system

Abstract

The system according to this embodiment aims to understand the availability of seats, adjust the environment according to the weather, and promote communication in public places. [Solution] The system according to the embodiment comprises a collection unit, a guidance unit, an adjustment unit, a provision unit, and a supply unit. The collection unit collects vacancy information. The guidance unit guides users to available benches based on the vacancy information collected by the collection unit. The adjustment unit adjusts the bench temperature according to the weather and temperature. The provision unit connects with nearby bench users, and AI supports conversations by providing topics of discussion. The supply unit supplies energy using solar panels.

Description

Technical Field

[0006] , , ,

[0003] ,

[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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to grasp the vacancy situation, it is difficult to use in an environment that is easily affected by the weather, and there is a problem that communication in public places is scarce.

[0005] The system according to the embodiment aims to grasp the vacancy situation, perform environment adjustment according to the weather, and promote communication in public places.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a guidance unit, an adjustment unit, a provision unit, and a supply unit. The collection unit collects information on available seats. The guidance unit guides users to available benches based on the information collected by the collection unit. The adjustment unit adjusts the temperature of the benches according to the weather and temperature. The provision unit connects with nearby bench users, and AI supports conversations by providing topics of discussion. The supply unit supplies energy using solar panels. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the availability of seats, adjust the environment according to the weather, and promote communication in public places. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The bench usage system according to an embodiment of the present invention is a system that improves the convenience and comfort of using benches in public places. By combining the components of a seat availability guidance system, an automatic environmental adjustment function, a communication mode, and energy efficiency using solar panels, this bench usage system can improve user convenience and comfort, realize environmentally friendly public facilities, and revitalize interaction in public places. For example, the bench usage system can obtain information on available benches in real time through a dedicated app. For example, the bench usage system can check the availability of benches in a park and guide users to the nearest available bench. This system eliminates the need for users to walk around looking for an empty seat. Next, the bench usage system has a function to automatically adjust the temperature of the bench according to the weather and temperature. For example, it raises the bench temperature on cold days and lowers it on hot days to ensure users can spend their time comfortably. This function improves comfort in public places. Furthermore, the bench usage system has a function to connect with nearby bench users, and a generating AI supports conversations by providing topics. For example, when a user turns on the communication mode through the dedicated app, it connects with other users nearby, and the generating AI provides common topics. This feature stimulates interaction in public spaces and reduces feelings of isolation. Finally, the bench utilization system has the function of supplying energy using sunlight by installing solar panels on the benches. This makes the benches eco-friendly public facilities and enables environmentally friendly operation. In this way, the bench utilization system can improve user convenience and comfort, realize environmentally friendly public facilities, and stimulate interaction in public spaces.

[0029] The bench utilization system according to this embodiment comprises a collection unit, a guidance unit, an adjustment unit, a provision unit, and a supply unit. The collection unit collects vacancy information. The collection unit can collect information on available benches in real time, for example, through a dedicated app. The collection unit collects information such as the location of the bench, the number of available seats, and the available time. The collection unit can also collect vacancy information using AI, for example. The guidance unit guides users to available benches based on the vacancy information collected by the collection unit. The guidance unit can guide users to the nearest available bench, for example. The guidance unit guides users to benches based on methods for measuring distance and checking for available seats, for example. The guidance unit can also guide users to available benches using AI, for example. The adjustment unit adjusts the bench temperature according to the weather and temperature. The adjustment unit can automatically adjust the bench temperature using a temperature sensor, for example. The adjustment unit can raise the bench temperature on cold days and lower it on hot days, for example. The adjustment unit can also adjust the bench temperature using AI, for example. The supply unit connects with nearby bench users, and a generating AI supports conversations by providing topics. For example, when a user turns on communication mode via a dedicated app, the supply unit connects with other nearby users, and the generating AI provides common topics. The supply unit provides topics based on, for example, topic selection criteria and conversation progression methods. The supply unit can also provide topics using, for example, AI. The supply unit supplies energy using solar panels. For example, the supply unit can install solar panels on benches and supply energy using sunlight. The supply unit supplies energy based on, for example, the type and installation method of the solar panels. The supply unit can also supply energy using, for example, AI. As a result, the bench usage system according to this embodiment can collect vacancy information, provide guidance, adjust temperature, support conversations, and supply energy.

[0030] The data collection unit collects information on available seats. For example, the data collection unit can collect information on available benches in real time through a dedicated app. Specifically, the dedicated app receives data from sensors installed on the benches to determine whether there are available seats and their usage status. These sensors, such as pressure sensors and infrared sensors, detect whether someone is sitting on the bench. The data collection unit collects information such as the bench's location, the number of available seats, and the available time. This allows users to check the location of currently available benches and the available time slots through the app. The data collection unit can also collect information on available seats using AI. The AI ​​analyzes data from sensors and updates the bench usage status in real time. For example, the AI ​​can predict bench usage trends at specific times of day or under specific weather conditions based on past usage data, providing more accurate information on available seats. Furthermore, the data collection unit can store this data on a cloud server and link it with other systems and departments. For example, the collected data can be made accessible to the guidance and coordination departments, improving the overall efficiency of the system. In addition, the data collection unit can adjust the frequency and accuracy of data collection, enabling 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 guidance unit directs users to available benches based on vacancy information collected by the data collection unit. For example, the guidance unit can direct users to the nearest available bench. Specifically, it obtains the user's current location through a dedicated app and displays the nearest available bench. The guidance unit also directs users to benches based on methods such as distance measurement and seat availability confirmation. For distance measurement, it uses GPS or Wi-Fi location information, and for seat availability confirmation, it uses real-time data provided by the data collection unit. The guidance unit can also use AI to guide users to available benches. The AI ​​analyzes the user's current location and past usage history to recommend the most suitable bench. For example, the AI ​​can learn the location and time of benches the user has used in the past and prioritize benches that match the user's preferences. Furthermore, the guidance unit can collect user feedback and continuously improve the accuracy and effectiveness of its guidance. For example, it can collect user evaluations and usage status of the benches they are directed to as feedback, and the AI ​​learns from this to improve the accuracy of future guidance. In addition, the guidance unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through notifications from a dedicated app, but also through voice guidance and vibration notifications. This allows the guidance system to quickly and reliably direct users to available benches, improving convenience.

[0032] The adjustment unit adjusts the bench temperature according to the weather and ambient temperature. For example, the adjustment unit can automatically adjust the bench temperature using a temperature sensor. Specifically, a temperature sensor installed on the bench measures the ambient temperature and transmits this data to the adjustment unit. The adjustment unit can, for example, raise the bench temperature on cold days and lower it on hot days. This allows users to use the bench at a comfortable temperature. The adjustment unit can also adjust the bench temperature using AI. The AI ​​learns the optimal temperature setting based on past temperature data and user feedback and adjusts it automatically. For example, the AI ​​can predict the optimal temperature setting for a specific time of day or weather conditions and adjust the bench temperature in advance. Furthermore, the adjustment unit can also provide temperature settings according to the user's preferences. For example, if a user inputs their desired temperature through a dedicated app, the adjustment unit adjusts the bench to that temperature. In addition, the adjustment unit can adjust the temperature with the minimum necessary energy, taking energy efficiency into consideration. For example, it can adjust the bench temperature using energy supplied from solar panels. This allows the adjustment unit to efficiently and effectively adjust the bench temperature and provide users with a comfortable environment.

