unit, and a monitoring unit. The collection unit collects data from the user's online activities and IoT devices. For example, the collection unit can collect website browsing history and social media posts from the user's online activities. The collection unit can also collect data from smart home devices and wearable devices from IoT devices. Furthermore, the collection unit can store the data collected from the user's online activities and IoT devices in a database for centralized management. The management unit centrally manages the data collected by the collection unit. For example, the management unit can organize the data using the database and set access permissions. The management unit can also regularly back up the data to ensure its security. Furthermore, the management unit can monitor data usage and detect unauthorized access. The negotiation unit negotiates the optimal recipients and conditions for receiving compensation for providing the data managed by the management unit. For example, the negotiation unit can evaluate the reliability of data recipients and set optimal provision conditions. The negotiation unit can also calculate the compensation for providing the data and notify the user. Furthermore, the Negotiation Department can manage contracts with data providers and monitor compliance with contract terms. The Analysis Department analyzes market data provided by the Negotiation Department. The Analysis Department can, for example, collect competitor data and consumer trend data to help select data providers. The Analysis Department can also evaluate the performance of data providers and set optimal provision conditions. Furthermore, the Analysis Department can predict market trends of data providers and formulate data provision strategies. The Monitoring Department understands data usage in real time. The Monitoring Department can, for example, monitor data usage and detect fraudulent use. The Monitoring Department can also analyze data usage and understand usage patterns. Furthermore, the Monitoring Department can monitor data usage in real time and issue alerts if anomalies are detected. As a result, the AI ​​agent system according to this embodiment can efficiently collect, manage, negotiate, analyze, and monitor user data.

[0030] The data collection unit collects data from users' online activities and IoT devices. Specifically, it collects online activity data such as browsing history of websites visited by users, social media posts, and search history. This allows for an understanding of users' interests and preferences. The data collection unit also collects data from smart home devices and wearable devices. For example, it collects data such as lighting on / off status, room temperature, and security camera footage from smart home devices, and health data such as heart rate, steps taken, and sleep patterns from wearable devices. This data is used to understand users' lifestyles and health status. The data collection unit stores this diverse data in a database for centralized management. The database efficiently organizes the collected data and allows for quick access when needed. Furthermore, the data collection unit can flexibly set the frequency and method of data collection, ensuring that necessary data is collected while protecting user privacy. As a result, the data collection unit can efficiently collect diverse user data and improve the overall system performance.

[0031] The management department centrally manages the data collected by the collection department. Specifically, it organizes the collected data using a database and classifies it according to its type and importance. This allows for quick access to the necessary data. The management department also sets data access permissions and provides different access levels for each user. For example, general users can be given view-only permissions, while administrators can be given permission to edit and delete data. Furthermore, the management department ensures data security by regularly backing up the data. Backups involve saving multiple copies in different locations to protect against data loss or corruption. The management department also monitors data usage and implements security measures to detect unauthorized access and use. For example, if an abnormal access pattern is detected, an alert can be issued, allowing for a quick response. In this way, the management department can manage the collected data safely and efficiently, improving the reliability of the entire system.

[0032] The Negotiation Department negotiates the optimal recipients and terms for receiving compensation for providing data managed by the Management Department. Specifically, it evaluates the reliability of data recipients and determines whether they will use the data appropriately. For example, it investigates the past performance and reputation of recipients and selects highly reliable recipients. The Negotiation Department also sets the conditions for data provision and determines the details of the contract with recipients. For example, it negotiates the purpose and duration of data use, the amount of compensation and payment method, and sets terms that are acceptable to both parties. Furthermore, the Negotiation Department calculates the compensation for data provision and notifies the user. Compensation varies depending on the type and amount of data and the recipient's purpose of use, so the Negotiation Department calculates the compensation considering these factors. The Negotiation Department also manages contracts with data recipients and monitors compliance with contract terms. For example, if a recipient violates the contract terms, it can take appropriate measures. In this way, the Negotiation Department can efficiently negotiate regarding data provision and provide users with appropriate compensation.

