system

Abstract

The system according to this embodiment aims to efficiently optimize the user's packet consumption. [Solution] The system according to the embodiment comprises a monitoring unit, an optimization unit, and a provision unit. The monitoring unit monitors packet consumption. The optimization unit optimizes packet consumption based on the data monitored by the monitoring unit. The provision unit provides the results of the packet consumption optimized by the optimization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently manage and adjust the user's packet consumption, and there is a risk of wasteful packet consumption.

[0005] The system according to the embodiment aims to efficiently optimize the user's packet consumption.

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring unit, an optimization unit, and a providing unit. The monitoring unit monitors packet consumption. The optimization unit optimizes packet consumption based on the data monitored by the monitoring unit. The providing unit provides the result of the optimized packet consumption by the optimization unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently optimize the user's packet consumption. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The packet management system according to an embodiment of the present invention is a new packet plan that dynamically manages and adjusts user packet consumption using an AI agent. This packet management system aims to optimize the amount of packets consumed in real time, enabling users to use the internet efficiently without waste. The packet management system aims to acquire 60 million new users and achieve customer migration through network optimization and the most competitive pricing in the industry. By providing flexible support to users and optimizing data consumption, it aims to increase user satisfaction and build a sustainable business model. For example, the packet management system uses an AI agent to monitor user packet consumption in real time and optimize packet consumption based on network congestion and user behavior data. For example, it maximizes network efficiency by reducing packet consumption during high-demand times and in low-demand times and in low-demand times. The packet management system also provides the optimal packet plan for each individual user based on user attributes and behavior data. Furthermore, the AI ​​agent performs demand forecasting and price optimization, adjusting the price of packet consumption in real time. This allows users to use the internet at the most cost-effective rate. For example, pricing can be tailored to various scenarios, such as event-linked data consumption and premium pricing for data consumption. This system allows users to use the internet efficiently without waste, prevents network congestion, and improves overall performance. For instance, smartphone users can enjoy a comfortable internet experience without being hindered by data speed restrictions. Furthermore, because the AI ​​agent autonomously solves complex tasks, users can enjoy the optimal data plan without any hassle. This new data plan aims to acquire 60 million new users and attract new customers by offering the lowest prices in the industry and a less congested network. This will increase user satisfaction and help build a sustainable business model.This allows the packet management system to efficiently manage user packet consumption and provide optimal internet usage.

[0029] The packet management system according to this embodiment comprises a monitoring unit, an optimization unit, and a provision unit. The monitoring unit monitors the user's packet consumption. The monitoring unit can, for example, monitor the user's data traffic and the usage of specific applications. The monitoring unit can, for example, monitor the user's packet consumption in real time and grasp fluctuations in data traffic. The monitoring unit can also collect user behavior data and analyze packet consumption patterns. For example, the monitoring unit collects user access logs and application usage history to grasp trends in packet consumption. The optimization unit optimizes packet consumption based on the data monitored by the monitoring unit. For example, the optimization unit can reduce packet consumption during times or in areas with high demand and increase it during times or in areas with low demand. The optimization unit can, for example, use an AI algorithm to optimize packet consumption. The optimization unit can also provide an optimal packet plan to individual users based on user attributes and behavior data. For example, the optimization unit proposes an optimal packet plan according to the user's age, gender, and usage. The provision unit provides the packet consumption results optimized by the optimization unit. The service provider can, for example, notify the user of the results of optimized packet consumption. For example, the service provider can send a notification to the user's smartphone and display the results of optimized packet consumption. The service provider can also save the user's packet consumption history so that it can be referenced later. For example, the service provider can save the user's packet consumption history to the cloud so that the user can access it at any time. As a result, the packet management system according to the embodiment can efficiently monitor, optimize, and provide the user's packet consumption.

[0030] The monitoring unit monitors user packet consumption. For example, the monitoring unit can monitor a user's data traffic and the usage of specific applications. Specifically, the monitoring unit acquires data traffic in real time from the user's device, such as a smartphone or tablet, and sends this data to a central server. This allows for a detailed understanding of which applications the user is using and to what extent, and at what times data traffic is concentrated. Furthermore, the monitoring unit can collect user behavior data and analyze packet consumption patterns. For example, if a user frequently uses video streaming services during a specific time period, a surge in data traffic during that time can be predicted. By understanding such patterns, the monitoring unit can analyze user packet consumption trends in detail and predict future data traffic demand. The monitoring unit also collects user access logs and application usage history, and uses this data to understand packet consumption trends. For example, if a user frequently uses a particular application, it can be seen that the data traffic of that application has a significant impact on overall packet consumption. This allows the monitoring unit to closely monitor user packet consumption and understand fluctuations in data traffic in real time. Furthermore, the monitoring department can centrally manage this data and collaborate with other systems and departments as needed. For example, data collected by the monitoring department can be stored on a cloud server and made accessible to the optimization and provision departments. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the monitoring department to collect data efficiently and effectively, improving the overall system performance.

[0031] The optimization unit optimizes packet consumption based on data monitored by the monitoring unit. For example, the optimization unit can reduce packet consumption during high-demand times and in low-demand times and in low-demand times and in low-demand areas. Specifically, the optimization unit uses an AI algorithm to optimize packet consumption. The AI ​​algorithm analyzes data provided by the monitoring unit to understand the patterns and trends of user data communication. For example, if data communication is concentrated during a specific time period, the optimization unit can adjust the priority of data communication to reduce packet consumption during that time. Conversely, by increasing packet consumption during low-demand times and in low-demand areas, it can promote efficient network utilization. Furthermore, the optimization unit can also provide individual users with the optimal packet plan based on user attributes and behavioral data. For example, the optimization unit proposes the optimal packet plan according to the user's age, gender, and usage. Younger users tend to use many data-intensive applications such as video streaming and online games, so it is appropriate to propose a large-capacity packet plan. On the other hand, older users tend to mainly use applications that use less data, such as email and web browsing, so it is appropriate to propose a small-capacity packet plan. This allows the optimization unit to provide the optimal packet plan tailored to user needs, thereby improving user satisfaction. Furthermore, the optimization unit can continuously modify the optimization results based on real-time updated data, adapting to the latest situations. For example, if a user's data traffic changes rapidly, the optimization unit immediately incorporates the new data and updates the optimization results. The optimization unit can also perform more accurate optimization by considering regional characteristics and past data traffic history. As a result, the optimization unit can always provide highly accurate packet consumption optimization based on the latest information, supporting efficient network utilization.

[0032] The service provider provides the results of packet consumption optimized by the optimization unit. For example, the service provider can notify users of the optimized packet consumption results. Specifically, the service provider sends a notification to the user's smartphone displaying the optimized packet consumption results. Through the smartphone notification, users can check their packet consumption status and optimization results in real time. For example, the service provider displays the usage status of the user's current packet plan and how much data has been saved through optimization. The service provider can also save the user's packet consumption history for later reference. For example, the service provider saves the user's packet consumption history to the cloud, allowing the user to access it at any time. This allows users to review past packet consumption trends and plan their future data usage. Furthermore, the service provider can collect user feedback and provide it to the optimization and monitoring units. For example, it can collect whether users are satisfied with the provided packet plan, and gather opinions and requests regarding the optimization results, which can then be used to improve the entire system. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to deliver optimized data usage results to users quickly and reliably, thereby improving user satisfaction.

[0033] The forecasting unit can perform demand forecasting. For example, the forecasting unit can predict future demand based on a user's past packet consumption data. For example, the forecasting unit can perform demand forecasting using AI algorithms. For example, the forecasting unit can perform demand forecasting considering user behavior data and network congestion. For example, the forecasting unit predicts future packet consumption based on a user's past data communication volume and application usage history. The forecasting unit can also monitor network congestion in real time and perform demand forecasting. For example, the forecasting unit monitors network traffic volume and latency to predict fluctuations in demand. As a result, demand forecasting improves the optimization of packet consumption.

