system

Abstract

The system according to the embodiment aims to achieve efficient use of power by automatically controlling the amount of data communication and performing the minimum necessary processing. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a control unit, and a processing unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and aggregates information that has something in common. The control unit automatically controls the amount of data communication based on the data analyzed by the analysis unit. The processing unit performs the minimum necessary processing based on the data controlled by the control unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not provide efficient data communication and processing to meet the increasing demand for electricity that comes with the development of generative AI, posing a risk of power shortages.

[0005] The system according to the embodiment aims to achieve efficient use of power by automatically controlling the amount of data communication and performing the minimum necessary processing. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a processing unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and aggregates information that has something in common. The control unit automatically controls the amount of data communication based on the data analyzed by the analysis unit. The processing unit performs the minimum necessary processing based on the data controlled by the control unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically control the amount of data communication and perform the minimum necessary processing, thereby enabling efficient use of power. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An energy management system according to an embodiment of the present invention utilizes a generation AI to efficiently manage energy demand and prevent future energy shortages. In this energy management system, the generation AI collects various data and aggregates common information. The generation AI then automatically controls data traffic, minimizing the amount of data traffic and minimizing the amount of processing required. This system efficiently manages energy demand and prevents energy shortages. For example, the generation AI collects various data, including data on energy demand and other information processed by the generation AI. For example, data on energy usage by households and businesses, weather data, and power plant operation status may be collected. This allows for detailed information on energy demand to be identified. The generation AI then analyzes the collected data and aggregates common information. For example, data on households and businesses that use a lot of energy in the same area during the same time period may be aggregated. This prevents data duplication and enables efficient information management. Furthermore, the generation AI automatically controls data traffic. For example, it controls the communication of only necessary information and prevents the communication of unnecessary information. This reduces communication traffic and avoids unnecessary energy waste. Finally, the generation AI performs the minimum necessary processing. For example, it adjusts the operation of power plants according to times when electricity demand is high. It also reduces the operation of power plants during times when electricity demand is low. This allows for a balanced management of electricity supply and demand. This system efficiently manages electricity demand and prevents future power shortages. For example, during peak summer hours, the generation AI predicts electricity demand and adjusts the operation of power plants, preventing power shortages. Furthermore, during winter when heating demand is high, the generation AI can predict electricity demand and efficiently supply electricity. This prevents power shortages that would have a significant impact on people's lives across Japan. This allows the power management system to efficiently manage electricity demand and prevent future power shortages.

[0029] The power management system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a processing unit. The collection unit collects various data. For example, the collection unit collects data such as power usage, weather data, and the operating status of power plants. The collection unit can, for example, collect power usage data from each household or business in real time. The collection unit can also acquire weather data from weather sensors or a weather database. The collection unit can also acquire the operating status of power plants from a monitoring system. The analysis unit analyzes the data collected by the collection unit and aggregates information that has commonalities. For example, the analysis unit aggregates data from households or businesses that use a lot of power in the same area during the same time period. For example, the analysis unit integrates data from households or businesses that use a lot of power in the same area during the same time period to prevent data duplication. The analysis unit can also analyze data patterns and predict power demand. The control unit automatically controls the amount of data communication based on the data analyzed by the analysis unit. For example, the control unit controls communication to communicate only necessary information and not communicate unnecessary information. The control unit determines the priority of communication based on, for example, the importance of the data. The control unit can also adjust the frequency of communication to reduce the amount of communication. The processing unit performs the minimum necessary processing based on the data controlled by the control unit. For example, the processing unit adjusts the operation of the power plant according to the time period when the demand for electricity is high. For example, the processing unit increases the operation of the power plant during the time period when the demand for electricity is high. The processing unit can also reduce the operation of the power plant during the time period when the demand for electricity is low. This makes it possible to manage the supply and demand of electricity in a balanced manner. As a result, the power management system according to the embodiment can efficiently manage the demand for electricity and prevent future power shortages.

[0030] The collection unit can collect data on power usage, weather data, and the operating status of power plants. For example, the collection unit can collect the power usage of each household or business in real time. For example, the collection unit can measure the power usage of each household or business using a smart meter and collect the data. The collection unit can also acquire weather data from a weather sensor or a weather database. For example, the collection unit collects weather data such as temperature, humidity, and wind speed. The collection unit can also acquire the operating status of power plants from a monitoring system. For example, the collection unit collects data such as the power generation amount, operating hours, and maintenance status of the power plant. This makes it possible to grasp detailed information regarding power demand. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the power usage data of each household or business into the generation AI, which then automates data collection.

[0031] The analysis unit can aggregate data on households and businesses that use a large amount of electricity in the same area during the same time period. For example, the analysis unit aggregates data on households and businesses that use a large amount of electricity in the same area during the same time period. For example, the analysis unit may integrate data on households and businesses that use a large amount of electricity in the same area during the same time period to prevent data duplication. The analysis unit can also analyze data patterns and forecast electricity demand. For example, the analysis unit can use an algorithm to forecast future electricity demand based on past data. Furthermore, the analysis unit can cleanse the data and remove noise and outliers. This improves data quality and allows for accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input collected data into the generation AI, which then analyzes the data.

[0032] The control unit can control the communication so that only necessary information is communicated. For example, the control unit controls the communication so that only necessary information is communicated and unnecessary information is not communicated. For example, the control unit determines the priority of communication based on the importance of the data. For example, the control unit prioritizes the communication of important data and postpones the communication of less important data. The control unit can also adjust the frequency of communication to reduce the amount of communication. For example, the control unit communicates necessary information in real time and periodically deletes unnecessary information. Furthermore, the control unit can optimize the communication protocol to improve communication efficiency. This reduces the amount of communication and avoids unnecessary power consumption. Some or all of the above-mentioned processing in the control unit may be performed using or without the generation AI. For example, the control unit can input the data to be communicated into the generation AI, and the generation AI can control the communication.

