system

Abstract

The system according to this embodiment aims to grasp the congestion status of a bicycle parking area in real time and provide this information to the user. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data from a sensor or camera. The analysis unit analyzes the data collected by the collection unit and determines the congestion status of the bicycle parking area. The provision unit provides the congestion status determined by the analysis unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to grasp the congestion situation of a bicycle parking lot in real time, and there is a problem that the problem of insufficient bicycle parking lots has not been solved.

[0005] The system according to the embodiment aims to grasp the congestion situation of a bicycle parking lot in real time and provide it to a user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data from a sensor or a camera. The analysis unit analyzes the data collected by the collection unit and determines the congestion situation of the bicycle parking lot. The provision unit provides the congestion situation determined by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the congestion status of a bicycle parking area in real time and provide this information to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52. [[ID=二十]]

[0020] [[ID=二十一]] 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 bicycle parking lot congestion status confirmation system according to an embodiment of the present invention is an application system that allows users to check the congestion status of a bicycle parking lot in real time. This system collects data from sensors and cameras installed in the bicycle parking lot and uses AI to analyze it, providing the user with the current congestion status of the bicycle parking lot. Users can check the availability of the nearest bicycle parking lot through the app and use the parking lot efficiently. For example, a user can check the availability of a bicycle parking lot on their way to their destination and choose another parking lot if it is crowded. This mechanism is expected to improve the efficiency of bicycle parking lot use and alleviate the shortage of bicycle parking lots. First, a collection unit is provided to collect data from sensors and cameras installed in the bicycle parking lot. Next, an analysis unit is provided to analyze the collected data and determine the congestion status of the bicycle parking lot. Furthermore, a provision unit is provided to provide the analysis results to the user. These components are interconnected and constitute a system that provides the congestion status of bicycle parking lots in real time. Specifically, the collection unit collects data from sensors and cameras at a certain frequency and transmits it to the analysis unit. The analysis unit uses AI to analyze the data and determine the congestion status. The provision unit provides the analysis results to the user. Users can check the availability of the nearest bicycle parking area through the app and use the parking area efficiently. For example, a user can check the availability of a parking area on their way to their destination and choose a different parking area if it is crowded. This system is expected to improve the efficiency of bicycle parking area use and alleviate the shortage of parking spaces. In this way, the bicycle parking area congestion check system aims to alleviate the shortage of parking spaces by providing real-time information on the congestion status of parking areas.

[0029] The bicycle parking area congestion status confirmation system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data from sensors or cameras. The collection unit collects data from sensors or cameras installed in the bicycle parking area at a certain frequency and transmits it to the analysis unit. Data from sensors includes, for example, the number of available spaces and the number of parked bicycles in the bicycle parking area. Data from cameras includes, for example, image data of the bicycle parking area. The collection unit can collect this data in real time and transmit it to the analysis unit. The analysis unit analyzes the data collected by the collection unit using AI to determine the congestion status of the bicycle parking area. The analysis unit analyzes the data using, for example, deep learning or machine learning algorithms. The analysis unit can determine the congestion status of the bicycle parking area based on information such as the percentage of available spaces and the number of parked bicycles. The provision unit provides the congestion status determined by the analysis unit to the user. The provision unit can provide the analysis results to the user, for example, through a smartphone app or website. The provision unit can also send the analysis results to the user as a notification message. This allows users to check the availability of bicycle parking spaces in real time and use them efficiently. By providing real-time information on parking space congestion, the bicycle parking congestion check system aims to alleviate the shortage of bicycle parking spaces.

[0030] The data collection unit can collect data from sensors or cameras at a fixed frequency. For example, the data collection unit can collect data from sensors or cameras every minute. The data collection unit can also collect data at different frequencies, such as every hour or every day. For example, the data collection unit can adjust the frequency of data collection according to the congestion level of the bicycle parking area. The data collection unit can increase the frequency of data collection during times when congestion is expected and decrease the frequency of data collection during times when congestion is low. This allows the data collection unit to perform stable data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from sensors or cameras into AI and have the AI ​​adjust the frequency of data collection. This allows the data collection unit to perform stable data collection.