[0033] The service provider connects with nearby bench users, and a generative AI supports conversations by providing topics. For example, when a user turns on communication mode through a dedicated app, the service provider connects with other nearby users, and the generative AI provides common topics. Specifically, the generative AI selects topics of common interest based on the user's profile information and past conversation history. The service provider provides topics based on, for example, topic selection criteria and conversation flow methods. The generative AI uses natural language processing technology to facilitate smooth conversations between users. For example, the generative AI analyzes user statements and provides appropriate responses and additional topics. The generative AI can also switch topics at the appropriate time depending on the tone and content of the conversation. The service provider can also provide topics using AI. The AI ​​collects user feedback and continuously improves topic selection criteria and conversation flow methods. For example, it collects user evaluations of provided topics and the progress of conversations as feedback, and the AI ​​learns from this to improve the accuracy of topic provision in the future. Furthermore, the service provider will implement a system to anonymize conversation content and protect personal information in order to protect user privacy. This will enable the service provider to provide users with an environment where they can enjoy communicating with peace of mind.

[0034] The supply unit provides energy using solar panels. For example, the supply unit can install solar panels on a bench and use sunlight to supply energy. Specifically, solar panels can be installed on the backrest and seat of the bench to absorb sunlight during the day and generate electricity. The supply unit supplies energy based on the type and installation method of the solar panels. For example, it can use high-efficiency monocrystalline silicon solar panels and install them in accordance with the bench's design. The supply unit can also supply energy using AI. The AI ​​creates an optimal energy supply plan based on weather data and sunshine hours. For example, on cloudy days or at night, it can supply energy using electricity stored in a battery. The AI ​​also analyzes energy consumption data and performs efficient energy management. For example, it monitors the bench's temperature control and lighting usage and adjusts the energy supply as needed. Furthermore, the supply unit can monitor the energy supply status in real time and respond quickly if an anomaly occurs. For example, it can detect solar panel failures or battery abnormalities and perform maintenance. The supply unit can also notify users of the energy supply status to encourage energy conservation. This enables the supply unit to achieve efficient and sustainable energy supply, thereby improving the reliability of the entire system and reducing its environmental impact.

[0035] The data collection unit can collect information on available benches in real time through a dedicated app. For example, the unit collects information such as the bench's location, the number of available seats, and the available time through the dedicated app. The data collection unit can also update the availability information in real time through the dedicated app. The data collection unit can also collect availability information through the dedicated app using AI, for example. This allows the data collection unit to collect information on available benches in real time.

[0036] The guidance unit can guide users to the nearest available bench based on the vacancy information collected by the data collection unit. For example, the guidance unit guides users to the nearest available bench based on the vacancy information collected by the data collection unit. For example, the guidance unit guides users to benches based on methods for measuring distance and checking for vacancies. The guidance unit can also guide users to the nearest available bench using AI, for example. This allows the guidance unit to guide users to the nearest available bench.

[0037] The adjustment unit can automatically adjust the bench temperature according to the weather and temperature. For example, the adjustment unit can automatically adjust the bench temperature using a temperature sensor. For example, the adjustment unit can raise the bench temperature on cold days and lower it on hot days. The adjustment unit can also automatically adjust the bench temperature using AI, for example. This allows the adjustment unit to automatically adjust the bench temperature according to the weather and temperature.

[0038] The service provider can connect with nearby bench users, and its generative AI can provide common topics of conversation. For example, when a user turns on communication mode through a dedicated app, the service provider connects with other users nearby, and its generative AI provides common topics of conversation. The service provider provides topics based on criteria for topic selection and how to conduct a conversation, for example. The service provider can also provide common topics using AI, for example. This allows the service provider to connect with nearby bench users, and its generative AI can provide common topics of conversation.

[0039] The supply unit can install solar panels and supply energy using sunlight. For example, the supply unit can install solar panels on a bench and supply energy using sunlight. The supply unit can supply energy based on, for example, the type and installation method of the solar panels. The supply unit can also supply energy using, for example, AI. This allows the supply unit to install solar panels and supply energy using sunlight.

[0040] The data collection unit can analyze the user's past bench usage history and select the optimal collection method. For example, the data collection unit can prioritize collecting information on benches that the user has frequently used in the past. For example, the data collection unit can focus on collecting information on benches that the user uses during specific time periods. For example, the data collection unit can select the optimal collection timing to avoid congestion based on the user's past usage history. In this way, the data collection unit can analyze the user's past bench usage history and select the optimal collection method.

[0041] The data collection unit can filter available seat information based on the user's current location and movement patterns. For example, the unit prioritizes collecting available seat information for benches closest to the user's current location. For example, the unit analyzes the user's movement patterns and collects available seat information for benches along predicted routes. For example, if the user is in a specific area, the unit focuses on collecting available seat information for benches within that area. This allows the data collection unit to filter available seat information based on the user's current location and movement patterns.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information when collecting vacancy information. For example, the data collection unit can collect information on vacant benches near locations where the user has checked in on social media. For example, the data collection unit can collect information on vacant benches near events that the user has shown interest in on social media. For example, the data collection unit can collect information on vacant benches used by friends the user follows on social media. In this way, the data collection unit can analyze the user's social media activity and collect relevant vacancy information.

[0043] The data collection unit can adjust the information it collects when gathering seat availability information, taking into account surrounding events. For example, the collection unit can prioritize collecting seat availability information for benches that are expected to be crowded, taking into account the impact of nearby events. For example, the collection unit can adjust the frequency of seat availability information collection to match the start time of events. For example, the collection unit can prioritize collecting seat availability information for specific benches depending on the type of event. This allows the collection unit to collect seat availability information while taking surrounding events into consideration.

[0044] The guidance unit can adjust the level of detail in the guidance based on how often the benches are used. For example, the guidance unit provides concise guidance for frequently used benches, and detailed guidance for less frequently used benches. The guidance unit also adjusts the display time of the guidance according to the frequency of use. This allows the guidance unit to adjust the level of detail in the guidance based on how often the benches are used.

[0045] The guidance system can apply different guidance algorithms depending on the bench category. For example, for benches in a park, the guidance system provides guidance including information about the scenery and surrounding facilities. For benches in front of a train station, the guidance system provides guidance including information about accessibility and surrounding transportation. For benches in a commercial facility, the guidance system provides guidance including information about nearby shops. This allows the guidance system to apply different guidance algorithms depending on the bench category.

[0046] The guidance system can determine the priority of guidance based on the location of the benches. For example, the guidance system will prioritize providing guidance to benches located in areas with many users. For example, the guidance system will postpone providing guidance to benches located in areas with few users. For example, the guidance system will consider the impact of an event when providing guidance to benches located in areas where a specific event is being held. In this way, the guidance system can determine the priority of guidance based on the location of the benches.