[0033] The Analysis Department analyzes market data for the Negotiation Department. Specifically, it collects data on competitors and consumer trends to help select data recipients. For example, by analyzing competitor data, it can understand the market share and growth rate of recipients and select the most suitable recipients. By analyzing consumer trend data, it can understand the target market and consumer needs of recipients and optimize the provision conditions. Furthermore, the Analysis Department evaluates the performance of data recipients and sets optimal provision conditions. For example, it evaluates the data usage and results of recipients and reviews the amount of compensation and provision conditions. In addition, the Analysis Department forecasts market trends of data recipients and formulates data provision strategies. For example, it forecasts market trends of recipients and develops strategies to respond to future fluctuations in demand and supply. In this way, the Analysis Department can select data recipients and optimize provision conditions, thereby improving the overall efficiency of the system.

[0034] The monitoring department monitors data usage in real time. Specifically, it monitors data usage and detects unauthorized use. For example, it analyzes data usage patterns and issues alerts if unusual usage is detected. The monitoring department also analyzes data usage and understands usage patterns. This allows for an understanding of data usage trends and frequencies, enabling the optimization of data delivery strategies. Furthermore, the monitoring department monitors data usage in real time and responds quickly if an anomaly is detected. For example, if unauthorized data use is detected, access is immediately blocked and relevant parties are notified. The monitoring department also regularly reports on data usage to ensure transparency throughout the system. This allows the monitoring department to efficiently monitor data usage and improve the overall security and reliability of the system.

[0035] The data collection unit can analyze the user's past online activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites that the user has frequently accessed in the past. The data collection unit can also concentrate data collection on specific time periods based on the user's past online activity. Furthermore, the data collection unit can analyze the user's past online activity history and select the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the user's past online activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past online activity data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the collection unit can prioritize collecting relevant data based on the content of the website the user is currently browsing. Furthermore, if the user is using a specific application, the collection unit can also collect data related to that application. In addition, the collection unit can efficiently collect data by filtering out unnecessary data based on the user's current activities. This enables efficient data collection by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current activity data into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can also collect data related to their travel destination. In addition, the data collection unit can collect the most relevant data based on the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can collect the most relevant data based on the content of the user's social media posts. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The management department can adjust the level of detail in data management based on the importance of the data. For example, the management department can manage highly important data in detail and less important data in a simplified manner. The management department can also adjust the frequency of management according to the importance of the data. Furthermore, the management department can apply stricter security measures to highly important data. In this way, important data can be managed in detail by adjusting the level of detail in management based on the importance of the data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in management.

[0040] The management department can apply different management algorithms depending on the data category during data management. For example, the management department can apply a strict management algorithm to personal information data. Alternatively, it can apply a simplified management algorithm to general activity data. Furthermore, the management department can select the optimal management algorithm depending on the data category. This allows for optimal data management by applying different management algorithms depending on the data category. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the data category into a generating AI and have the generating AI execute the application of the management algorithm.

[0041] The negotiation unit can adjust the level of detail in negotiations based on the importance of the data. For example, the negotiation unit will conduct detailed negotiations for highly important data. Conversely, it can conduct simplified negotiations for less important data. Furthermore, the negotiation unit can adjust the level of detail in negotiations according to the importance of the data. This allows for detailed negotiations for important data by adjusting the level of detail in negotiations based on the importance of the data. Some or all of the above processes in the negotiation unit may be performed using AI, for example, or not using AI. For example, the negotiation unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in negotiations.