[0031] The analysis unit can analyze the collected data using AI to determine the congestion level of the bicycle parking area. The analysis unit can analyze the data using, for example, deep learning or machine learning algorithms. The analysis unit can determine the congestion level of the bicycle parking area based on information such as the percentage of available spaces and the number of bicycles parked. For example, the analysis unit can use deep learning to analyze image data of the bicycle parking area and determine the number of available spaces. The analysis unit can also use machine learning algorithms to analyze data from sensors and determine the congestion level of the bicycle parking area. In this way, the analysis unit can improve the accuracy of congestion level determination by using AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the congestion level determination. In this way, the analysis unit can improve the accuracy of congestion level determination by using AI.

[0032] The service provider can provide the analysis results to the user. The service provider can provide the analysis results to the user, for example, through a smartphone app or a website. The service provider can also send the analysis results to the user as a notification message. For example, the service provider can provide the user with real-time information on the availability of bicycle parking spaces through a smartphone app. The service provider can also provide the user with information on the availability of bicycle parking spaces through a website. Furthermore, the service provider can send the analysis results to the user as a notification message, allowing the user to check the availability of bicycle parking spaces. In this way, the service provider can enable the user to check the availability of bicycle parking spaces by providing the user with the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​select the information to provide to the user. In this way, the service provider can enable the user to check the availability of bicycle parking spaces by providing the user with the analysis results.

[0033] The service provider allows users to check the availability of bicycle parking spaces on their way to their destination and select an alternative parking space if the current one is crowded. For example, the service provider can enable users to check the availability of bicycle parking spaces on their way to their destination through a navigation app. The service provider can also provide users with real-time notifications regarding the availability of bicycle parking spaces. This allows the service provider to enable users to check the availability of bicycle parking spaces on their way to their destination and utilize them efficiently. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's current location information into the AI ​​and have the AI ​​select the optimal parking space. This allows the service provider to enable users to check the availability of bicycle parking spaces on their way to their destination and utilize them efficiently.

[0034] The data collection unit can analyze the usage patterns of bicycle parking lots and increase the frequency of data collection during peak times. For example, the data collection unit can analyze the usage patterns of bicycle parking lots and increase the frequency of data collection during peak times. The data collection unit can increase the frequency of data collection during weekday morning and evening commute hours. The data collection unit can also increase the frequency of data collection at bicycle parking lots around shopping malls on weekends. Furthermore, the data collection unit can increase the frequency of data collection at nearby bicycle parking lots when events are held. In this way, the data collection unit can improve the accuracy of understanding congestion by increasing the frequency of data collection during peak times. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input bicycle parking lot usage pattern data into AI and have the AI ​​adjust the frequency of data collection. In this way, the data collection unit can improve the accuracy of understanding congestion by increasing the frequency of data collection during peak times.

[0035] The data collection unit can adjust the placement of sensors or cameras to improve the accuracy of data collection. For example, the data collection unit can install sensors at the entrance and exit of a bicycle parking area to accurately count the number of bicycles entering and exiting. It can also install cameras in the center of the bicycle parking area to understand the overall congestion level. Furthermore, the data collection unit can distribute sensors throughout each area of ​​the bicycle parking area to collect detailed congestion information for each area. In this way, the data collection unit can improve the accuracy of data collection by optimizing the placement of sensors and cameras. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input sensor and camera placement data into AI and have the AI ​​select the optimal placement. In this way, the data collection unit can improve the accuracy of data collection by optimizing the placement of sensors and cameras.

