System

Abstract

To provide a system according to an embodiment which efficiently manages and optimizes data of each city and shares the data on a year-by-year basis.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a sharing unit, an anonymization unit, and an encryption unit. The collection unit collects data of each city. The analysis unit analyzes data collected by the collection unit. The sharing unit shares the result of analysis obtained by the analysis unit on a year-by-year basis. The anonymization unit anonymizes the data collected by the collection unit. The encryption unit encrypts the data collected by the collection unit.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies are not yet capable of effectively managing and optimizing city-specific data and sharing it year-on-year, leaving room for improvement.

[0005] The system according to the embodiment aims to effectively manage and optimize data for each city and share it annually. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a sharing unit, an anonymization unit, and an encryption unit. The collection unit collects data for each city. The analysis unit analyzes the data collected by the collection unit. The sharing unit shares the analysis results obtained by the analysis unit by year. The anonymization unit anonymizes the data collected by the collection unit. The encryption unit encrypts the data collected by the collection unit. Effect of the Invention

[0007] The system according to the embodiment can effectively manage and optimize data for each city and share it on an annual basis. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The smart city management system according to the embodiment of the present invention is a system that packages data held by each city and shares effective management and optimization on an annual basis using AI. This system collects and packages data for each city. For example, it includes traffic data, energy consumption data, and public service usage data. These data are collected from sensors and databases in each city. Next, the collected data is analyzed using AI to achieve traffic control, energy optimization, and service efficiency. For example, traffic data can be analyzed to predict traffic congestion and propose optimal traffic routes. It is also possible to analyze energy consumption data to propose efficient energy usage methods. Furthermore, the analysis results are shared annually so that each city can effectively utilize data. This will promote data sharing between cities and improve the efficiency of the overall smart city management. It also focuses on data privacy and quality control, protecting the privacy of collected data and maintaining the quality of data. For example, data is anonymized and encrypted to prevent unauthorized use of data. In addition, the quality of data is regularly checked and accurate data is provided. This system aims to pursue social value and innovation and build a sustainable city. Participants are asked to make proposals that emphasize fairness and ethics, and propose specific ideas for building a sustainable city. For example, new algorithms for traffic control and energy optimization and new systems for improving the efficiency of public services are conceivable. In this way, a system that packages data held by each city and uses AI to effectively manage and optimize data on an annual basis will play an important role in smart city management. By focusing on data privacy and quality management and pursuing social value and innovation, a sustainable city can be built. This allows the smart city management system to effectively manage and optimize data for each city and share it on an annual basis.

[0029] The smart city management system according to the embodiment includes a collection unit, an analysis unit, a sharing unit, an anonymization unit, and an encryption unit. The collection unit collects data for each city. The data for each city includes, for example, traffic data, energy consumption data, and public service usage data, but is not limited to such examples. The collection unit collects data from, for example, sensors and databases in each city. The sensors include sensors that measure traffic volume, sensors that measure energy consumption, and sensors that measure the usage status of public services. The database refers to data accumulated in the management system of each city. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes traffic data to predict traffic congestion and propose an optimal traffic route. The analysis unit can also analyze energy consumption data to propose an efficient energy usage method. The sharing unit shares the analysis results obtained by the analysis unit by year. The sharing is performed, for example, based on the format of data and the frequency of sharing, but is not limited to such examples. For example, the sharing unit compiles the analysis results as an annual report and distributes it to each city. The sharing unit can also share the analysis results through an online platform. The anonymization unit anonymizes the data collected by the collection unit. For example, the anonymization unit deletes personal information and masks the data, but is not limited to such examples. For example, the anonymization unit deletes information that can identify an individual from the collected data to protect the privacy of the data. The encryption unit encrypts the data collected by the collection unit. The encryption is performed, for example, based on an encryption algorithm and a key management method to be used, but is not limited to such examples. For example, the encryption unit encrypts the collected data using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized use of the data. As a result, the smart city management system according to the embodiment can effectively manage and optimize data for each city and share it every year. Some or all of the above-mentioned processes in the collection unit, analysis unit, sharing unit, anonymization unit, and encryption unit may be performed using AI, for example, or may be performed without using AI.For example, the collection unit can input data acquired from a sensor to the generation AI and cause the generation AI to collect data. The analysis unit can input collected data to the generation AI and cause the generation AI to analyze the data. The sharing unit can input the analysis result to the generation AI and cause the generation AI to share the data. The anonymization unit can input collected data to the generation AI and cause the generation AI to anonymize the data. The encryption unit can input collected data to the generation AI and cause the generation AI to encrypt the data. As a result, the smart city management system according to the embodiment can effectively manage and optimize data for each city and share it on an annual basis.

[0030] The collection unit collects data for each city. The data for each city includes, for example, traffic data, energy consumption data, and public service usage data, but is not limited to such examples. The collection unit collects data from, for example, sensors and databases for each city. The sensors include sensors that measure traffic volume, sensors that measure energy consumption, and sensors that measure the usage of public services. Specifically, the sensors that measure traffic volume acquire data from cameras installed on roads and GPS devices mounted on vehicles, and monitor traffic flow in real time. The sensors that measure energy consumption collect data from power meters and gas meters in homes and businesses, and grasp the energy usage status in detail. The sensors that measure the usage status of public services measure, for example, the number of passengers boarding and alighting public transportation and the number of users of libraries, and grasp the usage status of the services. The database refers to data accumulated in the management system of each city. For example, past traffic data and accident information are accumulated in the city's traffic management system, and the collection unit acquires these data. The energy management system records past energy consumption data and consumption patterns at peak times, and the collection unit also acquires these data. The collection unit centrally collects data from these diverse data sources and integrates them into a central database. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, when a traffic jam occurs, the frequency of data collection from the traffic sensor is increased to grasp the detailed situation in real time. Also, when energy consumption increases sharply, the frequency of data collection from the energy sensor is increased to identify the cause. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the analysis unit analyzes traffic data to predict traffic congestion and proposes an optimal traffic route. For example, the analysis unit uses a machine learning algorithm to learn past traffic data and predicts the occurrence pattern of traffic congestion in a specific time period or weather conditions. In addition, the analysis unit analyzes real-time traffic data and calculates an optimal detour route based on the current traffic situation. Furthermore, the analysis unit can analyze energy consumption data to propose an efficient method of using energy. For example, the analysis unit analyzes energy consumption patterns of homes and businesses and proposes measures to reduce consumption during peak hours. Specifically, the analysis unit predicts the time period when energy consumption will reach its peak and proposes a schedule for distributing energy consumption during that time period. The analysis unit can also analyze public service usage data to propose an efficient operation method of the service. For example, the analysis unit analyzes the usage status of public transportation and proposes additional operation during the time period when the number of passengers is high. In this way, the analysis unit can quickly and accurately analyze the collected data and provide information for optimizing the operation of the city. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, based on past traffic data, the analysis unit can predict traffic congestion trends in specific areas or time periods and plan future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The sharing unit shares the analysis results obtained by the analysis unit every year. Sharing is performed, for example, based on the format of data and the frequency of sharing, but is not limited to such examples. Specifically, the analysis results are compiled as an annual report and distributed to each city. The annual report contains detailed information on the occurrence of traffic congestion, trends in energy consumption, and the use of public services, and serves as a reference material for each city to review its own operations. The sharing unit can also share the analysis results through an online platform. For example, through a dedicated website or application, the person in charge of each city can access the analysis results and obtain the necessary information. Furthermore, the sharing unit can provide a dashboard for visually displaying the analysis results in an easy-to-understand manner. The dashboard displays the locations of traffic congestion, peak hours of energy consumption, and the use of public services in graphs and maps, allowing the person in charge to intuitively grasp the situation. This allows the sharing unit to effectively share the analysis results and provide information for each city to take prompt and appropriate action. Furthermore, the sharing unit can adjust the frequency of sharing the analysis results, allowing flexible responses according to specific situations and conditions. For example, in areas where traffic congestion is frequent, analysis results can be shared monthly or weekly to allow for prompt countermeasures. Also, during seasons when energy consumption increases sharply, analysis results can be shared frequently to promote efficient energy use. This allows the shared parts to share analysis results efficiently and effectively, improving the performance of the entire system.

[0033] The anonymization unit anonymizes the data collected by the collection unit. For example, the anonymization may delete personal information or mask the data, but is not limited to such examples. Specifically, information that can identify an individual is deleted from the collected data to protect the privacy of the data. For example, traffic data may include vehicle license plates and personal information of drivers, but by deleting this information, the individual cannot be identified. In addition, energy consumption data may include detailed consumption patterns of homes and businesses, but by masking this data, the privacy of individuals and businesses is protected. Furthermore, the anonymization unit can use an algorithm for automating the anonymization process of data. For example, a machine learning algorithm is used to automatically detect information that can identify an individual from the data, and delete or mask the information. In addition, the anonymization unit also has a function for verifying whether the anonymization process of data is performed appropriately. For example, the anonymized data is reanalyzed to check whether any information that can identify an individual remains. In this way, the anonymization unit can safely use the collected data while ensuring the privacy of the data. Furthermore, the anonymization unit can flexibly adjust the anonymization process of data. For example, by performing more rigorous anonymization processing on a specific data set and performing the minimum necessary anonymization processing on other data sets, the utility value of the data can be maximized. This allows the anonymization unit to anonymize data efficiently and effectively, improving the performance of the entire system.