[0033] The processing unit can adjust the operation of the power plant according to time periods when electricity demand is high. For example, the processing unit increases the operation of the power plant during time periods when electricity demand is high. For example, the processing unit increases the amount of electricity generated by the power plant during time periods when electricity demand is high. The processing unit can also reduce the operation of the power plant during time periods when electricity demand is low. For example, the processing unit reduces the amount of electricity generated by the power plant during time periods when electricity demand is low. This allows for a balanced management of electricity supply and demand. Furthermore, the processing unit can monitor the operation status of the power plant in real time and adjust the operation as needed. For example, the processing unit monitors the operation status of the power plant and adjusts the amount of electricity generated according to demand. This allows for efficient management of electricity supply and demand. Some or all of the above-described processing in the processing unit may be performed using or without the generation AI. For example, the processing unit can input power plant operation status data into the generation AI, which can then adjust the operation.

[0034] The processing unit can reduce the operation of the power plant during times when electricity demand is low. For example, the processing unit reduces the amount of power generated by the power plant during times when electricity demand is low. For example, the processing unit suppresses the operation of the power plant during times when electricity demand is low. The processing unit can also increase the operation of the power plant during times when electricity demand is high. For example, the processing unit increases the amount of power generated by the power plant during times when electricity demand is high. This allows for a balanced management of electricity supply and demand. Furthermore, the processing unit can monitor the operation status of the power plant in real time and adjust the operation as needed. For example, the processing unit monitors the operation status of the power plant and adjusts the amount of power generated according to demand. This allows for efficient management of electricity supply and demand. Some or all of the above-mentioned processing in the processing unit may be performed using or without the generation AI. For example, the processing unit can input power plant operation status data into the generation AI, which can then adjust the operation.

[0035] The collection unit can analyze past power usage history and select the optimal data collection method. For example, the collection unit prioritizes data collection during peak hours based on the past power usage history. For example, the collection unit analyzes past power usage history and prioritizes data collection during peak hours. The collection unit can also concentrate data collection during specific time periods based on the past power usage history. For example, the collection unit analyzes past power usage history and concentrates data collection during specific time periods. The collection unit can also analyze past power usage history and eliminate unnecessary data collection. For example, the collection unit analyzes past power usage history and eliminates unnecessary data collection. This enables efficient data collection based on the past power usage history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input past power usage history data into the generation AI, which can select the optimal data collection method.

[0036] When collecting data, the collection unit can filter the data to be collected based on a specific event or season. For example, the collection unit prioritizes collecting data related to air conditioning use during peak summer hours. For example, the collection unit prioritizes collecting data related to air conditioning use during peak summer hours. The collection unit can also prioritize collecting data related to heating use during periods when heating demand is high in winter. For example, the collection unit prioritizes collecting data related to heating use during periods when heating demand is high in winter. Furthermore, the collection unit can predict fluctuations in electricity usage based on a specific event (e.g., a long holiday) and collect data. For example, the collection unit predicts fluctuations in electricity usage based on a long holiday and collects data. This enables efficient data collection based on a specific event or season. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data related to a specific event or season into the generation AI, and the generation AI can filter the data to be collected.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, if power usage is high in a specific area, the collection unit prioritizes collecting data for that area. For example, if power usage is high in a specific area, the collection unit prioritizes collecting data for that area. The collection unit can also prioritize collecting data for areas with large fluctuations in power usage based on geographical location information. For example, the collection unit prioritizes collecting data for areas with large fluctuations in power usage based on geographical location information. The collection unit can also prioritize collecting data for areas with unstable power supply by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data for areas with unstable power supply by taking geographical location information into consideration. This enables efficient data collection by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input geographical location information data to the generation AI, which can then prioritize collecting highly relevant data.

[0038] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit analyzes posts about electricity usage on social media and collects related data. For example, the collection unit analyzes posts about electricity usage on social media and collects related data. The collection unit can also analyze posts about weather on social media and collect data useful for predicting electricity usage. For example, the collection unit analyzes posts about weather on social media and collects data useful for predicting electricity usage. Furthermore, the collection unit can analyze event information on social media and collect data for predicting fluctuations in electricity usage. For example, the collection unit analyzes event information on social media and collects data for predicting fluctuations in electricity usage. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input social media activity data to the generation AI, which then collects related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a detailed analysis on important data based on the importance of the data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit performs a simplified analysis on less important data based on the importance of the data. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit optimally allocates analysis resources based on the importance of the data. This enables efficient analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a time series analysis algorithm to power usage data. For example, the analysis unit applies a time series analysis algorithm to power usage data. The analysis unit can also apply a weather forecasting algorithm to weather data. For example, the analysis unit applies a weather forecasting algorithm to weather data. The analysis unit can also apply a machine learning algorithm to power plant operation status data. For example, the analysis unit applies a machine learning algorithm to power plant operation status data. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the data category to the generation AI, which can select an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was collected. The analysis unit can also analyze trends based on past data. For example, the analysis unit analyzes trends based on past data based on the time when the data was collected. Furthermore, the analysis unit can optimally allocate analysis resources according to the time when the data was collected. For example, the analysis unit optimally allocates analysis resources based on the time when the data was collected. This enables efficient analysis according to the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data based on the relevance of the data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data based on the relevance of the data. Furthermore, the analysis unit can also optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit optimally allocates analysis resources based on the relevance of the data. This enables efficient analysis according to the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI, and the generation AI can adjust the order of analysis.

[0043] The control unit can improve the accuracy of communication by taking into account the interrelationships of data when controlling communication. The control unit, for example, prioritizes communication of highly related data. For example, the control unit prioritizes communication of highly related data by taking into account the interrelationships of data. The control unit can also optimize the order of communication based on the interrelationships of data. For example, the control unit optimizes the order of communication based on the interrelationships of data. Furthermore, the control unit can also optimally allocate communication resources by taking into account the interrelationships of data. For example, the control unit optimally allocates communication resources by taking into account the interrelationships of data. As a result, the accuracy of communication is improved by taking into account the interrelationships of data. Some or all of the above-mentioned processing in the control unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the control unit can input the interrelationships of data into the generation AI, which can improve the accuracy of communication.