[0042] The negotiation department can apply different negotiation algorithms depending on the data category during negotiations. For example, the negotiation department may apply a rigorous negotiation algorithm to personal information data. It may also apply a simplified negotiation algorithm to general activity data. Furthermore, the negotiation department can select the optimal negotiation algorithm depending on the data category. This allows for optimal negotiation by applying different negotiation algorithms depending on the data category. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not. For example, the negotiation department can input the data category into a generating AI and have the generating AI apply the negotiation algorithm.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a rigorous analysis algorithm to personal information data. It can also apply a simplified analysis algorithm to general activity data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the data category. This allows for optimal analysis by applying different analysis algorithms depending on the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The monitoring unit can adjust the level of detail of monitoring based on the importance of the data during monitoring. For example, the monitoring unit will perform detailed monitoring of high-importance data. It can also perform simplified monitoring of low-importance data. Furthermore, the monitoring unit can adjust the level of detail of monitoring according to the importance of the data. This allows for detailed monitoring of important data by adjusting the level of detail of monitoring based on the importance of the data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of monitoring.

[0046] The monitoring unit can apply different monitoring algorithms depending on the data category during monitoring. For example, the monitoring unit can apply a strict monitoring algorithm to personal information data. It can also apply a simplified monitoring algorithm to general activity data. Furthermore, the monitoring unit can select the optimal monitoring algorithm depending on the data category. This enables optimal monitoring by applying different monitoring algorithms depending on the data category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the data category into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The data collection unit collects user health data and can adjust the frequency of data collection based on the user's health status. For example, if a user is in good health, the frequency of data collection can be reduced. Conversely, if a user's health is deteriorating, the frequency of data collection can be increased to collect more detailed data. Furthermore, the data collection unit can analyze the user's health data and provide insights for health management. This allows for more appropriate data collection by adjusting the frequency of data collection according to the user's health status.

[0049] The negotiation team can analyze a user's past negotiation history and select the optimal negotiation strategy. For example, it can refer to past successful negotiation patterns and adopt a similar strategy. It can also analyze the success rate of negotiations under specific conditions based on past negotiation history and set optimal conditions. Furthermore, it can understand the characteristics of the negotiating partner based on past negotiation history and select an appropriate approach. In this way, the optimal negotiation strategy can be selected by analyzing a user's past negotiation history.

[0050] The data collection unit can adjust the frequency of data collection considering the battery status of the user's device. For example, if the device's battery is low, the data collection frequency can be reduced. Conversely, if the device is charging, the data collection frequency can be increased to collect more detailed data. Furthermore, the timing of data collection can be adjusted according to the device's battery status. This allows for efficient data collection by considering the device's battery status.

[0051] The management department can improve data security by decentralizing data storage locations. For example, critical data can be stored across multiple servers. Data backups can also be stored in different geographical locations. Furthermore, security risks can be reduced by regularly changing data storage locations. In this way, decentralizing data storage locations improves data security.

[0052] The analysis department can evaluate the reliability of the data and prioritize the analysis of highly reliable data. For example, it can perform detailed analysis on highly reliable data, and simplified analysis on less reliable data. Furthermore, it can adjust the analysis method according to the reliability of the data. In this way, by evaluating the reliability of the data, it is possible to prioritize the analysis of highly reliable data.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The data collection unit collects data from the user's online activities and IoT devices. For example, it can collect website browsing history and social media posts from the user's online activities. The data collection unit can also collect data from smart home devices and wearable devices from IoT devices. Furthermore, the data collection unit can store the data collected from the user's online activities and IoT devices in a database for centralized management. Step 2: The management department centrally manages the data collected by the collection department. For example, it can organize the data using a database and set access permissions. The management department can also ensure data security by regularly backing up the data. Furthermore, the management department can monitor data usage and detect unauthorized access. Step 3: The Negotiation Department negotiates the best recipients and terms for compensation for providing data managed by the Management Department. For example, they can evaluate the reliability of data recipients and set optimal provision terms. The Negotiation Department can also calculate compensation for data provision and notify users. Furthermore, the Negotiation Department can manage contracts with data recipients and monitor compliance with contract terms. Step 4: The analysis department analyzes market data from the negotiation department. For example, they can collect competitor data and consumer trend data to help select data providers. The analysis department can also evaluate the performance of data providers and set optimal provision conditions. Furthermore, the analysis department can predict market trends for data providers and formulate data provision strategies. Step 5: The monitoring unit understands data usage in real time. For example, it can monitor data usage and detect unauthorized use. The monitoring unit can also analyze data usage and understand usage patterns. Furthermore, the monitoring unit can monitor data usage in real time and issue alerts if anomalies are detected.