[0034] The encryption unit encrypts the data collected by the collection unit. The encryption is performed, for example, based on the encryption algorithm and key management method used, but is not limited to such examples. Specifically, the collected data is encrypted using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized use of the data. For example, all collected data, such as traffic data, energy consumption data, and public service usage data, is encrypted to ensure the confidentiality of the data. Furthermore, the encryption unit also has a function for properly managing the keys of the encrypted data. For example, a hardware security module (HSM) for safely storing the encryption key is used to prevent the key from leaking. Furthermore, the encryption unit can use an algorithm for automating the encryption process of the data. For example, a machine learning algorithm is used to automatically detect highly confidential information from the data and perform encryption. This allows the encryption unit to safely use the collected data while ensuring the confidentiality of the data. Furthermore, the encryption unit also has a function for verifying whether the encryption process of the data is performed appropriately. For example, the encrypted data is reanalyzed to check whether the encryption is performed appropriately. This allows the encryption unit to safely use the collected data while ensuring the confidentiality of the data. Furthermore, the encryption unit can flexibly adjust the encryption process of the data. For example, it is possible to maximize the value of the data by performing more strict encryption process on a specific data set and performing the minimum necessary encryption process on other data sets. This allows the encryption unit to encrypt data efficiently and effectively, improving the performance of the entire system.

[0035] The analysis unit can analyze traffic data, predict traffic congestion, and propose an appropriate traffic route. The analysis unit, for example, analyzes traffic data to predict traffic congestion. For example, based on past traffic data, calculates the probability of traffic congestion occurring in a specific time period or day of the week. The analysis unit can also analyze real-time traffic data to grasp the current traffic situation and predict the occurrence of traffic congestion. For example, the analysis unit analyzes real-time data acquired from a traffic sensor to predict the occurrence of traffic congestion. Furthermore, the analysis unit proposes an optimal traffic route based on the result of predicting traffic congestion. For example, an alternative route to avoid traffic congestion is proposed. The analysis unit can also propose a method of controlling traffic signals to minimize the impact of traffic congestion. For example, the timing of traffic signals is adjusted to smooth the flow of traffic. This predicts traffic congestion and proposes an optimal traffic route, thereby improving traffic efficiency. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input traffic data to the generation AI and cause the generation AI to predict traffic congestion and propose an optimal traffic route.

[0036] The analysis unit can analyze the energy consumption data and propose an appropriate method of using energy. The analysis unit can, for example, analyze the energy consumption data to propose an efficient method of using energy. For example, based on past energy consumption data, the energy consumption pattern in a specific time period or season is analyzed. The analysis unit can also analyze real-time energy consumption data to grasp the current energy consumption situation and propose an efficient method of using energy. For example, the analysis unit can analyze real-time data acquired from an energy sensor and make a proposal to prevent energy waste. Furthermore, the analysis unit can propose a method of promoting the use of renewable energy in order to propose an efficient method of using energy. For example, the analysis unit can propose the introduction of solar power generation or wind power generation. The analysis unit can also propose a peak shift of energy consumption. For example, the analysis unit can propose a method of dispersing energy consumption to avoid peak hours of energy consumption. As a result, energy can be optimized by proposing an efficient method of using energy. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the energy consumption data to the generation AI and cause the generation AI to execute a proposal for an efficient method of using energy.

[0037] The sharing unit can share the analysis results annually so that each city can effectively utilize the data. For example, the sharing unit compiles the analysis results as an annual report and distributes it to each city. The annual report includes a detailed explanation of the analysis results and suggestions for each city to utilize the data. The sharing unit can also share the analysis results through an online platform. For example, the analysis results can be stored in a digital format and made accessible to each city. Furthermore, the sharing unit can provide tools for visually displaying the analysis results. For example, the analysis results can be displayed in an easy-to-understand manner using graphs and charts. This allows each city to effectively utilize the data by sharing the analysis results annually. Some or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI or may be performed without using AI. For example, the sharing unit can input the analysis results to the generation AI and cause the generation AI to create an annual report and share it on an online platform.

[0038] The anonymization unit can anonymize the collected data and protect the privacy of the data. For example, the anonymization unit deletes information that can identify an individual from the collected data. For example, personal information such as a name and an address is deleted to protect the privacy of the data. The anonymization unit can also mask the data. For example, a specific data field is masked so that an individual cannot be identified. Furthermore, the anonymization unit can periodically check the anonymization of the data and maintain the quality of the anonymization. For example, the anonymized data is periodically reviewed and additional anonymization processing is performed as necessary. This can prevent unauthorized use of the data by protecting the privacy of the data. Some or all of the above-mentioned processing in the anonymization unit may be performed, for example, using AI or may be performed without using AI. For example, the anonymization unit can input the collected data to the generation AI and cause the generation AI to perform anonymization of the data.

[0039] The encryption unit can encrypt the collected data and prevent unauthorized use of the data. For example, the encryption unit encrypts the collected data using an encryption algorithm such as AES (Advanced Encryption Standard). For example, by encrypting the data, unauthorized use of the data is prevented. The encryption unit can also perform key management of the encrypted data. For example, the encryption unit provides a procedure for safely storing the encryption key and decrypting it as necessary. Furthermore, the encryption unit can periodically check the quality of the encryption and maintain the strength of the encryption. For example, the encryption algorithm is updated and the encryption key is periodically changed. This prevents unauthorized use of the data, thereby improving the security of the data. A part or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input the collected data to the generation AI and cause the generation AI to encrypt the data.

[0040] The collection unit can select an appropriate data collection method based on the characteristics of each city. The collection unit optimizes the placement of sensors based on the population density of the city, for example. For example, many sensors are placed in areas with high population density to collect detailed data. The collection unit can also adjust the collection method of traffic data based on the traffic pattern of the city. For example, data collection is strengthened during times of high traffic volume and data collection is reduced during times of low traffic volume. Furthermore, the collection unit can adjust the collection method of energy data based on the energy consumption pattern of the city. For example, data collection is strengthened during times of peak energy consumption and data collection is reduced during times of low energy consumption. This improves the efficiency of data collection by selecting the optimal data collection method based on the characteristics of each city. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input characteristic data of each city to the generation AI and cause the generation AI to select the optimal data collection method.

[0041] The collection unit can adjust the type of data to be collected in consideration of city events and seasonal fluctuations when collecting data. For example, when a large-scale event is held in the city, the collection unit strengthens collection of traffic data. For example, the collection unit predicts that traffic volume will increase during the event period and increases the frequency of collection of traffic data. The collection unit can also adjust the collection of energy consumption data in accordance with seasonal fluctuations. For example, the collection unit predicts that energy consumption will increase in summer and winter and increases the frequency of collection of energy data. Furthermore, the collection unit can strengthen collection of tourism data in accordance with the city's tourist season. For example, the frequency of collection of tourism data is increased during a period when tourists increase, and tourist trends are grasped. This allows more accurate data to be collected by adjusting data collection in consideration of city events and seasonal fluctuations. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input city event information and seasonal fluctuation data to the generation AI and cause the generation AI to adjust the type of data to be collected.

[0042] The collection unit can prioritize collection of highly relevant data in consideration of the geographical characteristics of the city when collecting data. The collection unit selects a collection point of traffic data based on, for example, the topography of the city. For example, the collection point of traffic data is optimized according to the topography, such as mountainous areas and plains. The collection unit can also select a collection point of energy data based on the climatic conditions of the city. For example, the collection point of energy data is optimized according to the climatic conditions, such as cold areas and warm areas. Furthermore, the collection unit can also select a collection point of public service data based on the population distribution of the city. For example, the collection point of public service data is optimized according to areas with high population density and areas with low population density. As a result, highly relevant data can be efficiently collected by collecting data in consideration of the geographical characteristics of the city. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical characteristics data of the city to the generation AI and cause the generation AI to select the collection point of highly relevant data.

[0043] The collection unit can analyze the social media activity of the city and collect related data when collecting data. The collection unit, for example, analyzes trends on social media and collects related traffic data. For example, if posts about traffic congestion are increasing on social media, the collection of traffic data is strengthened. The collection unit can also analyze discussions about energy consumption on social media and collect related energy data. For example, if posts about energy consumption are increasing, the collection of energy data is strengthened. Furthermore, the collection unit can analyze feedback about public services on social media and collect related data. For example, if posts about public services are increasing, the collection of public service data is strengthened. This allows more diverse data to be collected by analyzing social media activities and collecting data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to the generation AI and cause the generation AI to collect related data.

