system

Abstract

The system according to this embodiment aims to optimally switch the amount of power generated and the advertising effect according to the weather and time of day. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a control unit. The collection unit collects weather and date / time data. The analysis unit analyzes the data collected by the collection unit and determines the amount of power generated and the advertising effect. The control unit switches between power generation and advertising display based on the results determined by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the optimal switching between the power generation amount and the advertising effect according to the weather and time has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to optimally switch the power generation amount and the advertising effect according to the weather and time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a control unit. The collection unit collects weather and time data. The analysis unit analyzes the data collected by the collection unit and determines the power generation amount and the advertising effect. The control unit switches between power generation and advertisement display based on the result determined by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can optimally switch the amount of power generated and the advertising effect according to the weather and time of day. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that installs panels combining organic EL and perovskite on the walls of buildings and apartments, and uses a generating AI to automatically switch between power generation and advertising display. In this system, the generating AI automatically determines the amount of power generated based on weather and time of day, and the advertising effect based on pedestrian traffic. If power generation is possible, it generates power; if power generation is not possible, it displays advertisements for a fee. This system makes it possible to keep the rent of buildings and apartments low and cover the difference with advertising costs. It also makes it possible to increase the number of tenants by offering electricity as a service. For example, panels combining organic EL and perovskite are installed on the walls of buildings and apartments. These panels have both power generation and advertising display functions. For example, they can generate power on sunny days and display advertisements on cloudy days or at night. Next, the generating AI automatically determines the amount of power generated based on weather and time of day, and the advertising effect based on pedestrian traffic. The generating AI analyzes weather forecast data, date and time data, and pedestrian traffic data to determine whether power generation is possible. For example, it can be set to prioritize power generation on sunny days and display advertisements on cloudy days or at night. If power generation is possible, the panels generate power and store electricity. For example, on sunny days, the system can generate electricity using sunlight and store it in batteries. This makes it possible to offer electricity as a service, improving tenant satisfaction. When power generation is not possible, the panels can display advertisements for a fee. For example, advertisements can be displayed on cloudy days or at night, generating advertising revenue. This allows for lower rents, with the savings covered by advertising. This system allows for lower rents for buildings and apartments, with the savings covered by advertising. Furthermore, offering free electricity can increase the number of tenants. For example, setting low rents and providing free electricity can reduce vacancy rates. In addition, this system utilizes a new material called perovskite, enabling efficient power generation. Perovskite generates electricity more efficiently than conventional solar cells, increasing the amount of power generated. This ensures that enough electricity is available to offer free electricity. As a result, the system allows for lower rents for buildings and apartments, with the savings covered by advertising.Furthermore, offering electricity as a service can increase the number of tenants.

[0029] The power generation advertising system according to this embodiment comprises a data collection unit, an analysis unit, and a control unit. The data collection unit collects weather and date / time data. For example, the data collection unit can acquire weather forecast data from the internet in real time. The data collection unit can also acquire date / time data from a clock within the system. Furthermore, the data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments. For example, the data collection unit can acquire weather forecast data from the internet in real time to improve the accuracy of power generation prediction. Furthermore, the data collection unit can accurately acquire date / time data from a clock within the system to optimize the timing of power generation and advertising display. Furthermore, the data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments to improve the accuracy of advertising effectiveness prediction. The analysis unit analyzes the data collected by the data collection unit to determine the amount of power generated and the effectiveness of the advertising. For example, the analysis unit can predict the amount of power generated based on weather forecast data. Furthermore, the analysis unit can predict the effectiveness of the advertising based on pedestrian flow data. For example, the analysis unit can predict the amount of power generated based on weather forecast data to improve the efficiency of power generation. Furthermore, the analysis unit can predict advertising effectiveness based on pedestrian flow data and maximize the effectiveness of advertising displays. The control unit switches between power generation and advertising display based on the results determined by the analysis unit. For example, the control unit can prioritize power generation when the amount of power generated is above a certain level, and display advertisements when it is below that level. The control unit can also stop power generation and start advertising display when switching from power generation to advertising display. For example, by prioritizing power generation when the amount of power generated is above a certain level and displaying advertisements when it is below that level, the control unit enables efficient energy use. Also, by stopping power generation and starting advertising display when switching from power generation to advertising display, the control unit enables a smooth transition. As a result, the power generation advertising system according to this embodiment can automatically switch between power generation and advertising display based on weather and date / time data. As a result, the power generation advertising system can keep building and apartment rental fees low and cover that difference with advertising costs. It is also possible to increase the number of tenants by offering electricity as a service.

[0030] The data collection unit collects weather and time-of-day data. For example, the data collection unit can obtain weather forecast data in real time from the internet. Specifically, the data collection unit connects to a weather data provision service via API and periodically obtains the latest weather forecast data. This allows for a quick response to weather changes and improves the accuracy of power generation forecasts. The data collection unit can also obtain time-of-day data from the system's internal clock. The system's internal clock is synchronized with a high-precision NTP server, providing always accurate time-of-day information. This allows for the optimization of the timing of power generation and advertising display. Furthermore, the data collection unit can also collect pedestrian flow data from sensors installed around buildings and apartments. These sensors, for example, use infrared sensors and cameras to detect people's movements and collect data in real time. The collected pedestrian flow data is used to understand people's movements and dwell times around buildings and apartments. This provides data to maximize the effectiveness of advertising display. The data collection unit centrally manages this diverse data and provides it to the analysis and control units. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to determine power generation and advertising effectiveness. For example, the analysis unit can predict power generation based on weather forecast data. Specifically, it uses weather forecast data to calculate the efficiency of solar power generation and predicts power generation considering factors such as solar radiation and cloud cover. By utilizing AI-based machine learning algorithms and building models based on past weather data and power generation performance, the accuracy of predictions can be improved. The analysis unit can also predict advertising effectiveness based on pedestrian flow data. Specifically, it analyzes pedestrian flow data around buildings and apartments to understand patterns of people's movement during specific times of day and on specific days of the week. This allows for the identification of the optimal timing and location for displaying advertisements, maximizing advertising effectiveness. Furthermore, the analysis unit integrates the collected data and makes decisions to optimize the balance between power generation and advertising display. For example, if power generation is lower than predicted, it prioritizes advertising display to secure revenue; if power generation is high, it prioritizes power generation to improve energy efficiency. The analysis unit makes these decisions in real time, maximizing the overall efficiency and effectiveness of the system.

[0032] The control unit switches between power generation and advertising display based on the results determined by the analysis unit. For example, the control unit can prioritize power generation when the power generation amount is above a certain level, and display advertisements when it is below that level. Specifically, the control unit has an interface for controlling both the power generation system and the advertising display system, and switches between these systems according to instructions from the analysis unit. When the power generation amount is high, the control unit operates the power generation system and supplies the generated electricity to the common areas of the building or apartment. On the other hand, when the power generation amount is low, the control unit operates the advertising display system and displays advertisements on digital signage or LED displays. When switching from power generation to advertising display, the control unit stops power generation and starts advertising display, enabling a smooth transition. Furthermore, the control unit can configure settings to optimize the content and timing of advertising display. For example, it can change the content of advertisements to match specific time periods or events to maximize advertising effectiveness. The control unit also has a function to constantly monitor the system status and issue alerts if an abnormality occurs. This allows the control unit to efficiently switch between power generation and advertising display and optimize the overall system performance.

[0033] The data collection unit can acquire weather forecast data from the internet in real time. For example, by acquiring weather forecast data from the internet in real time, the data collection unit can improve the accuracy of power generation forecasts. The data collection unit can also collect and integrate data from multiple weather forecast services. For example, it can acquire data from multiple weather forecast services in real time, calculate the average, and improve accuracy. Furthermore, the data collection unit can analyze the historical forecast accuracy of each weather forecast service and prioritize the use of data from the most reliable service. In addition, the data collection unit can integrate data from weather forecast services and, if different forecast results are obtained, adopt the most conservative forecast. This allows the data collection unit to improve the accuracy of power generation forecasts by acquiring weather forecast data in real time. The specific definition and criteria of "real time" need to be clarified, including data acquisition delay time and update frequency. For example, the data collection unit can acquire weather forecast data in real time and minimize data acquisition delay time. Furthermore, by increasing the update frequency of weather forecast data, the data collection unit can make more accurate forecasts. This allows the data collection unit to acquire weather forecast data in real time and improve the accuracy of power generation forecasts.