[0044] The control unit can perform communication while taking into account attribute information of the data sender when controlling communication. For example, the control unit prioritizes communication when the sender has important data. For example, the control unit prioritizes communication when the sender has important data, taking into account the attribute information of the data sender. The control unit can also optimize the order of communication based on the attribute information of the sender. For example, the control unit optimizes the order of communication based on the attribute information of the data sender. Furthermore, the control unit can also optimally allocate communication resources by taking into account the attribute information of the sender. For example, the control unit optimally allocates communication resources by taking into account the attribute information of the data sender. This enables efficient communication by taking into account the attribute information of the data sender. Some or all of the above-described processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input the attribute information of the data sender to the generation AI, and the generation AI can perform communication.

[0045] The control unit can perform communication taking into consideration the geographical distribution of data when controlling communication. For example, the control unit prioritizes communication of geographically close data. For example, the control unit prioritizes communication of geographically close data taking into consideration the geographical distribution of data. The control unit can also optimize the order of communication based on the geographical distribution. For example, the control unit optimizes the order of communication based on the geographical distribution of data. Furthermore, the control unit can also optimally allocate communication resources taking into consideration the geographical distribution. For example, the control unit optimally allocates communication resources taking into consideration the geographical distribution of data. This enables efficient communication taking into consideration the geographical distribution. Some or all of the above-described processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input geographical distribution data to the generation AI, and the generation AI can perform communication.

[0046] The control unit can improve the accuracy of communication by referring to related literature when controlling communication. The control unit, for example, improves the accuracy of communication based on related literature. For example, the control unit improves the accuracy of communication by referring to related literature. The control unit can also optimize the order of communication by referring to related literature. For example, the control unit optimizes the order of communication by referring to related literature. Furthermore, the control unit can also optimally allocate communication resources by taking related literature into consideration. For example, the control unit optimally allocates communication resources by taking related literature into consideration. As a result, the accuracy of communication is improved by referring to related literature. Some or all of the above-mentioned processing in the control unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the control unit can input related literature data into the generation AI, which can improve the accuracy of communication.

[0047] During processing, the processing unit can select an optimal processing method by referring to past processing data. The processing unit, for example, selects an optimal processing method based on past processing data. For example, the processing unit selects an optimal processing method by referring to past processing data. The processing unit can also analyze past processing data and select an efficient processing method. For example, the processing unit analyzes past processing data and selects an efficient processing method. Furthermore, the processing unit can also eliminate unnecessary processing by referring to past processing data. For example, the processing unit eliminates unnecessary processing by referring to past processing data. This enables efficient processing based on past processing data. Some or all of the above-mentioned processing in the processing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the processing unit can input past processing data into a generation AI, which then selects an optimal processing method.

[0048] During processing, the processing unit can customize the processing means based on the current situation. The processing unit selects the optimal processing means based on, for example, the current power demand. For example, the processing unit selects the optimal processing means based on the current power demand. The processing unit can also customize the processing means based on the current weather conditions. For example, the processing unit customizes the processing means based on the current weather conditions. Furthermore, the processing unit can also adjust the processing means based on the current operating status of the power plant. For example, the processing unit adjusts the processing means based on the current operating status of the power plant. This makes it possible to select the optimal processing means according to the current situation. Some or all of the above-mentioned processing in the processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the processing unit can input current situation data into the generation AI, which can customize the processing means.

[0049] During processing, the processing unit can select an optimal processing method by taking into account the geographical location information. For example, the processing unit prioritizes processing of geographically close data. For example, the processing unit prioritizes processing of geographically close data by taking into account the geographical location information. The processing unit can also select an optimal processing method based on the geographical location information. For example, the processing unit selects an optimal processing method based on the geographical location information. Furthermore, the processing unit can also optimally allocate processing resources by taking into account the geographical location information. For example, the processing unit optimally allocates processing resources by taking into account the geographical location information. This enables efficient processing by taking into account the geographical location information. Some or all of the above-mentioned processing in the processing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the processing unit can input geographical location information data to the generation AI, which can select an optimal processing method.

[0050] During processing, the processing unit can analyze social media activity and suggest a processing measure. For example, the processing unit analyzes posts about electricity usage on social media and suggests an optimal processing measure. For example, the processing unit analyzes posts about electricity usage on social media and suggests an optimal processing measure. The processing unit can also analyze posts about weather on social media and suggest a processing measure useful for predicting electricity usage. For example, the processing unit analyzes posts about weather on social media and suggests a processing measure useful for predicting electricity usage. Furthermore, the processing unit can analyze event information on social media and suggest a processing measure for predicting fluctuations in electricity usage. For example, the processing unit analyzes event information on social media and suggests a processing measure for predicting fluctuations in electricity usage. In this way, the optimal processing measure can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the processing unit may be performed using or without the generation AI. For example, the processing unit can input social media activity data to the generation AI, which then suggests the optimal processing measure.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze past electricity usage history and select the optimal data collection method. For example, it can prioritize data collection during peak hours based on past electricity usage history. It can also concentrate data collection on specific time periods based on past electricity usage history. It can also analyze past electricity usage history and eliminate unnecessary data collection. This enables efficient data collection based on past electricity usage history. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input past electricity usage history data into the generation AI, which can select the optimal data collection method.

[0053] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, analysis resources can be optimally allocated according to the importance of the data. This enables efficient analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0054] The control unit can improve the accuracy of communication by taking into account the interrelationships of data when controlling communication. For example, highly related data can be communicated preferentially. The order of communication can also be optimized based on the interrelationships of data. Furthermore, communication resources can be optimally allocated by taking into account the interrelationships of data. In this way, the accuracy of communication is improved by taking into account the interrelationships of data. Some or all of the above-mentioned processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input the interrelationships of data into the generation AI, which can then improve the accuracy of communication.