[0034] The pricing unit can perform price optimization. For example, the pricing unit can set optimal rates based on user packet consumption data. For example, the pricing unit can perform price optimization using AI algorithms. For example, the pricing unit can perform price optimization considering demand forecast data and network congestion. For example, the pricing unit proposes the optimal pricing plan based on the user's past packet consumption data and behavioral data. The pricing unit can also monitor network congestion in real time and perform price optimization. For example, the pricing unit monitors network traffic volume and latency and sets rates according to demand. By optimizing prices in this way, it becomes possible to set the most optimal rates for users.

[0035] The data collection unit can collect user behavior data. For example, the data collection unit can collect user access logs and app usage history. For example, the data collection unit can use AI algorithms to collect behavior data. For example, the data collection unit can collect data from the user's device and use it to optimize packet consumption. For example, the data collection unit collects access logs and app usage history from the user's smartphone and analyzes packet consumption patterns. The data collection unit can also collect user behavior data in real time and use it to optimize packet consumption. For example, the data collection unit collects data from the user's device in real time and understands fluctuations in packet consumption. As a result, packet consumption optimization improves by collecting user behavior data.

[0036] The monitoring unit can monitor network congestion. For example, the monitoring unit can monitor network traffic volume and latency. For example, the monitoring unit can monitor network congestion using AI algorithms. For example, the monitoring unit can monitor network congestion in real time and detect anomalies. For example, the monitoring unit can detect an anomaly and issue an alert if network traffic volume suddenly increases. The monitoring unit can also detect an anomaly and take countermeasures if network latency increases. For example, the monitoring unit can detect an anomaly if network latency exceeds a certain threshold and distribute traffic. This improves the optimization of packet consumption by monitoring network congestion.

[0037] The optimization unit can reduce packet consumption during high-demand times and in high-demand areas, and increase it during low-demand times and in low-demand areas. For example, the optimization unit can use AI algorithms to optimize packet consumption according to demand. For example, the optimization unit can monitor demand in real time and adjust packet consumption accordingly. For instance, the optimization unit reduces packet consumption during high-demand times to alleviate network load. Conversely, the optimization unit can increase packet consumption during low-demand times to maximize network efficiency. For example, the optimization unit can provide users with additional packets during low-demand times to encourage internet usage. This enables the optimization of packet consumption according to demand.

[0038] The service provider can provide users with the results of optimized packet consumption. For example, the service provider can notify users of the results of optimized packet consumption. For example, the service provider can provide the results of optimized packet consumption using an AI algorithm. For example, the service provider can send a notification to the user's smartphone and display the results of optimized packet consumption. For example, the service provider can notify users of the results of optimized packet consumption in real time, allowing them to understand their internet usage. The service provider can also save the user's packet consumption history and make it available for later reference. For example, the service provider can save the user's packet consumption history to the cloud, allowing the user to access it at any time. By providing users with the results of optimized packet consumption, user satisfaction can be improved.

[0039] The monitoring unit can monitor network congestion in real time and detect anomalies. For example, the monitoring unit can monitor network traffic volume in real time and detect abnormal increases. For example, the monitoring unit can monitor network latency in real time and detect abnormal delays. For example, the monitoring unit can monitor network packet loss rate in real time and detect abnormal losses. This enables rapid response by detecting network anomalies in real time. 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 network traffic data into a generating AI and have the generating AI perform anomaly detection.

[0040] The monitoring unit can monitor the user's device usage and propose optimal packet consumption. For example, the monitoring unit can monitor the user's device's battery level and propose packet consumption that reduces battery consumption. For example, the monitoring unit can monitor the user's device's CPU usage and propose packet consumption that reduces CPU load. For example, the monitoring unit can monitor the user's device's memory usage and propose packet consumption that reduces memory consumption. This enables efficient use by proposing optimal packet consumption according to the user's device usage. 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 user device data into a generating AI and have the generating AI execute a proposal for optimal packet consumption.

[0041] The monitoring unit can adjust its monitoring range by considering geographical factors when monitoring network congestion. For example, the monitoring unit can prioritize monitoring congestion in urban areas to identify areas prone to congestion. For example, the monitoring unit can monitor congestion in suburban areas to identify areas less prone to congestion. For example, the monitoring unit can monitor congestion in areas where a specific event is held and predict congestion during the event period. This allows for efficient monitoring by adjusting the monitoring range by considering geographical factors. 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 geographical data into a generating AI and have the generating AI adjust the monitoring range.

[0042] The monitoring unit can monitor a user's social media activity and identify related packet consumption patterns. For example, if a user frequently watches videos on social media, the monitoring unit can identify packet consumption patterns related to video viewing. For example, if a user frequently posts images on social media, the monitoring unit can identify packet consumption patterns related to image posting. For example, if a user frequently sends messages on social media, the monitoring unit can identify packet consumption patterns related to message sending. By identifying packet consumption patterns based on the user's social media activity, efficient packet consumption becomes possible. 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 social media data into a generating AI and have the generating AI perform the identification of packet consumption patterns.

[0043] The optimization unit can improve the accuracy of optimization by referring to past data when optimizing packet consumption. For example, the optimization unit can refer to the user's past packet consumption data to identify the optimal consumption pattern. For example, the optimization unit can refer to past network congestion data to identify the optimal consumption pattern that avoids congestion. For example, the optimization unit can refer to the user's past behavior data to identify the optimal consumption pattern based on behavior patterns. As a result, the accuracy of optimization is improved by referring to past data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past data into a generating AI and have the generating AI perform the task of identifying the optimal consumption pattern.

[0044] The optimization unit can perform optimization while considering the user's device characteristics when optimizing packet consumption. For example, the optimization unit can consider the user's device's battery level and perform optimization to reduce battery consumption. For example, the optimization unit can consider the user's device's CPU usage and perform optimization to reduce CPU load. For example, the optimization unit can consider the user's device's memory usage and perform optimization to reduce memory consumption. This makes efficient optimization possible by considering the user's device characteristics. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user device data into a generating AI and have the generating AI perform the optimization.

[0045] The optimization unit can optimize packet consumption by taking into account the user's geographical location information. For example, if the user is in an urban area, the optimization unit can perform optimization to avoid congestion. For example, if the user is in a suburban area, the optimization unit can perform optimization to consume packets efficiently. For example, if the user is participating in a specific event, the optimization unit can perform optimization related to that event. This makes efficient optimization possible by taking into account the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the user's geographical location data into a generating AI and have the generating AI perform the optimization.

[0046] The optimization unit can analyze the user's social media activity and select an optimization method when optimizing packet consumption. For example, if a user frequently watches videos on social media, the optimization unit can select the optimal consumption pattern for video viewing. For example, if a user frequently posts images on social media, the optimization unit can select the optimal consumption pattern for image posting. For example, if a user frequently sends messages on social media, the optimization unit can select the optimal consumption pattern for message sending. This enables efficient optimization by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input social media data into a generating AI and have the generating AI select an optimization method.

[0047] The service provider can select the optimal service delivery method by referring to the user's past usage history when providing the results of optimized packet consumption. For example, the service provider can refer to the user's past usage history and select the optimal service delivery method. For example, the service provider can prioritize providing frequently used information from the user's past usage history. For example, the service provider can analyze the user's past usage history and select the most efficient service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history into a generating AI and have the generating AI select the optimal service delivery method.