[0034] The data collection unit can obtain date and time data from the system's clock. For example, the data collection unit can accurately obtain date and time data from the system's clock and optimize the timing of power generation and advertisement display. The data collection unit can also adjust the timing of power generation and advertisement display based on the date and time data. For example, the data collection unit can optimize the timing of power generation and advertisement display based on the date and time data to achieve efficient energy use. The specific format and acquisition method of the date and time data, such as timestamps and date formats, needs to be clearly defined. For example, the data collection unit can obtain date and time data using timestamps and adjust the timing of power generation and advertisement display. Furthermore, the data collection unit can obtain date and time data using date formats and optimize the timing of power generation and advertisement display. This allows the data collection unit to optimize the timing of power generation and advertisement display by accurately obtaining date and time data.

[0035] The data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments. For example, by collecting pedestrian flow data from sensors installed around buildings and apartments, the data collection unit can improve the accuracy of advertising effectiveness predictions. The data collection unit can also predict advertising effectiveness based on pedestrian flow data. For example, the data collection unit can predict advertising effectiveness based on pedestrian flow data and maximize the effectiveness of advertising displays. The specific types of pedestrian flow data and collection methods need to be clearly defined, such as people counting, movement routes, and dwell time. For example, the data collection unit can collect pedestrian flow data using people counting and predict advertising effectiveness. Furthermore, the data collection unit can collect pedestrian flow data using movement routes and predict advertising effectiveness. In this way, the data collection unit can improve the accuracy of advertising effectiveness predictions by collecting pedestrian flow data.

[0036] The analysis unit can predict power generation based on weather forecast data. For example, the analysis unit can improve power generation efficiency by predicting power generation based on weather forecast data. The analysis unit can use prediction algorithms to predict power generation based on weather forecast data. For example, the analysis unit can use machine learning algorithms to predict power generation based on weather forecast data. The analysis unit can also use statistical models to predict power generation based on weather forecast data. Furthermore, the analysis unit can use simulation models to predict power generation based on weather forecast data. This allows the analysis unit to improve power generation efficiency by predicting power generation based on weather forecast data. The specific methods and types of data used for predicting power generation need to be clearly defined, including the prediction algorithm and the type of weather forecast data used. For example, the analysis unit can analyze weather forecast data using machine learning algorithms to predict power generation. Furthermore, the analysis unit can analyze weather forecast data using statistical models to predict power generation. Furthermore, the analysis unit can analyze weather forecast data using simulation models to predict power generation. This allows the analysis unit to improve power generation efficiency by predicting power generation based on weather forecast data.

[0037] The analysis unit can predict advertising effectiveness based on pedestrian flow data. For example, the analysis unit can predict advertising effectiveness based on pedestrian flow data and maximize the effectiveness of ad displays. The analysis unit can use prediction algorithms to predict advertising effectiveness based on pedestrian flow data. For example, the analysis unit can use machine learning algorithms to predict advertising effectiveness based on pedestrian flow data. The analysis unit can also use statistical models to predict advertising effectiveness based on pedestrian flow data. Furthermore, the analysis unit can use simulation models to predict advertising effectiveness based on pedestrian flow data. This allows the analysis unit to maximize the effectiveness of ad displays by predicting advertising effectiveness based on pedestrian flow data. The specific methods and types of data used for predicting advertising effectiveness need to be clearly defined, including the prediction algorithms and the types of pedestrian flow data used. For example, the analysis unit can analyze pedestrian flow data using machine learning algorithms and predict advertising effectiveness. Furthermore, the analysis unit can analyze pedestrian flow data using statistical models and predict advertising effectiveness. Furthermore, the analysis unit can analyze pedestrian flow data using simulation models and predict advertising effectiveness. This allows the analysis unit to maximize the effectiveness of ad displays by predicting advertising effectiveness based on pedestrian flow data.

[0038] The control unit can prioritize power generation when the power generation amount is above a certain level, and display advertisements when it is below that level. For example, by prioritizing power generation when the power generation amount is above a certain level and displaying advertisements when it is below that level, the control unit can enable efficient energy use. The control unit can set a threshold for power generation amount to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level. For example, the control unit can set a threshold for power generation amount to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level, and then prioritize power generation when the power generation amount exceeds the threshold. The control unit can also set a threshold for advertising effectiveness to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level. For example, the control unit can set a threshold for advertising effectiveness to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level, and then display advertisements when the advertising effectiveness exceeds the threshold. As a result, the control unit can enable efficient energy use by prioritizing power generation when the power generation amount is above a certain level and displaying advertisements when it is below that level. The specific criteria and numerical values ​​for "a certain level" need to be clearly defined, such as the threshold for power generation amount and the threshold for advertising effectiveness. For example, the control unit can set a threshold for power generation and prioritize power generation if the power generation exceeds that threshold. The control unit can also set a threshold for advertising effectiveness and display advertisements if the advertising effectiveness exceeds that threshold. This allows the control unit to prioritize power generation when it is above a certain level and display advertisements when it is below that level, enabling efficient energy utilization.

[0039] The control unit can stop power generation and start displaying advertisements when switching from power generation to advertisement display. For example, the control unit can enable a smooth transition by stopping power generation and starting advertisement display when switching from power generation to advertisement display. The control unit can set the timing for stopping power generation and starting advertisement display when switching from power generation to advertisement display. For example, the control unit can set the timing for stopping power generation and starting advertisement display when switching from power generation to advertisement display, and stop power generation and start advertisement display when the power generation amount falls below a certain level. Furthermore, the control unit can also set the procedure for stopping power generation and starting advertisement display when switching from power generation to advertisement display. For example, the control unit can set the procedure for stopping power generation and starting advertisement display when switching from power generation to advertisement display, and start advertisement display after stopping power generation. This allows the control unit to enable a smooth transition by stopping power generation and starting advertisement display when switching from power generation to advertisement display. The specific method and conditions of the switch, such as the timing and procedure, need to be clearly defined. For example, the control unit can set the timing for the switch, and stop power generation and start advertisement display when the power generation amount falls below a certain level. Furthermore, the control unit can set a switching procedure and start displaying advertisements after stopping power generation. This allows the control unit to smoothly switch from power generation to advertisement display by stopping power generation and then starting advertisement display.

[0040] The data collection unit can collect and integrate data from multiple weather forecasting services to improve the accuracy of weather data. For example, the unit can acquire data from multiple weather forecasting services in real time, calculate the average, and improve accuracy. The unit can also analyze the historical prediction accuracy of each weather forecasting service and prioritize the use of data from the most reliable service. Furthermore, the unit can integrate data from weather forecasting services and, if different prediction results are obtained, adopt the most conservative prediction. This allows the data collection unit to collect and integrate data from multiple weather forecasting services to improve the accuracy of weather data. The specific types of weather forecasting services and collection methods need to be clearly defined, such as the Japan Meteorological Agency or private weather services. For example, the data collection unit can collect weather data using data from the Japan Meteorological Agency and improve accuracy. Alternatively, the data collection unit can collect weather data using data from private weather services and improve accuracy. This allows the data collection unit to collect and integrate data from multiple weather forecasting services to improve the accuracy of weather data.

[0041] The data collection unit can adjust the data to account for the impact of specific events and festivals when collecting pedestrian flow data. For example, during an event, the unit can apply a correction factor to normal pedestrian flow data to collect accurate data. The unit can also refer to past event data and adjust the predictive model if similar events are held. Furthermore, the unit can dynamically change the correction factor according to the scale and type of event to improve data accuracy. This allows the unit to collect accurate pedestrian flow data by adjusting the data to account for the impact of specific events and festivals. The specific type and impact of a particular event or festival need to be clearly defined, including the scale and duration of the event. For example, the unit can set a larger correction factor when a large-scale event is held to collect accurate pedestrian flow data. Also, the unit can dynamically change the correction factor during long-term festivals to improve data accuracy. This allows the unit to collect accurate pedestrian flow data by adjusting the data to account for the impact of specific events and festivals.