[0044] 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 data with high importance. For example, for data that is important in city management, such as traffic data and energy data, the analysis unit performs a detailed statistical analysis or an analysis using a machine learning algorithm. The analysis unit can also perform a simplified analysis on data with low importance. For example, for data with relatively low importance, such as public service usage data, a simple summary or a basic statistical analysis is performed. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. For example, for data with medium importance, such as city event data and seasonal fluctuation data, an analysis with a moderate level of detail is performed. This allows efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a traffic congestion prediction algorithm to traffic data. For example, the analysis unit calculates the probability of traffic congestion occurring in a specific time period or day of the week based on past traffic data. The analysis unit can also apply an energy consumption optimization algorithm to energy data. For example, the analysis unit proposes an efficient energy usage method based on past energy consumption data. Furthermore, the analysis unit can apply a service efficiency algorithm to public service data. For example, the analysis unit analyzes the usage status of public services and makes proposals for improving the efficiency of the services. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the category of data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of the analysis based on the time when the data was collected. For example, the analysis unit preferentially analyzes the latest data. For example, data collected in real time is preferentially analyzed to provide the latest information. The analysis unit can also analyze the latest data while referring to past data. For example, past data is compared with the latest data to analyze trends and patterns. Furthermore, the analysis unit can also preferentially analyze data from a specific period. For example, data from a specific event period is preferentially analyzed to evaluate the impact of the event. As a result, the latest data can be preferentially analyzed by determining the priority of the analysis based on the time when the data was collected. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and cause the generation AI to determine the priority of the analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of data. The analysis unit adjusts the order of analysis, for example, taking into account the relevance of traffic data and energy data. For example, the correlation between traffic data and energy data is analyzed, and highly relevant data is analyzed preferentially. The analysis unit can also adjust the order of analysis, for example, taking into account the relevance of public service data and energy data. For example, the relationship between the usage status of public services and energy consumption is analyzed, and highly relevant data is analyzed preferentially. The analysis unit can also adjust the order of analysis, for example, taking into account the relevance of traffic data and public service data. For example, the relationship between traffic volume and the usage status of public services is analyzed, and highly relevant data is analyzed preferentially. This allows efficient data analysis by adjusting the order of analysis based on the relevance of data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The sharing unit can select the optimal sharing method based on the characteristics of each city. The sharing unit adjusts the data sharing method based on the population density of the city, for example. For example, for cities with high population density, data is shared through an online platform, and for cities with low population density, annual reports are distributed. The sharing unit can also adjust the traffic data sharing method based on the traffic pattern of the city. For example, for cities with high traffic volume, traffic data is shared in real time, and for cities with low traffic volume, traffic data is shared periodically. Furthermore, the sharing unit can adjust the energy data sharing method based on the energy consumption pattern of the city. For example, detailed energy data is shared for cities with high energy consumption, and simplified energy data is shared for cities with low energy consumption. As a result, the efficiency of data sharing is improved by selecting the optimal sharing method based on the characteristics of each city. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input characteristic data of each city to the generation AI and cause the generation AI to select the optimal sharing method.

[0049] The sharing unit can adjust the level of detail of the sharing based on the importance of the data. For example, the sharing unit performs detailed sharing for data with high importance. For example, for data that is important in city management, such as traffic data and energy data, the sharing unit uses detailed reports and graphs to share the data. The sharing unit can also perform simplified sharing for data with low importance. For example, for data with relatively low importance, such as public service usage data, the sharing unit uses simple summaries and basic statistical information to share the data. Furthermore, the sharing unit can also perform sharing with a moderate level of detail for data with medium importance. For example, for data with medium importance, such as city event data and seasonal fluctuation data, the sharing unit uses reports with a moderate level of detail to share the data. This enables efficient data sharing by adjusting the level of detail of the sharing based on the importance of the data. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the sharing.

[0050] The sharing unit can preferentially share highly relevant data in consideration of the geographical characteristics of the city. The sharing unit selects a sharing point for traffic data based on, for example, the topography of the city. For example, the sharing point for traffic data is optimized according to the topography, such as mountainous areas or plains. The sharing unit can also select a sharing point for energy data based on the climatic conditions of the city. For example, the sharing point for energy data is optimized according to climatic conditions, such as cold areas or warm areas. Furthermore, the sharing unit can also select a sharing point for public service data based on the population distribution of the city. For example, the sharing point for public service data is optimized according to areas with high or low population density. As a result, highly relevant data can be shared efficiently by sharing data in consideration of the geographical characteristics of the city. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the geographical characteristics data of the city to the generation AI and cause the generation AI to select a sharing point for highly relevant data.

[0051] The sharing unit can analyze the city's social media activities and share relevant data. The sharing unit, for example, analyzes trends on social media and shares relevant traffic data. For example, when posts about traffic congestion are increasing on social media, the sharing unit strengthens the sharing of traffic data. The sharing unit can also analyze discussions about energy consumption on social media and share relevant energy data. For example, when posts about energy consumption are increasing, the sharing unit strengthens the sharing of energy data. Furthermore, the sharing unit can analyze feedback about public services on social media and share relevant data. For example, when posts about public services are increasing, the sharing unit strengthens the sharing of public service data. In this way, by analyzing social media activities and sharing data, more diverse data can be shared. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input social media data to the generation AI and cause the generation AI to execute the sharing of related data.

[0052] The anonymization unit can adjust the level of detail of anonymization based on the importance of the data. For example, the anonymization unit performs detailed anonymization on data with high importance. For example, for data including personal information or confidential information, detailed data masking or deletion of personal information is performed. The anonymization unit can also perform simplified anonymization on data with low importance. For example, simple data masking is performed on general statistical data or public information. Furthermore, the anonymization unit can also perform anonymization with a moderate level of detail on data with medium importance. For example, data masking with a moderate level of detail is performed on business data or operational data. As a result, efficient data anonymization is possible by adjusting the level of detail of anonymization based on the importance of the data. Part or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of anonymization.

[0053] The anonymization unit can adjust the anonymization method based on the time when the data was collected. For example, the anonymization unit preferentially anonymizes the latest data. For example, data collected in real time is preferentially anonymized to protect the latest information. The anonymization unit can also anonymize the latest data while referring to past data. For example, past data is compared with the latest data to analyze trends and patterns. Furthermore, the anonymization unit can also preferentially anonymize data from a specific period. For example, data from a specific event period is preferentially anonymized to evaluate the impact of the event. This allows the latest data to be preferentially anonymized by adjusting the anonymization method based on the time when the data was collected. Part or all of the above-mentioned processing in the anonymization unit may be performed, for example, using AI or may be performed without using AI. For example, the anonymization unit can input the time when the data was collected to the generation AI and cause the generation AI to adjust the anonymization method.

[0054] The encryption unit can adjust the level of detail of encryption based on the importance of the data. For example, the encryption unit performs detailed encryption on data of high importance. For example, for data including personal information or confidential information, the data is encrypted using a detailed encryption algorithm. The encryption unit can also perform simplified encryption on data of low importance. For example, for general statistical data or public information, the data is encrypted using a simple encryption algorithm. Furthermore, the encryption unit can also perform encryption with a moderate level of detail on data of medium importance. For example, for business data or operational data, the data is encrypted using an encryption algorithm with a moderate level of detail. As a result, efficient data encryption is possible by adjusting the level of detail of encryption based on the importance of the data. A part or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of encryption.

[0055] The encryption unit can adjust the encryption method based on the time when the data was collected. For example, the encryption unit preferentially encrypts the latest data. For example, data collected in real time is preferentially encrypted to protect the latest information. The encryption unit can also encrypt the latest data while referring to past data. For example, past data is compared with the latest data to analyze trends and patterns. Furthermore, the encryption unit can also preferentially encrypt data from a specific period. For example, data from a specific event period is preferentially encrypted to evaluate the impact of the event. This allows the latest data to be preferentially encrypted by adjusting the encryption method based on the time when the data was collected. Some or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input the time when the data was collected to the generation AI and cause the generation AI to adjust the encryption method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze the city's weather data and propose optimization of transportation routes and energy consumption based on weather conditions. For example, traffic congestion is likely to occur in rainy weather, so the analysis unit proposes optimal transportation routes based on traffic data for rainy weather. In addition, energy consumption is predicted to increase on hot days, so the analysis unit can propose efficient energy usage methods based on temperature data. Furthermore, the analysis unit can adjust the way public services are provided based on weather data. For example, in bad weather, public transportation schedules can be adjusted to ensure the safety of users. This makes it possible to operate the city optimally according to weather conditions.

[0058] The common unit can collect feedback from city residents and reflect it in the analysis results. For example, if residents have complaints about traffic congestion, the common unit collects that feedback and provides it to the analysis unit. The analysis unit can reanalyze traffic data based on the residents' feedback and propose more effective traffic routes. In addition, if residents make suggestions regarding energy consumption, the common unit collects the suggestions and provides it to the analysis unit. The analysis unit can reanalyze energy consumption data based on the residents' suggestions and propose efficient methods of using energy. In addition, if residents have opinions about public services, the common unit collects those opinions and provides them to the analysis unit. The analysis unit can reanalyze public service data based on the residents' opinions and improve the efficiency of services. This makes it possible to operate a city that reflects residents' feedback.

[0059] The collection unit can collect infrastructure data of the city and monitor the state of the infrastructure. For example, the damage state of the road is detected by a sensor, and the collection unit collects the data. The structural soundness of bridges and tunnels can also be monitored, and the collection unit can collect the data. Furthermore, the state of water supply and sewerage can be monitored, and the collection unit can collect the data. This makes it possible to grasp the state of the city's infrastructure in real time and perform necessary maintenance promptly. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input infrastructure data to the generation AI and cause the generation AI to monitor the state of the infrastructure.