[0055] During processing, the processing unit can select the optimal processing method by referring to past processing data. For example, the processing unit can select the optimal processing method based on the past processing data. It can also analyze the past processing data and select an efficient processing method. Furthermore, it can eliminate unnecessary processing by referring to the past processing data. This enables efficient processing based on the past processing data. Some or all of the above-mentioned processing in the processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the processing unit can input the past processing data into the generation AI, which can select the optimal processing method.

[0056] When collecting data, the collection unit can filter the data to be collected based on specific events or seasons. For example, during peak summer hours, data related to air conditioning usage can be collected with priority. Also, during periods of high heating demand in winter, data related to heating usage can be collected with priority. Furthermore, fluctuations in electricity usage can be predicted and data collected based on specific events (e.g., long holidays). This enables efficient data collection based on specific events or seasons. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data related to specific events or seasons into the generation AI, which then filters the data to be collected.

[0057] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a time series analysis algorithm can be applied to power consumption data. A weather forecasting algorithm can be applied to weather data. Furthermore, a machine learning algorithm can be applied to power plant operation status data. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category into the generation AI, which then selects the appropriate analysis algorithm.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects various data. For example, the collection unit collects data such as power usage, weather data, and the operating status of power plants. The collection unit can collect the power usage of each household or business in real time. The collection unit can also obtain weather data from weather sensors and weather databases. Furthermore, the collection unit can obtain the operating status of power plants from a monitoring system. Step 2: The analysis unit analyzes the data collected by the collection unit and aggregates information that has commonalities. For example, the analysis unit aggregates data on households and businesses that use a lot of electricity in the same area during the same time period. To prevent data duplication, the analysis unit integrates data on households and businesses that use a lot of electricity in the same area during the same time period. The analysis unit can also analyze data patterns and forecast electricity demand. Step 3: The control unit automatically controls the amount of data communication based on the data analyzed by the analysis unit. For example, the control unit controls communication so that only necessary information is communicated and unnecessary information is not communicated. The control unit determines the priority of communication based on the importance of the data. The control unit can also adjust the frequency of communication to reduce the amount of communication. Step 4: The processing unit performs the minimum necessary processing based on the data controlled by the control unit. For example, the processing unit adjusts the operation of the power plant to match the time periods when electricity demand is high. The processing unit increases the operation of the power plant during times when electricity demand is high. The processing unit can also reduce the operation of the power plant during times when electricity demand is low. This allows for a balanced management of electricity supply and demand.

[0060] (Example 2) An energy management system according to an embodiment of the present invention utilizes a generation AI to efficiently manage energy demand and prevent future energy shortages. In this energy management system, the generation AI collects various data and aggregates common information. The generation AI then automatically controls data traffic, minimizing the amount of data traffic and minimizing the amount of processing required. This system efficiently manages energy demand and prevents energy shortages. For example, the generation AI collects various data, including data on energy demand and other information processed by the generation AI. For example, data on energy usage by households and businesses, weather data, and power plant operation status may be collected. This allows for detailed information on energy demand to be identified. The generation AI then analyzes the collected data and aggregates common information. For example, data on households and businesses that use a lot of energy in the same area during the same time period may be aggregated. This prevents data duplication and enables efficient information management. Furthermore, the generation AI automatically controls data traffic. For example, it controls the communication of only necessary information and prevents the communication of unnecessary information. This reduces communication traffic and avoids unnecessary energy waste. Finally, the generation AI performs the minimum necessary processing. For example, it adjusts the operation of power plants according to times when electricity demand is high. It also reduces the operation of power plants during times when electricity demand is low. This allows for a balanced management of electricity supply and demand. This system efficiently manages electricity demand and prevents future power shortages. For example, during peak summer hours, the generation AI predicts electricity demand and adjusts the operation of power plants, preventing power shortages. Furthermore, during winter when heating demand is high, the generation AI can predict electricity demand and efficiently supply electricity. This prevents power shortages that would have a significant impact on people's lives across Japan. This allows the power management system to efficiently manage electricity demand and prevent future power shortages.

[0061] The power management system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a processing unit. The collection unit collects various data. For example, the collection unit collects data such as power usage, weather data, and the operating status of power plants. The collection unit can, for example, collect power usage data from each household or business in real time. The collection unit can also acquire weather data from weather sensors or a weather database. The collection unit can also acquire the operating status of power plants from a monitoring system. The analysis unit analyzes the data collected by the collection unit and aggregates information that has commonalities. For example, the analysis unit aggregates data from households or businesses that use a lot of power in the same area during the same time period. For example, the analysis unit integrates data from households or businesses that use a lot of power in the same area during the same time period to prevent data duplication. The analysis unit can also analyze data patterns and predict power demand. The control unit automatically controls the amount of data communication based on the data analyzed by the analysis unit. For example, the control unit controls communication to communicate only necessary information and not communicate unnecessary information. The control unit determines the priority of communication based on, for example, the importance of the data. The control unit can also adjust the frequency of communication to reduce the amount of communication. The processing unit performs the minimum necessary processing based on the data controlled by the control unit. For example, the processing unit adjusts the operation of the power plant according to the time period when the demand for electricity is high. For example, the processing unit increases the operation of the power plant during the time period when the demand for electricity is high. The processing unit can also reduce the operation of the power plant during the time period when the demand for electricity is low. This makes it possible to manage the supply and demand of electricity in a balanced manner. As a result, the power management system according to the embodiment can efficiently manage the demand for electricity and prevent future power shortages.