[0042] The data collection unit can improve prediction accuracy by referencing past weather patterns when collecting weather data. For example, the data collection unit can correct the accuracy of current weather forecasts based on past weather data. Furthermore, the data collection unit can analyze seasonal weather patterns and adjust the prediction model. In addition, the data collection unit can compare past and current weather data to detect outliers and improve prediction accuracy. This allows the data collection unit to improve prediction accuracy by referencing past weather patterns when collecting weather data. The specific data and analysis methods for past weather patterns need to be clearly defined, including past meteorological data and analysis algorithms. For example, the data collection unit can collect weather data using past meteorological data to improve prediction accuracy. Furthermore, the data collection unit can analyze past weather data using analysis algorithms to improve prediction accuracy. This allows the data collection unit to improve prediction accuracy by referencing past weather patterns when collecting weather data.

[0043] The data collection unit can make more accurate pedestrian flow predictions by using surrounding traffic data in conjunction with pedestrian flow data. For example, the data collection unit can acquire surrounding traffic data in real time and integrate it with pedestrian flow data to improve prediction accuracy. The data collection unit can also correct pedestrian flow data by considering traffic congestion information. Furthermore, the data collection unit can adjust the pedestrian flow prediction model by referring to the operating status of public transportation. As a result, the data collection unit can make more accurate pedestrian flow predictions by using surrounding traffic data in conjunction with pedestrian flow data when collecting pedestrian flow data. The specific types of traffic data and collection methods need to be clearly defined, such as traffic volume and traffic congestion information. For example, the data collection unit can collect pedestrian flow data using traffic volume data to improve prediction accuracy. Furthermore, the data collection unit can correct pedestrian flow data using traffic congestion information to improve prediction accuracy. Furthermore, the data collection unit can adjust the pedestrian flow prediction model using the operating status of public transportation to improve prediction accuracy. As a result, the data collection unit can make more accurate pedestrian flow predictions by using surrounding traffic data in conjunction with pedestrian flow data when collecting pedestrian flow data.

[0044] The analysis unit can predict power generation by considering seasonal characteristics when analyzing weather forecast data. For example, the analysis unit can predict power generation by considering seasonal sunshine hours. Furthermore, the analysis unit can analyze seasonal weather patterns and adjust the power generation prediction model. In addition, the analysis unit can correct power generation efficiency by considering seasonal temperature fluctuations. This allows the analysis unit to predict power generation by considering seasonal characteristics when analyzing weather forecast data. The specific content and methods of considering seasonal characteristics need to be clearly defined, such as seasonal temperature changes and sunshine hours fluctuations. For example, the analysis unit can predict power generation by considering seasonal temperature changes. Furthermore, the analysis unit can predict power generation by considering seasonal sunshine hours fluctuations. This allows the analysis unit to predict power generation by considering seasonal characteristics when analyzing weather forecast data.

[0045] The analysis unit can predict advertising effectiveness by considering fluctuations in pedestrian flow data across different time periods. For example, the analysis unit can analyze pedestrian flow data by time period and predict advertising effectiveness. Furthermore, the analysis unit can optimize the timing of ad displays by considering peaks in pedestrian flow during specific time periods. In addition, the analysis unit can adjust the advertising effectiveness prediction model based on pedestrian flow patterns by time period. This allows the analysis unit to predict advertising effectiveness by considering fluctuations in pedestrian flow data across different time periods. The specific content and methods of considering these fluctuations by time period need to be clearly defined, such as commuting hours or lunch break times. For example, the analysis unit can predict advertising effectiveness by considering pedestrian flow data during commuting hours. Similarly, the analysis unit can predict advertising effectiveness by considering pedestrian flow data during lunch breaks. This allows the analysis unit to predict advertising effectiveness by considering fluctuations in pedestrian flow data across different time periods.

[0046] The analysis unit can predict power generation by considering regional characteristics when analyzing weather forecast data. For example, the analysis unit can predict power generation by considering the amount of sunshine in each region. Furthermore, the analysis unit can analyze regional weather patterns and adjust the power generation prediction model. In addition, the analysis unit can correct power generation efficiency by considering regional temperature fluctuations. This allows the analysis unit to predict power generation by considering regional characteristics when analyzing weather forecast data. The specific content and methods of considering regional characteristics need to be clearly defined, such as regional climate conditions and geographical characteristics. For example, the analysis unit can predict power generation by considering regional climate conditions. Furthermore, the analysis unit can predict power generation by considering regional geographical characteristics. This allows the analysis unit to predict power generation by considering regional characteristics when analyzing weather forecast data.

[0047] The analysis unit can predict advertising effectiveness by considering the influence of surrounding commercial facilities when analyzing pedestrian flow data. For example, the analysis unit can predict advertising effectiveness by considering the operating hours of surrounding commercial facilities. Furthermore, the analysis unit can refer to event information of commercial facilities and adjust the advertising effectiveness prediction model. In addition, the analysis unit can analyze the ability of commercial facilities to attract customers and correct the advertising effectiveness. This allows the analysis unit to predict advertising effectiveness by considering the influence of surrounding commercial facilities when analyzing pedestrian flow data. The specific details and methods of considering the influence of commercial facilities need to be clearly defined, such as the size and operating hours of the commercial facilities. For example, the analysis unit can predict advertising effectiveness by considering the size of commercial facilities. Furthermore, the analysis unit can predict advertising effectiveness by considering the operating hours of commercial facilities. This allows the analysis unit to predict advertising effectiveness by considering the influence of surrounding commercial facilities when analyzing pedestrian flow data.

[0048] The control unit can automatically optimize the content of advertisements when switching from power generation to advertisement display. For example, the control unit can automatically select the most profitable advertisement when switching from power generation to advertisement display. The control unit can also dynamically change the content of advertisements in accordance with the timing of advertisement display. Furthermore, the control unit can optimize the content of advertisements based on the user's interests and preferences. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display. The specific methods and criteria for optimizing the content of advertisements need to be clearly defined, such as the attributes of the target user and the type of advertisement. For example, the control unit can optimize the content of advertisements by considering the attributes of the target user. The control unit can also optimize the content of advertisements by considering the type of advertisement. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display.

[0049] The control unit can prioritize advertisements even when power generation is above a certain level if it determines that the advertisement is highly effective. For example, even when power generation is above a certain level, the control unit can prioritize advertisements if the advertising revenue is high. The control unit can also temporarily suspend power generation and display advertisements during times when advertising is highly effective. Furthermore, the control unit can suspend power generation and prioritize advertisements during events where advertising is highly effective. In this way, even when power generation is above a certain level, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective. The specific evaluation criteria and measurement methods for high advertising effectiveness need to be clearly defined, such as click-through rate, viewing time, and conversion rate. For example, the control unit can evaluate the effectiveness of advertisements based on the click-through rate and prioritize them. The control unit can also evaluate the effectiveness of advertisements based on viewing time and prioritize them. In this way, even when power generation is above a certain level, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective.

[0050] The control unit can optimize the display time of advertisements when switching from power generation to advertisement display. For example, the control unit can optimize the display time of advertisements based on user interests and preferences. It can also dynamically adjust the display time of advertisements based on profitability. Furthermore, the control unit can optimize the display time of advertisements to match events or specific time periods. This allows the control unit to maximize revenue by optimizing the display time of advertisements when switching from power generation to advertisement display. The specific optimization methods and criteria for advertisement display time need to be clearly defined, including the length of display time and the frequency of display. For example, the control unit can optimize the display time of advertisements considering the length of display time. It can also optimize the display time of advertisements considering the frequency of display. This allows the control unit to maximize revenue by optimizing the display time of advertisements when switching from power generation to advertisement display.