[0055] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that enables individuals to have complete control over their own data and utilize it as an asset. This system collects and centrally manages data from the user's online activities and IoT devices. Next, the AI ​​negotiates the optimal recipient and conditions for receiving compensation in exchange for providing data. Furthermore, it monitors data usage in real time and analyzes user data to provide insights such as health management and optimization of consumer behavior. This allows individuals to enjoy the value of their data while protecting their privacy, and enables companies to acquire high-quality data in a legitimate manner. For example, the collection unit collects data from the user's online activities and IoT devices. The management unit centrally manages the data collected by the collection unit. The negotiation unit negotiates the optimal recipient and conditions for receiving compensation in exchange for providing the data managed by the management unit. The analysis unit analyzes the market data from the negotiation unit. The monitoring unit monitors data usage in real time. This allows individuals to enjoy the value of their data while protecting their privacy, and enables companies to acquire high-quality data in a legitimate manner. This allows AI agent systems to enable individuals to have complete control over their own data and utilize it as an asset.

[0056] The AI ​​agent system according to this embodiment comprises a collection unit, a management unit, a negotiation unit, an analysis unit, and a monitoring unit. The collection unit collects data from the user's online activities and IoT devices. For example, the collection unit can collect website browsing history and social media posts from the user's online activities. The collection unit can also collect data from smart home devices and wearable devices from IoT devices. Furthermore, the collection unit can store the data collected from the user's online activities and IoT devices in a database for centralized management. The management unit centrally manages the data collected by the collection unit. For example, the management unit can organize the data using the database and set access permissions. The management unit can also regularly back up the data to ensure its security. Furthermore, the management unit can monitor data usage and detect unauthorized access. The negotiation unit negotiates the optimal recipients and conditions for receiving compensation for providing the data managed by the management unit. For example, the negotiation unit can evaluate the reliability of data recipients and set optimal provision conditions. The negotiation unit can also calculate the compensation for providing the data and notify the user. Furthermore, the Negotiation Department can manage contracts with data providers and monitor compliance with contract terms. The Analysis Department analyzes market data provided by the Negotiation Department. The Analysis Department can, for example, collect competitor data and consumer trend data to help select data providers. The Analysis Department can also evaluate the performance of data providers and set optimal provision conditions. Furthermore, the Analysis Department can predict market trends of data providers and formulate data provision strategies. The Monitoring Department understands data usage in real time. The Monitoring Department can, for example, monitor data usage and detect fraudulent use. The Monitoring Department can also analyze data usage and understand usage patterns. Furthermore, the Monitoring Department can monitor data usage in real time and issue alerts if anomalies are detected. As a result, the AI ​​agent system according to this embodiment can efficiently collect, manage, negotiate, analyze, and monitor user data.

[0057] The data collection unit collects data from users' online activities and IoT devices. Specifically, it collects online activity data such as browsing history of websites visited by users, social media posts, and search history. This allows for an understanding of users' interests and preferences. The data collection unit also collects data from smart home devices and wearable devices. For example, it collects data such as lighting on / off status, room temperature, and security camera footage from smart home devices, and health data such as heart rate, steps taken, and sleep patterns from wearable devices. This data is used to understand users' lifestyles and health status. The data collection unit stores this diverse data in a database for centralized management. The database efficiently organizes the collected data and allows for quick access when needed. Furthermore, the data collection unit can flexibly set the frequency and method of data collection, ensuring that necessary data is collected while protecting user privacy. As a result, the data collection unit can efficiently collect diverse user data and improve the overall system performance.