[0060] The analysis unit can analyze crime data for a city and propose crime prevention measures. For example, it can predict the risk of crime occurring in a specific area or time period based on past crime data. It can also analyze real-time crime data to understand the current crime situation and propose crime prevention measures. For example, it can propose strengthening police patrols in areas with high crime rates. Furthermore, the analysis unit can optimize the placement of public facilities based on crime data. For example, it can propose installing security cameras in areas with high crime rates. In this way, it can improve the safety of the city by proposing crime prevention measures.

[0061] The sharing department can collect educational data for a city and make proposals to improve the quality of education. For example, it can collect school grade data and attendance data to evaluate the quality of education. It can also collect teacher evaluation data and student feedback to identify areas for improvement in education. Furthermore, the sharing department can propose optimizing educational programs based on educational data. For example, if grades are low in a particular subject, it can make a proposal to review the educational program for that subject. This makes it possible to make specific proposals to improve the quality of education.

[0062] The analysis unit can analyze the city's health data and propose optimization of health management. For example, based on the health checkup data of residents, it can predict the risk of a particular disease. It can also analyze real-time health data, grasp the current health status, and propose optimization of health management. For example, based on the health data of residents, it can propose a health education program for areas with high health risks. Furthermore, the analysis unit can propose optimal allocation of medical resources based on the health data. For example, it makes a proposal to optimize the allocation of medical facilities for areas with a high incidence of a particular disease. In this way, it is possible to improve the health of residents by proposing optimization of health management.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection unit collects data for each city. The data for each city includes traffic data, energy consumption data, and public service usage data. The collection unit collects data from sensors and databases in each city. The sensors include sensors that measure traffic volume, sensors that measure energy consumption, and sensors that measure the usage of public services. The database refers to the data accumulated in the management system of each city. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, traffic data can be analyzed to predict traffic congestion and propose optimal traffic routes. Energy consumption data can also be analyzed to propose efficient energy usage methods. Step 3: The Sharing Department will share the analysis results obtained by the Analysis Department on an annual basis. Sharing will be based on the format of data and frequency of sharing. For example, the analysis results may be compiled as an annual report and distributed to each city. Analysis results may also be shared through an online platform. Step 4: The anonymization unit anonymizes the data collected by the collection unit. Anonymization involves removing personal information or masking the data. For example, it removes information that can identify an individual from the collected data to protect the privacy of the data. Step 5: The encryption unit encrypts the data collected by the collection unit. The encryption is performed based on the encryption algorithm and key management method to be used. For example, the collected data is encrypted using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized use of the data.

[0065] (Example 2) The smart city management system according to the embodiment of the present invention is a system that packages data held by each city and shares effective management and optimization on an annual basis using AI. This system collects and packages data for each city. For example, it includes traffic data, energy consumption data, and public service usage data. These data are collected from sensors and databases in each city. Next, the collected data is analyzed using AI to achieve traffic control, energy optimization, and service efficiency. For example, traffic data can be analyzed to predict traffic congestion and propose optimal traffic routes. It is also possible to analyze energy consumption data to propose efficient energy usage methods. Furthermore, the analysis results are shared annually so that each city can effectively utilize data. This will promote data sharing between cities and improve the efficiency of the overall smart city management. It also focuses on data privacy and quality control, protecting the privacy of collected data and maintaining the quality of data. For example, data is anonymized and encrypted to prevent unauthorized use of data. In addition, the quality of data is regularly checked and accurate data is provided. This system aims to pursue social value and innovation and build a sustainable city. Participants are asked to make proposals that emphasize fairness and ethics, and propose specific ideas for building a sustainable city. For example, new algorithms for traffic control and energy optimization and new systems for improving the efficiency of public services are conceivable. In this way, a system that packages data held by each city and uses AI to effectively manage and optimize data on an annual basis will play an important role in smart city management. By focusing on data privacy and quality management and pursuing social value and innovation, a sustainable city can be built. This allows the smart city management system to effectively manage and optimize data for each city and share it on an annual basis.

[0066] The smart city management system according to the embodiment includes a collection unit, an analysis unit, a sharing unit, an anonymization unit, and an encryption unit. The collection unit collects data for each city. The data for each city includes, for example, traffic data, energy consumption data, and public service usage data, but is not limited to such examples. The collection unit collects data from, for example, sensors and databases in each city. The sensors include sensors that measure traffic volume, sensors that measure energy consumption, and sensors that measure the usage status of public services. The database refers to data accumulated in the management system of each city. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes traffic data to predict traffic congestion and propose an optimal traffic route. The analysis unit can also analyze energy consumption data to propose an efficient energy usage method. The sharing unit shares the analysis results obtained by the analysis unit by year. The sharing is performed, for example, based on the format of data and the frequency of sharing, but is not limited to such examples. For example, the sharing unit compiles the analysis results as an annual report and distributes it to each city. The sharing unit can also share the analysis results through an online platform. The anonymization unit anonymizes the data collected by the collection unit. For example, the anonymization unit deletes personal information and masks the data, but is not limited to such examples. For example, the anonymization unit deletes information that can identify an individual from the collected data to protect the privacy of the data. The encryption unit encrypts the data collected by the collection unit. The encryption is performed, for example, based on an encryption algorithm and a key management method to be used, but is not limited to such examples. For example, the encryption unit encrypts the collected data using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized use of the data. As a result, the smart city management system according to the embodiment can effectively manage and optimize data for each city and share it every year. Some or all of the above-mentioned processes in the collection unit, analysis unit, sharing unit, anonymization unit, and encryption unit may be performed using AI, for example, or may be performed without using AI.For example, the collection unit can input data acquired from a sensor to the generation AI and cause the generation AI to collect data. The analysis unit can input collected data to the generation AI and cause the generation AI to analyze the data. The sharing unit can input the analysis result to the generation AI and cause the generation AI to share the data. The anonymization unit can input collected data to the generation AI and cause the generation AI to anonymize the data. The encryption unit can input collected data to the generation AI and cause the generation AI to encrypt the data. As a result, the smart city management system according to the embodiment can effectively manage and optimize data for each city and share it on an annual basis.

[0067] The collection unit collects data for each city. The data for each city includes, for example, traffic data, energy consumption data, and public service usage data, but is not limited to such examples. The collection unit collects data from, for example, sensors and databases for each city. The sensors include sensors that measure traffic volume, sensors that measure energy consumption, and sensors that measure the usage of public services. Specifically, the sensors that measure traffic volume acquire data from cameras installed on roads and GPS devices mounted on vehicles, and monitor traffic flow in real time. The sensors that measure energy consumption collect data from power meters and gas meters in homes and businesses, and grasp the energy usage status in detail. The sensors that measure the usage status of public services measure, for example, the number of passengers boarding and alighting public transportation and the number of users of libraries, and grasp the usage status of the services. The database refers to data accumulated in the management system of each city. For example, past traffic data and accident information are accumulated in the city's traffic management system, and the collection unit acquires these data. The energy management system records past energy consumption data and consumption patterns at peak times, and the collection unit also acquires these data. The collection unit centrally collects data from these diverse data sources and integrates them into a central database. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, when a traffic jam occurs, the frequency of data collection from the traffic sensor is increased to grasp the detailed situation in real time. Also, when energy consumption increases sharply, the frequency of data collection from the energy sensor is increased to identify the cause. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0068] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the analysis unit analyzes traffic data to predict traffic congestion and proposes an optimal traffic route. For example, the analysis unit uses a machine learning algorithm to learn past traffic data and predicts the occurrence pattern of traffic congestion in a specific time period or weather conditions. In addition, the analysis unit analyzes real-time traffic data and calculates an optimal detour route based on the current traffic situation. Furthermore, the analysis unit can analyze energy consumption data to propose an efficient method of using energy. For example, the analysis unit analyzes energy consumption patterns of homes and businesses and proposes measures to reduce consumption during peak hours. Specifically, the analysis unit predicts the time period when energy consumption will reach its peak and proposes a schedule for distributing energy consumption during that time period. The analysis unit can also analyze public service usage data to propose an efficient operation method of the service. For example, the analysis unit analyzes the usage status of public transportation and proposes additional operation during the time period when the number of passengers is high. In this way, the analysis unit can quickly and accurately analyze the collected data and provide information for optimizing the operation of the city. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, based on past traffic data, the analysis unit can predict traffic congestion trends in specific areas or time periods and plan future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The sharing unit shares the analysis results obtained by the analysis unit every year. Sharing is performed, for example, based on the format of data and the frequency of sharing, but is not limited to such examples. Specifically, the analysis results are compiled as an annual report and distributed to each city. The annual report contains detailed information on the occurrence of traffic congestion, trends in energy consumption, and the use of public services, and serves as a reference material for each city to review its own operations. The sharing unit can also share the analysis results through an online platform. For example, through a dedicated website or application, the person in charge of each city can access the analysis results and obtain the necessary information. Furthermore, the sharing unit can provide a dashboard for visually displaying the analysis results in an easy-to-understand manner. The dashboard displays the locations of traffic congestion, peak hours of energy consumption, and the use of public services in graphs and maps, allowing the person in charge to intuitively grasp the situation. This allows the sharing unit to effectively share the analysis results and provide information for each city to take prompt and appropriate action. Furthermore, the sharing unit can adjust the frequency of sharing the analysis results, allowing flexible responses according to specific situations and conditions. For example, in areas where traffic congestion is frequent, analysis results can be shared monthly or weekly to allow for prompt countermeasures. Also, during seasons when energy consumption increases sharply, analysis results can be shared frequently to promote efficient energy use. This allows the shared parts to share analysis results efficiently and effectively, improving the performance of the entire system.