[0047] The guidance system can adjust the order of guidance when providing directions, taking into account the attribute information of the bench users. For example, the guidance system can prioritize guidance for benches used by the elderly. For example, the guidance system can provide guidance that prioritizes safety for benches frequently used by families with children. For example, the guidance system can provide guidance that prioritizes convenience for benches used by business people. In this way, the guidance system can adjust the order of guidance by taking into account the attribute information of the bench users.

[0048] The adjustment unit can select the optimal adjustment method by referring to past weather data when adjusting the temperature. For example, the adjustment unit sets the optimal temperature for each season based on past weather data. For example, the adjustment unit analyzes past weather data and selects the optimal temperature adjustment method for specific weather conditions. For example, the adjustment unit refers to past weather data and adjusts the temperature according to the predicted weather. In this way, the adjustment unit can select the optimal temperature adjustment method by referring to past weather data.

[0049] The adjustment unit can adjust the temperature while considering the user's attributes. For example, if an elderly person is using the bench, the adjustment unit will set the temperature slightly higher. If a child is using the bench, the adjustment unit will adjust the temperature within a safe range. If a businessman is using the bench, the adjustment unit will maintain a comfortable temperature. In this way, the adjustment unit can adjust the temperature while considering the user's attributes.

[0050] The adjustment unit can acquire and adjust ambient weather information in real time when adjusting the temperature. For example, the adjustment unit adjusts the bench temperature based on real-time temperature information. For example, the adjustment unit sets the optimal temperature considering real-time humidity information. For example, the adjustment unit adjusts the temperature considering the effect of wind based on real-time wind speed information. In this way, the adjustment unit can acquire ambient weather information in real time and adjust the temperature.

[0051] The adjustment unit can adjust the temperature while considering the characteristics of the bench's installation location. For example, the adjustment unit will set the temperature lower for benches installed in sunny locations. For example, the adjustment unit will set the temperature higher for benches installed in well-ventilated locations. For example, the adjustment unit will adjust the temperature to match the ambient temperature for benches installed in covered areas. In this way, the adjustment unit can adjust the temperature while considering the characteristics of the bench's installation location.

[0052] The topic provider can select the most suitable topic by referring to past conversation history when providing a topic. For example, the provider can prioritize providing topics that the user has shown interest in in the past. For example, the provider can provide topics of common interest from the user's past conversation history. For example, the provider can exclude topics that the user has avoided in the past. In this way, the provider can select the most suitable topic by referring to past conversation history.

[0053] The topic provider can offer topics while considering the user's attributes. For example, if the user is elderly, the provider can offer topics related to health and hobbies. If the user is a child, the provider can offer topics related to play and learning. If the user is a business person, the provider can offer topics related to work and business. In this way, the provider can offer topics while considering the user's attributes.

[0054] The content provider can provide topics while considering information about surrounding events. For example, the content provider can provide topics related to events held in the surrounding area. For example, the content provider can provide relevant topics in line with the start time of an event. For example, the content provider can prioritize providing specific topics depending on the type of event. In this way, the content provider can provide topics while considering information about surrounding events.

[0055] The content provider can provide topics based on the interests of the users on the benchmark when providing topics. For example, the content provider can provide topics related to topics that the user has shown interest in. For example, the content provider can provide topics that are likely to interest the user based on the user's past behavior history. For example, the content provider can provide topics related to topics on social media that the user follows. In this way, the content provider can provide topics based on the interests of the users on the benchmark.

[0056] The supply unit can optimize the installation angle of the solar panels and supply energy efficiently. For example, the supply unit adjusts the installation angle of the solar panels seasonally to maximize sunlight reception. For example, the supply unit adjusts the installation angle of the solar panels to match the amount of sunlight to supply energy efficiently. For example, the supply unit optimizes the installation angle of the solar panels considering the influence of surrounding buildings and trees. In this way, the supply unit can optimize the installation angle of the solar panels and supply energy efficiently.

[0057] The supply unit can collect maintenance information for solar panels in real time and perform maintenance at the optimal time. For example, the supply unit can detect dirt or damage to solar panels in real time and perform maintenance. For example, the supply unit can perform maintenance if the power generation efficiency of solar panels decreases. For example, the supply unit can set a regular maintenance schedule for solar panels and perform maintenance at the optimal time. As a result, the supply unit can collect maintenance information for solar panels in real time and perform maintenance at the optimal time.

[0058] The supply unit can monitor the power generation of the solar panels and adjust the energy supply as needed. For example, the supply unit can monitor the power generation of the solar panels in real time and adjust the energy supply. For example, if the power generation of the solar panels decreases, the supply unit can increase the supply from other energy sources. For example, if the power generation of the solar panels is excessive, the supply unit can store the surplus energy in a battery. This allows the supply unit to monitor the power generation of the solar panels and adjust the energy supply as needed.

[0059] The supply unit can optimize the placement of solar panels and supply energy efficiently. For example, the supply unit may install solar panels in sunny locations to maximize sunlight exposure. For example, the supply unit may install solar panels in well-ventilated locations to enhance cooling. For example, the supply unit may install solar panels in locations unaffected by surrounding buildings or trees. In this way, the supply unit can optimize the placement of solar panels and supply energy efficiently.

[0060] The supply unit can improve energy efficiency by sharing the power generated by solar panels with other public facilities. For example, the supply unit can share the power generated by solar panels with public facilities such as streetlights and surveillance cameras. For example, the supply unit can improve energy efficiency by sharing the power generated by solar panels with other facilities in a park. For example, the supply unit can efficiently supply energy by linking the power generated by solar panels with the local energy management system. In this way, the supply unit can improve energy efficiency by sharing the power generated by solar panels with other public facilities.

[0061] The supply unit can store the electricity generated by the solar panels in a battery and supply energy as needed. For example, the supply unit can store the electricity generated by the solar panels in a battery and supply energy at night or on cloudy days. For example, if the amount of electricity generated by the solar panels is excessive, the supply unit can store the surplus energy in the battery. For example, the supply unit can efficiently manage the energy in the battery and supply energy as needed. In this way, the supply unit can store the electricity generated by the solar panels in a battery and supply energy as needed.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can monitor the user's health status and adjust the frequency of collecting seat availability information according to that status. For example, if the user is tired, the unit will collect seat availability information frequently so that the user can rest immediately. If the user is healthy and active, the unit will reduce the frequency of collection and update the information only when necessary. If the user has a specific health problem, the unit will prioritize collecting information on benches that address that problem. In this way, the data collection unit can adjust the frequency of collecting seat availability information according to the user's health status.

[0064] The guidance unit can analyze the user's past travel history and provide the optimal route. For example, it can guide the user to the most efficient route based on routes the user has used in the past. The guidance unit can also consider routes the user has avoided in the past and suggest alternative routes. For example, it can analyze the user's travel patterns and guide the user to benches along the predicted route. In this way, the guidance unit can analyze the user's past travel history and provide the optimal route.