[0051] The control unit can provide an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. For example, the control unit can continue generating electricity when electricity demand is high, even if the amount of electricity generated falls below a certain level. It can also continue generating electricity when electricity prices are soaring, even if the amount of electricity generated falls below a certain level. Furthermore, the control unit can continue generating electricity during specific events, even if the amount of electricity generated falls below a certain level. This allows the control unit to respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. The specific content and criteria of these conditions need to be clearly defined, taking into account factors such as electricity demand and generation costs. For example, the control unit can consider electricity demand and continue generating electricity even when the amount of electricity generated falls below a certain level. It can also consider generation costs and continue generating electricity even when the amount of electricity generated falls below a certain level. This allows the control unit to respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can further collect ambient noise data and adjust the timing of ad display. For example, it can display ads when the surroundings are quiet and refrain from displaying them when the noise level is high. The data collection unit can also adjust the volume of ads based on the noise data. For example, it can increase the volume in quiet environments and decrease it in noisy environments. Furthermore, the data collection unit can change the content of the ads based on the noise data. For example, it can display visual ads in quiet environments and text-based ads in noisy environments. In this way, the data collection unit can optimize the timing and content of ad display by collecting ambient noise data.

[0054] The data collection unit can further collect user health data and adjust the timing of power generation and ad display. For example, it can prioritize power generation when the user's heart rate is high and display ads when the heart rate is stable. The data collection unit can also adjust the timing of ad display based on the user's sleep data. For example, it can refrain from displaying ads while the user is sleeping and display them after they wake up. Furthermore, the data collection unit can adjust the timing of power generation and ad display based on the user's exercise data. For example, it can prioritize power generation when the user is exercising and display ads after the exercise. In this way, the data collection unit can optimize the timing of power generation and ad display by collecting user health data.

[0055] The control unit can also provide an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. For example, it can continue generating electricity if electricity demand is high, even if the amount of electricity generated falls below a certain level. Furthermore, the control unit can continue generating electricity if electricity prices are soaring, even if the amount of electricity generated falls below a certain level. In this way, the control unit can respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. The specific content and criteria of these conditions need to be clearly defined, taking into account factors such as electricity demand and generation costs. For example, the control unit can consider electricity demand and continue generating electricity even when the amount of electricity generated falls below a certain level. Furthermore, the control unit can consider generation costs and continue generating electricity even when the amount of electricity generated falls below a certain level. In this way, the control unit can respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level.

[0056] The control unit can also automatically optimize the content of advertisements when switching from power generation to advertisement display. For example, it can automatically select the most profitable advertisement when switching from power generation to advertisement display. The control unit can also dynamically change the content of advertisements in accordance with the timing of advertisement display. Furthermore, the control unit can optimize the content of advertisements based on the user's interests and preferences. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display. The specific methods and criteria for optimizing the content of advertisements need to be clearly defined, such as the attributes of the target user and the type of advertisement. For example, the control unit can optimize the content of advertisements by considering the attributes of the target user. The control unit can also optimize the content of advertisements by considering the type of advertisement. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display.

[0057] The control unit can prioritize advertisements even when the power generation level is above a certain point, if it determines that the advertisement is highly effective. For example, even when the power generation level is above a certain point, it can prioritize advertisements if the advertising revenue is high. The control unit can also temporarily suspend power generation and display advertisements during times when advertising is highly effective. Furthermore, the control unit can suspend power generation and prioritize advertisements during events where advertising is highly effective. In this way, even when the power generation level is above a certain point, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective. The specific evaluation criteria and measurement methods for high advertising effectiveness need to be clearly defined, such as click-through rate, viewing time, and conversion rate. For example, the control unit can evaluate the effectiveness of advertisements based on the click-through rate and prioritize them. The control unit can also evaluate the effectiveness of advertisements based on viewing time and prioritize them. In this way, even when the power generation level is above a certain point, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects weather and time-of-day data. For example, the data collection unit can obtain weather forecast data in real time from the internet. It can also obtain time-of-day data from the system's internal clock. Furthermore, the data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the amount of power generated and the effectiveness of the advertising. For example, the analysis unit can predict the amount of power generated based on weather forecast data. The analysis unit can also predict the effectiveness of the advertising based on pedestrian flow data. Step 3: The control unit switches between power generation and advertisement display based on the results determined by the analysis unit. For example, the control unit can prioritize power generation if the amount of power generated is above a certain level, and display advertisements if it is below that level. The control unit can also stop power generation and start displaying advertisements when switching from power generation to advertisement display.

[0060] (Example of form 2) The system according to an embodiment of the present invention is a system that installs panels combining organic EL and perovskite on the walls of buildings and apartments, and uses a generating AI to automatically switch between power generation and advertising display. In this system, the generating AI automatically determines the amount of power generated based on weather and time of day, and the advertising effect based on pedestrian traffic. If power generation is possible, it generates power; if power generation is not possible, it displays advertisements for a fee. This system makes it possible to keep the rent of buildings and apartments low and cover the difference with advertising costs. It also makes it possible to increase the number of tenants by offering electricity as a service. For example, panels combining organic EL and perovskite are installed on the walls of buildings and apartments. These panels have both power generation and advertising display functions. For example, they can generate power on sunny days and display advertisements on cloudy days or at night. Next, the generating AI automatically determines the amount of power generated based on weather and time of day, and the advertising effect based on pedestrian traffic. The generating AI analyzes weather forecast data, date and time data, and pedestrian traffic data to determine whether power generation is possible. For example, it can be set to prioritize power generation on sunny days and display advertisements on cloudy days or at night. If power generation is possible, the panels generate power and store electricity. For example, on sunny days, the system can generate electricity using sunlight and store it in batteries. This makes it possible to offer electricity as a service, improving tenant satisfaction. When power generation is not possible, the panels can display advertisements for a fee. For example, advertisements can be displayed on cloudy days or at night, generating advertising revenue. This allows for lower rents, with the savings covered by advertising. This system allows for lower rents for buildings and apartments, with the savings covered by advertising. Furthermore, offering free electricity can increase the number of tenants. For example, setting low rents and providing free electricity can reduce vacancy rates. In addition, this system utilizes a new material called perovskite, enabling efficient power generation. Perovskite generates electricity more efficiently than conventional solar cells, increasing the amount of power generated. This ensures that enough electricity is available to offer free electricity. As a result, the system allows for lower rents for buildings and apartments, with the savings covered by advertising.Furthermore, offering electricity as a service can increase the number of tenants.

[0061] The power generation advertising system according to this embodiment comprises a data collection unit, an analysis unit, and a control unit. The data collection unit collects weather and date / time data. For example, the data collection unit can acquire weather forecast data from the internet in real time. The data collection unit can also acquire date / time data from a clock within the system. Furthermore, the data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments. For example, the data collection unit can acquire weather forecast data from the internet in real time to improve the accuracy of power generation prediction. Furthermore, the data collection unit can accurately acquire date / time data from a clock within the system to optimize the timing of power generation and advertising display. Furthermore, the data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments to improve the accuracy of advertising effectiveness prediction. The analysis unit analyzes the data collected by the data collection unit to determine the amount of power generated and the effectiveness of the advertising. For example, the analysis unit can predict the amount of power generated based on weather forecast data. Furthermore, the analysis unit can predict the effectiveness of the advertising based on pedestrian flow data. For example, the analysis unit can predict the amount of power generated based on weather forecast data to improve the efficiency of power generation. Furthermore, the analysis unit can predict advertising effectiveness based on pedestrian flow data and maximize the effectiveness of advertising displays. The control unit switches between power generation and advertising display based on the results determined by the analysis unit. For example, the control unit can prioritize power generation when the amount of power generated is above a certain level, and display advertisements when it is below that level. The control unit can also stop power generation and start advertising display when switching from power generation to advertising display. For example, by prioritizing power generation when the amount of power generated is above a certain level and displaying advertisements when it is below that level, the control unit enables efficient energy use. Also, by stopping power generation and starting advertising display when switching from power generation to advertising display, the control unit enables a smooth transition. As a result, the power generation advertising system according to this embodiment can automatically switch between power generation and advertising display based on weather and date / time data. As a result, the power generation advertising system can keep building and apartment rental fees low and cover that difference with advertising costs. It is also possible to increase the number of tenants by offering electricity as a service.