[0048] The information provider can customize the information it provides, taking into account the user's device characteristics. For example, the provider can provide information in an optimal display format, taking into account the screen size of the user's device. For example, the provider can provide information in a format that minimizes battery consumption, taking into account the user's device's battery level. For example, the provider can provide information in a format that minimizes memory consumption, taking into account the user's device's memory usage. This enables efficient information provision by considering the user's device characteristics. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user device data into a generating AI and have the generating AI perform the information customization.

[0049] The service provider can adjust its delivery method by considering the user's geographical location when providing the results of optimized packet consumption. For example, if the user is in an urban area, the service provider can provide information to avoid congestion. For example, if the user is in a suburban area, the service provider can provide information to ensure efficient packet consumption. For example, if the user is participating in a specific event, the service provider can provide information related to that event. This enables efficient information delivery by considering the user's geographical location. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0050] The information provider can customize the information it provides to reflect the user's social media activity. For example, if a user frequently watches videos on social media, the provider can prioritize providing information related to video viewing. For example, if a user frequently posts images on social media, the provider can prioritize providing information related to image posting. For example, if a user frequently sends messages on social media, the provider can prioritize providing information related to message sending. This enables efficient information provision by reflecting the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input social media data into a generating AI and have the generating AI perform the information customization.

[0051] The forecasting unit can improve the accuracy of its forecasts by referring to past data when forecasting demand. For example, the forecasting unit can improve the accuracy of its demand forecasts by referring to users' past packet consumption data. For example, the forecasting unit can perform demand forecasts that avoid congestion by referring to past network congestion data. For example, the forecasting unit can perform demand forecasts based on users' past behavior data by referring to users' past behavior data. In this way, the accuracy of demand forecasts is improved by referring to past data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input past data into a generating AI and have the generating AI perform the demand forecast.

[0052] The forecasting unit can perform demand forecasting while considering the user's device characteristics. For example, the forecasting unit can consider the user's device's battery level and perform demand forecasting that reduces battery consumption. For example, the forecasting unit can consider the user's device's CPU usage and perform demand forecasting that reduces CPU load. For example, the forecasting unit can consider the user's device's memory usage and perform demand forecasting that reduces memory consumption. This makes efficient demand forecasting possible by considering the user's device characteristics. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input user device data into a generating AI and have the generating AI perform the demand forecasting.

[0053] The forecasting unit can perform demand forecasting while considering the user's geographical location information. For example, if the user is in an urban area, the forecasting unit can perform demand forecasting that avoids congestion. For example, if the user is in a suburban area, the forecasting unit can perform demand forecasting that optimizes packet consumption. For example, if the user is participating in a specific event, the forecasting unit can perform demand forecasting related to that event. This makes efficient demand forecasting possible by considering the user's geographical location information. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the user's geographical location data into a generating AI and have the generating AI perform the demand forecasting.

[0054] The forecasting unit can analyze users' social media activity and select a forecasting method when forecasting demand. For example, if a user frequently watches videos on social media, the forecasting unit can forecast demand related to video viewing. For example, if a user frequently posts images on social media, the forecasting unit can forecast demand related to image posting. For example, if a user frequently sends messages on social media, the forecasting unit can forecast demand related to message sending. This enables efficient demand forecasting by analyzing users' social media activity. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input social media data into a generating AI and have the generating AI select a forecasting method.

[0055] The pricing unit can improve the accuracy of price optimization by referring to historical data during price optimization. For example, the pricing unit can improve the accuracy of price optimization by referring to the user's past packet consumption data. For example, the pricing unit can perform price optimization that avoids congestion by referring to historical network congestion data. For example, the pricing unit can perform price optimization based on behavioral patterns by referring to the user's past behavior data. In this way, the accuracy of price optimization is improved by referring to historical data. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without using AI. For example, the pricing unit can input historical data into a generating AI and have the generating AI perform price optimization.

[0056] The pricing unit can optimize prices by considering the user's device characteristics. For example, the pricing unit can optimize prices by considering the user's device's battery level to reduce battery consumption. For example, the pricing unit can optimize prices by considering the user's device's CPU usage to reduce CPU load. For example, the pricing unit can optimize prices by considering the user's device's memory usage to reduce memory consumption. This enables efficient price optimization by considering the user's device characteristics. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input user device data into a generating AI and have the generating AI perform the price optimization.

[0057] The pricing unit can optimize prices by taking into account the user's geographical location. For example, if the user is in an urban area, the pricing unit can optimize prices to avoid congestion. For example, if the user is in a suburban area, the pricing unit can optimize prices to ensure efficient packet consumption. For example, if the user is participating in a specific event, the pricing unit can optimize prices related to that event. This enables efficient price optimization by considering the user's geographical location. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the user's geographical location data into a generating AI and have the generating AI perform the price optimization.

[0058] The pricing unit can analyze users' social media activity and select optimization methods when optimizing prices. For example, if a user frequently watches videos on social media, the pricing unit can perform price optimization related to video viewing. For example, if a user frequently posts images on social media, the pricing unit can perform price optimization related to image posting. For example, if a user frequently sends messages on social media, the pricing unit can perform price optimization related to message sending. This enables efficient price optimization by analyzing users' social media activity. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input social media data into a generating AI and have the generating AI select optimization methods.

[0059] The data collection unit can improve the accuracy of data collection by referring to past data when collecting behavioral data. For example, the data collection unit can improve the accuracy of data collection by referring to the user's past behavioral data. For example, the data collection unit can refer to past network congestion data and use a collection method that avoids congestion. For example, the data collection unit can refer to the user's past packet consumption data and use a collection method based on consumption patterns. This improves the accuracy of behavioral data collection by referring to past data. 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 past data into a generating AI and have the generating AI execute the collection method.

[0060] The data collection unit can collect behavioral data while considering the user's device characteristics. For example, the data collection unit can consider the user's device's battery level and use a collection method that reduces battery consumption. For example, the data collection unit can consider the user's device's CPU usage and use a collection method that reduces CPU load. For example, the data collection unit can consider the user's device's memory usage and use a collection method that reduces memory consumption. This enables efficient data collection by considering the user's device characteristics. 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 user device data into a generating AI and have the generating AI execute the data collection method.

[0061] The data collection unit can collect behavioral data while considering the user's geographical location. For example, if the user is in an urban area, the data collection unit can prioritize collecting behavioral data to avoid congestion. For example, if the user is in a suburban area, the data collection unit can collect behavioral data to efficiently consume data packets. For example, if the user is participating in a specific event, the data collection unit can collect behavioral data related to that event. This enables efficient data collection by considering the user's geographical location. 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 execute the data collection method.

[0062] The data collection unit can analyze the user's social media activity and select a data collection method when collecting behavioral data. For example, if a user frequently watches videos on social media, the data collection unit can collect behavioral data related to video viewing. For example, if a user frequently posts images on social media, the data collection unit can collect behavioral data related to image posting. For example, if a user frequently sends messages on social media, the data collection unit can collect behavioral data related to message sending. This enables efficient data collection 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 social media data into a generating AI and have the generating AI select a data collection method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The packet management system can optimize packet consumption by considering the user's device characteristics. For example, it can monitor the user's device's battery level and suggest packet consumption that reduces battery consumption. It can also monitor the user's device's CPU usage and suggest packet consumption that reduces CPU load. Furthermore, it can monitor the user's device's memory usage and suggest packet consumption that reduces memory consumption. This enables optimal packet consumption tailored to the user's device characteristics.

[0065] The packet management system can optimize packet consumption by considering the user's geographical location. For example, if the user is in an urban area, packet consumption can be reduced to avoid congestion. Conversely, if the user is in a suburban area, packet consumption can be increased for more efficient use. Furthermore, if the user is participating in a specific event, packet consumption related to that event can be optimized. This enables optimal packet consumption tailored to the user's geographical location.