[0070] The anonymization unit anonymizes the data collected by the collection unit. For example, the anonymization may delete personal information or mask the data, but is not limited to such examples. Specifically, information that can identify an individual is deleted from the collected data to protect the privacy of the data. For example, traffic data may include vehicle license plates and personal information of drivers, but by deleting this information, the individual cannot be identified. In addition, energy consumption data may include detailed consumption patterns of homes and businesses, but by masking this data, the privacy of individuals and businesses is protected. Furthermore, the anonymization unit can use an algorithm for automating the anonymization process of data. For example, a machine learning algorithm is used to automatically detect information that can identify an individual from the data, and delete or mask the information. In addition, the anonymization unit also has a function for verifying whether the anonymization process of data is performed appropriately. For example, the anonymized data is reanalyzed to check whether any information that can identify an individual remains. In this way, the anonymization unit can safely use the collected data while ensuring the privacy of the data. Furthermore, the anonymization unit can flexibly adjust the anonymization process of data. For example, by performing more rigorous anonymization processing on a specific data set and performing the minimum necessary anonymization processing on other data sets, the utility value of the data can be maximized. This allows the anonymization unit to anonymize data efficiently and effectively, improving the performance of the entire system.

[0071] The encryption unit encrypts the data collected by the collection unit. The encryption is performed, for example, based on the encryption algorithm and key management method used, but is not limited to such examples. Specifically, the collected data is encrypted using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized use of the data. For example, all collected data, such as traffic data, energy consumption data, and public service usage data, is encrypted to ensure the confidentiality of the data. Furthermore, the encryption unit also has a function for properly managing the keys of the encrypted data. For example, a hardware security module (HSM) for safely storing the encryption key is used to prevent the key from leaking. Furthermore, the encryption unit can use an algorithm for automating the encryption process of the data. For example, a machine learning algorithm is used to automatically detect highly confidential information from the data and perform encryption. This allows the encryption unit to safely use the collected data while ensuring the confidentiality of the data. Furthermore, the encryption unit also has a function for verifying whether the encryption process of the data is performed appropriately. For example, the encrypted data is reanalyzed to check whether the encryption is performed appropriately. This allows the encryption unit to safely use the collected data while ensuring the confidentiality of the data. Furthermore, the encryption unit can flexibly adjust the encryption process of the data. For example, it is possible to maximize the value of the data by performing more strict encryption process on a specific data set and performing the minimum necessary encryption process on other data sets. This allows the encryption unit to encrypt data efficiently and effectively, improving the performance of the entire system.

[0072] The analysis unit can analyze traffic data, predict traffic congestion, and propose an appropriate traffic route. The analysis unit, for example, analyzes traffic data to predict traffic congestion. For example, based on past traffic data, calculates the probability of traffic congestion occurring in a specific time period or day of the week. The analysis unit can also analyze real-time traffic data to grasp the current traffic situation and predict the occurrence of traffic congestion. For example, the analysis unit analyzes real-time data acquired from a traffic sensor to predict the occurrence of traffic congestion. Furthermore, the analysis unit proposes an optimal traffic route based on the result of predicting traffic congestion. For example, an alternative route to avoid traffic congestion is proposed. The analysis unit can also propose a method of controlling traffic signals to minimize the impact of traffic congestion. For example, the timing of traffic signals is adjusted to smooth the flow of traffic. This predicts traffic congestion and proposes an optimal traffic route, thereby improving traffic efficiency. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input traffic data to the generation AI and cause the generation AI to predict traffic congestion and propose an optimal traffic route.

[0073] The analysis unit can analyze the energy consumption data and propose an appropriate method of using energy. The analysis unit can, for example, analyze the energy consumption data to propose an efficient method of using energy. For example, based on past energy consumption data, the energy consumption pattern in a specific time period or season is analyzed. The analysis unit can also analyze real-time energy consumption data to grasp the current energy consumption situation and propose an efficient method of using energy. For example, the analysis unit can analyze real-time data acquired from an energy sensor and make a proposal to prevent energy waste. Furthermore, the analysis unit can propose a method of promoting the use of renewable energy in order to propose an efficient method of using energy. For example, the analysis unit can propose the introduction of solar power generation or wind power generation. The analysis unit can also propose a peak shift of energy consumption. For example, the analysis unit can propose a method of dispersing energy consumption to avoid peak hours of energy consumption. As a result, energy can be optimized by proposing an efficient method of using energy. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the energy consumption data to the generation AI and cause the generation AI to execute a proposal for an efficient method of using energy.

[0074] The sharing unit can share the analysis results annually so that each city can effectively utilize the data. For example, the sharing unit compiles the analysis results as an annual report and distributes it to each city. The annual report includes a detailed explanation of the analysis results and suggestions for each city to utilize the data. The sharing unit can also share the analysis results through an online platform. For example, the analysis results can be stored in a digital format and made accessible to each city. Furthermore, the sharing unit can provide tools for visually displaying the analysis results. For example, the analysis results can be displayed in an easy-to-understand manner using graphs and charts. This allows each city to effectively utilize the data by sharing the analysis results annually. Some or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI or may be performed without using AI. For example, the sharing unit can input the analysis results to the generation AI and cause the generation AI to create an annual report and share it on an online platform.

[0075] The anonymization unit can anonymize the collected data and protect the privacy of the data. For example, the anonymization unit deletes information that can identify an individual from the collected data. For example, personal information such as a name and an address is deleted to protect the privacy of the data. The anonymization unit can also mask the data. For example, a specific data field is masked so that an individual cannot be identified. Furthermore, the anonymization unit can periodically check the anonymization of the data and maintain the quality of the anonymization. For example, the anonymized data is periodically reviewed and additional anonymization processing is performed as necessary. This makes it possible to prevent unauthorized use of the data by protecting the privacy of the data. Some or all of the above-mentioned processing in the anonymization unit may be performed, for example, using AI or may be performed without using AI. For example, the anonymization unit can input the collected data to the generation AI and cause the generation AI to perform anonymization of the data.

[0076] The encryption unit can encrypt the collected data and prevent unauthorized use of the data. For example, the encryption unit encrypts the collected data using an encryption algorithm such as AES (Advanced Encryption Standard). For example, by encrypting the data, unauthorized use of the data is prevented. The encryption unit can also perform key management of the encrypted data. For example, the encryption unit provides a procedure for safely storing the encryption key and decrypting it as necessary. Furthermore, the encryption unit can periodically check the quality of the encryption and maintain the strength of the encryption. For example, the encryption algorithm is updated and the encryption key is periodically changed. This prevents unauthorized use of the data, thereby improving the security of the data. A part or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input the collected data to the generation AI and cause the generation AI to encrypt the data.

[0077] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the collection unit can increase the frequency of data collection and collect detailed data when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, the timing of data collection is adjusted to collect more data. Furthermore, the collection unit can temporarily stop data collection when the user is in a hurry and resume it later. For example, when the user's emotion is estimated to be in a hurry, the data collection is temporarily stopped to reduce the burden on the user. This allows the burden on the user to be reduced by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0078] The collection unit can select an appropriate data collection method based on the characteristics of each city. The collection unit optimizes the placement of sensors based on the population density of the city, for example. For example, many sensors are placed in areas with high population density to collect detailed data. The collection unit can also adjust the collection method of traffic data based on the traffic pattern of the city. For example, data collection is strengthened during times of high traffic volume and data collection is reduced during times of low traffic volume. Furthermore, the collection unit can adjust the collection method of energy data based on the energy consumption pattern of the city. For example, data collection is strengthened during times of peak energy consumption and data collection is reduced during times of low energy consumption. This improves the efficiency of data collection by selecting the optimal data collection method based on the characteristics of each city. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input characteristic data of each city to the generation AI and cause the generation AI to select the optimal data collection method.

[0079] The collection unit can adjust the type of data to be collected in consideration of city events and seasonal fluctuations when collecting data. For example, when a large-scale event is held in the city, the collection unit strengthens collection of traffic data. For example, the collection unit predicts that traffic volume will increase during the event period and increases the frequency of collection of traffic data. The collection unit can also adjust the collection of energy consumption data in accordance with seasonal fluctuations. For example, the collection unit predicts that energy consumption will increase in summer and winter and increases the frequency of collection of energy data. Furthermore, the collection unit can strengthen collection of tourism data in accordance with the city's tourist season. For example, the frequency of collection of tourism data is increased during a period when tourists increase, and tourist trends are grasped. This allows more accurate data to be collected by adjusting data collection in consideration of city events and seasonal fluctuations. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input city event information and seasonal fluctuation data to the generation AI and cause the generation AI to adjust the type of data to be collected.