[0062] The data collection unit collects weather and time-of-day data. For example, the data collection unit can obtain weather forecast data in real time from the internet. Specifically, the data collection unit connects to a weather data provision service via API and periodically obtains the latest weather forecast data. This allows for a quick response to weather changes and improves the accuracy of power generation forecasts. The data collection unit can also obtain time-of-day data from the system's internal clock. The system's internal clock is synchronized with a high-precision NTP server, providing always accurate time-of-day information. This allows for the optimization of the timing of power generation and advertising display. Furthermore, the data collection unit can also collect pedestrian flow data from sensors installed around buildings and apartments. These sensors, for example, use infrared sensors and cameras to detect people's movements and collect data in real time. The collected pedestrian flow data is used to understand people's movements and dwell times around buildings and apartments. This provides data to maximize the effectiveness of advertising display. The data collection unit centrally manages this diverse data and provides it to the analysis and control units. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0063] The analysis unit analyzes data collected by the data collection unit to determine power generation and advertising effectiveness. For example, the analysis unit can predict power generation based on weather forecast data. Specifically, it uses weather forecast data to calculate the efficiency of solar power generation and predicts power generation considering factors such as solar radiation and cloud cover. By utilizing AI-based machine learning algorithms and building models based on past weather data and power generation performance, the accuracy of predictions can be improved. The analysis unit can also predict advertising effectiveness based on pedestrian flow data. Specifically, it analyzes pedestrian flow data around buildings and apartments to understand patterns of people's movement during specific times of day and on specific days of the week. This allows for the identification of the optimal timing and location for displaying advertisements, maximizing advertising effectiveness. Furthermore, the analysis unit integrates the collected data and makes decisions to optimize the balance between power generation and advertising display. For example, if power generation is lower than predicted, it prioritizes advertising display to secure revenue; if power generation is high, it prioritizes power generation to improve energy efficiency. The analysis unit makes these decisions in real time, maximizing the overall efficiency and effectiveness of the system.

[0064] The control unit switches between power generation and advertising display based on the results determined by the analysis unit. For example, the control unit can prioritize power generation when the power generation amount is above a certain level, and display advertisements when it is below that level. Specifically, the control unit has an interface for controlling both the power generation system and the advertising display system, and switches between these systems according to instructions from the analysis unit. When the power generation amount is high, the control unit operates the power generation system and supplies the generated electricity to the common areas of the building or apartment. On the other hand, when the power generation amount is low, the control unit operates the advertising display system and displays advertisements on digital signage or LED displays. When switching from power generation to advertising display, the control unit stops power generation and starts advertising display, enabling a smooth transition. Furthermore, the control unit can configure settings to optimize the content and timing of advertising display. For example, it can change the content of advertisements to match specific time periods or events to maximize advertising effectiveness. The control unit also has a function to constantly monitor the system status and issue alerts if an abnormality occurs. This allows the control unit to efficiently switch between power generation and advertising display and optimize the overall system performance.

[0065] The data collection unit can acquire weather forecast data from the internet in real time. For example, by acquiring weather forecast data from the internet in real time, the data collection unit can improve the accuracy of power generation forecasts. The data collection unit can also collect and integrate data from multiple weather forecast services. For example, it can acquire data from multiple weather forecast services in real time, calculate the average, and improve accuracy. Furthermore, the data collection unit can analyze the historical forecast accuracy of each weather forecast service and prioritize the use of data from the most reliable service. In addition, the data collection unit can integrate data from weather forecast services and, if different forecast results are obtained, adopt the most conservative forecast. This allows the data collection unit to improve the accuracy of power generation forecasts by acquiring weather forecast data in real time. The specific definition and criteria of "real time" need to be clarified, including data acquisition delay time and update frequency. For example, the data collection unit can acquire weather forecast data in real time and minimize data acquisition delay time. Furthermore, by increasing the update frequency of weather forecast data, the data collection unit can make more accurate forecasts. This allows the data collection unit to acquire weather forecast data in real time and improve the accuracy of power generation forecasts.

[0066] The data collection unit can obtain date and time data from the system's clock. For example, the data collection unit can accurately obtain date and time data from the system's clock and optimize the timing of power generation and advertisement display. The data collection unit can also adjust the timing of power generation and advertisement display based on the date and time data. For example, the data collection unit can optimize the timing of power generation and advertisement display based on the date and time data to achieve efficient energy use. The specific format and acquisition method of the date and time data, such as timestamps and date formats, needs to be clearly defined. For example, the data collection unit can obtain date and time data using timestamps and adjust the timing of power generation and advertisement display. Furthermore, the data collection unit can obtain date and time data using date formats and optimize the timing of power generation and advertisement display. This allows the data collection unit to optimize the timing of power generation and advertisement display by accurately obtaining date and time data.

[0067] The data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments. For example, by collecting pedestrian flow data from sensors installed around buildings and apartments, the data collection unit can improve the accuracy of advertising effectiveness predictions. The data collection unit can also predict advertising effectiveness based on pedestrian flow data. For example, the data collection unit can predict advertising effectiveness based on pedestrian flow data and maximize the effectiveness of advertising displays. The specific types of pedestrian flow data and collection methods need to be clearly defined, such as people counting, movement routes, and dwell time. For example, the data collection unit can collect pedestrian flow data using people counting and predict advertising effectiveness. Furthermore, the data collection unit can collect pedestrian flow data using movement routes and predict advertising effectiveness. In this way, the data collection unit can improve the accuracy of advertising effectiveness predictions by collecting pedestrian flow data.

[0068] The analysis unit can predict power generation based on weather forecast data. For example, the analysis unit can improve power generation efficiency by predicting power generation based on weather forecast data. The analysis unit can use prediction algorithms to predict power generation based on weather forecast data. For example, the analysis unit can use machine learning algorithms to predict power generation based on weather forecast data. The analysis unit can also use statistical models to predict power generation based on weather forecast data. Furthermore, the analysis unit can use simulation models to predict power generation based on weather forecast data. This allows the analysis unit to improve power generation efficiency by predicting power generation based on weather forecast data. The specific methods and types of data used for predicting power generation need to be clearly defined, including the prediction algorithm and the type of weather forecast data used. For example, the analysis unit can analyze weather forecast data using machine learning algorithms to predict power generation. Furthermore, the analysis unit can analyze weather forecast data using statistical models to predict power generation. Furthermore, the analysis unit can analyze weather forecast data using simulation models to predict power generation. This allows the analysis unit to improve power generation efficiency by predicting power generation based on weather forecast data.

[0069] The analysis unit can predict advertising effectiveness based on pedestrian flow data. For example, the analysis unit can predict advertising effectiveness based on pedestrian flow data and maximize the effectiveness of ad displays. The analysis unit can use prediction algorithms to predict advertising effectiveness based on pedestrian flow data. For example, the analysis unit can use machine learning algorithms to predict advertising effectiveness based on pedestrian flow data. The analysis unit can also use statistical models to predict advertising effectiveness based on pedestrian flow data. Furthermore, the analysis unit can use simulation models to predict advertising effectiveness based on pedestrian flow data. This allows the analysis unit to maximize the effectiveness of ad displays by predicting advertising effectiveness based on pedestrian flow data. The specific methods and types of data used for predicting advertising effectiveness need to be clearly defined, including the prediction algorithms and the types of pedestrian flow data used. For example, the analysis unit can analyze pedestrian flow data using machine learning algorithms and predict advertising effectiveness. Furthermore, the analysis unit can analyze pedestrian flow data using statistical models and predict advertising effectiveness. Furthermore, the analysis unit can analyze pedestrian flow data using simulation models and predict advertising effectiveness. This allows the analysis unit to maximize the effectiveness of ad displays by predicting advertising effectiveness based on pedestrian flow data.