[0066] The packet management system can analyze a user's social media activity and optimize packet consumption. For example, if a user frequently watches videos on social media, it can optimize packet consumption related to video viewing. Similarly, if a user frequently posts images on social media, it can optimize packet consumption related to image posting. Furthermore, if a user frequently sends messages on social media, it can optimize packet consumption related to message sending. This enables optimal packet consumption based on the user's social media activity.

[0067] A packet management system can optimize packet consumption by referring to a user's past packet consumption data. For example, it can identify the optimal consumption pattern by referring to a user's past data traffic. It can also identify the optimal consumption pattern to avoid congestion by referring to past network congestion data. Furthermore, it can identify the optimal consumption pattern based on a user's past behavioral data. As a result, packet consumption optimization is improved by referring to past data.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The monitoring unit monitors user packet consumption. Specifically, it monitors the amount of data traffic and the usage of specific applications in real time to understand fluctuations in data traffic. It also collects user behavior data and analyzes packet consumption patterns. For example, it collects user access logs and application usage history to understand trends in packet consumption. Step 2: The optimization unit optimizes packet consumption based on data monitored by the monitoring unit. Specifically, it can reduce packet consumption during high-demand times and in low-demand times and in low-demand times. Furthermore, it uses an AI algorithm to optimize packet consumption and provides the optimal packet plan for each individual user based on user attributes and behavioral data. For example, it can suggest the optimal packet plan according to the user's age, gender, and usage. Step 3: The service provider provides the results of the packet consumption optimized by the optimization unit. Specifically, it notifies the user of the optimized packet consumption results and sends a notification to the user's smartphone to display the results. It also saves the user's packet consumption history so that it can be referenced later. For example, the user's packet consumption history is saved to the cloud so that the user can access it at any time.

[0070] (Example of form 2) The packet management system according to an embodiment of the present invention is a new packet plan that dynamically manages and adjusts user packet consumption using an AI agent. This packet management system aims to optimize the amount of packets consumed in real time, enabling users to use the internet efficiently without waste. The packet management system aims to acquire 60 million new users and achieve customer migration through network optimization and the most competitive pricing in the industry. By providing flexible support to users and optimizing data consumption, it aims to increase user satisfaction and build a sustainable business model. For example, the packet management system uses an AI agent to monitor user packet consumption in real time and optimize packet consumption based on network congestion and user behavior data. For example, it maximizes network efficiency by reducing packet consumption during high-demand times and in low-demand times and in low-demand times. The packet management system also provides the optimal packet plan for each individual user based on user attributes and behavior data. Furthermore, the AI ​​agent performs demand forecasting and price optimization, adjusting the price of packet consumption in real time. This allows users to use the internet at the most cost-effective rate. For example, pricing can be tailored to various scenarios, such as event-linked data consumption and premium pricing for data consumption. This system allows users to use the internet efficiently without waste, prevents network congestion, and improves overall performance. For instance, smartphone users can enjoy a comfortable internet experience without being hindered by data speed restrictions. Furthermore, because the AI ​​agent autonomously solves complex tasks, users can enjoy the optimal data plan without any hassle. This new data plan aims to acquire 60 million new users and attract new customers by offering the lowest prices in the industry and a less congested network. This will increase user satisfaction and help build a sustainable business model.This allows the packet management system to efficiently manage user packet consumption and provide optimal internet usage.

[0071] The packet management system according to this embodiment comprises a monitoring unit, an optimization unit, and a provision unit. The monitoring unit monitors the user's packet consumption. The monitoring unit can, for example, monitor the user's data traffic and the usage of specific applications. The monitoring unit can, for example, monitor the user's packet consumption in real time and grasp fluctuations in data traffic. The monitoring unit can also collect user behavior data and analyze packet consumption patterns. For example, the monitoring unit collects user access logs and application usage history to grasp trends in packet consumption. The optimization unit optimizes packet consumption based on the data monitored by the monitoring unit. For example, the optimization unit can reduce packet consumption during times or in areas with high demand and increase it during times or in areas with low demand. The optimization unit can, for example, use an AI algorithm to optimize packet consumption. The optimization unit can also provide an optimal packet plan to individual users based on user attributes and behavior data. For example, the optimization unit proposes an optimal packet plan according to the user's age, gender, and usage. The provision unit provides the packet consumption results optimized by the optimization unit. The service provider can, for example, notify the user of the results of optimized packet consumption. For example, the service provider can send a notification to the user's smartphone and display the results of optimized packet consumption. The service provider can also save the user's packet consumption history so that it can be referenced later. For example, the service provider can save the user's packet consumption history to the cloud so that the user can access it at any time. As a result, the packet management system according to the embodiment can efficiently monitor, optimize, and provide the user's packet consumption.

[0072] The monitoring unit monitors user packet consumption. For example, the monitoring unit can monitor a user's data traffic and the usage of specific applications. Specifically, the monitoring unit acquires data traffic in real time from the user's device, such as a smartphone or tablet, and sends this data to a central server. This allows for a detailed understanding of which applications the user is using and to what extent, and at what times data traffic is concentrated. Furthermore, the monitoring unit can collect user behavior data and analyze packet consumption patterns. For example, if a user frequently uses video streaming services during a specific time period, a surge in data traffic during that time can be predicted. By understanding such patterns, the monitoring unit can analyze user packet consumption trends in detail and predict future data traffic demand. The monitoring unit also collects user access logs and application usage history, and uses this data to understand packet consumption trends. For example, if a user frequently uses a particular application, it can be seen that the data traffic of that application has a significant impact on overall packet consumption. This allows the monitoring unit to closely monitor user packet consumption and understand fluctuations in data traffic in real time. Furthermore, the monitoring department can centrally manage this data and collaborate with other systems and departments as needed. For example, data collected by the monitoring department can be stored on a cloud server and made accessible to the optimization and provision departments. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the monitoring department to collect data efficiently and effectively, improving the overall system performance.

[0073] The optimization unit optimizes packet consumption based on data monitored by the monitoring unit. For example, the optimization unit can reduce packet consumption during high-demand times and in low-demand times and in low-demand times and in low-demand areas. Specifically, the optimization unit uses an AI algorithm to optimize packet consumption. The AI ​​algorithm analyzes data provided by the monitoring unit to understand the patterns and trends of user data communication. For example, if data communication is concentrated during a specific time period, the optimization unit can adjust the priority of data communication to reduce packet consumption during that time. Conversely, by increasing packet consumption during low-demand times and in low-demand areas, it can promote efficient network utilization. Furthermore, the optimization unit can also provide individual users with the optimal packet plan based on user attributes and behavioral data. For example, the optimization unit proposes the optimal packet plan according to the user's age, gender, and usage. Younger users tend to use many data-intensive applications such as video streaming and online games, so it is appropriate to propose a large-capacity packet plan. On the other hand, older users tend to mainly use applications that use less data, such as email and web browsing, so it is appropriate to propose a small-capacity packet plan. This allows the optimization unit to provide the optimal packet plan tailored to user needs, thereby improving user satisfaction. Furthermore, the optimization unit can continuously modify the optimization results based on real-time updated data, adapting to the latest situations. For example, if a user's data traffic changes rapidly, the optimization unit immediately incorporates the new data and updates the optimization results. The optimization unit can also perform more accurate optimization by considering regional characteristics and past data traffic history. As a result, the optimization unit can always provide highly accurate packet consumption optimization based on the latest information, supporting efficient network utilization.