[0062] The collection unit can collect data on power usage, weather data, and the operating status of power plants. For example, the collection unit can collect the power usage of each household or business in real time. For example, the collection unit can measure the power usage of each household or business using a smart meter and collect the data. The collection unit can also acquire weather data from a weather sensor or a weather database. For example, the collection unit collects weather data such as temperature, humidity, and wind speed. The collection unit can also acquire the operating status of power plants from a monitoring system. For example, the collection unit collects data such as the power generation amount, operating hours, and maintenance status of the power plant. This makes it possible to grasp detailed information regarding power demand. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the power usage data of each household or business into the generation AI, which then automates data collection.

[0063] The analysis unit can aggregate data on households and businesses that use a large amount of electricity in the same area during the same time period. For example, the analysis unit aggregates data on households and businesses that use a large amount of electricity in the same area during the same time period. For example, the analysis unit may integrate data on households and businesses that use a large amount of electricity in the same area during the same time period to prevent data duplication. The analysis unit can also analyze data patterns and forecast electricity demand. For example, the analysis unit can use an algorithm to forecast future electricity demand based on past data. Furthermore, the analysis unit can cleanse the data and remove noise and outliers. This improves data quality and allows for accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input collected data into the generation AI, which then analyzes the data.

[0064] The control unit can control the communication so that only necessary information is communicated. For example, the control unit controls the communication so that only necessary information is communicated and unnecessary information is not communicated. For example, the control unit determines the priority of communication based on the importance of the data. For example, the control unit prioritizes the communication of important data and postpones the communication of less important data. The control unit can also adjust the frequency of communication to reduce the amount of communication. For example, the control unit communicates necessary information in real time and periodically deletes unnecessary information. Furthermore, the control unit can optimize the communication protocol to improve communication efficiency. This reduces the amount of communication and avoids unnecessary power consumption. Some or all of the above-mentioned processing in the control unit may be performed using or without the generation AI. For example, the control unit can input the data to be communicated into the generation AI, and the generation AI can control the communication.

[0065] The processing unit can adjust the operation of the power plant according to time periods when electricity demand is high. For example, the processing unit increases the operation of the power plant during time periods when electricity demand is high. For example, the processing unit increases the amount of electricity generated by the power plant during time periods when electricity demand is high. The processing unit can also reduce the operation of the power plant during time periods when electricity demand is low. For example, the processing unit reduces the amount of electricity generated by the power plant during time periods when electricity demand is low. This allows for a balanced management of electricity supply and demand. Furthermore, the processing unit can monitor the operation status of the power plant in real time and adjust the operation as needed. For example, the processing unit monitors the operation status of the power plant and adjusts the amount of electricity generated according to demand. This allows for efficient management of electricity supply and demand. Some or all of the above-described processing in the processing unit may be performed using or without the generation AI. For example, the processing unit can input power plant operation status data into the generation AI, which can then adjust the operation.

[0066] The processing unit can reduce the operation of the power plant during times when electricity demand is low. For example, the processing unit reduces the amount of power generated by the power plant during times when electricity demand is low. For example, the processing unit suppresses the operation of the power plant during times when electricity demand is low. The processing unit can also increase the operation of the power plant during times when electricity demand is high. For example, the processing unit increases the amount of power generated by the power plant during times when electricity demand is high. This allows for a balanced management of electricity supply and demand. Furthermore, the processing unit can monitor the operation status of the power plant in real time and adjust the operation as needed. For example, the processing unit monitors the operation status of the power plant and adjusts the amount of power generated according to demand. This allows for efficient management of electricity supply and demand. Some or all of the above-mentioned processing in the processing unit may be performed using or without the generation AI. For example, the processing unit can input power plant operation status data into the generation AI, which can then adjust the operation.

[0067] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, the collection unit estimates the user's emotions and reduces the frequency of data collection when the user is feeling stressed. The collection unit can also increase the frequency of data collection and collect more detailed data when the user is relaxed. For example, the collection unit estimates the user's emotions and increases the frequency of data collection when the user is relaxed. Furthermore, the collection unit can quickly collect data when the user is in a hurry to quickly obtain necessary information. For example, the collection unit estimates the user's emotions and quickly collects data when the user is in a hurry. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI, and the generation AI may adjust the timing of data collection.

[0068] The collection unit can analyze past power usage history and select the optimal data collection method. For example, the collection unit prioritizes data collection during peak hours based on the past power usage history. For example, the collection unit analyzes past power usage history and prioritizes data collection during peak hours. The collection unit can also concentrate data collection during specific time periods based on the past power usage history. For example, the collection unit analyzes past power usage history and concentrates data collection during specific time periods. The collection unit can also analyze past power usage history and eliminate unnecessary data collection. For example, the collection unit analyzes past power usage history and eliminates unnecessary data collection. This enables efficient data collection based on the past power usage history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input past power usage history data into the generation AI, which can select the optimal data collection method.

[0069] When collecting data, the collection unit can filter the data to be collected based on a specific event or season. For example, the collection unit prioritizes collecting data related to air conditioning use during peak summer hours. For example, the collection unit prioritizes collecting data related to air conditioning use during peak summer hours. The collection unit can also prioritize collecting data related to heating use during periods when heating demand is high in winter. For example, the collection unit prioritizes collecting data related to heating use during periods when heating demand is high in winter. Furthermore, the collection unit can predict fluctuations in electricity usage based on a specific event (e.g., a long holiday) and collect data. For example, the collection unit predicts fluctuations in electricity usage based on a long holiday and collects data. This enables efficient data collection based on a specific event or season. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data related to a specific event or season into the generation AI, and the generation AI can filter the data to be collected.