[0058] The management department centrally manages the data collected by the collection department. Specifically, it organizes the collected data using a database and classifies it according to its type and importance. This allows for quick access to the necessary data. The management department also sets data access permissions and provides different access levels for each user. For example, general users can be given view-only permissions, while administrators can be given permission to edit and delete data. Furthermore, the management department ensures data security by regularly backing up the data. Backups involve saving multiple copies in different locations to protect against data loss or corruption. The management department also monitors data usage and implements security measures to detect unauthorized access and use. For example, if an abnormal access pattern is detected, an alert can be issued, allowing for a quick response. In this way, the management department can manage the collected data safely and efficiently, improving the reliability of the entire system.

[0059] The Negotiation Department negotiates the optimal recipients and terms for receiving compensation for providing data managed by the Management Department. Specifically, it evaluates the reliability of data recipients and determines whether they will use the data appropriately. For example, it investigates the past performance and reputation of recipients and selects highly reliable recipients. The Negotiation Department also sets the conditions for data provision and determines the details of the contract with recipients. For example, it negotiates the purpose and duration of data use, the amount of compensation and payment method, and sets terms that are acceptable to both parties. Furthermore, the Negotiation Department calculates the compensation for data provision and notifies the user. Compensation varies depending on the type and amount of data and the recipient's purpose of use, so the Negotiation Department calculates the compensation considering these factors. The Negotiation Department also manages contracts with data recipients and monitors compliance with contract terms. For example, if a recipient violates the contract terms, it can take appropriate measures. In this way, the Negotiation Department can efficiently negotiate regarding data provision and provide users with appropriate compensation.

[0060] The Analysis Department analyzes market data for the Negotiation Department. Specifically, it collects data on competitors and consumer trends to help select data recipients. For example, by analyzing competitor data, it can understand the market share and growth rate of recipients and select the most suitable recipients. By analyzing consumer trend data, it can understand the target market and consumer needs of recipients and optimize the provision conditions. Furthermore, the Analysis Department evaluates the performance of data recipients and sets optimal provision conditions. For example, it evaluates the data usage and results of recipients and reviews the amount of compensation and provision conditions. In addition, the Analysis Department forecasts market trends of data recipients and formulates data provision strategies. For example, it forecasts market trends of recipients and develops strategies to respond to future fluctuations in demand and supply. In this way, the Analysis Department can select data recipients and optimize provision conditions, thereby improving the overall efficiency of the system.

[0061] The monitoring department monitors data usage in real time. Specifically, it monitors data usage and detects unauthorized use. For example, it analyzes data usage patterns and issues alerts if unusual usage is detected. The monitoring department also analyzes data usage and understands usage patterns. This allows for an understanding of data usage trends and frequencies, enabling the optimization of data delivery strategies. Furthermore, the monitoring department monitors data usage in real time and responds quickly if an anomaly is detected. For example, if unauthorized data use is detected, access is immediately blocked and relevant parties are notified. The monitoring department also regularly reports on data usage to ensure transparency throughout the system. This allows the monitoring department to efficiently monitor data usage and improve the overall security and reliability of the system.

[0062] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can actively collect detailed data. Furthermore, if the user is in a hurry, the data collection unit can minimize data collection and collect detailed data later. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0063] The data collection unit can analyze the user's past online activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites that the user has frequently accessed in the past. The data collection unit can also concentrate data collection on specific time periods based on the user's past online activity. Furthermore, the data collection unit can analyze the user's past online activity history and select the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the user's past online activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past online activity data into a generating AI and have the generating AI select the optimal data collection method.

[0064] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the collection unit can prioritize collecting relevant data based on the content of the website the user is currently browsing. Furthermore, if the user is using a specific application, the collection unit can also collect data related to that application. In addition, the collection unit can efficiently collect data by filtering out unnecessary data based on the user's current activities. This enables efficient data collection by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current activity data into a generating AI and have the generating AI perform data filtering.