[0074] The service provider provides the results of packet consumption optimized by the optimization unit. For example, the service provider can notify users of the optimized packet consumption results. Specifically, the service provider sends a notification to the user's smartphone displaying the optimized packet consumption results. Through the smartphone notification, users can check their packet consumption status and optimization results in real time. For example, the service provider displays the usage status of the user's current packet plan and how much data has been saved through optimization. The service provider can also save the user's packet consumption history for later reference. For example, the service provider saves the user's packet consumption history to the cloud, allowing the user to access it at any time. This allows users to review past packet consumption trends and plan their future data usage. Furthermore, the service provider can collect user feedback and provide it to the optimization and monitoring units. For example, it can collect whether users are satisfied with the provided packet plan, and gather opinions and requests regarding the optimization results, which can then be used to improve the entire system. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to deliver optimized data usage results to users quickly and reliably, thereby improving user satisfaction.

[0075] The forecasting unit can perform demand forecasting. For example, the forecasting unit can predict future demand based on a user's past packet consumption data. For example, the forecasting unit can perform demand forecasting using AI algorithms. For example, the forecasting unit can perform demand forecasting considering user behavior data and network congestion. For example, the forecasting unit predicts future packet consumption based on a user's past data communication volume and application usage history. The forecasting unit can also monitor network congestion in real time and perform demand forecasting. For example, the forecasting unit monitors network traffic volume and latency to predict fluctuations in demand. As a result, demand forecasting improves the optimization of packet consumption.

[0076] The pricing unit can perform price optimization. For example, the pricing unit can set optimal rates based on user packet consumption data. For example, the pricing unit can perform price optimization using AI algorithms. For example, the pricing unit can perform price optimization considering demand forecast data and network congestion. For example, the pricing unit proposes the optimal pricing plan based on the user's past packet consumption data and behavioral data. The pricing unit can also monitor network congestion in real time and perform price optimization. For example, the pricing unit monitors network traffic volume and latency and sets rates according to demand. By optimizing prices in this way, it becomes possible to set the most optimal rates for users.

[0077] The data collection unit can collect user behavior data. For example, the data collection unit can collect user access logs and app usage history. For example, the data collection unit can use AI algorithms to collect behavior data. For example, the data collection unit can collect data from the user's device and use it to optimize packet consumption. For example, the data collection unit collects access logs and app usage history from the user's smartphone and analyzes packet consumption patterns. The data collection unit can also collect user behavior data in real time and use it to optimize packet consumption. For example, the data collection unit collects data from the user's device in real time and understands fluctuations in packet consumption. As a result, packet consumption optimization improves by collecting user behavior data.

[0078] The monitoring unit can monitor network congestion. For example, the monitoring unit can monitor network traffic volume and latency. For example, the monitoring unit can monitor network congestion using AI algorithms. For example, the monitoring unit can monitor network congestion in real time and detect anomalies. For example, the monitoring unit can detect an anomaly and issue an alert if network traffic volume suddenly increases. The monitoring unit can also detect an anomaly and take countermeasures if network latency increases. For example, the monitoring unit can detect an anomaly if network latency exceeds a certain threshold and distribute traffic. This improves the optimization of packet consumption by monitoring network congestion.

[0079] The optimization unit can reduce packet consumption during high-demand times and in high-demand areas, and increase it during low-demand times and in low-demand areas. For example, the optimization unit can use AI algorithms to optimize packet consumption according to demand. For example, the optimization unit can monitor demand in real time and adjust packet consumption accordingly. For instance, the optimization unit reduces packet consumption during high-demand times to alleviate network load. Conversely, the optimization unit can increase packet consumption during low-demand times to maximize network efficiency. For example, the optimization unit can provide users with additional packets during low-demand times to encourage internet usage. This enables the optimization of packet consumption according to demand.

[0080] The service provider can provide users with the results of optimized packet consumption. For example, the service provider can notify users of the results of optimized packet consumption. For example, the service provider can provide the results of optimized packet consumption using an AI algorithm. For example, the service provider can send a notification to the user's smartphone and display the results of optimized packet consumption. For example, the service provider can notify users of the results of optimized packet consumption in real time, allowing them to understand their internet usage. The service provider can also save the user's packet consumption history and make it available for later reference. For example, the service provider can save the user's packet consumption history to the cloud, allowing the user to access it at any time. By providing users with the results of optimized packet consumption, user satisfaction can be improved.

[0081] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can set the monitoring frequency low to reduce the user's burden. For example, if the user is relaxed, the monitoring unit can set the monitoring frequency high to collect detailed data. For example, if the user is in a hurry, the monitoring unit can set the monitoring frequency to a moderate level to collect only the necessary data. In this way, the user's burden is reduced by adjusting the monitoring frequency 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 without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The monitoring unit can monitor network congestion in real time and detect anomalies. For example, the monitoring unit can monitor network traffic volume in real time and detect abnormal increases. For example, the monitoring unit can monitor network latency in real time and detect abnormal delays. For example, the monitoring unit can monitor network packet loss rate in real time and detect abnormal losses. This enables rapid response by detecting network anomalies in real time. 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 network traffic data into a generating AI and have the generating AI perform anomaly detection.

[0083] The monitoring unit can monitor the user's device usage and propose optimal packet consumption. For example, the monitoring unit can monitor the user's device's battery level and propose packet consumption that reduces battery consumption. For example, the monitoring unit can monitor the user's device's CPU usage and propose packet consumption that reduces CPU load. For example, the monitoring unit can monitor the user's device's memory usage and propose packet consumption that reduces memory consumption. This enables efficient use by proposing optimal packet consumption according to the user's device usage. 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 user device data into a generating AI and have the generating AI execute a proposal for optimal packet consumption.

[0084] The monitoring unit can estimate the user's emotions and determine the priority of data to monitor based on the estimated user emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring only important data. For example, if the user is relaxed, the monitoring unit can prioritize monitoring detailed data. For example, if the user is in a hurry, the monitoring unit can prioritize monitoring only the minimum necessary data. This enables efficient data collection by determining the priority of data to monitor 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 determine the priority of the data.

[0085] The monitoring unit can adjust its monitoring range by considering geographical factors when monitoring network congestion. For example, the monitoring unit can prioritize monitoring congestion in urban areas to identify areas prone to congestion. For example, the monitoring unit can monitor congestion in suburban areas to identify areas less prone to congestion. For example, the monitoring unit can monitor congestion in areas where a specific event is held and predict congestion during the event period. This allows for efficient monitoring by adjusting the monitoring range by considering geographical factors. 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 geographical data into a generating AI and have the generating AI adjust the monitoring range.

[0086] The monitoring unit can monitor a user's social media activity and identify related packet consumption patterns. For example, if a user frequently watches videos on social media, the monitoring unit can identify packet consumption patterns related to video viewing. For example, if a user frequently posts images on social media, the monitoring unit can identify packet consumption patterns related to image posting. For example, if a user frequently sends messages on social media, the monitoring unit can identify packet consumption patterns related to message sending. By identifying packet consumption patterns based on the user's social media activity, efficient packet consumption becomes possible. 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 social media data into a generating AI and have the generating AI perform the identification of packet consumption patterns.

[0087] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated emotions. For example, if the user is stressed, the optimization unit can use a simple optimization algorithm to reduce the user's burden. For example, if the user is relaxed, the optimization unit can use a detailed optimization algorithm to improve the accuracy of the optimization. For example, if the user is in a hurry, the optimization unit can use a rapid optimization algorithm to prioritize the speed of optimization. This allows for efficient optimization by adjusting the optimization algorithm 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 optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the optimization algorithm.