[0036] The data collection unit can predict the usage status of bicycle parking lots by taking weather information into consideration when collecting data. For example, the data collection unit can predict the usage status of bicycle parking lots by taking weather information into consideration when collecting data. When it rains, the data collection unit can prioritize collecting data on the usage status of covered bicycle parking lots. When it is sunny, the data collection unit can collect data on the usage status of all bicycle parking lots evenly. Furthermore, when it snows, the data collection unit can collect data on the usage status of bicycle parking lots along less slippery routes. In this way, the data collection unit can improve the accuracy of predicting the usage status of bicycle parking lots by taking weather information into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input weather information data into AI and have the AI ​​perform predictions of the usage status of bicycle parking lots. In this way, the data collection unit can improve the accuracy of predicting the usage status of bicycle parking lots by taking weather information into consideration.

[0037] The data collection unit can predict parking lot congestion by considering surrounding traffic conditions when collecting data. For example, the data collection unit can predict parking lot congestion by considering surrounding traffic conditions when collecting data. The data collection unit can collect data considering the possibility of increased parking lot usage when traffic congestion occurs. The data collection unit can also predict parking lot usage by considering the operation status of public transportation. Furthermore, the data collection unit can collect information on the usage status of parking lots on detour routes based on surrounding road construction information. In this way, the data collection unit can improve the accuracy of parking lot congestion predictions by considering surrounding traffic conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input traffic condition data into AI and have the AI ​​perform parking lot congestion predictions. In this way, the data collection unit can improve the accuracy of parking lot congestion predictions by considering surrounding traffic conditions.

[0038] The analysis unit can improve its analysis accuracy by learning congestion patterns in bicycle parking lots by referring to past data. For example, the analysis unit can learn congestion patterns during specific time periods based on past data. It can also learn congestion patterns on specific days of the week based on past data. Furthermore, it can learn congestion patterns during specific events based on past data. In this way, the analysis unit can improve its analysis accuracy by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into AI and have the AI ​​learn congestion patterns. In this way, the analysis unit can improve its analysis accuracy by referring to past data.

[0039] The analysis unit can analyze congestion levels by considering the user attributes of the bicycle parking area. For example, the analysis unit can analyze congestion levels by considering the age group of the users of the bicycle parking area. It can also analyze congestion levels by considering the gender of the users of the bicycle parking area. Furthermore, the analysis unit can analyze congestion levels by considering the purpose of use of the bicycle parking area (commuting, shopping, etc.). In this way, the analysis unit can improve the accuracy of its congestion analysis by considering the user attributes of the bicycle parking area. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user attribute data into AI and have the AI ​​perform the congestion analysis. In this way, the analysis unit can improve the accuracy of its congestion analysis by considering the user attributes of the bicycle parking area.

[0040] The analysis unit can predict congestion by considering surrounding event information during analysis. For example, the analysis unit can predict congestion in the bicycle parking area if a large-scale event is held nearby. The analysis unit can also predict congestion in the bicycle parking area by considering event information at nearby schools. Furthermore, the analysis unit can predict congestion in the bicycle parking area based on sales information from nearby commercial facilities. In this way, the analysis unit can improve the accuracy of congestion prediction by considering surrounding event information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input event information data into AI and have the AI ​​perform bicycle parking area congestion prediction. In this way, the analysis unit can improve the accuracy of congestion prediction by considering surrounding event information.

[0041] The analysis unit can predict congestion by considering the facilities of the bicycle parking area during analysis. For example, the analysis unit can predict congestion by considering the facilities of the bicycle parking area (e.g., covered, equipped with surveillance cameras). The analysis unit can also predict congestion based on the number of bicycles that can be accommodated in the bicycle parking area. Furthermore, the analysis unit can predict congestion by considering the location conditions of the bicycle parking area (e.g., near a train station, near a commercial facility). In this way, the analysis unit can improve the accuracy of congestion prediction by considering the facilities of the bicycle parking area. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input facility status data into AI and have the AI ​​perform bicycle parking area congestion prediction. In this way, the analysis unit can improve the accuracy of congestion prediction by considering the facilities of the bicycle parking area.