[0065] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. Conversely, if the user is relaxed, the data collection unit can prioritize the collection of highly important data. Furthermore, if the user is in a hurry, the data collection unit can collect only the most important data and collect detailed data later. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0066] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can also collect data related to their travel destination. In addition, the data collection unit can collect the most relevant data based on the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant data.

[0067] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can collect the most relevant data based on the content of the user's social media posts. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0068] The management unit can estimate the user's emotions and adjust data management methods based on the estimated emotions. For example, if the user is stressed, the management unit can simplify data management operations. If the user is relaxed, the management unit can also provide more detailed data management options. Furthermore, if the user is in a hurry, the management unit can prioritize managing only the most important data. This allows for more appropriate data management by adjusting data management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the data management methods.

[0069] The management department can adjust the level of detail in data management based on the importance of the data. For example, the management department can manage highly important data in detail and less important data in a simplified manner. The management department can also adjust the frequency of management according to the importance of the data. Furthermore, the management department can apply stricter security measures to highly important data. In this way, important data can be managed in detail by adjusting the level of detail in management based on the importance of the data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in management.

[0070] The management department can apply different management algorithms depending on the data category during data management. For example, the management department can apply a strict management algorithm to personal information data. Alternatively, it can apply a simplified management algorithm to general activity data. Furthermore, the management department can select the optimal management algorithm depending on the data category. This allows for optimal data management by applying different management algorithms depending on the data category. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the data category into a generating AI and have the generating AI execute the application of the management algorithm.

[0071] The negotiation unit can estimate the user's emotions and adjust the negotiation's presentation based on those emotions. For example, if the user is nervous, the negotiation unit will use a simple and easy-to-understand presentation. If the user is relaxed, the negotiation unit may use a presentation that includes detailed information. Furthermore, if the user is in a hurry, the negotiation unit may use a quick and concise presentation. This allows for more effective negotiation by adjusting the negotiation's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation's presentation.

[0072] The negotiation unit can adjust the level of detail in negotiations based on the importance of the data. For example, the negotiation unit will conduct detailed negotiations for highly important data. Conversely, it can conduct simplified negotiations for less important data. Furthermore, the negotiation unit can adjust the level of detail in negotiations according to the importance of the data. This allows for detailed negotiations for important data by adjusting the level of detail in negotiations based on the importance of the data. Some or all of the above processes in the negotiation unit may be performed using AI, for example, or not using AI. For example, the negotiation unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in negotiations.

[0073] The negotiation department can apply different negotiation algorithms depending on the data category during negotiations. For example, the negotiation department may apply a rigorous negotiation algorithm to personal information data. It may also apply a simplified negotiation algorithm to general activity data. Furthermore, the negotiation department can select the optimal negotiation algorithm depending on the data category. This allows for optimal negotiation by applying different negotiation algorithms depending on the data category. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not. For example, the negotiation department can input the data category into a generating AI and have the generating AI apply the negotiation algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a simplified analysis. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a rigorous analysis algorithm to personal information data. It can also apply a simplified analysis algorithm to general activity data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the data category. This allows for optimal analysis by applying different analysis algorithms depending on the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0077] The monitoring unit can estimate the user's emotions and adjust its monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of monitoring. Conversely, if the user is relaxed, the monitoring unit can increase the frequency of monitoring. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring only important data. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring method.

[0078] The monitoring unit can adjust the level of detail of monitoring based on the importance of the data during monitoring. For example, the monitoring unit will perform detailed monitoring of high-importance data. It can also perform simplified monitoring of low-importance data. Furthermore, the monitoring unit can adjust the level of detail of monitoring according to the importance of the data. This allows for detailed monitoring of important data by adjusting the level of detail of monitoring based on the importance of the data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of monitoring.