[0070] The control unit can prioritize power generation when the power generation amount is above a certain level, and display advertisements when it is below that level. For example, by prioritizing power generation when the power generation amount is above a certain level and displaying advertisements when it is below that level, the control unit can enable efficient energy use. The control unit can set a threshold for power generation amount to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level. For example, the control unit can set a threshold for power generation amount to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level, and then prioritize power generation when the power generation amount exceeds the threshold. The control unit can also set a threshold for advertising effectiveness to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level. For example, the control unit can set a threshold for advertising effectiveness to prioritize power generation when the power generation amount is above a certain level and display advertisements when it is below that level, and then display advertisements when the advertising effectiveness exceeds the threshold. As a result, the control unit can enable efficient energy use by prioritizing power generation when the power generation amount is above a certain level and displaying advertisements when it is below that level. The specific criteria and numerical values ​​for "a certain level" need to be clearly defined, such as the threshold for power generation amount and the threshold for advertising effectiveness. For example, the control unit can set a threshold for power generation and prioritize power generation if the power generation exceeds that threshold. The control unit can also set a threshold for advertising effectiveness and display advertisements if the advertising effectiveness exceeds that threshold. This allows the control unit to prioritize power generation when it is above a certain level and display advertisements when it is below that level, enabling efficient energy utilization.

[0071] The control unit can stop power generation and start displaying advertisements when switching from power generation to advertisement display. For example, the control unit can enable a smooth transition by stopping power generation and starting advertisement display when switching from power generation to advertisement display. The control unit can set the timing for stopping power generation and starting advertisement display when switching from power generation to advertisement display. For example, the control unit can set the timing for stopping power generation and starting advertisement display when switching from power generation to advertisement display, and stop power generation and start advertisement display when the power generation amount falls below a certain level. Furthermore, the control unit can also set the procedure for stopping power generation and starting advertisement display when switching from power generation to advertisement display. For example, the control unit can set the procedure for stopping power generation and starting advertisement display when switching from power generation to advertisement display, and start advertisement display after stopping power generation. This allows the control unit to enable a smooth transition by stopping power generation and starting advertisement display when switching from power generation to advertisement display. The specific method and conditions of the switch, such as the timing and procedure, need to be clearly defined. For example, the control unit can set the timing for the switch, and stop power generation and start advertisement display when the power generation amount falls below a certain level. Furthermore, the control unit can set a switching procedure and start displaying advertisements after stopping power generation. This allows the control unit to smoothly switch from power generation to advertisement display by stopping power generation and then starting advertisement display.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the system load. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only important data and perform rapid analysis. In this way, the data collection unit can reduce the system load by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions.

[0073] The data collection unit can collect and integrate data from multiple weather forecasting services to improve the accuracy of weather data. For example, the unit can acquire data from multiple weather forecasting services in real time, calculate the average, and improve accuracy. The unit can also analyze the historical prediction accuracy of each weather forecasting service and prioritize the use of data from the most reliable service. Furthermore, the unit can integrate data from weather forecasting services and, if different prediction results are obtained, adopt the most conservative prediction. This allows the data collection unit to collect and integrate data from multiple weather forecasting services to improve the accuracy of weather data. The specific types of weather forecasting services and collection methods need to be clearly defined, such as the Japan Meteorological Agency or private weather services. For example, the data collection unit can collect weather data using data from the Japan Meteorological Agency and improve accuracy. Alternatively, the data collection unit can collect weather data using data from private weather services and improve accuracy. This allows the data collection unit to collect and integrate data from multiple weather forecasting services to improve the accuracy of weather data.

[0074] The data collection unit can adjust the data to account for the impact of specific events and festivals when collecting pedestrian flow data. For example, during an event, the unit can apply a correction factor to normal pedestrian flow data to collect accurate data. The unit can also refer to past event data and adjust the predictive model if similar events are held. Furthermore, the unit can dynamically change the correction factor according to the scale and type of event to improve data accuracy. This allows the unit to collect accurate pedestrian flow data by adjusting the data to account for the impact of specific events and festivals. The specific type and impact of a particular event or festival need to be clearly defined, including the scale and duration of the event. For example, the unit can set a larger correction factor when a large-scale event is held to collect accurate pedestrian flow data. Also, the unit can dynamically change the correction factor during long-term festivals to improve data accuracy. This allows the unit to collect accurate pedestrian flow data by adjusting the data to account for the impact of specific events and festivals.

[0075] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data to reduce the system load. If the user is relaxed, the data collection unit can prioritize collecting detailed data to improve the accuracy of the analysis. Furthermore, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly to perform analysis quickly. In this way, the system efficiency is improved by the data collection unit prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows the data collection unit to estimate the user's emotions and determine the priority of the data to collect based on the estimated user emotions.

[0076] The data collection unit can improve prediction accuracy by referencing past weather patterns when collecting weather data. For example, the data collection unit can correct the accuracy of current weather forecasts based on past weather data. Furthermore, the data collection unit can analyze seasonal weather patterns and adjust the prediction model. In addition, the data collection unit can compare past and current weather data to detect outliers and improve prediction accuracy. This allows the data collection unit to improve prediction accuracy by referencing past weather patterns when collecting weather data. The specific data and analysis methods for past weather patterns need to be clearly defined, including past meteorological data and analysis algorithms. For example, the data collection unit can collect weather data using past meteorological data to improve prediction accuracy. Furthermore, the data collection unit can analyze past weather data using analysis algorithms to improve prediction accuracy. This allows the data collection unit to improve prediction accuracy by referencing past weather patterns when collecting weather data.

[0077] The data collection unit can make more accurate pedestrian flow predictions by using surrounding traffic data in conjunction with pedestrian flow data. For example, the data collection unit can acquire surrounding traffic data in real time and integrate it with pedestrian flow data to improve prediction accuracy. The data collection unit can also correct pedestrian flow data by considering traffic congestion information. Furthermore, the data collection unit can adjust the pedestrian flow prediction model by referring to the operating status of public transportation. As a result, the data collection unit can make more accurate pedestrian flow predictions by using surrounding traffic data in conjunction with pedestrian flow data when collecting pedestrian flow data. The specific types of traffic data and collection methods need to be clearly defined, such as traffic volume and traffic congestion information. For example, the data collection unit can collect pedestrian flow data using traffic volume data to improve prediction accuracy. Furthermore, the data collection unit can correct pedestrian flow data using traffic congestion information to improve prediction accuracy. Furthermore, the data collection unit can adjust the pedestrian flow prediction model using the operating status of public transportation to improve prediction accuracy. As a result, the data collection unit can make more accurate pedestrian flow predictions by using surrounding traffic data in conjunction with pedestrian flow data when collecting pedestrian flow data.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit deepens the user's understanding by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions.

[0079] The analysis unit can predict power generation by considering seasonal characteristics when analyzing weather forecast data. For example, the analysis unit can predict power generation by considering seasonal sunshine hours. Furthermore, the analysis unit can analyze seasonal weather patterns and adjust the power generation prediction model. In addition, the analysis unit can correct power generation efficiency by considering seasonal temperature fluctuations. This allows the analysis unit to predict power generation by considering seasonal characteristics when analyzing weather forecast data. The specific content and methods of considering seasonal characteristics need to be clearly defined, such as seasonal temperature changes and sunshine hours fluctuations. For example, the analysis unit can predict power generation by considering seasonal temperature changes. Furthermore, the analysis unit can predict power generation by considering seasonal sunshine hours fluctuations. This allows the analysis unit to predict power generation by considering seasonal characteristics when analyzing weather forecast data.

[0080] The analysis unit can predict advertising effectiveness by considering fluctuations in pedestrian flow data across different time periods. For example, the analysis unit can analyze pedestrian flow data by time period and predict advertising effectiveness. Furthermore, the analysis unit can optimize the timing of ad displays by considering peaks in pedestrian flow during specific time periods. In addition, the analysis unit can adjust the advertising effectiveness prediction model based on pedestrian flow patterns by time period. This allows the analysis unit to predict advertising effectiveness by considering fluctuations in pedestrian flow data across different time periods. The specific content and methods of considering these fluctuations by time period need to be clearly defined, such as commuting hours or lunch break times. For example, the analysis unit can predict advertising effectiveness by considering pedestrian flow data during commuting hours. Similarly, the analysis unit can predict advertising effectiveness by considering pedestrian flow data during lunch breaks. This allows the analysis unit to predict advertising effectiveness by considering fluctuations in pedestrian flow data across different time periods.