[0080] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects only data with high importance. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the collection unit can preferentially collect detailed data when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, the priority of data to be collected is adjusted and more data is collected. Furthermore, the collection unit can limit the type of data to be collected when the user is in a hurry. For example, when the user's emotion is estimated to be in a hurry, the type of data to be collected is limited to reduce the burden on the user. In this way, important data can be preferentially collected by determining the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the data to be collected.

[0081] The collection unit can prioritize collection of highly relevant data in consideration of the geographical characteristics of the city when collecting data. The collection unit selects a collection point of traffic data based on, for example, the topography of the city. For example, the collection point of traffic data is optimized according to the topography, such as mountainous areas and plains. The collection unit can also select a collection point of energy data based on the climatic conditions of the city. For example, the collection point of energy data is optimized according to the climatic conditions, such as cold areas and warm areas. Furthermore, the collection unit can also select a collection point of public service data based on the population distribution of the city. For example, the collection point of public service data is optimized according to areas with high population density and areas with low population density. As a result, highly relevant data can be efficiently collected by collecting data in consideration of the geographical characteristics of the city. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical characteristics data of the city to the generation AI and cause the generation AI to select the collection point of highly relevant data.

[0082] The collection unit can analyze the social media activity of the city and collect related data when collecting data. The collection unit, for example, analyzes trends on social media and collects related traffic data. For example, if posts about traffic congestion are increasing on social media, the collection of traffic data is strengthened. The collection unit can also analyze discussions about energy consumption on social media and collect related energy data. For example, if posts about energy consumption are increasing, the collection of energy data is strengthened. Furthermore, the collection unit can analyze feedback about public services on social media and collect related data. For example, if posts about public services are increasing, the collection of public service data is strengthened. This allows more diverse data to be collected by analyzing social media activities and collecting data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to the generation AI and cause the generation AI to collect related data.

[0083] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis result based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit provides a simple and highly visible analysis result. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, the analysis result is provided using detailed graphs and charts. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. For example, when the user's emotion is estimated to be in a hurry, a concise text summary is provided. In this way, by adjusting the expression method of the analysis result according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis result.

[0084] 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 data with high importance. For example, for data that is important in city management, such as traffic data and energy data, the analysis unit performs a detailed statistical analysis or an analysis using a machine learning algorithm. The analysis unit can also perform a simplified analysis on data with low importance. For example, for data with relatively low importance, such as public service usage data, a simple summary or a basic statistical analysis is performed. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. For example, for data with medium importance, such as city event data and seasonal fluctuation data, an analysis with a moderate level of detail is performed. This allows efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a traffic congestion prediction algorithm to traffic data. For example, the analysis unit calculates the probability of traffic congestion occurring in a specific time period or day of the week based on past traffic data. The analysis unit can also apply an energy consumption optimization algorithm to energy data. For example, the analysis unit proposes an efficient energy usage method based on past energy consumption data. Furthermore, the analysis unit can apply a service efficiency algorithm to public service data. For example, the analysis unit analyzes the usage status of public services and makes proposals for improving the efficiency of the services. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the category of data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis result based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit provides a short and to the point analysis result. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, a detailed report and graphs are provided. Furthermore, the analysis unit can also provide a concise analysis result when the user is in a hurry. For example, when the user's emotion is estimated to be in a hurry, a concise summary is provided. In this way, by adjusting the length of the analysis result according to the user's emotion, an appropriate amount of information can be provided for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis result.

[0087] The analysis unit can determine the priority of the analysis based on the time when the data was collected. For example, the analysis unit preferentially analyzes the latest data. For example, data collected in real time is preferentially analyzed to provide the latest information. The analysis unit can also analyze the latest data while referring to past data. For example, past data is compared with the latest data to analyze trends and patterns. Furthermore, the analysis unit can also preferentially analyze data from a specific period. For example, data from a specific event period is preferentially analyzed to evaluate the impact of the event. As a result, the latest data can be preferentially analyzed by determining the priority of the analysis based on the time when the data was collected. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and cause the generation AI to determine the priority of the analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of data. The analysis unit adjusts the order of analysis, for example, taking into account the relevance of traffic data and energy data. For example, the correlation between traffic data and energy data is analyzed, and highly relevant data is analyzed preferentially. The analysis unit can also adjust the order of analysis, for example, taking into account the relevance of public service data and energy data. For example, the relationship between the usage status of public services and energy consumption is analyzed, and highly relevant data is analyzed preferentially. The analysis unit can also adjust the order of analysis, for example, taking into account the relevance of traffic data and public service data. For example, the relationship between traffic volume and the usage status of public services is analyzed, and highly relevant data is analyzed preferentially. This allows efficient data analysis by adjusting the order of analysis based on the relevance of data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The sharing unit can estimate the user's emotion and adjust the display method of the data to be shared based on the estimated user's emotion. For example, when the user is feeling stressed, the sharing unit provides a simple and highly visible display method. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the sharing unit can provide a display method including detailed information when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, the data is displayed using detailed graphs and charts. Furthermore, when the user is in a hurry, the sharing unit can provide a display method that focuses on the main points. For example, when the user's emotion is estimated to be in a hurry, a concise text summary is provided. This allows the display method of the data to be easily understood by the user by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit may input the user's emotion data to the generating AI and cause the generating AI to adjust the display method of the data.

[0090] The sharing unit can select the optimal sharing method based on the characteristics of each city. The sharing unit adjusts the data sharing method based on the population density of the city, for example. For example, for cities with high population density, data is shared through an online platform, and for cities with low population density, annual reports are distributed. The sharing unit can also adjust the traffic data sharing method based on the traffic pattern of the city. For example, for cities with high traffic volume, traffic data is shared in real time, and for cities with low traffic volume, traffic data is shared periodically. Furthermore, the sharing unit can adjust the energy data sharing method based on the energy consumption pattern of the city. For example, detailed energy data is shared for cities with high energy consumption, and simplified energy data is shared for cities with low energy consumption. As a result, the efficiency of data sharing is improved by selecting the optimal sharing method based on the characteristics of each city. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input characteristic data of each city to the generation AI and cause the generation AI to select the optimal sharing method.

[0091] The sharing unit can adjust the level of detail of the sharing based on the importance of the data. For example, the sharing unit performs detailed sharing for data with high importance. For example, for data that is important in city management, such as traffic data and energy data, the sharing unit uses detailed reports and graphs to share the data. The sharing unit can also perform simplified sharing for data with low importance. For example, for data with relatively low importance, such as public service usage data, the sharing unit uses simple summaries and basic statistical information to share the data. Furthermore, the sharing unit can also perform sharing with a moderate level of detail for data with medium importance. For example, for data with medium importance, such as city event data and seasonal fluctuation data, the sharing unit uses reports with a moderate level of detail to share the data. This enables efficient data sharing by adjusting the level of detail of the sharing based on the importance of the data. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the sharing.

[0092] The sharing unit can estimate the user's emotion and determine the priority of data to be shared based on the estimated user's emotion. For example, when the user is feeling stressed, the sharing unit preferentially shares only data with high importance. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the sharing unit can preferentially share detailed data when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, data is shared using detailed reports and graphs. Furthermore, the sharing unit can also limit the type of data to be shared when the user is in a hurry. For example, when the user's emotion is estimated to be in a hurry, data is shared using a concise summary. In this way, important data can be preferentially shared by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit may input the user's emotion data to the generating AI and cause the generating AI to determine the priority order of the data to be shared.

[0093] The sharing unit can preferentially share highly relevant data in consideration of the geographical characteristics of the city. The sharing unit selects a sharing point for traffic data based on, for example, the topography of the city. For example, the sharing point for traffic data is optimized according to the topography, such as mountainous areas or plains. The sharing unit can also select a sharing point for energy data based on the climatic conditions of the city. For example, the sharing point for energy data is optimized according to climatic conditions, such as cold areas or warm areas. Furthermore, the sharing unit can also select a sharing point for public service data based on the population distribution of the city. For example, the sharing point for public service data is optimized according to areas with high or low population density. As a result, highly relevant data can be shared efficiently by sharing data in consideration of the geographical characteristics of the city. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input the geographical characteristics data of the city to the generation AI and cause the generation AI to select a sharing point for highly relevant data.

[0094] The sharing unit can analyze the social media activity of the city and share related data. The sharing unit, for example, analyzes trends on social media and shares related traffic data. For example, when posts about traffic congestion are increasing on social media, the sharing unit strengthens the sharing of traffic data. The sharing unit can also analyze discussions about energy consumption on social media and share related energy data. For example, when posts about energy consumption are increasing, the sharing unit strengthens the sharing of energy data. Furthermore, the sharing unit can analyze feedback about public services on social media and share related data. For example, when posts about public services are increasing, the sharing unit strengthens the sharing of public service data. In this way, by analyzing social media activity and sharing data, more diverse data can be shared. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input social media data to the generation AI and cause the generation AI to execute the sharing of related data.

[0095] The anonymization unit can estimate the user's emotion and adjust the anonymization method based on the estimated user's emotion. For example, when the user is stressed, the anonymization unit applies a simplified anonymization method. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. The anonymization unit can also apply a detailed anonymization method when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, detailed data masking and deletion of personal information are performed. Furthermore, the anonymization unit can also apply a method for quickly performing anonymization when the user is in a hurry. For example, when the user's emotion is estimated to be in a hurry, a simplified procedure for quickly performing anonymization is applied. This allows the burden on the user to be reduced by adjusting the anonymization method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the anonymization method.