[0070] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important data. For example, the collection unit estimates the user's emotions and prioritizes collecting only important data when the user is feeling stressed. The collection unit can also prioritize collecting detailed data when the user is relaxed. For example, the collection unit estimates the user's emotions and prioritizes collecting detailed data when the user is relaxed. Furthermore, the collection unit can also prioritize collecting data that can be collected quickly when the user is in a hurry. For example, the collection unit estimates the user's emotions and prioritizes collecting data that can be collected quickly when the user is in a hurry. This enables more appropriate data collection by determining the priority of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and determine the priority of the data to be collected by the generation AI.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, if power usage is high in a specific area, the collection unit prioritizes collecting data for that area. For example, if power usage is high in a specific area, the collection unit prioritizes collecting data for that area. The collection unit can also prioritize collecting data for areas with large fluctuations in power usage based on geographical location information. For example, the collection unit prioritizes collecting data for areas with large fluctuations in power usage based on geographical location information. The collection unit can also prioritize collecting data for areas with unstable power supply by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data for areas with unstable power supply by taking geographical location information into consideration. This enables efficient data collection by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input geographical location information data to the generation AI, which can then prioritize collecting highly relevant data.

[0072] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit analyzes posts about electricity usage on social media and collects related data. For example, the collection unit analyzes posts about electricity usage on social media and collects related data. The collection unit can also analyze posts about weather on social media and collect data useful for predicting electricity usage. For example, the collection unit analyzes posts about weather on social media and collects data useful for predicting electricity usage. Furthermore, the collection unit can analyze event information on social media and collect data for predicting fluctuations in electricity usage. For example, the collection unit analyzes event information on social media and collects data for predicting fluctuations in electricity usage. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input social media activity data to the generation AI, which then collects related data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the user is nervous, the analysis unit can estimate the user's emotions and provide a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can also provide a summary analysis result if the user is in a hurry. For example, the analysis unit can estimate the user's emotions and provide a summary analysis result if the user is in a hurry. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then adjust the way the analysis is expressed.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a detailed analysis on important data based on the importance of the data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit performs a simplified analysis on less important data based on the importance of the data. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit optimally allocates analysis resources based on the importance of the data. This enables efficient analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a time series analysis algorithm to power usage data. For example, the analysis unit applies a time series analysis algorithm to power usage data. The analysis unit can also apply a weather forecasting algorithm to weather data. For example, the analysis unit applies a weather forecasting algorithm to weather data. The analysis unit can also apply a machine learning algorithm to power plant operation status data. For example, the analysis unit applies a machine learning algorithm to power plant operation status data. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the data category to the generation AI, which can select an appropriate analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit can estimate the user's emotions and provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, the analysis unit can estimate the user's emotions and provide a visually stimulating analysis result when the user is excited. This allows for adjusting the length of the analysis according to the user's emotions to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was collected. The analysis unit can also analyze trends based on past data. For example, the analysis unit analyzes trends based on past data based on the time when the data was collected. Furthermore, the analysis unit can optimally allocate analysis resources according to the time when the data was collected. For example, the analysis unit optimally allocates analysis resources based on the time when the data was collected. This enables efficient analysis according to the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data based on the relevance of the data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data based on the relevance of the data. Furthermore, the analysis unit can also optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit optimally allocates analysis resources based on the relevance of the data. This enables efficient analysis according to the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI, and the generation AI can adjust the order of analysis.

[0079] The control unit can estimate the user's emotions and adjust the communication control criteria based on the estimated user emotions. For example, if the user is nervous, the control unit reduces the communication volume to reduce the burden. For example, the control unit estimates the user's emotions and reduces the communication volume when the user is nervous. The control unit can also increase the communication volume and transmit detailed data when the user is relaxed. For example, the control unit estimates the user's emotions and increases the communication volume when the user is relaxed. Furthermore, the control unit can communicate quickly and immediately transmit necessary information when the user is in a hurry. For example, the control unit estimates the user's emotions and communicates quickly when the user is in a hurry. This enables more appropriate communication control by adjusting the communication control criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input the user's emotional data into the generation AI, which can then adjust the communication control criteria.

[0080] The control unit can improve the accuracy of communication by taking into account the interrelationships of data when controlling communication. The control unit, for example, prioritizes communication of highly related data. For example, the control unit prioritizes communication of highly related data by taking into account the interrelationships of data. The control unit can also optimize the order of communication based on the interrelationships of data. For example, the control unit optimizes the order of communication based on the interrelationships of data. Furthermore, the control unit can also optimally allocate communication resources by taking into account the interrelationships of data. For example, the control unit optimally allocates communication resources by taking into account the interrelationships of data. As a result, the accuracy of communication is improved by taking into account the interrelationships of data. Some or all of the above-mentioned processing in the control unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the control unit can input the interrelationships of data into the generation AI, which can improve the accuracy of communication.

[0081] The control unit can perform communication while taking into account attribute information of the data sender when controlling communication. For example, the control unit prioritizes communication when the sender has important data. For example, the control unit prioritizes communication when the sender has important data, taking into account the attribute information of the data sender. The control unit can also optimize the order of communication based on the attribute information of the sender. For example, the control unit optimizes the order of communication based on the attribute information of the data sender. Furthermore, the control unit can also optimally allocate communication resources by taking into account the attribute information of the sender. For example, the control unit optimally allocates communication resources by taking into account the attribute information of the data sender. This enables efficient communication by taking into account the attribute information of the data sender. Some or all of the above-described processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input the attribute information of the data sender to the generation AI, and the generation AI can perform communication.

[0082] The control unit can estimate the user's emotions and adjust the display order of communication results based on the estimated user emotions. For example, if the user is nervous, the control unit displays important information first. For example, the control unit estimates the user's emotions and displays important information first when the user is nervous. The control unit can also display detailed information later when the user is relaxed. For example, the control unit estimates the user's emotions and displays detailed information later when the user is relaxed. Furthermore, the control unit can also display information that emphasizes the main points first when the user is in a hurry. For example, the control unit estimates the user's emotions and displays information that emphasizes the main points first when the user is in a hurry. This enables more appropriate information to be provided by adjusting the display order of communication results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using the generation AI or without the generation AI. For example, the control unit can input the user's emotional data into the generation AI, which can then adjust the display order of the communication results.