[0088] The optimization unit can improve the accuracy of optimization by referring to past data when optimizing packet consumption. For example, the optimization unit can refer to the user's past packet consumption data to identify the optimal consumption pattern. For example, the optimization unit can refer to past network congestion data to identify the optimal consumption pattern that avoids congestion. For example, the optimization unit can refer to the user's past behavior data to identify the optimal consumption pattern based on behavior patterns. As a result, the accuracy of optimization is improved by referring to past data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past data into a generating AI and have the generating AI perform the task of identifying the optimal consumption pattern.

[0089] The optimization unit can perform optimization while considering the user's device characteristics when optimizing packet consumption. For example, the optimization unit can consider the user's device's battery level and perform optimization to reduce battery consumption. For example, the optimization unit can consider the user's device's CPU usage and perform optimization to reduce CPU load. For example, the optimization unit can consider the user's device's memory usage and perform optimization to reduce memory consumption. This makes efficient optimization possible by considering the user's device characteristics. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user device data into a generating AI and have the generating AI perform the optimization.

[0090] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize processing important optimization items. For example, if the user is relaxed, the optimization unit can prioritize processing detailed optimization items. For example, if the user is in a hurry, the optimization unit can prioritize processing quick optimization items. This enables efficient optimization by determining optimization priorities 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 optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI determine the optimization priorities.

[0091] The optimization unit can optimize packet consumption by taking into account the user's geographical location information. For example, if the user is in an urban area, the optimization unit can perform optimization to avoid congestion. For example, if the user is in a suburban area, the optimization unit can perform optimization to consume packets efficiently. For example, if the user is participating in a specific event, the optimization unit can perform optimization related to that event. This makes efficient optimization possible by taking into account the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the user's geographical location data into a generating AI and have the generating AI perform the optimization.

[0092] The optimization unit can analyze the user's social media activity and select an optimization method when optimizing packet consumption. For example, if a user frequently watches videos on social media, the optimization unit can select the optimal consumption pattern for video viewing. For example, if a user frequently posts images on social media, the optimization unit can select the optimal consumption pattern for image posting. For example, if a user frequently sends messages on social media, the optimization unit can select the optimal consumption pattern for message sending. This enables efficient optimization by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input social media data into a generating AI and have the generating AI select an optimization method.

[0093] The information provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is stressed, the information provider can provide information in a simple format. For example, if the user is relaxed, the information provider can provide information in a detailed format. For example, if the user is in a hurry, the information provider can provide information in a concise format. This allows for efficient information provision by adjusting the format of the information 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the information format.

[0094] The service provider can select the optimal service delivery method by referring to the user's past usage history when providing the results of optimized packet consumption. For example, the service provider can refer to the user's past usage history and select the optimal service delivery method. For example, the service provider can prioritize providing frequently used information from the user's past usage history. For example, the service provider can analyze the user's past usage history and select the most efficient service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history into a generating AI and have the generating AI select the optimal service delivery method.

[0095] The information provider can customize the information it provides, taking into account the user's device characteristics. For example, the provider can provide information in an optimal display format, taking into account the screen size of the user's device. For example, the provider can provide information in a format that minimizes battery consumption, taking into account the user's device's battery level. For example, the provider can provide information in a format that minimizes memory consumption, taking into account the user's device's memory usage. This enables efficient information provision by considering the user's device characteristics. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user device data into a generating AI and have the generating AI perform the information customization.

[0096] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider can prioritize providing only important information. For example, if the user is relaxed, the information provider can prioritize providing detailed information. For example, if the user is in a hurry, the information provider can prioritize providing only the minimum necessary information. This enables efficient information provision by prioritizing information 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 information provider may be performed using AI, for example, or without AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0097] The service provider can adjust its delivery method by considering the user's geographical location when providing the results of optimized packet consumption. For example, if the user is in an urban area, the service provider can provide information to avoid congestion. For example, if the user is in a suburban area, the service provider can provide information to ensure efficient packet consumption. For example, if the user is participating in a specific event, the service provider can provide information related to that event. This enables efficient information delivery by considering the user's geographical location. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0098] The information provider can customize the information it provides to reflect the user's social media activity. For example, if a user frequently watches videos on social media, the provider can prioritize providing information related to video viewing. For example, if a user frequently posts images on social media, the provider can prioritize providing information related to image posting. For example, if a user frequently sends messages on social media, the provider can prioritize providing information related to message sending. This enables efficient information provision by reflecting the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input social media data into a generating AI and have the generating AI perform the information customization.

[0099] The forecasting unit can estimate the user's emotions and adjust the demand forecasting algorithm based on the estimated emotions. For example, if the user is stressed, the forecasting unit can use a simple demand forecasting algorithm to reduce the user's burden. For example, if the user is relaxed, the forecasting unit can use a detailed demand forecasting algorithm to improve the accuracy of the forecast. For example, if the user is in a hurry, the forecasting unit can use a rapid demand forecasting algorithm to prioritize the speed of the forecast. This allows for efficient demand forecasting by adjusting the demand forecasting algorithm 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 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 forecasting unit may be performed using AI or not. For example, the forecasting unit can input user emotion data into the generative AI and have the generative AI adjust the demand forecasting algorithm.

[0100] The forecasting unit can improve the accuracy of its forecasts by referring to past data when forecasting demand. For example, the forecasting unit can improve the accuracy of its demand forecasts by referring to users' past packet consumption data. For example, the forecasting unit can perform demand forecasts that avoid congestion by referring to past network congestion data. For example, the forecasting unit can perform demand forecasts based on users' past behavior data by referring to users' past behavior data. In this way, the accuracy of demand forecasts is improved by referring to past data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input past data into a generating AI and have the generating AI perform the demand forecast.

[0101] The forecasting unit can perform demand forecasting while considering the user's device characteristics. For example, the forecasting unit can consider the user's device's battery level and perform demand forecasting that reduces battery consumption. For example, the forecasting unit can consider the user's device's CPU usage and perform demand forecasting that reduces CPU load. For example, the forecasting unit can consider the user's device's memory usage and perform demand forecasting that reduces memory consumption. This makes efficient demand forecasting possible by considering the user's device characteristics. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input user device data into a generating AI and have the generating AI perform the demand forecasting.

[0102] The forecasting unit can estimate the user's emotions and determine the priority of demand forecasts based on the estimated emotions. For example, if the user is stressed, the forecasting unit can prioritize processing important demand forecast items. For example, if the user is relaxed, the forecasting unit can prioritize processing detailed demand forecast items. For example, if the user is in a hurry, the forecasting unit can prioritize processing urgent demand forecast items. This enables efficient demand forecasting by determining the priority of demand forecasts 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 forecasting unit may be performed using AI or not using AI. For example, the forecasting unit can input user emotion data into a generative AI and have the generative AI determine the priority of demand forecasts.

[0103] The forecasting unit can perform demand forecasting while considering the user's geographical location information. For example, if the user is in an urban area, the forecasting unit can perform demand forecasting that avoids congestion. For example, if the user is in a suburban area, the forecasting unit can perform demand forecasting that optimizes packet consumption. For example, if the user is participating in a specific event, the forecasting unit can perform demand forecasting related to that event. This makes efficient demand forecasting possible by considering the user's geographical location information. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the user's geographical location data into a generating AI and have the generating AI perform the demand forecasting.

[0104] The forecasting unit can analyze users' social media activity and select a forecasting method when forecasting demand. For example, if a user frequently watches videos on social media, the forecasting unit can forecast demand related to video viewing. For example, if a user frequently posts images on social media, the forecasting unit can forecast demand related to image posting. For example, if a user frequently sends messages on social media, the forecasting unit can forecast demand related to message sending. This enables efficient demand forecasting by analyzing users' social media activity. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input social media data into a generating AI and have the generating AI select a forecasting method.