[0081] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying only the most important analysis results. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can quickly display the analysis results. In this way, the analysis unit can prioritize providing important information by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, the analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions.

[0082] The analysis unit can predict power generation by considering regional characteristics when analyzing weather forecast data. For example, the analysis unit can predict power generation by considering the amount of sunshine in each region. Furthermore, the analysis unit can analyze regional weather patterns and adjust the power generation prediction model. In addition, the analysis unit can correct power generation efficiency by considering regional temperature fluctuations. This allows the analysis unit to predict power generation by considering regional characteristics when analyzing weather forecast data. The specific content and methods of considering regional characteristics need to be clearly defined, such as regional climate conditions and geographical characteristics. For example, the analysis unit can predict power generation by considering regional climate conditions. Furthermore, the analysis unit can predict power generation by considering regional geographical characteristics. This allows the analysis unit to predict power generation by considering regional characteristics when analyzing weather forecast data.

[0083] The analysis unit can predict advertising effectiveness by considering the influence of surrounding commercial facilities when analyzing pedestrian flow data. For example, the analysis unit can predict advertising effectiveness by considering the operating hours of surrounding commercial facilities. Furthermore, the analysis unit can refer to event information of commercial facilities and adjust the advertising effectiveness prediction model. In addition, the analysis unit can analyze the ability of commercial facilities to attract customers and correct the advertising effectiveness. This allows the analysis unit to predict advertising effectiveness by considering the influence of surrounding commercial facilities when analyzing pedestrian flow data. The specific details and methods of considering the influence of commercial facilities need to be clearly defined, such as the size and operating hours of the commercial facilities. For example, the analysis unit can predict advertising effectiveness by considering the size of commercial facilities. Furthermore, the analysis unit can predict advertising effectiveness by considering the operating hours of commercial facilities. This allows the analysis unit to predict advertising effectiveness by considering the influence of surrounding commercial facilities when analyzing pedestrian flow data.

[0084] The control unit can estimate the user's emotions and adjust the timing of switching between power generation and ad display based on the estimated emotions. For example, if the user is stressed, the control unit can prioritize power generation and reduce the frequency of ad display. Conversely, if the user is relaxed, the control unit can increase the frequency of ad display to maximize revenue. Furthermore, if the user is in a hurry, the control unit can quickly switch between power generation and ad display. This improves the efficiency of the system by adjusting the timing of switching between power generation and ad display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows the control unit to estimate the user's emotions and adjust the timing of switching between power generation and ad display based on the estimated emotions.

[0085] The control unit can automatically optimize the content of advertisements when switching from power generation to advertisement display. For example, the control unit can automatically select the most profitable advertisement when switching from power generation to advertisement display. The control unit can also dynamically change the content of advertisements in accordance with the timing of advertisement display. Furthermore, the control unit can optimize the content of advertisements based on the user's interests and preferences. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display. The specific methods and criteria for optimizing the content of advertisements need to be clearly defined, such as the attributes of the target user and the type of advertisement. For example, the control unit can optimize the content of advertisements by considering the attributes of the target user. The control unit can also optimize the content of advertisements by considering the type of advertisement. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display.

[0086] The control unit can prioritize advertisements even when power generation is above a certain level if it determines that the advertisement is highly effective. For example, even when power generation is above a certain level, the control unit can prioritize advertisements if the advertising revenue is high. The control unit can also temporarily suspend power generation and display advertisements during times when advertising is highly effective. Furthermore, the control unit can suspend power generation and prioritize advertisements during events where advertising is highly effective. In this way, even when power generation is above a certain level, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective. The specific evaluation criteria and measurement methods for high advertising effectiveness need to be clearly defined, such as click-through rate, viewing time, and conversion rate. For example, the control unit can evaluate the effectiveness of advertisements based on the click-through rate and prioritize them. The control unit can also evaluate the effectiveness of advertisements based on viewing time and prioritize them. In this way, even when power generation is above a certain level, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective.

[0087] The control unit can estimate the user's emotions and determine the priority of power generation and ad display based on the estimated emotions. For example, if the user is stressed, the control unit can prioritize power generation and reduce the frequency of ad display. Conversely, if the user is relaxed, the control unit can increase the frequency of ad display to maximize revenue. Furthermore, if the user is in a hurry, the control unit can quickly switch between power generation and ad display. This improves the efficiency of the system by allowing the control unit to determine the priority of power generation and ad display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows the control unit to estimate the user's emotions and determine the priority of power generation and ad display based on the estimated emotions.

[0088] The control unit can optimize the display time of advertisements when switching from power generation to advertisement display. For example, the control unit can optimize the display time of advertisements based on user interests and preferences. It can also dynamically adjust the display time of advertisements based on profitability. Furthermore, the control unit can optimize the display time of advertisements to match events or specific time periods. This allows the control unit to maximize revenue by optimizing the display time of advertisements when switching from power generation to advertisement display. The specific optimization methods and criteria for advertisement display time need to be clearly defined, including the length of display time and the frequency of display. For example, the control unit can optimize the display time of advertisements considering the length of display time. It can also optimize the display time of advertisements considering the frequency of display. This allows the control unit to maximize revenue by optimizing the display time of advertisements when switching from power generation to advertisement display.

[0089] The control unit can provide an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. For example, the control unit can continue generating electricity when electricity demand is high, even if the amount of electricity generated falls below a certain level. It can also continue generating electricity when electricity prices are soaring, even if the amount of electricity generated falls below a certain level. Furthermore, the control unit can continue generating electricity during specific events, even if the amount of electricity generated falls below a certain level. This allows the control unit to respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. The specific content and criteria of these conditions need to be clearly defined, taking into account factors such as electricity demand and generation costs. For example, the control unit can consider electricity demand and continue generating electricity even when the amount of electricity generated falls below a certain level. It can also consider generation costs and continue generating electricity even when the amount of electricity generated falls below a certain level. This allows the control unit to respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The power-generating advertising system can further estimate the user's emotions and dynamically change the ad content based on those emotions. For example, if the user is stressed, it can display relaxing ads. If the user is excited, it can display energetic ads. Furthermore, if the user is sad, it can display ads containing encouraging messages. In this way, the power-generating advertising system can maximize advertising effectiveness by displaying ads that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the power-generating advertising system may be performed using AI or not. For example, the power-generating advertising system can input user emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, the power-generating advertising system can estimate the user's emotions and dynamically change the ad content based on those emotions.

[0092] The data collection unit can further collect ambient noise data and adjust the timing of ad display. For example, it can display ads when the surroundings are quiet and refrain from displaying them when the noise level is high. The data collection unit can also adjust the volume of ads based on the noise data. For example, it can increase the volume in quiet environments and decrease it in noisy environments. Furthermore, the data collection unit can change the content of the ads based on the noise data. For example, it can display visual ads in quiet environments and text-based ads in noisy environments. In this way, the data collection unit can optimize the timing and content of ad display by collecting ambient noise data.

[0093] The data collection unit can further collect user health data and adjust the timing of power generation and ad display. For example, it can prioritize power generation when the user's heart rate is high and display ads when the heart rate is stable. The data collection unit can also adjust the timing of ad display based on the user's sleep data. For example, it can refrain from displaying ads while the user is sleeping and display them after they wake up. Furthermore, the data collection unit can adjust the timing of power generation and ad display based on the user's exercise data. For example, it can prioritize power generation when the user is exercising and display ads after the exercise. In this way, the data collection unit can optimize the timing of power generation and ad display by collecting user health data.

[0094] The data collection unit can further estimate the user's emotions and adjust the timing of power generation and ad display based on the estimated emotions. For example, if the user is stressed, power generation can be prioritized and the frequency of ad display can be reduced. Conversely, if the user is relaxed, the frequency of ad display can be increased to maximize revenue. Furthermore, if the user is in a hurry, power generation and ad display can be quickly switched. This improves the efficiency of the system by allowing the data collection unit to adjust the timing of power generation and ad display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows the data collection unit to estimate the user's emotions and adjust the timing of power generation and ad display based on the estimated emotions.