[0083] The control unit can perform communication taking into consideration the geographical distribution of data when controlling communication. For example, the control unit prioritizes communication of geographically close data. For example, the control unit prioritizes communication of geographically close data taking into consideration the geographical distribution of data. The control unit can also optimize the order of communication based on the geographical distribution. For example, the control unit optimizes the order of communication based on the geographical distribution of data. Furthermore, the control unit can also optimally allocate communication resources taking into consideration the geographical distribution. For example, the control unit optimally allocates communication resources taking into consideration the geographical distribution of data. This enables efficient communication taking into consideration the geographical distribution. Some or all of the above-described processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input geographical distribution data to the generation AI, and the generation AI can perform communication.

[0084] The control unit can improve the accuracy of communication by referring to related literature when controlling communication. The control unit, for example, improves the accuracy of communication based on related literature. For example, the control unit improves the accuracy of communication by referring to related literature. The control unit can also optimize the order of communication by referring to related literature. For example, the control unit optimizes the order of communication by referring to related literature. Furthermore, the control unit can also optimally allocate communication resources by taking related literature into consideration. For example, the control unit optimally allocates communication resources by taking related literature into consideration. As a result, the accuracy of communication is improved by referring to related literature. Some or all of the above-mentioned processing in the control unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the control unit can input related literature data into the generation AI, which can improve the accuracy of communication.

[0085] The processing unit can estimate the user's emotion and adjust the processing method based on the estimated user's emotion. For example, the processing unit provides a simple and quick processing method when the user is nervous. For example, the processing unit can estimate the user's emotion and provide a simple and quick processing method when the user is nervous. The processing unit can also provide a detailed processing method when the user is relaxed. For example, the processing unit can estimate the user's emotion and provide a detailed processing method when the user is relaxed. Furthermore, the processing unit can also provide a processing method that focuses on the main points when the user is in a hurry. For example, the processing unit can estimate the user's emotion and provide a processing method that focuses on the main points when the user is in a hurry. This enables more appropriate processing by adjusting the processing method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the processing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the processing unit can input the user's emotional data into the generation AI, which can then adjust the processing method.

[0086] During processing, the processing unit can select an optimal processing method by referring to past processing data. The processing unit, for example, selects an optimal processing method based on past processing data. For example, the processing unit selects an optimal processing method by referring to past processing data. The processing unit can also analyze past processing data and select an efficient processing method. For example, the processing unit analyzes past processing data and selects an efficient processing method. Furthermore, the processing unit can also eliminate unnecessary processing by referring to past processing data. For example, the processing unit eliminates unnecessary processing by referring to past processing data. This enables efficient processing based on past processing data. Some or all of the above-mentioned processing in the processing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the processing unit can input past processing data into a generation AI, which then selects an optimal processing method.

[0087] During processing, the processing unit can customize the processing means based on the current situation. The processing unit selects the optimal processing means based on, for example, the current power demand. For example, the processing unit selects the optimal processing means based on the current power demand. The processing unit can also customize the processing means based on the current weather conditions. For example, the processing unit customizes the processing means based on the current weather conditions. Furthermore, the processing unit can also adjust the processing means based on the current operating status of the power plant. For example, the processing unit adjusts the processing means based on the current operating status of the power plant. This makes it possible to select the optimal processing means according to the current situation. Some or all of the above-mentioned processing in the processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the processing unit can input current situation data into the generation AI, which can customize the processing means.

[0088] The processing unit can estimate the user's emotions and determine processing priorities based on the estimated user emotions. For example, if the user is nervous, the processing unit prioritizes important processing. For example, the processing unit estimates the user's emotions and prioritizes important processing when the user is nervous. The processing unit can also prioritize detailed processing when the user is relaxed. For example, the processing unit estimates the user's emotions and prioritizes detailed processing when the user is relaxed. Furthermore, the processing unit can also quickly process when the user is in a hurry. For example, the processing unit estimates the user's emotions and quickly process when the user is in a hurry. This enables more appropriate processing by determining processing priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the processing unit can input the user's emotional data into the generation AI, and the generation AI can determine the processing priority.

[0089] During processing, the processing unit can select an optimal processing method by taking into account the geographical location information. For example, the processing unit prioritizes processing of geographically close data. For example, the processing unit prioritizes processing of geographically close data by taking into account the geographical location information. The processing unit can also select an optimal processing method based on the geographical location information. For example, the processing unit selects an optimal processing method based on the geographical location information. Furthermore, the processing unit can also optimally allocate processing resources by taking into account the geographical location information. For example, the processing unit optimally allocates processing resources by taking into account the geographical location information. This enables efficient processing by taking into account the geographical location information. Some or all of the above-mentioned processing in the processing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the processing unit can input geographical location information data to the generation AI, which can select an optimal processing method.