[0105] The pricing unit can estimate the user's emotions and adjust the price optimization algorithm based on the estimated emotions. For example, if the user is stressed, the pricing unit can use a simple price optimization algorithm to reduce the user's burden. For example, if the user is relaxed, the pricing unit can use a detailed price optimization algorithm to improve the accuracy of the optimization. For example, if the user is in a hurry, the pricing unit can use a rapid price optimization algorithm to prioritize the speed of optimization. This allows for efficient price optimization by adjusting the price optimization algorithm 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 pricing unit may be performed using AI, for example, or not using AI. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the price optimization algorithm.

[0106] The pricing unit can improve the accuracy of price optimization by referring to historical data during price optimization. For example, the pricing unit can improve the accuracy of price optimization by referring to the user's past packet consumption data. For example, the pricing unit can perform price optimization that avoids congestion by referring to historical network congestion data. For example, the pricing unit can perform price optimization based on behavioral patterns by referring to the user's past behavior data. In this way, the accuracy of price optimization is improved by referring to historical data. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without using AI. For example, the pricing unit can input historical data into a generating AI and have the generating AI perform price optimization.

[0107] The pricing unit can optimize prices by considering the user's device characteristics. For example, the pricing unit can optimize prices by considering the user's device's battery level to reduce battery consumption. For example, the pricing unit can optimize prices by considering the user's device's CPU usage to reduce CPU load. For example, the pricing unit can optimize prices by considering the user's device's memory usage to reduce memory consumption. This enables efficient price optimization by considering the user's device characteristics. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input user device data into a generating AI and have the generating AI perform the price optimization.

[0108] The pricing unit can estimate the user's emotions and determine the priority of price optimization based on the estimated emotions. For example, if the user is stressed, the pricing unit can prioritize important price optimization items. If the user is relaxed, the pricing unit can prioritize detailed price optimization items. If the user is in a hurry, the pricing unit can prioritize quick price optimization items. This enables efficient price optimization by determining the priority of price optimization 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 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 pricing unit may be performed using AI or not. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI determine the priority of price optimization.

[0109] The pricing unit can optimize prices by taking into account the user's geographical location. For example, if the user is in an urban area, the pricing unit can optimize prices to avoid congestion. For example, if the user is in a suburban area, the pricing unit can optimize prices to ensure efficient packet consumption. For example, if the user is participating in a specific event, the pricing unit can optimize prices related to that event. This enables efficient price optimization by considering the user's geographical location. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the user's geographical location data into a generating AI and have the generating AI perform the price optimization.

[0110] The pricing unit can analyze users' social media activity and select optimization methods when optimizing prices. For example, if a user frequently watches videos on social media, the pricing unit can perform price optimization related to video viewing. For example, if a user frequently posts images on social media, the pricing unit can perform price optimization related to image posting. For example, if a user frequently sends messages on social media, the pricing unit can perform price optimization related to message sending. This enables efficient price optimization by analyzing users' social media activity. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input social media data into a generating AI and have the generating AI select optimization methods.

[0111] The data collection unit can estimate the user's emotions and adjust the method of collecting behavioral data based on the estimated user emotions. For example, if the user is stressed, the data collection unit can use a simpler collection method to reduce the user's burden. For example, if the user is relaxed, the data collection unit can use a more detailed collection method to improve the accuracy of the collection. For example, if the user is in a hurry, the data collection unit can use a faster collection method to prioritize the speed of collection. This allows for efficient data collection by adjusting the method of collecting behavioral data 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 using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection method.

[0112] The data collection unit can improve the accuracy of data collection by referring to past data when collecting behavioral data. For example, the data collection unit can improve the accuracy of data collection by referring to the user's past behavioral data. For example, the data collection unit can refer to past network congestion data and use a collection method that avoids congestion. For example, the data collection unit can refer to the user's past packet consumption data and use a collection method based on consumption patterns. This improves the accuracy of behavioral data collection by referring to past data. 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 past data into a generating AI and have the generating AI execute the collection method.

[0113] The data collection unit can collect behavioral data while considering the user's device characteristics. For example, the data collection unit can consider the user's device's battery level and use a collection method that reduces battery consumption. For example, the data collection unit can consider the user's device's CPU usage and use a collection method that reduces CPU load. For example, the data collection unit can consider the user's device's memory usage and use a collection method that reduces memory consumption. This enables efficient data collection by considering the user's device characteristics. 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 user device data into a generating AI and have the generating AI execute the data collection method.

[0114] 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 prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only the minimum necessary data. This enables efficient data collection by prioritizing the data to be collected 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0115] The data collection unit can collect behavioral data while considering the user's geographical location. For example, if the user is in an urban area, the data collection unit can prioritize collecting behavioral data to avoid congestion. For example, if the user is in a suburban area, the data collection unit can collect behavioral data to efficiently consume data packets. For example, if the user is participating in a specific event, the data collection unit can collect behavioral data related to that event. This enables efficient data collection by considering the user's geographical location. 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 execute the data collection method.

[0116] The data collection unit can analyze the user's social media activity and select a data collection method when collecting behavioral data. For example, if a user frequently watches videos on social media, the data collection unit can collect behavioral data related to video viewing. For example, if a user frequently posts images on social media, the data collection unit can collect behavioral data related to image posting. For example, if a user frequently sends messages on social media, the data collection unit can collect behavioral data related to message sending. This enables efficient data collection 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 social media data into a generating AI and have the generating AI select a data collection method.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The packet management system can efficiently manage user packet consumption by estimating user emotions and optimizing packet consumption based on those emotions. For example, if a user is stressed, reducing packet consumption can alleviate the user's burden. Conversely, if a user is relaxed, increasing packet consumption can provide a more comfortable internet experience. Furthermore, if a user is in a hurry, packet consumption can be set to a moderate level, providing only the necessary data. This enables the optimization of packet consumption according to the user's emotions.

[0119] The packet management system can optimize packet consumption by considering the user's device characteristics. For example, it can monitor the user's device's battery level and suggest packet consumption that reduces battery consumption. It can also monitor the user's device's CPU usage and suggest packet consumption that reduces CPU load. Furthermore, it can monitor the user's device's memory usage and suggest packet consumption that reduces memory consumption. This enables optimal packet consumption tailored to the user's device characteristics.

[0120] The packet management system can optimize packet consumption by considering the user's geographical location. For example, if the user is in an urban area, packet consumption can be reduced to avoid congestion. Conversely, if the user is in a suburban area, packet consumption can be increased for more efficient use. Furthermore, if the user is participating in a specific event, packet consumption related to that event can be optimized. This enables optimal packet consumption tailored to the user's geographical location.

[0121] The packet management system can analyze a user's social media activity and optimize packet consumption. For example, if a user frequently watches videos on social media, it can optimize packet consumption related to video viewing. Similarly, if a user frequently posts images on social media, it can optimize packet consumption related to image posting. Furthermore, if a user frequently sends messages on social media, it can optimize packet consumption related to message sending. This enables optimal packet consumption based on the user's social media activity.

[0122] A packet management system can optimize packet consumption by referring to a user's past packet consumption data. For example, it can identify the optimal consumption pattern by referring to a user's past data traffic. It can also identify the optimal consumption pattern to avoid congestion by referring to past network congestion data. Furthermore, it can identify the optimal consumption pattern based on a user's past behavioral data. As a result, packet consumption optimization is improved by referring to past data.

[0123] The packet management system can estimate the user's emotions and prioritize packet consumption based on those emotions. For example, if the user is stressed, only essential data can be consumed first. If the user is relaxed, detailed data can be consumed first. Furthermore, if the user is in a hurry, only the minimum necessary data can be consumed first. By prioritizing packet consumption according to the user's emotions, efficient data consumption becomes possible.