[0042] The service provider can provide optimal information by referring to the user's past usage history at the time of provision. For example, the service provider can prioritize providing information on bicycle parking lots that the user has used in the past. It can also provide information on bicycle parking lots that are less crowded based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and provide information on the most efficient bicycle parking lots. In this way, the service provider can provide more appropriate information by referring to past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history data into AI and have the AI ​​select the optimal information. In this way, the service provider can provide more appropriate information by referring to past usage history.

[0043] The service provider can suggest the most suitable bicycle parking area by considering the user's current location information at the time of service provision. For example, the service provider can provide information on the bicycle parking area closest to the user's current location. It can also suggest the most suitable bicycle parking area by considering the distance from the user's current location. Furthermore, the service provider can provide information on bicycle parking areas that are easily accessible from the user's current location. In this way, the service provider can suggest the most suitable bicycle parking area to the user by considering the user's current location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input current location data into AI and have the AI ​​select the most suitable bicycle parking area. In this way, the service provider can suggest the most suitable bicycle parking area to the user by considering the user's current location information.

[0044] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the user with the optimal display method by considering device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information data into AI and have the AI ​​select the optimal display method. In this way, the service provider can provide the user with the optimal display method by considering device information.

[0045] The service provider can suggest the most suitable parking location by considering the user's schedule information at the time of service provision. For example, the service provider can suggest the most suitable parking location based on the user's schedule. Furthermore, the service provider can suggest parking locations related to specific events based on the user's schedule information. In addition, the service provider can suggest the most suitable route based on the user's schedule information. Thus, by considering the schedule information, the service provider can suggest the most suitable parking location to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input schedule information data into AI and have the AI ​​select the most suitable parking location. Thus, by considering the schedule information, the service provider can suggest the most suitable parking location to the user.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The data collection unit can collect user attribute data for bicycle parking areas and transmit it to the analysis unit. For example, the data collection unit can collect user age groups, genders, and purposes of use (commuting, shopping, etc.). This allows the analysis unit to analyze the congestion status of bicycle parking areas with greater accuracy based on user attributes. Furthermore, the data collection unit can collect user attribute data in real time and transmit it to the analysis unit. This allows the analysis unit to analyze congestion status based on the latest user attribute data. In addition, the data collection unit can collect user attribute data anonymized. This allows for the collection of user attribute data while protecting privacy.

[0048] The service provider can suggest the most suitable parking location based on the user's past usage history. For example, the service provider can collect data on parking locations previously used by the user and transmit it to the analysis unit. This allows the analysis unit to suggest the most suitable parking location based on the user's past usage history. The service provider can also suggest parking locations that avoid congestion based on the user's past usage history. Furthermore, the service provider can suggest the most efficient parking location based on the user's past usage history. In short, the service provider can suggest the most suitable parking location based on the user's past usage history.

[0049] The data collection unit can analyze parking lot usage patterns and increase the frequency of data collection during peak times. For example, it can increase the frequency of data collection during weekday morning and evening commute hours. It can also increase the frequency of data collection at parking lots around shopping malls on weekends. Furthermore, it can increase the frequency of data collection at nearby parking lots when events are being held. By increasing the frequency of data collection during peak times, the data collection unit can improve the accuracy of understanding congestion levels.

[0050] The analysis unit can improve its analysis accuracy by learning congestion patterns in bicycle parking lots by referring to past data. For example, the analysis unit can learn congestion patterns during specific time periods based on past data. It can also learn congestion patterns on specific days of the week based on past data. Furthermore, it can learn congestion patterns during specific events based on past data. In this way, the analysis unit can improve its analysis accuracy by referring to past data.