[0096] The anonymization unit can adjust the level of detail of anonymization based on the importance of the data. For example, the anonymization unit performs detailed anonymization on data with high importance. For example, for data including personal information or confidential information, detailed data masking or deletion of personal information is performed. The anonymization unit can also perform simplified anonymization on data with low importance. For example, simple data masking is performed on general statistical data or public information. Furthermore, the anonymization unit can also perform anonymization with a moderate level of detail on data with medium importance. For example, data masking with a moderate level of detail is performed on business data or operational data. As a result, efficient data anonymization is possible by adjusting the level of detail of anonymization based on the importance of the data. Part or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of anonymization.

[0097] The anonymization unit can estimate the user's emotion and determine the priority of anonymization based on the estimated user's emotion. For example, when the user is feeling stressed, the anonymization unit preferentially anonymizes only data with high importance. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the anonymization unit can preferentially anonymize detailed data when the user is relaxed. For example, when it is estimated that the user's emotion is relaxed, detailed data masking and deletion of personal information are performed. Furthermore, the anonymization unit can limit the type of data to be anonymized when the user is in a hurry. For example, when it is estimated that the user's emotion is in a hurry, a simplified procedure for quickly performing anonymization is applied. As a result, important data can be preferentially anonymized by determining the priority of anonymization according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the anonymization unit may be performed using, for example, AI, or may be performed without using AI. For example, the anonymization unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of anonymization.

[0098] The anonymization unit can adjust the anonymization method based on the time when the data was collected. For example, the anonymization unit preferentially anonymizes the latest data. For example, data collected in real time is preferentially anonymized to protect the latest information. The anonymization unit can also anonymize the latest data while referring to past data. For example, past data is compared with the latest data to analyze trends and patterns. Furthermore, the anonymization unit can also preferentially anonymize data from a specific period. For example, data from a specific event period is preferentially anonymized to evaluate the impact of the event. This allows the latest data to be preferentially anonymized by adjusting the anonymization method based on the time when the data was collected. Part or all of the above-mentioned processing in the anonymization unit may be performed, for example, using AI or may be performed without using AI. For example, the anonymization unit can input the time when the data was collected to the generation AI and cause the generation AI to adjust the anonymization method.

[0099] The encryption unit can estimate the user's emotion and adjust the encryption method based on the estimated user's emotion. For example, when the user is stressed, the encryption unit applies a simplified encryption method. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. The encryption unit can also apply a detailed encryption method when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, the detailed encryption algorithm is used to encrypt data. Furthermore, when the user is in a hurry, the encryption unit can also apply a method for performing encryption quickly. For example, when the user's emotion is estimated to be in a hurry, a simplified procedure for performing encryption quickly is applied. This allows the user's burden to be reduced by adjusting the encryption method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the encryption unit may be performed using AI, or may be performed without using AI. For example, the encryption unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the encryption method.

[0100] The encryption unit can adjust the level of detail of encryption based on the importance of the data. For example, the encryption unit performs detailed encryption on data of high importance. For example, for data including personal information or confidential information, the data is encrypted using a detailed encryption algorithm. The encryption unit can also perform simplified encryption on data of low importance. For example, for general statistical data or public information, the data is encrypted using a simple encryption algorithm. Furthermore, the encryption unit can also perform encryption with a moderate level of detail on data of medium importance. For example, for business data or operational data, the data is encrypted using an encryption algorithm with a moderate level of detail. As a result, efficient data encryption is possible by adjusting the level of detail of encryption based on the importance of the data. A part or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of encryption.

[0101] The encryption unit can estimate the user's emotion and determine the encryption priority based on the estimated user's emotion. For example, when the user is feeling stressed, the encryption unit preferentially encrypts only data with high importance. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, the encryption unit can also preferentially encrypt detailed data when the user is relaxed. For example, when the user's emotion is estimated to be relaxed, the encryption unit encrypts data using a detailed encryption algorithm. Furthermore, when the user is in a hurry, the encryption unit can also limit the type of data to be encrypted. For example, when the user's emotion is estimated to be in a hurry, a simplified procedure is applied to perform encryption quickly. In this way, important data can be preferentially encrypted by determining the encryption priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the encryption unit may be performed using AI, or may be performed without using AI. For example, the encryption unit may input the user's emotion data to the generation AI and have the generation AI determine the encryption priority.

[0102] The encryption unit can adjust the encryption method based on the time when the data was collected. For example, the encryption unit preferentially encrypts the latest data. For example, data collected in real time is preferentially encrypted to protect the latest information. The encryption unit can also encrypt the latest data while referring to past data. For example, past data is compared with the latest data to analyze trends and patterns. Furthermore, the encryption unit can also preferentially encrypt data from a specific period. For example, data from a specific event period is preferentially encrypted to evaluate the impact of the event. This allows the latest data to be preferentially encrypted by adjusting the encryption method based on the time when the data was collected. Some or all of the above-mentioned processing in the encryption unit may be performed using, for example, AI, or may be performed without using AI. For example, the encryption unit can input the time when the data was collected to the generation AI and cause the generation AI to adjust the encryption method.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The analysis unit can analyze the city's weather data and propose optimization of transportation routes and energy consumption based on weather conditions. For example, traffic congestion is likely to occur in rainy weather, so the analysis unit proposes optimal transportation routes based on traffic data for rainy weather. In addition, energy consumption is predicted to increase on hot days, so the analysis unit can propose efficient energy usage methods based on temperature data. Furthermore, the analysis unit can adjust the way public services are provided based on weather data. For example, in bad weather, public transportation schedules can be adjusted to ensure the safety of users. This makes it possible to operate the city optimally according to weather conditions.

[0105] The common unit can collect feedback from city residents and reflect it in the analysis results. For example, if residents have complaints about traffic congestion, the common unit collects that feedback and provides it to the analysis unit. The analysis unit can reanalyze traffic data based on the residents' feedback and propose more effective traffic routes. In addition, if residents make suggestions regarding energy consumption, the common unit collects the suggestions and provides it to the analysis unit. The analysis unit can reanalyze energy consumption data based on the residents' suggestions and propose efficient methods of using energy. In addition, if residents have opinions about public services, the common unit collects those opinions and provides them to the analysis unit. The analysis unit can reanalyze public service data based on the residents' opinions and improve the efficiency of services. This makes it possible to operate a city that reflects residents' feedback.

[0106] The collection unit can collect infrastructure data of the city and monitor the state of the infrastructure. For example, the damage state of the road is detected by a sensor, and the collection unit collects the data. The structural soundness of bridges and tunnels can also be monitored, and the collection unit can collect the data. Furthermore, the state of water supply and sewerage can be monitored, and the collection unit can collect the data. This makes it possible to grasp the state of the city's infrastructure in real time and perform necessary maintenance promptly. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input infrastructure data to the generation AI and cause the generation AI to monitor the state of the infrastructure.

[0107] The analysis unit can analyze crime data for a city and propose crime prevention measures. For example, it can predict the risk of crime occurring in a specific area or time period based on past crime data. It can also analyze real-time crime data to understand the current crime situation and propose crime prevention measures. For example, it can propose strengthening police patrols in areas with high crime rates. Furthermore, the analysis unit can optimize the placement of public facilities based on crime data. For example, it can propose installing security cameras in areas with high crime rates. In this way, it can improve the safety of the city by proposing crime prevention measures.

[0108] The sharing department can collect educational data for a city and make proposals to improve the quality of education. For example, it can collect school grade data and attendance data to evaluate the quality of education. It can also collect teacher evaluation data and student feedback to identify areas for improvement in education. Furthermore, the sharing department can propose optimizing educational programs based on educational data. For example, if grades are low in a particular subject, it can make a proposal to review the educational program for that subject. This makes it possible to make specific proposals to improve the quality of education.

[0109] The analysis unit can estimate the user's emotion and adjust the notification method of the analysis result based on the estimated user's emotion. For example, if the user is feeling stressed, a notification that briefly summarizes the analysis result is sent. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, if the user is relaxed, a notification including a detailed analysis result can be sent. For example, if the user's emotion is estimated to be relaxed, a notification including a detailed report and graphs is sent. Furthermore, if the user is in a hurry, a notification that covers the main points can be sent. For example, if the user's emotion is estimated to be in a hurry, a notification including a concise text summary is sent. In this way, by adjusting the notification method of the analysis result according to the user's emotion, it is possible to provide appropriate information to the user.

[0110] The collection unit can estimate the user's emotion and adjust the frequency of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the frequency of data collection is reduced to reduce the burden on the user. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, when the user is relaxed, the frequency of data collection can be increased to collect detailed data. For example, when the user's emotion is estimated to be relaxed, the frequency of data collection is adjusted to collect more data. Furthermore, when the user is in a hurry, the data collection can be temporarily stopped and later resumed. For example, when the user's emotion is estimated to be in a hurry, the data collection is temporarily stopped to reduce the burden on the user. In this way, the frequency of data collection can be adjusted according to the user's emotion to reduce the burden on the user.