[0065] The adjustment unit can adjust the bench temperature while taking the surrounding noise environment into consideration. For example, if the surroundings are noisy, the adjustment unit can reduce user stress by maintaining a comfortable temperature. If the surroundings are quiet, for example, the adjustment unit can set the temperature slightly lower to provide a relaxing environment. The adjustment unit can change the method of temperature adjustment according to a specific noise environment, for example. This allows the adjustment unit to adjust the temperature while taking the surrounding noise environment into consideration.

[0066] The supply unit can monitor the power generation of the solar panels in real time and adjust the energy supply according to the amount of power generated. For example, if the amount of power generated decreases, the supply unit will increase the supply from other energy sources. If the amount of power generated is excessive, for example, the supply unit will store the surplus energy in a battery. If the amount of power generated falls below a certain standard, for example, the supply unit will take measures to reduce energy consumption. In this way, the supply unit can monitor the power generation of the solar panels in real time and adjust the energy supply according to the amount of power generated.

[0067] The supply unit can optimize the installation angle of the solar panels and supply energy efficiently. For example, it can adjust the installation angle of the solar panels seasonally to maximize sunlight reception. The supply unit can, for example, adjust the installation angle of the solar panels to match the amount of sunlight to supply energy efficiently. The supply unit can, for example, optimize the installation angle of the solar panels considering the influence of surrounding buildings and trees. In this way, the supply unit can optimize the installation angle of the solar panels and supply energy efficiently.

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

[0069] Step 1: The collection unit collects seat availability information. The collection unit can collect information on available benches in real time, for example, through a dedicated app. The collection unit collects information such as the bench's location, the number of available seats, and the available time. The collection unit can also collect seat availability information using AI, for example. Step 2: The guidance unit guides users to available benches based on the vacancy information collected by the data collection unit. For example, the guidance unit can guide users to the nearest available bench. The guidance unit can also guide users to benches based on methods such as distance measurement and seat availability confirmation. For example, the guidance unit can also use AI to guide users to available benches. Step 3: The adjustment unit adjusts the bench temperature according to the weather and temperature. The adjustment unit can, for example, automatically adjust the bench temperature using a temperature sensor. For example, the adjustment unit can raise the bench temperature on cold days and lower it on hot days. The adjustment unit can also adjust the bench temperature using, for example, AI. Step 4: The service provider connects with nearby bench users, and the generative AI supports conversations by providing topics. For example, when a user turns on communication mode through a dedicated app, the service provider connects with other users nearby, and the generative AI provides common topics. The service provider provides topics based on criteria for topic selection and how to proceed with the conversation, for example. The service provider can also provide topics using AI, for example. Step 5: The supply unit supplies energy using solar panels. The supply unit can, for example, install solar panels on a bench and supply energy using sunlight. The supply unit supplies energy based on, for example, the type and installation method of the solar panels. The supply unit can also supply energy using, for example, AI.

[0070] (Example of form 2) The bench usage system according to an embodiment of the present invention is a system that improves the convenience and comfort of using benches in public places. By combining the components of a seat availability guidance system, an automatic environmental adjustment function, a communication mode, and energy efficiency using solar panels, this bench usage system can improve user convenience and comfort, realize environmentally friendly public facilities, and revitalize interaction in public places. For example, the bench usage system can obtain information on available benches in real time through a dedicated app. For example, the bench usage system can check the availability of benches in a park and guide users to the nearest available bench. This system eliminates the need for users to walk around looking for an empty seat. Next, the bench usage system has a function to automatically adjust the temperature of the bench according to the weather and temperature. For example, it raises the bench temperature on cold days and lowers it on hot days to ensure users can spend their time comfortably. This function improves comfort in public places. Furthermore, the bench usage system has a function to connect with nearby bench users, and a generating AI supports conversations by providing topics. For example, when a user turns on the communication mode through the dedicated app, it connects with other users nearby, and the generating AI provides common topics. This feature stimulates interaction in public spaces and reduces feelings of isolation. Finally, the bench utilization system has the function of supplying energy using sunlight by installing solar panels on the benches. This makes the benches eco-friendly public facilities and enables environmentally friendly operation. In this way, the bench utilization system can improve user convenience and comfort, realize environmentally friendly public facilities, and stimulate interaction in public spaces.

[0071] The bench utilization system according to this embodiment comprises a collection unit, a guidance unit, an adjustment unit, a provision unit, and a supply unit. The collection unit collects vacancy information. The collection unit can collect information on available benches in real time, for example, through a dedicated app. The collection unit collects information such as the location of the bench, the number of available seats, and the available time. The collection unit can also collect vacancy information using AI, for example. The guidance unit guides users to available benches based on the vacancy information collected by the collection unit. The guidance unit can guide users to the nearest available bench, for example. The guidance unit guides users to benches based on methods for measuring distance and checking for available seats, for example. The guidance unit can also guide users to available benches using AI, for example. The adjustment unit adjusts the bench temperature according to the weather and temperature. The adjustment unit can automatically adjust the bench temperature using a temperature sensor, for example. The adjustment unit can raise the bench temperature on cold days and lower it on hot days, for example. The adjustment unit can also adjust the bench temperature using AI, for example. The supply unit connects with nearby bench users, and a generating AI supports conversations by providing topics. For example, when a user turns on communication mode via a dedicated app, the supply unit connects with other nearby users, and the generating AI provides common topics. The supply unit provides topics based on, for example, topic selection criteria and conversation progression methods. The supply unit can also provide topics using, for example, AI. The supply unit supplies energy using solar panels. For example, the supply unit can install solar panels on benches and supply energy using sunlight. The supply unit supplies energy based on, for example, the type and installation method of the solar panels. The supply unit can also supply energy using, for example, AI. As a result, the bench usage system according to this embodiment can collect vacancy information, provide guidance, adjust temperature, support conversations, and supply energy.

[0072] The data collection unit collects information on available seats. For example, the data collection unit can collect information on available benches in real time through a dedicated app. Specifically, the dedicated app receives data from sensors installed on the benches to determine whether there are available seats and their usage status. These sensors, such as pressure sensors and infrared sensors, detect whether someone is sitting on the bench. The data collection unit collects information such as the bench's location, the number of available seats, and the available time. This allows users to check the location of currently available benches and the available time slots through the app. The data collection unit can also collect information on available seats using AI. The AI ​​analyzes data from sensors and updates the bench usage status in real time. For example, the AI ​​can predict bench usage trends at specific times of day or under specific weather conditions based on past usage data, providing more accurate information on available seats. Furthermore, the data collection unit can store this data on a cloud server and link it with other systems and departments. For example, the collected data can be made accessible to the guidance and coordination departments, improving the overall efficiency of the system. In addition, the data collection unit can adjust the frequency and accuracy of data collection, enabling 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.