[0051] The data collection unit can predict the usage status of bicycle parking lots by taking weather information into account during data collection. For example, during rainy weather, the data collection unit can prioritize collecting data on the usage status of covered bicycle parking lots. During sunny weather, the data collection unit can collect data on the usage status of all bicycle parking lots evenly. Furthermore, on snowy days, the data collection unit can collect data on the usage status of bicycle parking lots along less slippery routes. In this way, the data collection unit can improve the accuracy of predicting bicycle parking lot usage status by taking weather information into account.

[0052] The service provider can suggest the most suitable parking location for the user, taking into account the user's schedule information. For example, the service provider can suggest the most suitable parking location based on the user's schedule. Furthermore, the service provider can suggest parking locations related to specific events based on the user's schedule information. In addition, the service provider can suggest the most suitable route based on the user's schedule information. Thus, by considering the user's schedule information, the service provider can suggest the most suitable parking location for the user.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The data collection unit collects data from sensors or cameras. For example, the data collection unit collects data from sensors or cameras installed in a bicycle parking area at a certain frequency and transmits it to the analysis unit. Data from sensors includes the number of available spaces and the number of bicycles parked in the bicycle parking area, while data from cameras includes image data of the bicycle parking area. The data collection unit can collect this data in real time and transmit it to the analysis unit. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit and determine the congestion level of the bicycle parking area. The analysis unit uses deep learning and machine learning algorithms to analyze the data and determines the congestion level based on information such as the percentage of available spaces and the number of bicycles parked in the bicycle parking area. Step 3: The service provider provides users with the congestion status determined by the analysis unit. The service provider can also provide the analysis results to users via a smartphone app or website, and can send them to users as notification messages. This allows users to check the availability of bicycle parking spaces in real time and use the parking spaces efficiently.

[0055] (Example of form 2) The bicycle parking lot congestion status confirmation system according to an embodiment of the present invention is an application system that allows users to check the congestion status of a bicycle parking lot in real time. This system collects data from sensors and cameras installed in the bicycle parking lot and uses AI to analyze it, providing the user with the current congestion status of the bicycle parking lot. Users can check the availability of the nearest bicycle parking lot through the app and use the parking lot efficiently. For example, a user can check the availability of a bicycle parking lot on their way to their destination and choose another parking lot if it is crowded. This mechanism is expected to improve the efficiency of bicycle parking lot use and alleviate the shortage of bicycle parking lots. First, a collection unit is provided to collect data from sensors and cameras installed in the bicycle parking lot. Next, an analysis unit is provided to analyze the collected data and determine the congestion status of the bicycle parking lot. Furthermore, a provision unit is provided to provide the analysis results to the user. These components are interconnected and constitute a system that provides the congestion status of bicycle parking lots in real time. Specifically, the collection unit collects data from sensors and cameras at a certain frequency and transmits it to the analysis unit. The analysis unit uses AI to analyze the data and determine the congestion status. The provision unit provides the analysis results to the user. Users can check the availability of the nearest bicycle parking area through the app and use the parking area efficiently. For example, a user can check the availability of a parking area on their way to their destination and choose a different parking area if it is crowded. This system is expected to improve the efficiency of bicycle parking area use and alleviate the shortage of parking spaces. In this way, the bicycle parking area congestion check system aims to alleviate the shortage of parking spaces by providing real-time information on the congestion status of parking areas.

[0056] The bicycle parking area congestion status confirmation system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data from sensors or cameras. The collection unit collects data from sensors or cameras installed in the bicycle parking area at a certain frequency and transmits it to the analysis unit. Data from sensors includes, for example, the number of available spaces and the number of parked bicycles in the bicycle parking area. Data from cameras includes, for example, image data of the bicycle parking area. The collection unit can collect this data in real time and transmit it to the analysis unit. The analysis unit analyzes the data collected by the collection unit using AI to determine the congestion status of the bicycle parking area. The analysis unit analyzes the data using, for example, deep learning or machine learning algorithms. The analysis unit can determine the congestion status of the bicycle parking area based on information such as the percentage of available spaces and the number of parked bicycles. The provision unit provides the congestion status determined by the analysis unit to the user. The provision unit can provide the analysis results to the user, for example, through a smartphone app or website. The provision unit can also send the analysis results to the user as a notification message. This allows users to check the availability of bicycle parking spaces in real time and use them efficiently. By providing real-time information on parking space congestion, the bicycle parking congestion check system aims to alleviate the shortage of bicycle parking spaces.