[0111] The analysis unit can estimate the user's emotion and adjust the display format of the analysis result based on the estimated user's emotion. For example, when the user is feeling stressed, a simple and highly visible display format is provided. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expression, voice, and sensor data to estimate the user's emotion. In addition, when the user is relaxed, a display format including detailed information can be provided. For example, when the user's emotion is estimated to be relaxed, the analysis result is displayed using detailed graphs and charts. Furthermore, when the user is in a hurry, a display format that focuses on the main points can be provided. For example, when the user's emotion is estimated to be in a hurry, a concise text summary is provided. As a result, by adjusting the display format of the analysis result according to the user's emotion, it is possible to display it in a way that is easy for the user to understand.

[0112] The sharing unit can estimate the user's emotion and adjust the amount of data to be shared based on the estimated user's emotion. For example, when the user is feeling stressed, the amount of data to be shared is reduced to reduce the burden on the user. For example, an emotion recognition algorithm is used to estimate the user's emotion. The emotion recognition algorithm analyzes the user's facial expressions, voice, and sensor data to estimate the user's emotion. In addition, when the user is relaxed, detailed data can be shared. For example, when the user's emotion is estimated to be relaxed, data is shared using detailed reports and graphs. Furthermore, when the user is in a hurry, data with the main points can be shared. For example, when the user's emotion is estimated to be in a hurry, data is shared using a concise text summary. In this way, by adjusting the amount of data to be shared according to the user's emotion, it is possible to provide appropriate information to the user.

[0113] The analysis unit can analyze the city's health data and propose optimization of health management. For example, based on the health checkup data of residents, it can predict the risk of a particular disease. It can also analyze real-time health data, grasp the current health status, and propose optimization of health management. For example, based on the health data of residents, it can propose a health education program for areas with high health risks. Furthermore, the analysis unit can propose optimal allocation of medical resources based on the health data. For example, it makes a proposal to optimize the allocation of medical facilities for areas with a high incidence of a particular disease. In this way, it is possible to improve the health of residents by proposing optimization of health management.

[0114] The process flow of the second embodiment will be briefly described below.

[0115] Step 1: The collection unit collects data for each city. The data for each city includes traffic data, energy consumption data, and public service usage data. The collection unit collects data from sensors and databases in each city. The sensors include sensors that measure traffic volume, sensors that measure energy consumption, and sensors that measure the usage of public services. The database refers to the data accumulated in the management system of each city. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, traffic data can be analyzed to predict traffic congestion and propose optimal traffic routes. Energy consumption data can also be analyzed to propose efficient energy usage methods. Step 3: The Sharing Department will share the analysis results obtained by the Analysis Department on an annual basis. Sharing will be based on the format of data and frequency of sharing. For example, the analysis results may be compiled as an annual report and distributed to each city. Analysis results may also be shared through an online platform. Step 4: The anonymization unit anonymizes the data collected by the collection unit. Anonymization involves removing personal information or masking the data. For example, it removes information that can identify an individual from the collected data to protect the privacy of the data. Step 5: The encryption unit encrypts the data collected by the collection unit. The encryption is performed based on the encryption algorithm and key management method to be used. For example, the collected data is encrypted using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized use of the data.

[0116] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0117] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0118] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements including the above-mentioned collection unit, analysis unit, sharing unit, anonymization unit, and encryption unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized as a processing unit that collects data from a sensor or a database of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compiles the analysis results as an annual report and distributes it to each city. The anonymization unit is realized, for example, by the control unit 46A of the smart device 14, and deletes information that can identify an individual from the collected data. The encryption unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encrypts the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0123] 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.

[0124] 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 the voice according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0126] 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 performed in a secure state.

[0127] Fig. 4 shows an example of 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.

[0128] 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.

[0129] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0132] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0133] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0134] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements including the above-mentioned collection unit, analysis unit, sharing unit, anonymization unit, and encryption 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 is realized as a processing unit that collects data from a sensor or a database of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compiles the analysis results as an annual report and distributes it to each city. The anonymization unit is realized, for example, by the control unit 46A of the smart glasses 214, and deletes information that can identify an individual from the collected data. The encryption unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encrypts the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0139] 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.

[0140] 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 the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0142] 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 performed in a secure state.

[0143] 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.

[0144] 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.

[0145] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements including the above-mentioned collection unit, analysis unit, sharing unit, anonymization unit, and encryption 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 is realized as a processing unit that collects data from a sensor or a database of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compiles the analysis results as an annual report and distributes it to each city. The anonymization unit is realized, for example, by the control unit 46A of the headset type terminal 314, and deletes information that can identify an individual from the collected data. The encryption unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encrypts the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0155] 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.

[0156] 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 the voice according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0158] 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 performed in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0160] 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.

[0161] 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.

[0162] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0164] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] 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 a voice indicating a user input for 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.

[0166] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0168] Each of the multiple elements including the above-mentioned collection unit, analysis unit, sharing unit, anonymization unit, and encryption unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized as a processing unit that collects data from a sensor or a database of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compiles the analysis results as an annual report and distributes it to each city. The anonymization unit is realized, for example, by the control unit 46A of the robot 414, and deletes information that can identify an individual from the collected data. The encryption unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encrypts the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0170] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0172] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0174] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0176] 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.

[0177] 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 Universal Serial Bus (USB) 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.

[0178] In addition, 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 upon request from the data processing device 12.

[0179] 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.

[0180] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0181] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0182] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0183] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0185] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0187] (Appendix 1) A collection department that collects data for each city; an analysis unit that analyzes the data collected by the collection unit; a sharing unit that shares the analysis results obtained by the analysis unit on an annual basis; an anonymization unit that anonymizes the data collected by the collection unit; An encryption unit that encrypts the data collected by the collection unit. A system characterized by: (Appendix 2) The analysis unit is Analyze traffic data, predict traffic congestion, and suggest optimal traffic routes 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing energy consumption data and proposing appropriate energy usage methods 2. The system of claim 1, (Appendix 4) The common part is The analysis results will be shared annually to help cities use the data effectively 2. The system of claim 1, (Appendix 5) The anonymization unit includes: Anonymize collected data and protect data privacy 2. The system of claim 1, (Appendix 6) The encryption unit includes: Encrypt the collected data to prevent unauthorized use of the data 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Select appropriate data collection methods based on the characteristics of each city 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting data, adjust the type of data collected to account for city events and seasonal variations 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting relevant data by taking into account the geographical characteristics of the city. 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze the city's social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is Adjust the granularity of your analysis based on the importance of your data 2. The system of claim 1, (Appendix 15) The analysis unit is Apply different analysis algorithms to different categories of data 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring user emotion and adjusting the length of the analysis result based on the estimated user emotion 2. The system of claim 1, (Appendix 17) The analysis unit is Prioritize your analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is Order your analysis based on data relevance 2. The system of claim 1, (Appendix 19) The common part is Infer user sentiment and adjust how shared data is displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The common part is Select the optimal sharing method based on the characteristics of each city 2. The system of claim 1, (Appendix 21) The common part is Adjust sharing granularity based on data sensitivity 2. The system of claim 1, (Appendix 22) The common part is Infer user sentiment and prioritize data sharing based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The common part is Prioritize sharing of relevant data based on the geographical characteristics of cities 2. The system of claim 1, (Appendix 24) The common part is Analyze city social media activity and share relevant data 2. The system of claim 1, (Appendix 25) The anonymization unit includes: Estimate user sentiment and adjust the anonymization method based on the estimated user sentiment 2. The system of claim 1, (Appendix 26) The anonymization unit includes: Adjust the granularity of anonymization based on the importance of the data 2. The system of claim 1, (Appendix 27) The anonymization unit includes: Estimate user sentiment and determine anonymization priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The anonymization unit includes: Tailor your anonymization methods based on when the data was collected 2. The system of claim 1, (Appendix 29) The encryption unit includes: Inferring user emotions and adjusting encryption methods based on the inferred user emotions 2. The system of claim 1, (Appendix 30) The encryption unit includes: Adjust encryption granularity based on data sensitivity 2. The system of claim 1, (Appendix 31) The encryption unit includes: Inferring user sentiment and determining encryption priorities based on the inferred user sentiment 2. The system of claim 1, (Appendix 32) The encryption unit includes: Tailor encryption methods based on when data is collected 2. The system of claim 1, [Explanation of symbols]

[0188] 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 department that collects data for each city; an analysis unit that analyzes the data collected by the collection unit; a sharing unit that shares the analysis results obtained by the analysis unit on an annual basis; an anonymization unit that anonymizes the data collected by the collection unit; An encryption unit that encrypts the data collected by the collection unit. A system characterized by:

2. The analysis unit is Analyze traffic data, predict traffic congestion, and suggest optimal traffic routes 2. The system of claim 1 .

3. The analysis unit is Analyzing energy consumption data and proposing appropriate energy usage methods 2. The system of claim 1 .

4. The common part is The analysis results will be shared annually to help cities use the data effectively 2. The system of claim 1 .

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

6. The collecting unit includes: Select appropriate data collection methods based on the characteristics of each city 2. The system of claim 1 .

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