[0090] During processing, the processing unit can analyze social media activity and suggest a processing measure. For example, the processing unit analyzes posts about electricity usage on social media and suggests an optimal processing measure. For example, the processing unit analyzes posts about electricity usage on social media and suggests an optimal processing measure. The processing unit can also analyze posts about weather on social media and suggest a processing measure useful for predicting electricity usage. For example, the processing unit analyzes posts about weather on social media and suggests a processing measure useful for predicting electricity usage. Furthermore, the processing unit can analyze event information on social media and suggest a processing measure for predicting fluctuations in electricity usage. For example, the processing unit analyzes event information on social media and suggests a processing measure for predicting fluctuations in electricity usage. In this way, the optimal processing measure can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the processing unit may be performed using or without the generation AI. For example, the processing unit can input social media activity data to the generation AI, which then suggests the optimal processing measure. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, control unit, and processing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects various data using the camera 42 or sensors of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and aggregates information that has something in common. The control unit is realized, for example, by the control unit 46A of the smart device 14, and automatically controls the amount of data communication. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs the minimum necessary processing. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, control unit, and processing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects various data using the camera 42 or sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and aggregates information that has something in common. The control unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically controls the amount of data communication. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs the minimum necessary processing. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, control unit, and processing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects various data using the camera 42 or sensors of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and aggregates information that has something in common. The control unit is realized, for example, by the control unit 46A of the headset type terminal 314, and automatically controls the amount of data communication. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs the minimum necessary processing. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, control unit, and processing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects various data using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and aggregates information that has something in common. The control unit is realized, for example, by the control unit 46A of the robot 414, and automatically controls the amount of data communication. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs the minimum necessary processing.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden. Furthermore, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be performed quickly to immediately obtain necessary information. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then adjust the timing of data collection.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. By adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0094] The control unit can estimate the user's emotions and adjust the communication control criteria based on the estimated user emotions. For example, if the user is nervous, the amount of communication can be reduced to reduce the burden. Furthermore, if the user is relaxed, the amount of communication can be increased and detailed data can be transmitted. Furthermore, if the user is in a hurry, communication can be performed quickly to immediately transmit necessary information. This enables more appropriate communication control by adjusting the communication control criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the control unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the control unit can input the user's emotion data into the generation AI, which can then adjust the communication control criteria.

[0095] The processing unit can estimate the user's emotions and adjust the processing method based on the estimated user's emotions. For example, if the user is nervous, a simple and quick processing method can be provided. If the user is relaxed, a detailed processing method can be provided. Furthermore, if the user is in a hurry, a processing method that focuses on the key points can be provided. This allows for more appropriate processing by adjusting the processing method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the processing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the processing unit can input the user's emotion data into the generation AI, which can then adjust the processing method.

[0096] The collection unit can analyze past electricity usage history and select the optimal data collection method. For example, it can prioritize data collection during peak hours based on past electricity usage history. It can also concentrate data collection on specific time periods based on past electricity usage history. It can also analyze past electricity usage history and eliminate unnecessary data collection. This enables efficient data collection based on past electricity usage history. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input past electricity usage history data into the generation AI, which can select the optimal data collection method.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, analysis resources can be optimally allocated according to the importance of the data. This enables efficient analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0098] The control unit can improve the accuracy of communication by taking into account the interrelationships of data when controlling communication. For example, highly related data can be communicated preferentially. The order of communication can also be optimized based on the interrelationships of data. Furthermore, communication resources can be optimally allocated by taking into account the interrelationships of data. In this way, the accuracy of communication is improved by taking into account the interrelationships of data. Some or all of the above-mentioned processing in the control unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the control unit can input the interrelationships of data into the generation AI, which can then improve the accuracy of communication.

[0099] During processing, the processing unit can select the optimal processing method by referring to past processing data. For example, the processing unit can select the optimal processing method based on the past processing data. It can also analyze the past processing data and select an efficient processing method. Furthermore, it can eliminate unnecessary processing by referring to the past processing data. This enables efficient processing based on the past processing data. Some or all of the above-mentioned processing in the processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the processing unit can input the past processing data into the generation AI, which can select the optimal processing method.

[0100] When collecting data, the collection unit can filter the data to be collected based on specific events or seasons. For example, during peak summer hours, data related to air conditioning usage can be collected with priority. Also, during periods of high heating demand in winter, data related to heating usage can be collected with priority. Furthermore, fluctuations in electricity usage can be predicted and data collected based on specific events (e.g., long holidays). This enables efficient data collection based on specific events or seasons. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data related to specific events or seasons into the generation AI, which then filters the data to be collected.

[0101] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a time series analysis algorithm can be applied to power consumption data. A weather forecasting algorithm can be applied to weather data. Furthermore, a machine learning algorithm can be applied to power plant operation status data. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category into the generation AI, which then selects the appropriate analysis algorithm.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects various data. For example, the collection unit collects data such as power usage, weather data, and the operating status of power plants. The collection unit can collect the power usage of each household or business in real time. The collection unit can also obtain weather data from weather sensors and weather databases. Furthermore, the collection unit can obtain the operating status of power plants from a monitoring system. Step 2: The analysis unit analyzes the data collected by the collection unit and aggregates information that has commonalities. For example, the analysis unit aggregates data on households and businesses that use a lot of electricity in the same area during the same time period. To prevent data duplication, the analysis unit integrates data on households and businesses that use a lot of electricity in the same area during the same time period. The analysis unit can also analyze data patterns and forecast electricity demand. Step 3: The control unit automatically controls the amount of data communication based on the data analyzed by the analysis unit. For example, the control unit controls communication so that only necessary information is communicated and unnecessary information is not communicated. The control unit determines the priority of communication based on the importance of the data. The control unit can also adjust the frequency of communication to reduce the amount of communication. Step 4: The processing unit performs the minimum necessary processing based on the data controlled by the control unit. For example, the processing unit adjusts the operation of the power plant to match the time periods when electricity demand is high. The processing unit increases the operation of the power plant during times when electricity demand is high. The processing unit can also reduce the operation of the power plant during times when electricity demand is low. This allows for a balanced management of electricity supply and demand.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a 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.

[0142] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit and aggregates information that has something in common; a control unit that automatically controls the amount of data communication based on the data analyzed by the analysis unit; a processing unit that performs the minimum necessary processing based on the data controlled by the control unit. A system characterized by:

2. The collecting unit Collect data on power usage, weather data, and power plant operation status 2. The system of claim 1.

3. The analysis unit Aggregating data on households and businesses that use large amounts of electricity in the same area at the same time 2. The system of claim 1.

4. The control unit Controlling communication so that only necessary information is transmitted 2. The system of claim 1.

5. The processing unit Adjusting power plant operation to coincide with times of high demand for electricity 2. The system of claim 1.

6. The processing unit Reduce power plant operation during times of low demand 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past electricity usage history and select appropriate data collection methods 2. The system of claim 1.

Images