[0073] The guidance unit directs users to available benches based on vacancy information collected by the data collection unit. For example, the guidance unit can direct users to the nearest available bench. Specifically, it obtains the user's current location through a dedicated app and displays the nearest available bench. The guidance unit also directs users to benches based on methods such as distance measurement and seat availability confirmation. For distance measurement, it uses GPS or Wi-Fi location information, and for seat availability confirmation, it uses real-time data provided by the data collection unit. The guidance unit can also use AI to guide users to available benches. The AI ​​analyzes the user's current location and past usage history to recommend the most suitable bench. For example, the AI ​​can learn the location and time of benches the user has used in the past and prioritize benches that match the user's preferences. Furthermore, the guidance unit can collect user feedback and continuously improve the accuracy and effectiveness of its guidance. For example, it can collect user evaluations and usage status of the benches they are directed to as feedback, and the AI ​​learns from this to improve the accuracy of future guidance. In addition, the guidance unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through notifications from a dedicated app, but also through voice guidance and vibration notifications. This allows the guidance system to quickly and reliably direct users to available benches, improving convenience.

[0074] The adjustment unit adjusts the bench temperature according to the weather and ambient temperature. For example, the adjustment unit can automatically adjust the bench temperature using a temperature sensor. Specifically, a temperature sensor installed on the bench measures the ambient temperature and transmits this data to the adjustment unit. The adjustment unit can, for example, raise the bench temperature on cold days and lower it on hot days. This allows users to use the bench at a comfortable temperature. The adjustment unit can also adjust the bench temperature using AI. The AI ​​learns the optimal temperature setting based on past temperature data and user feedback and adjusts it automatically. For example, the AI ​​can predict the optimal temperature setting for a specific time of day or weather conditions and adjust the bench temperature in advance. Furthermore, the adjustment unit can also provide temperature settings according to the user's preferences. For example, if a user inputs their desired temperature through a dedicated app, the adjustment unit adjusts the bench to that temperature. In addition, the adjustment unit can adjust the temperature with the minimum necessary energy, taking energy efficiency into consideration. For example, it can adjust the bench temperature using energy supplied from solar panels. This allows the adjustment unit to efficiently and effectively adjust the bench temperature and provide users with a comfortable environment.

[0075] The service provider connects with nearby bench users, and a generative AI supports conversations by providing topics. For example, when a user turns on communication mode through a dedicated app, the service provider connects with other nearby users, and the generative AI provides common topics. Specifically, the generative AI selects topics of common interest based on the user's profile information and past conversation history. The service provider provides topics based on, for example, topic selection criteria and conversation flow methods. The generative AI uses natural language processing technology to facilitate smooth conversations between users. For example, the generative AI analyzes user statements and provides appropriate responses and additional topics. The generative AI can also switch topics at the appropriate time depending on the tone and content of the conversation. The service provider can also provide topics using AI. The AI ​​collects user feedback and continuously improves topic selection criteria and conversation flow methods. For example, it collects user evaluations of provided topics and the progress of conversations as feedback, and the AI ​​learns from this to improve the accuracy of topic provision in the future. Furthermore, the service provider will implement a system to anonymize conversation content and protect personal information in order to protect user privacy. This will enable the service provider to provide users with an environment where they can enjoy communicating with peace of mind.

[0076] The supply unit provides energy using solar panels. For example, the supply unit can install solar panels on a bench and use sunlight to supply energy. Specifically, solar panels can be installed on the backrest and seat of the bench to absorb sunlight during the day and generate electricity. The supply unit supplies energy based on the type and installation method of the solar panels. For example, it can use high-efficiency monocrystalline silicon solar panels and install them in accordance with the bench's design. The supply unit can also supply energy using AI. The AI ​​creates an optimal energy supply plan based on weather data and sunshine hours. For example, on cloudy days or at night, it can supply energy using electricity stored in a battery. The AI ​​also analyzes energy consumption data and performs efficient energy management. For example, it monitors the bench's temperature control and lighting usage and adjusts the energy supply as needed. Furthermore, the supply unit can monitor the energy supply status in real time and respond quickly if an anomaly occurs. For example, it can detect solar panel failures or battery abnormalities and perform maintenance. The supply unit can also notify users of the energy supply status to encourage energy conservation. This enables the supply unit to achieve efficient and sustainable energy supply, thereby improving the reliability of the entire system and reducing its environmental impact.

[0077] The data collection unit can collect information on available benches in real time through a dedicated app. For example, the unit collects information such as the bench's location, the number of available seats, and the available time through the dedicated app. The data collection unit can also update the availability information in real time through the dedicated app. The data collection unit can also collect availability information through the dedicated app using AI, for example. This allows the data collection unit to collect information on available benches in real time.

[0078] The guidance unit can guide users to the nearest available bench based on the vacancy information collected by the data collection unit. For example, the guidance unit guides users to the nearest available bench based on the vacancy information collected by the data collection unit. For example, the guidance unit guides users to benches based on methods for measuring distance and checking for vacancies. The guidance unit can also guide users to the nearest available bench using AI, for example. This allows the guidance unit to guide users to the nearest available bench.

[0079] The adjustment unit can automatically adjust the bench temperature according to the weather and temperature. For example, the adjustment unit can automatically adjust the bench temperature using a temperature sensor. For example, the adjustment unit can raise the bench temperature on cold days and lower it on hot days. The adjustment unit can also automatically adjust the bench temperature using AI, for example. This allows the adjustment unit to automatically adjust the bench temperature according to the weather and temperature.

[0080] The service provider can connect with nearby bench users, and its generative AI can provide common topics of conversation. For example, when a user turns on communication mode through a dedicated app, the service provider connects with other users nearby, and its generative AI provides common topics of conversation. The service provider provides topics based on criteria for topic selection and how to conduct a conversation, for example. The service provider can also provide common topics using AI, for example. This allows the service provider to connect with nearby bench users, and its generative AI can provide common topics of conversation.

[0081] The supply unit can install solar panels and supply energy using sunlight. For example, the supply unit can install solar panels on a bench and supply energy using sunlight. The supply unit can supply energy based on, for example, the type and installation method of the solar panels. The supply unit can also supply energy using, for example, AI. This allows the supply unit to install solar panels and supply energy using sunlight.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of collecting seat availability information based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect seat availability information frequently and update it in real time. For example, if the user is relaxed, the data collection unit will collect seat availability information at regular intervals and update it only when necessary. For example, if the user is in a hurry, the data collection unit will immediately collect seat availability information and guide the user to the most readily available bench. In this way, the data collection unit can adjust the timing of collecting seat availability information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The data collection unit can analyze the user's past bench usage history and select the optimal collection method. For example, the data collection unit can prioritize collecting information on benches that the user has frequently used in the past. For example, the data collection unit can focus on collecting information on benches that the user uses during specific time periods. For example, the data collection unit can select the optimal collection timing to avoid congestion based on the user's past usage history. In this way, the data collection unit can analyze the user's past bench usage history and select the optimal collection method.

[0084] The data collection unit can filter available seat information based on the user's current location and movement patterns. For example, the unit prioritizes collecting available seat information for benches closest to the user's current location. For example, the unit analyzes the user's movement patterns and collects available seat information for benches along predicted routes. For example, if the user is in a specific area, the unit focuses on collecting available seat information for benches within that area. This allows the data collection unit to filter available seat information based on the user's current location and movement patterns.