[0124] The packet management system can estimate the user's emotions and adjust the packet consumption algorithm based on those emotions. For example, if the user is stressed, a simple algorithm can be used to reduce the user's burden. If the user is relaxed, a more detailed algorithm can be used to improve the accuracy of optimization. Furthermore, if the user is in a hurry, a rapid algorithm can be used to prioritize the speed of optimization. This allows for the adjustment of the packet consumption algorithm according to the user's emotions.

[0125] The packet management system can estimate the user's emotions and adjust the frequency of packet consumption based on those emotions. For example, if the user is stressed, the frequency of packet consumption can be set low to reduce the user's burden. If the user is relaxed, the frequency of packet consumption can be set high to collect detailed data. Furthermore, if the user is in a hurry, the frequency of packet consumption can be set to a moderate level to collect only the necessary data. This makes it possible to adjust the frequency of packet consumption according to the user's emotions.

[0126] The packet management system can estimate the user's emotions and adjust the format of the information it provides regarding packet consumption based on those emotions. For example, if the user is stressed, the information can be provided in a simple format. If the user is relaxed, the information can be provided in a detailed format. Furthermore, if the user is in a hurry, the information can be provided in a concise format. This allows for the adjustment of the information provided in accordance with the user's emotions.

[0127] The packet management system can estimate the user's emotions and, based on those emotions, prioritize the information provided as a result of packet consumption. For example, if the user is stressed, only essential information can be prioritized. If the user is relaxed, detailed information can be prioritized. Furthermore, if the user is in a hurry, only the bare minimum of information can be prioritized. This makes it possible to prioritize information provision according to the user's emotions.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The monitoring unit monitors user packet consumption. Specifically, it monitors the amount of data traffic and the usage of specific applications in real time to understand fluctuations in data traffic. It also collects user behavior data and analyzes packet consumption patterns. For example, it collects user access logs and application usage history to understand trends in packet consumption. Step 2: The optimization unit optimizes packet consumption based on data monitored by the monitoring unit. Specifically, it can reduce packet consumption during high-demand times and in low-demand times and in low-demand times. Furthermore, it uses an AI algorithm to optimize packet consumption and provides the optimal packet plan for each individual user based on user attributes and behavioral data. For example, it can suggest the optimal packet plan according to the user's age, gender, and usage. Step 3: The service provider provides the results of the packet consumption optimized by the optimization unit. Specifically, it notifies the user of the optimized packet consumption results and sends a notification to the user's smartphone to display the results. It also saves the user's packet consumption history so that it can be referenced later. For example, the user's packet consumption history is saved to the cloud so that the user can access it at any time.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the monitoring unit, optimization unit, provision unit, forecasting unit, pricing unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the user's packet consumption in real time. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes packet consumption. The provision unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the results of the optimized packet consumption. The forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs demand forecasting. The pricing unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs price optimization. The collection unit is implemented by the control unit 46A of the smart device 14 and collects user behavior data. 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.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the monitoring unit, optimization unit, provision unit, forecasting unit, pricing unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's packet consumption in real time. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes packet consumption. The provision unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of the results of the optimized packet consumption. The forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs demand forecasting. The pricing unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs price optimization. The collection unit is implemented by the control unit 46A of the smart glasses 214 and collects user behavior data. 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.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the monitoring unit, optimization unit, provision unit, forecasting unit, pricing unit, and collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the user's packet consumption in real time. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes packet consumption. The provision unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the results of the optimized packet consumption. The forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs demand forecasting. The pricing unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs price optimization. The collection unit is implemented by the control unit 46A of the headset terminal 314 and collects user behavior data. 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.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the monitoring unit, optimization unit, provision unit, forecasting unit, pricing unit, and collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the user's packet consumption in real time. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes packet consumption. The provision unit is implemented by the control unit 46A of the robot 414 and notifies the user of the results of the optimized packet consumption. The forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs demand forecasting. The pricing unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs price optimization. The collection unit is implemented by the control unit 46A of the robot 414 and collects user behavior data. 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) A monitoring unit that monitors packet consumption, An optimization unit that optimizes packet consumption based on data monitored by the aforementioned monitoring unit, The system includes a providing unit that provides the results of packet consumption optimized by the optimization unit. A system characterized by the following features. (Note 2) It is equipped with a forecasting unit that performs demand forecasting. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a pricing unit that optimizes prices. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a data collection unit that collects user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Monitor network congestion The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, Packet consumption is reduced during peak hours and in peak regions, and increased during peak hours and in peak regions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide users with the results of optimized packet consumption. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It monitors network congestion in real time and detects anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It monitors the user's device usage and suggests optimal data consumption. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, It estimates user sentiment and prioritizes data to monitor based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, When monitoring network congestion, adjust the monitoring range to take geographical factors into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, Monitor users' social media activity and identify related data usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, It estimates the user's emotions and adjusts the optimization algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, When optimizing packet consumption, historical data is referenced to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, When optimizing packet consumption, the optimization is performed taking into account the user's device characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, When optimizing packet consumption, the system takes the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, When optimizing packet consumption, the optimization method is selected by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the format of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing optimized packet consumption results, the system selects the optimal delivery method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The information provided is customized to take into account the user's device characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing optimized packet consumption results, the delivery method is adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The information provided will be customized to reflect the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, It estimates user sentiment and adjusts the demand forecasting algorithm based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The prediction unit, When forecasting demand, historical data is used to improve the accuracy of the forecast. The system described in Appendix 2, characterized by the features described herein. (Note 28) The prediction unit, When forecasting demand, the forecast should take into account the characteristics of the user's device. The system described in Appendix 2, characterized by the features described herein. (Note 29) The prediction unit, It estimates user sentiment and determines demand forecast priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 30) The prediction unit, When forecasting demand, the forecast takes into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 31) The prediction unit, When forecasting demand, we analyze users' social media activity to select a forecasting method. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned price section is, It estimates user sentiment and adjusts the price optimization algorithm based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned price section is, When optimizing prices, historical data is used to improve the accuracy of the optimization. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned price section is, When optimizing pricing, the user's device characteristics are taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned price section is, It estimates user sentiment and determines price optimization priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned price section is, When optimizing pricing, the system takes into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned price section is, When optimizing pricing, we analyze users' social media activity to select the appropriate optimization method. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting behavioral data based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned collection unit is When collecting behavioral data, we improve the accuracy of the collection by referring to past data. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned collection unit is When collecting behavioral data, the user's device characteristics should be taken into consideration during the collection process. The system described in Appendix 4, characterized by the features described herein. (Note 41) 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 4, characterized by the features described herein. (Note 42) The aforementioned collection unit is When collecting behavioral data, the user's geographical location information is taken into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned collection unit is When collecting behavioral data, we analyze users' social media activity to select the appropriate collection method. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0202] 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 monitoring unit that monitors packet consumption, An optimization unit that optimizes packet consumption based on data monitored by the aforementioned monitoring unit, The system includes a providing unit that provides the results of packet consumption optimized by the optimization unit. A system characterized by the following features.

2. It is equipped with a forecasting unit that performs demand forecasting. The system according to feature 1.

3. It includes a pricing unit that optimizes prices. The system according to feature 1.

4. It includes a data collection unit that collects user behavior data. The system according to feature 1.

5. The aforementioned monitoring unit, Monitor network congestion The system according to feature 1.

6. The optimization unit, Packet consumption is reduced during peak hours and in peak regions, and increased during peak hours and in peak regions. The system according to feature 1.

7. The aforementioned supply unit is, Provide users with the results of optimized packet consumption. The system according to feature 1.

8. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

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