[0057] The data collection unit can collect data from sensors or cameras at a fixed frequency. For example, the data collection unit can collect data from sensors or cameras every minute. The data collection unit can also collect data at different frequencies, such as every hour or every day. For example, the data collection unit can adjust the frequency of data collection according to the congestion level of the bicycle parking area. The data collection unit can increase the frequency of data collection during times when congestion is expected and decrease the frequency of data collection during times when congestion is low. This allows the data collection unit to perform stable data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from sensors or cameras into AI and have the AI ​​adjust the frequency of data collection. This allows the data collection unit to perform stable data collection.

[0058] The analysis unit can analyze the collected data using AI to determine the congestion level of the bicycle parking area. The analysis unit can analyze the data using, for example, deep learning or machine learning algorithms. The analysis unit can determine the congestion level of the bicycle parking area based on information such as the percentage of available spaces and the number of bicycles parked. For example, the analysis unit can use deep learning to analyze image data of the bicycle parking area and determine the number of available spaces. The analysis unit can also use machine learning algorithms to analyze data from sensors and determine the congestion level of the bicycle parking area. In this way, the analysis unit can improve the accuracy of congestion level determination by using AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the congestion level determination. In this way, the analysis unit can improve the accuracy of congestion level determination by using AI.

[0059] The service provider can provide the analysis results to the user. The service provider can provide the analysis results to the user, for example, through a smartphone app or a website. The service provider can also send the analysis results to the user as a notification message. For example, the service provider can provide the user with real-time information on the availability of bicycle parking spaces through a smartphone app. The service provider can also provide the user with information on the availability of bicycle parking spaces through a website. Furthermore, the service provider can send the analysis results to the user as a notification message, allowing the user to check the availability of bicycle parking spaces. In this way, the service provider can enable the user to check the availability of bicycle parking spaces by providing the user with the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​select the information to provide to the user. In this way, the service provider can enable the user to check the availability of bicycle parking spaces by providing the user with the analysis results.

[0060] The service provider allows users to check the availability of bicycle parking spaces on their way to their destination and select an alternative parking space if the current one is crowded. For example, the service provider can enable users to check the availability of bicycle parking spaces on their way to their destination through a navigation app. The service provider can also provide users with real-time notifications regarding the availability of bicycle parking spaces. This allows the service provider to enable users to check the availability of bicycle parking spaces on their way to their destination and utilize them efficiently. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's current location information into the AI ​​and have the AI ​​select the optimal parking space. This allows the service provider to enable users to check the availability of bicycle parking spaces on their way to their destination and utilize them efficiently.

[0061] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The data collection unit can adjust the frequency of data collection according to the user's emotions. For example, if the user is stressed, the data collection unit can increase the frequency of data collection to provide a more accurate congestion status. If the user is relaxed, the data collection unit can maintain a normal frequency of data collection. Furthermore, if the user is in a hurry, the data collection unit can maximize the frequency of data collection to provide real-time information. This allows the data collection unit to adjust the frequency of data collection according to the user's emotions and provide a more accurate congestion status. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation. This allows the data collection unit to adjust the frequency of data collection according to the user's emotions, providing a more accurate representation of congestion levels.