[0095] The analysis unit can further estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the analysis unit deepens the user's understanding by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions.

[0096] The analysis unit can further estimate the user's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the user is stressed, only important analysis results can be displayed preferentially. If the user is relaxed, detailed analysis results can be displayed preferentially. Furthermore, if the user is in a hurry, the analysis results can be displayed quickly. In this way, the analysis unit can prioritize important information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, the analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated emotions.

[0097] The control unit can also provide an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. For example, it can continue generating electricity if electricity demand is high, even if the amount of electricity generated falls below a certain level. Furthermore, the control unit can continue generating electricity if electricity prices are soaring, even if the amount of electricity generated falls below a certain level. In this way, the control unit can respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level. The specific content and criteria of these conditions need to be clearly defined, taking into account factors such as electricity demand and generation costs. For example, the control unit can consider electricity demand and continue generating electricity even when the amount of electricity generated falls below a certain level. Furthermore, the control unit can consider generation costs and continue generating electricity even when the amount of electricity generated falls below a certain level. In this way, the control unit can respond to electricity demand by providing an option to continue generating electricity under specific conditions, even when the amount of electricity generated falls below a certain level.

[0098] The control unit can also automatically optimize the content of advertisements when switching from power generation to advertisement display. For example, it can automatically select the most profitable advertisement when switching from power generation to advertisement display. The control unit can also dynamically change the content of advertisements in accordance with the timing of advertisement display. Furthermore, the control unit can optimize the content of advertisements based on the user's interests and preferences. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display. The specific methods and criteria for optimizing the content of advertisements need to be clearly defined, such as the attributes of the target user and the type of advertisement. For example, the control unit can optimize the content of advertisements by considering the attributes of the target user. The control unit can also optimize the content of advertisements by considering the type of advertisement. As a result, the control unit can maximize revenue by automatically optimizing the content of advertisements when switching from power generation to advertisement display.

[0099] The control unit can prioritize advertisements even when the power generation level is above a certain point, if it determines that the advertisement is highly effective. For example, even when the power generation level is above a certain point, it can prioritize advertisements if the advertising revenue is high. The control unit can also temporarily suspend power generation and display advertisements during times when advertising is highly effective. Furthermore, the control unit can suspend power generation and prioritize advertisements during events where advertising is highly effective. In this way, even when the power generation level is above a certain point, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective. The specific evaluation criteria and measurement methods for high advertising effectiveness need to be clearly defined, such as click-through rate, viewing time, and conversion rate. For example, the control unit can evaluate the effectiveness of advertisements based on the click-through rate and prioritize them. The control unit can also evaluate the effectiveness of advertisements based on viewing time and prioritize them. In this way, even when the power generation level is above a certain point, the control unit can maximize revenue by prioritizing advertisements when it determines that they are highly effective.

[0100] The control unit can further estimate the user's emotions and determine the priority of power generation and ad display based on the estimated emotions. For example, if the user is stressed, power generation can be prioritized and the frequency of ad display can be reduced. Conversely, if the user is relaxed, the frequency of ad display can be increased to maximize revenue. Furthermore, if the user is in a hurry, power generation and ad display can be quickly switched. This improves the efficiency of the system by allowing the control unit to determine the priority of power generation and ad display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows the control unit to estimate the user's emotions and determine the priority of power generation and ad display based on the estimated emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects weather and time-of-day data. For example, the data collection unit can obtain weather forecast data in real time from the internet. It can also obtain time-of-day data from the system's internal clock. Furthermore, the data collection unit can collect pedestrian flow data from sensors installed around buildings and apartments. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the amount of power generated and the effectiveness of the advertising. For example, the analysis unit can predict the amount of power generated based on weather forecast data. The analysis unit can also predict the effectiveness of the advertising based on pedestrian flow data. Step 3: The control unit switches between power generation and advertisement display based on the results determined by the analysis unit. For example, the control unit can prioritize power generation if the amount of power generated is above a certain level, and display advertisements if it is below that level. The control unit can also stop power generation and start displaying advertisements when switching from power generation to advertisement display.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, and control unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect weather and date / time data using the sensors and internet connection of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the amount of power generated and the effectiveness of the advertisement. The control unit is implemented in the control unit 46A of the smart device 14, and switches between power generation and advertisement display based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, and control unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect weather and date / time data using the sensors and internet connection of the smart glasses 214. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to determine the amount of power generated and the effectiveness of the advertisement. The control unit is implemented in, for example, the control unit 46A of the smart glasses 214, which switches between power generation and advertisement display based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, and control unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect weather and date / time data using the sensors and internet connection of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to determine the amount of power generated and the effectiveness of the advertisement. The control unit is implemented in the control unit 46A of the headset terminal 314, for example, and switches between power generation and advertisement display based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, and control unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect weather and date / time data using the robot 414's sensors and internet connection. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to determine the amount of power generated and the effectiveness of the advertisement. The control unit is implemented, for example, by the control unit 46A of the robot 414, which switches between power generation and advertisement display based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) The collection unit collects weather and date / time data, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the amount of power generated and the effectiveness of the advertising, The system includes a control unit that switches between power generation and advertising display based on the results determined by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Obtain weather forecast data from the internet in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Date and time data is obtained from the system's clock. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Pedestrian flow data is collected from sensors installed around buildings and apartments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Predicting power generation based on weather forecast data The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Predicting advertising effectiveness based on pedestrian flow data The system described in Appendix 1, characterized by the features described herein. (Note 7) The control unit, If the amount of electricity generated is above a certain level, electricity generation will be prioritized; otherwise, advertisements will be displayed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The control unit, When switching from power generation to displaying advertisements, power generation will stop and advertisements will begin. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is To improve the accuracy of weather data, we collect and integrate data from multiple weather forecasting services. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting pedestrian flow data, adjust the data to account for the influence of specific events or festivals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting weather data, we improve prediction accuracy by referring to past weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting pedestrian flow data, use surrounding traffic data in conjunction to make more accurate pedestrian flow predictions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing weather forecast data, we predict power generation by taking into account seasonal characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing pedestrian flow data, we predict advertising effectiveness by considering fluctuations over time. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing weather forecast data, power generation is predicted by considering the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing pedestrian flow data, we predict advertising effectiveness by considering the influence of surrounding commercial facilities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, The system estimates the user's emotions and adjusts the timing of switching between power generation and ad display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, When switching from power generation to displaying ads, the content of the ads displayed is automatically optimized. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, Even if the amount of electricity generated is above a certain level, advertising will be prioritized if it is determined that advertising is highly effective. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, It estimates the user's emotions and determines the priority of power generation and ad display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, When switching from power generation to displaying ads, optimize the ad display time. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, Even if the amount of electricity generated falls below a certain level, it provides an option to continue generating electricity under specific conditions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects weather and date / time data, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the amount of power generated and the effectiveness of the advertising, The system includes a control unit that switches between power generation and advertising display based on the results determined by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Obtain weather forecast data from the internet in real time. The system according to feature 1.

3. The aforementioned collection unit is Date and time data is obtained from the system's clock. The system according to feature 1.

4. The aforementioned collection unit is Pedestrian flow data is collected from sensors installed around buildings and apartments. The system according to feature 1.

5. The aforementioned analysis unit, Predicting power generation based on weather forecast data The system according to feature 1.

6. The aforementioned analysis unit, Predicting advertising effectiveness based on pedestrian flow data The system according to feature 1.

7. The control unit, If the amount of electricity generated is above a certain level, electricity generation will be prioritized; otherwise, advertisements will be displayed. The system according to feature 1.

8. The control unit, When switching from power generation to displaying advertisements, power generation will stop and advertisements will begin. The system according to feature 1.

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

10. The aforementioned collection unit is To improve the accuracy of weather data, we collect and integrate data from multiple weather forecasting services. The system according to feature 1.

Images