[0085] The data collection unit can estimate the user's emotions and determine the priority of vacancy information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information on the most available benches. If the user is relaxed, the data collection unit will prioritize collecting information on benches with good views. If the user is in a hurry, the data collection unit will prioritize collecting information on the nearest available bench. In this way, the data collection unit can determine the priority of vacancy information based on 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.

[0086] The data collection unit can analyze the user's social media activity and collect relevant information when collecting vacancy information. For example, the data collection unit can collect information on vacant benches near locations where the user has checked in on social media. For example, the data collection unit can collect information on vacant benches near events that the user has shown interest in on social media. For example, the data collection unit can collect information on vacant benches used by friends the user follows on social media. In this way, the data collection unit can analyze the user's social media activity and collect relevant vacancy information.

[0087] The data collection unit can adjust the information it collects when gathering seat availability information, taking into account surrounding events. For example, the collection unit can prioritize collecting seat availability information for benches that are expected to be crowded, taking into account the impact of nearby events. For example, the collection unit can adjust the frequency of seat availability information collection to match the start time of events. For example, the collection unit can prioritize collecting seat availability information for specific benches depending on the type of event. This allows the collection unit to collect seat availability information while taking surrounding events into consideration.

[0088] The guidance system can estimate the user's emotions and adjust the way it presents the guidance based on those emotions. For example, if the user is nervous, the guidance system will provide simple and easy-to-understand guidance. If the user is relaxed, the guidance system will provide detailed guidance. If the user is in a hurry, the guidance system will provide concise guidance that gets straight to the point. In this way, the guidance system can adjust the way it presents the guidance based on 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.

[0089] The guidance unit can adjust the level of detail in the guidance based on how often the benches are used. For example, the guidance unit provides concise guidance for frequently used benches, and detailed guidance for less frequently used benches. The guidance unit also adjusts the display time of the guidance according to the frequency of use. This allows the guidance unit to adjust the level of detail in the guidance based on how often the benches are used.

[0090] The guidance system can apply different guidance algorithms depending on the bench category. For example, for benches in a park, the guidance system provides guidance including information about the scenery and surrounding facilities. For benches in front of a train station, the guidance system provides guidance including information about accessibility and surrounding transportation. For benches in a commercial facility, the guidance system provides guidance including information about nearby shops. This allows the guidance system to apply different guidance algorithms depending on the bench category.

[0091] The guidance system can estimate the user's emotions and adjust the length of the guidance based on the estimated emotions. For example, if the user is in a hurry, the guidance system will provide a short, concise guide. For example, if the user is relaxed, the guidance system will provide a longer guide with detailed explanations. For example, if the user is excited, the guidance system will provide a guide with visually stimulating effects. In this way, the guidance system can adjust the length of the guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The guidance system can determine the priority of guidance based on the location of the benches. For example, the guidance system will prioritize providing guidance to benches located in areas with many users. For example, the guidance system will postpone providing guidance to benches located in areas with few users. For example, the guidance system will consider the impact of an event when providing guidance to benches located in areas where a specific event is being held. In this way, the guidance system can determine the priority of guidance based on the location of the benches.

[0093] The guidance system can adjust the order of guidance when providing directions, taking into account the attribute information of the bench users. For example, the guidance system can prioritize guidance for benches used by the elderly. For example, the guidance system can provide guidance that prioritizes safety for benches frequently used by families with children. For example, the guidance system can provide guidance that prioritizes convenience for benches used by business people. In this way, the guidance system can adjust the order of guidance by taking into account the attribute information of the bench users.

[0094] The adjustment unit can estimate the user's emotions and adjust the temperature control method based on the estimated emotions. For example, if the user is cold, the adjustment unit will raise the bench temperature. For example, if the user is hot, the adjustment unit will lower the bench temperature. For example, if the user is comfortable, the adjustment unit will maintain the current temperature. In this way, the adjustment unit can adjust the temperature control method based on 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.

[0095] The adjustment unit can select the optimal adjustment method by referring to past weather data when adjusting the temperature. For example, the adjustment unit sets the optimal temperature for each season based on past weather data. For example, the adjustment unit analyzes past weather data and selects the optimal temperature adjustment method for specific weather conditions. For example, the adjustment unit refers to past weather data and adjusts the temperature according to the predicted weather. In this way, the adjustment unit can select the optimal temperature adjustment method by referring to past weather data.

[0096] The adjustment unit can adjust the temperature while considering the user's attributes. For example, if an elderly person is using the bench, the adjustment unit will set the temperature slightly higher. If a child is using the bench, the adjustment unit will adjust the temperature within a safe range. If a businessman is using the bench, the adjustment unit will maintain a comfortable temperature. In this way, the adjustment unit can adjust the temperature while considering the user's attributes.

[0097] The adjustment unit can estimate the user's emotions and determine the priority of temperature adjustment based on the estimated emotions. For example, if the user is cold, the adjustment unit will prioritize raising the temperature. For example, if the user is hot, the adjustment unit will prioritize lowering the temperature. For example, if the user is comfortable, the adjustment unit will prioritize the temperature adjustments of other users. In this way, the adjustment unit can determine the priority of temperature adjustment based on 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.

[0098] The adjustment unit can acquire and adjust ambient weather information in real time when adjusting the temperature. For example, the adjustment unit adjusts the bench temperature based on real-time temperature information. For example, the adjustment unit sets the optimal temperature considering real-time humidity information. For example, the adjustment unit adjusts the temperature considering the effect of wind based on real-time wind speed information. In this way, the adjustment unit can acquire ambient weather information in real time and adjust the temperature.

[0099] The adjustment unit can adjust the temperature while considering the characteristics of the bench's installation location. For example, the adjustment unit will set the temperature lower for benches installed in sunny locations. For example, the adjustment unit will set the temperature higher for benches installed in well-ventilated locations. For example, the adjustment unit will adjust the temperature to match the ambient temperature for benches installed in covered areas. In this way, the adjustment unit can adjust the temperature while considering the characteristics of the bench's installation location.

[0100] The service provider can estimate the user's emotions and adjust the way it presents topics based on those emotions. For example, if the user is relaxed, the service provider will present topics at a relaxed pace. If the user is in a hurry, the service provider will present short, concise topics. If the user is excited, the service provider will present topics with visually stimulating effects. This allows the service provider to adjust its topic presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The topic provider can select the most suitable topic by referring to past conversation history when providing a topic. For example, the provider can prioritize providing topics that the user has shown interest in in the past. For example, the provider can provide topics of common interest from the user's past conversation history. For example, the provider can exclude topics that the user has avoided in the past. In this way, the provider can select the most suitable topic by referring to past conversation history.

[0102] The topic provider can offer topics while considering the user's attributes. For example, if the user is elderly, the provider can offer topics related to health and hobbies. If the user is a child, the provider can offer topics related to play and learning. If the user is a business person, the provider can offer topics related to work and business. In this way, the provider can offer topics while considering the user's attributes.