[0079] The monitoring unit can apply different monitoring algorithms depending on the data category during monitoring. For example, the monitoring unit can apply a strict monitoring algorithm to personal information data. It can also apply a simplified monitoring algorithm to general activity data. Furthermore, the monitoring unit can select the optimal monitoring algorithm depending on the data category. This enables optimal monitoring by applying different monitoring algorithms depending on the data category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the data category into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The data collection unit collects user health data and can adjust the frequency of data collection based on the user's health status. For example, if a user is in good health, the frequency of data collection can be reduced. Conversely, if a user's health is deteriorating, the frequency of data collection can be increased to collect more detailed data. Furthermore, the data collection unit can analyze the user's health data and provide insights for health management. This allows for more appropriate data collection by adjusting the frequency of data collection according to the user's health status.

[0082] The management department can estimate user emotions and adjust data visualization methods based on those estimates. For example, if a user is stressed, simple and easy-to-understand graphs and charts can be used. Conversely, if a user is relaxed, more complex visualizations including detailed data can be used. Furthermore, if a user is in a hurry, quickly understandable summarized data can be provided. This allows for more effective data management by adjusting data visualization methods according to user emotions.

[0083] The negotiation team can analyze a user's past negotiation history and select the optimal negotiation strategy. For example, it can refer to past successful negotiation patterns and adopt a similar strategy. It can also analyze the success rate of negotiations under specific conditions based on past negotiation history and set optimal conditions. Furthermore, it can understand the characteristics of the negotiating partner based on past negotiation history and select an appropriate approach. In this way, the optimal negotiation strategy can be selected by analyzing a user's past negotiation history.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on those estimates. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide a concise summary. Furthermore, if the user is excited, it can provide visually appealing graphical analysis results. By adjusting the presentation method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate analysis results.

[0085] The monitoring unit can estimate the user's emotions and adjust the alert notification method based on the estimated emotions. For example, if the user is stressed, the alert notification will be less frequent. Conversely, if the user is relaxed, a more detailed alert notification may be provided. Furthermore, if the user is in a hurry, only important alerts can be quickly notified. This allows for more appropriate monitoring by adjusting the alert notification method according to the user's emotions.

[0086] The data collection unit can adjust the frequency of data collection considering the battery status of the user's device. For example, if the device's battery is low, the data collection frequency can be reduced. Conversely, if the device is charging, the data collection frequency can be increased to collect more detailed data. Furthermore, the timing of data collection can be adjusted according to the device's battery status. This allows for efficient data collection by considering the device's battery status.

[0087] The management department can improve data security by decentralizing data storage locations. For example, critical data can be stored across multiple servers. Data backups can also be stored in different geographical locations. Furthermore, security risks can be reduced by regularly changing data storage locations. In this way, decentralizing data storage locations improves data security.

[0088] The negotiation team can estimate the user's emotions and adjust the timing of negotiations based on those estimates. For example, if the user is feeling stressed, the negotiation can be temporarily postponed. Conversely, if the user is relaxed, the negotiation can be advanced more actively. Furthermore, if the user is in a hurry, the negotiation can be expedited. By adjusting the timing of negotiations according to the user's emotions, more effective negotiations become possible.

[0089] The analysis department can evaluate the reliability of the data and prioritize the analysis of highly reliable data. For example, it can perform detailed analysis on highly reliable data, and simplified analysis on less reliable data. Furthermore, it can adjust the analysis method according to the reliability of the data. In this way, by evaluating the reliability of the data, it is possible to prioritize the analysis of highly reliable data.