[0103] The service provider can estimate the user's emotions and determine the priority of topics based on those emotions. For example, if the user is relaxed, the service provider will prioritize relaxing topics. If the user is in a hurry, the service provider will prioritize short, concise topics. If the user is excited, the service provider will prioritize visually stimulating topics. In this way, the service provider can determine the priority of topics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The content provider can provide topics while considering information about surrounding events. For example, the content provider can provide topics related to events held in the surrounding area. For example, the content provider can provide relevant topics in line with the start time of an event. For example, the content provider can prioritize providing specific topics depending on the type of event. In this way, the content provider can provide topics while considering information about surrounding events.

[0105] The content provider can provide topics based on the interests of the users on the benchmark when providing topics. For example, the content provider can provide topics related to topics that the user has shown interest in. For example, the content provider can provide topics that are likely to interest the user based on the user's past behavior history. For example, the content provider can provide topics related to topics on social media that the user follows. In this way, the content provider can provide topics based on the interests of the users on the benchmark.

[0106] The supply unit can optimize the installation angle of the solar panels and supply energy efficiently. For example, the supply unit adjusts the installation angle of the solar panels seasonally to maximize sunlight reception. For example, the supply unit adjusts the installation angle of the solar panels to match the amount of sunlight to supply energy efficiently. For example, the supply unit optimizes the installation angle of the solar panels considering the influence of surrounding buildings and trees. In this way, the supply unit can optimize the installation angle of the solar panels and supply energy efficiently.

[0107] The supply unit can collect maintenance information for solar panels in real time and perform maintenance at the optimal time. For example, the supply unit can detect dirt or damage to solar panels in real time and perform maintenance. For example, the supply unit can perform maintenance if the power generation efficiency of solar panels decreases. For example, the supply unit can set a regular maintenance schedule for solar panels and perform maintenance at the optimal time. As a result, the supply unit can collect maintenance information for solar panels in real time and perform maintenance at the optimal time.

[0108] The supply unit can monitor the power generation of the solar panels and adjust the energy supply as needed. For example, the supply unit can monitor the power generation of the solar panels in real time and adjust the energy supply. For example, if the power generation of the solar panels decreases, the supply unit can increase the supply from other energy sources. For example, if the power generation of the solar panels is excessive, the supply unit can store the surplus energy in a battery. This allows the supply unit to monitor the power generation of the solar panels and adjust the energy supply as needed.

[0109] The supply unit can optimize the placement of solar panels and supply energy efficiently. For example, the supply unit may install solar panels in sunny locations to maximize sunlight exposure. For example, the supply unit may install solar panels in well-ventilated locations to enhance cooling. For example, the supply unit may install solar panels in locations unaffected by surrounding buildings or trees. In this way, the supply unit can optimize the placement of solar panels and supply energy efficiently.

[0110] The supply unit can improve energy efficiency by sharing the power generated by solar panels with other public facilities. For example, the supply unit can share the power generated by solar panels with public facilities such as streetlights and surveillance cameras. For example, the supply unit can improve energy efficiency by sharing the power generated by solar panels with other facilities in a park. For example, the supply unit can efficiently supply energy by linking the power generated by solar panels with the local energy management system. In this way, the supply unit can improve energy efficiency by sharing the power generated by solar panels with other public facilities.

[0111] The supply unit can store the electricity generated by the solar panels in a battery and supply energy as needed. For example, the supply unit can store the electricity generated by the solar panels in a battery and supply energy at night or on cloudy days. For example, if the amount of electricity generated by the solar panels is excessive, the supply unit can store the surplus energy in the battery. For example, the supply unit can efficiently manage the energy in the battery and supply energy as needed. In this way, the supply unit can store the electricity generated by the solar panels in a battery and supply energy as needed.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can monitor the user's health status and adjust the frequency of collecting seat availability information according to that status. For example, if the user is tired, the unit will collect seat availability information frequently so that the user can rest immediately. If the user is healthy and active, the unit will reduce the frequency of collection and update the information only when necessary. If the user has a specific health problem, the unit will prioritize collecting information on benches that address that problem. In this way, the data collection unit can adjust the frequency of collecting seat availability information according to the user's health status.

[0114] The guidance unit can analyze the user's past travel history and provide the optimal route. For example, it can guide the user to the most efficient route based on routes the user has used in the past. The guidance unit can also consider routes the user has avoided in the past and suggest alternative routes. For example, it can analyze the user's travel patterns and guide the user to benches along the predicted route. In this way, the guidance unit can analyze the user's past travel history and provide the optimal route.

[0115] The adjustment unit can adjust the bench temperature while taking the surrounding noise environment into consideration. For example, if the surroundings are noisy, the adjustment unit can reduce user stress by maintaining a comfortable temperature. If the surroundings are quiet, for example, the adjustment unit can set the temperature slightly lower to provide a relaxing environment. The adjustment unit can change the method of temperature adjustment according to a specific noise environment, for example. This allows the adjustment unit to adjust the temperature while taking the surrounding noise environment into consideration.

[0116] The service provider can estimate the user's emotions and adjust the timing of topic delivery based on those emotions. For example, if the user is relaxed, the service provider will deliver topics at a slow pace. If the user is in a hurry, the service provider will deliver short, concise topics. If the user is excited, the service provider will deliver topics with visually stimulating effects. In this way, the service provider can adjust the timing of topic delivery based on the user's emotions.

[0117] The supply unit can monitor the power generation of the solar panels in real time and adjust the energy supply according to the amount of power generated. For example, if the amount of power generated decreases, the supply unit will increase the supply from other energy sources. If the amount of power generated is excessive, for example, the supply unit will store the surplus energy in a battery. If the amount of power generated falls below a certain standard, for example, the supply unit will take measures to reduce energy consumption. In this way, the supply unit can monitor the power generation of the solar panels in real time and adjust the energy supply according to the amount of power generated.

[0118] The data collection unit can estimate the user's emotions and adjust how it collects seat availability information based on those emotions. For example, if the user is stressed, the unit collects seat availability information frequently and updates it in real time. If the user is relaxed, for example, the unit collects seat availability information at regular intervals and updates it only when necessary. If the user is in a hurry, for example, the unit immediately collects seat availability information and guides them to the most readily available bench. In this way, the data collection unit can adjust how it collects seat availability information based on the user's emotions.

[0119] The guidance system can estimate the user's emotions and adjust the way it presents the guidance based on those emotions. For example, if the user is nervous, it provides simple and easy-to-understand guidance. If the user is relaxed, it provides detailed guidance. If the user is in a hurry, it provides concise guidance that gets straight to the point. In this way, the guidance system can adjust the way it presents the guidance based on the user's emotions.

[0120] The adjustment unit can estimate the user's emotions and adjust the temperature control method based on the estimated emotions. For example, if the user is feeling cold, the adjustment unit will raise the bench temperature. If the user is feeling hot, the adjustment unit will lower the bench temperature. If the user is feeling comfortable, the adjustment unit will maintain the current temperature. In this way, the adjustment unit can adjust the temperature control method based on the user's emotions.

[0121] The service provider can estimate the user's emotions and determine the priority of topics based on those emotions. For example, if the user is relaxed, it will prioritize relaxing topics. If the user is in a hurry, it will prioritize short, concise topics. If the user is excited, it will prioritize visually stimulating topics. In this way, the service provider can determine the priority of topics based on the user's emotions.