[0090] The monitoring unit can estimate the user's emotions and adjust the monitoring target based on those emotions. For example, if the user is stressed, it can monitor only important data. If the user is relaxed, it can monitor detailed data. Furthermore, if the user is in a hurry, it can prioritize monitoring only the most important data. This allows for more appropriate monitoring by adjusting the monitoring target according to the user's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The data collection unit collects data from the user's online activities and IoT devices. For example, it can collect website browsing history and social media posts from the user's online activities. The data collection unit can also collect data from smart home devices and wearable devices from IoT devices. Furthermore, the data collection unit can store the data collected from the user's online activities and IoT devices in a database for centralized management. Step 2: The management department centrally manages the data collected by the collection department. For example, it can organize the data using a database and set access permissions. The management department can also ensure data security by regularly backing up the data. Furthermore, the management department can monitor data usage and detect unauthorized access. Step 3: The Negotiation Department negotiates the best recipients and terms for compensation for providing data managed by the Management Department. For example, they can evaluate the reliability of data recipients and set optimal provision terms. The Negotiation Department can also calculate compensation for data provision and notify users. Furthermore, the Negotiation Department can manage contracts with data recipients and monitor compliance with contract terms. Step 4: The analysis department analyzes market data from the negotiation department. For example, they can collect competitor data and consumer trend data to help select data providers. The analysis department can also evaluate the performance of data providers and set optimal provision conditions. Furthermore, the analysis department can predict market trends for data providers and formulate data provision strategies. Step 5: The monitoring unit understands data usage in real time. For example, it can monitor data usage and detect unauthorized use. The monitoring unit can also analyze data usage and understand usage patterns. Furthermore, the monitoring unit can monitor data usage in real time and issue alerts if anomalies are detected.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] Each of the multiple elements described above, including the collection unit, management unit, negotiation unit, analysis unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data from the user's online activities and IoT devices using the camera 42 and microphone 38B of the smart device 14. The management unit is implemented in the specific processing unit 290 of the data processing unit 12 and centrally manages the collected data. The negotiation unit is implemented in the specific processing unit 290 of the data processing unit 12 and negotiates the optimal recipients and conditions for receiving compensation in exchange for providing data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes market data for the negotiation unit. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12 and monitors data usage in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, management unit, negotiation unit, analysis unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from the user's online activities and IoT devices using the camera 42 and microphone 238 of the smart glasses 214. The management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and centrally manages the collected data. The negotiation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and negotiates the optimal recipients and conditions for receiving compensation in exchange for providing data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and the negotiation unit analyzes market data. The monitoring unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and monitors data usage in real time. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0128] Each of the multiple elements described above, including the collection unit, management unit, negotiation unit, analysis unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data from the user's online activities and IoT devices using the camera 42 and microphone 238 of the headset terminal 314. The management unit is implemented in the specific processing unit 290 of the data processing unit 12 and centrally manages the collected data. The negotiation unit is implemented in the specific processing unit 290 of the data processing unit 12 and negotiates the optimal recipients and conditions for receiving compensation in exchange for providing data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes market data for the negotiation unit. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12 and monitors data usage in real time. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[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 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.

[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 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).

[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] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, management unit, negotiation unit, analysis unit, and monitoring unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data from the user's online activities and IoT devices using the camera 42 and microphone 238 of the robot 414. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and centrally manages the collected data. The negotiation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and negotiates the optimal recipient and conditions for receiving compensation in exchange for providing data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the negotiation unit analyzes market data. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and grasps the data usage status in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Note 1) A data collection unit that collects data from users' online activities and IoT devices, A management unit centrally manages the data collected by the aforementioned collection unit, A negotiation department that negotiates the optimal recipients and conditions for receiving compensation in exchange for providing data managed by the aforementioned management department, The aforementioned negotiation department and the analysis department, which analyzes market data, The system includes a monitoring unit that grasps the usage status of the aforementioned data in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past online activity history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, When managing data, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, When managing data, different management algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned negotiating body said, During negotiations, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned negotiating body said, During negotiations, different negotiation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, During monitoring, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from users' online activities and IoT devices, A management unit centrally manages the data collected by the aforementioned collection unit, A negotiation department that negotiates the optimal recipients and conditions for receiving compensation in exchange for providing data managed by the aforementioned management department, The aforementioned negotiation department and the analysis department, which analyzes market data, The system includes a monitoring unit that grasps the usage status of the aforementioned data in real time. A system characterized by the following features.

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

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

4. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

8. The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system according to feature 1.

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