[0122] The supply unit can optimize the installation angle of the solar panels and supply energy efficiently. For example, it can adjust the installation angle of the solar panels seasonally to maximize sunlight reception. The supply unit can, for example, adjust the installation angle of the solar panels to match the amount of sunlight to supply energy efficiently. The supply unit can, for example, optimize the installation angle of the solar panels considering the influence of surrounding buildings and trees. In this way, the supply unit can optimize the installation angle of the solar panels and supply energy efficiently.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects seat availability information. The collection unit can collect information on available benches in real time, for example, through a dedicated app. The collection unit collects information such as the bench's location, the number of available seats, and the available time. The collection unit can also collect seat availability information using AI, for example. Step 2: The guidance unit guides users to available benches based on the vacancy information collected by the data collection unit. For example, the guidance unit can guide users to the nearest available bench. The guidance unit can also guide users to benches based on methods such as distance measurement and seat availability confirmation. For example, the guidance unit can also use AI to guide users to available benches. Step 3: The adjustment unit adjusts the bench temperature according to the weather and temperature. The adjustment unit can, for example, automatically adjust the bench temperature using a temperature sensor. For example, the adjustment unit can raise the bench temperature on cold days and lower it on hot days. The adjustment unit can also adjust the bench temperature using, for example, AI. Step 4: The service provider connects with nearby bench users, and the generative AI supports conversations by providing topics. For example, when a user turns on communication mode through a dedicated app, the service provider connects with other users nearby, and the generative AI provides common topics. The service provider provides topics based on criteria for topic selection and how to proceed with the conversation, for example. The service provider can also provide topics using AI, for example. Step 5: The supply unit supplies energy using solar panels. The supply unit can, for example, install solar panels on a bench and supply energy using sunlight. The supply unit supplies energy based on, for example, the type and installation method of the solar panels. The supply unit can also supply energy using, for example, AI.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, guidance unit, adjustment unit, provision unit, and supply unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects vacancy information using the camera 42 and communication I / F 44 of the smart device 14 and processes it with the control unit 46A. The guidance unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and guides users to the optimal bench based on the collected vacancy information. The adjustment unit adjusts the bench temperature using, for example, the temperature sensor of the smart device 14 and the control unit 46A. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and a generating AI provides topics to support conversation. The supply unit supplies energy using, for example, a solar panel installed on the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, guidance unit, adjustment unit, provision unit, and supply unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects vacancy information using the camera 42 and communication I / F 44 of the smart glasses 214 and processes it with the control unit 46A. The guidance unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and guides the user to the optimal bench based on the collected vacancy information. The adjustment unit adjusts the bench temperature using, for example, the temperature sensor of the smart glasses 214 and the control unit 46A. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and a generating AI provides topics to support the conversation. The supply unit supplies energy using, for example, a solar panel installed on the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, guidance unit, adjustment unit, provision unit, and supply unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects vacancy information using the camera 42 and communication I / F 44 of the headset terminal 314 and processes it with the control unit 46A. The guidance unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and guides users to the optimal bench based on the collected vacancy information. The adjustment unit adjusts the bench temperature using, for example, the temperature sensor of the headset terminal 314 and the control unit 46A. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and a generating AI provides topics to support the conversation. The supply unit supplies energy using, for example, a solar panel installed on the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, guidance unit, adjustment unit, offering unit, and supply unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects vacancy information using the camera 42 and communication I / F 44 of the robot 414 and processes it with the control unit 46A. The guidance unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and guides the user to the optimal bench based on the collected vacancy information. The adjustment unit adjusts the bench temperature using, for example, the temperature sensor of the robot 414 and the control unit 46A. The offering unit is implemented, for example, by the control unit 46A of the robot 414, and a generating AI provides topics to support the conversation. The supply unit supplies energy using, for example, a solar panel installed on the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection department that collects information on available seats, A guidance unit that guides users to available benches based on the vacancy information collected by the aforementioned collection unit, A control unit that adjusts the bench temperature according to the weather and temperature, The service department connects with nearby bench users, and AI supports conversations by providing topics. It comprises a power supply unit that supplies energy using solar panels. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects information on available benches in real time through a dedicated app. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guide section is Based on the vacancy information collected by the aforementioned collection unit, the system guides the user to the nearest available bench. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, The bench temperature is automatically adjusted according to the weather and temperature. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Connect with nearby bench users, and AI will provide common topics of conversation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is Install solar panels and use sunlight to supply energy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting seat availability information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past benchmark usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting seat availability information, filtering is performed based on the user's current location and movement patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the collection of available seating information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information on available seats, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information on available seats, we adjust the information collected by taking into account information on nearby events. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned guide section is The system estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned guide section is When providing directions, adjust the level of detail based on how often the benches are used. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned guide section is When providing guidance, different guidance algorithms are applied depending on the bench category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is It estimates the user's emotions and adjusts the length of the guidance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is When providing directions, the priority of the directions will be determined based on the location of the benches. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is When providing guidance, the order of the guidance is adjusted considering the attribute information of the bench users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, It estimates the user's emotions and adjusts the temperature control method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, When adjusting the temperature, the optimal adjustment method is selected by referring to past weather data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, When adjusting the temperature, the user attribute information of the test bench is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, It estimates the user's emotions and determines the priority of temperature adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, When adjusting the temperature, the system acquires and adjusts based on real-time information about the surrounding weather. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When adjusting the temperature, take into account the characteristics of the bench's location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way topics are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When introducing a topic, the system selects the most appropriate topic by referring to past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When introducing a topic, consider the attribute information of the benchmark users. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of topics to discuss based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When introducing a topic, consider information about nearby events. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When introducing a topic, provide topics based on the interests of the benchmark users. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is Optimize the installation angle of solar panels to efficiently supply energy. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is We collect maintenance information for solar panels in real time and perform maintenance at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is Monitor the power generation from solar panels and adjust the energy supply as needed. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is Optimize the placement of solar panels to efficiently supply energy. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is Sharing the power generated by solar panels with other public utilities improves energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is The electricity generated by solar panels is stored in a battery, and energy is supplied as needed. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on available seats, A guidance unit that guides users to available benches based on the vacancy information collected by the aforementioned collection unit, A control unit that adjusts the bench temperature according to the weather and temperature, The service department connects with nearby bench users, and AI supports conversations by providing topics. It comprises a power supply unit that supplies energy using solar panels. A system characterized by the following features.

2. The aforementioned collection unit is The system collects information on available benches in real time through a dedicated app. The system according to feature 1.

3. The aforementioned guide section is Based on the vacancy information collected by the aforementioned collection unit, the system guides the user to the nearest available bench. The system according to feature 1.

4. The adjustment unit is, The bench temperature is automatically adjusted according to the weather and temperature. The system according to feature 1.

5. The aforementioned supply unit is, Connect with nearby bench users, and AI will provide common topics of conversation. The system according to feature 1.

6. The aforementioned supply unit is Install solar panels and use sunlight to supply energy. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting seat availability information based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past benchmark usage history and select the optimal data collection method. The system according to feature 1.

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