[0062] The data collection unit can analyze the usage patterns of bicycle parking lots and increase the frequency of data collection during peak times. For example, the data collection unit can analyze the usage patterns of bicycle parking lots and increase the frequency of data collection during peak times. The data collection unit can increase the frequency of data collection during weekday morning and evening commute hours. The data collection unit can also increase the frequency of data collection at bicycle parking lots around shopping malls on weekends. Furthermore, the data collection unit can increase the frequency of data collection at nearby bicycle parking lots when events are held. In this way, the data collection unit can improve the accuracy of understanding congestion by increasing the frequency of data collection during peak times. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input bicycle parking lot usage pattern data into AI and have the AI ​​adjust the frequency of data collection. In this way, the data collection unit can improve the accuracy of understanding congestion by increasing the frequency of data collection during peak times.

[0063] The data collection unit can adjust the placement of sensors or cameras to improve the accuracy of data collection. For example, the data collection unit can install sensors at the entrance and exit of a bicycle parking area to accurately count the number of bicycles entering and exiting. It can also install cameras in the center of the bicycle parking area to understand the overall congestion level. Furthermore, the data collection unit can distribute sensors throughout each area of ​​the bicycle parking area to collect detailed congestion information for each area. In this way, the data collection unit can improve the accuracy of data collection by optimizing the placement of sensors and cameras. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input sensor and camera placement data into AI and have the AI ​​select the optimal placement. In this way, the data collection unit can improve the accuracy of data collection by optimizing the placement of sensors and cameras.

[0064] The data collection unit can estimate the user's emotions and select the types of data to collect based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The data collection unit can select the types of data to collect according to the user's emotions. For example, if the user is feeling stressed, the data collection unit can collect detailed data on the availability of parking spaces. If the user is relaxed, the data collection unit can collect data on normal congestion levels. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of real-time availability data. This allows the data collection unit to select the types of data to collect according to the user's emotions and collect more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation. This allows the data collection unit to select the type of data to collect according to the user's emotions, thereby collecting more appropriate data.

[0065] The data collection unit can predict the usage status of bicycle parking lots by taking weather information into consideration when collecting data. For example, the data collection unit can predict the usage status of bicycle parking lots by taking weather information into consideration when collecting data. When it rains, the data collection unit can prioritize collecting data on the usage status of covered bicycle parking lots. When it is sunny, the data collection unit can collect data on the usage status of all bicycle parking lots evenly. Furthermore, when it snows, the data collection unit can collect data on the usage status of bicycle parking lots along less slippery routes. In this way, the data collection unit can improve the accuracy of predicting the usage status of bicycle parking lots by taking weather information into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input weather information data into AI and have the AI ​​perform predictions of the usage status of bicycle parking lots. In this way, the data collection unit can improve the accuracy of predicting the usage status of bicycle parking lots by taking weather information into consideration.

[0066] The data collection unit can predict parking lot congestion by considering surrounding traffic conditions when collecting data. For example, the data collection unit can predict parking lot congestion by considering surrounding traffic conditions when collecting data. The data collection unit can collect data considering the possibility of increased parking lot usage when traffic congestion occurs. The data collection unit can also predict parking lot usage by considering the operation status of public transportation. Furthermore, the data collection unit can collect information on the usage status of parking lots on detour routes based on surrounding road construction information. In this way, the data collection unit can improve the accuracy of parking lot congestion predictions by considering surrounding traffic conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input traffic condition data into AI and have the AI ​​perform parking lot congestion predictions. In this way, the data collection unit can improve the accuracy of parking lot congestion predictions by considering surrounding traffic conditions.

[0067] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can adjust the display method of the analysis results according to the user's emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into a generating AI, allowing the AI ​​to perform emotion estimation. This enables the analysis unit to adjust how the analysis results are displayed according to the user's emotions, providing more appropriate information.

[0068] The analysis unit can improve its analysis accuracy by learning congestion patterns in bicycle parking lots by referring to past data. For example, the analysis unit can learn congestion patterns during specific time periods based on past data. It can also learn congestion patterns on specific days of the week based on past data. Furthermore, it can learn congestion patterns during specific events based on past data. In this way, the analysis unit can improve its analysis accuracy by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into AI and have the AI ​​learn congestion patterns. In this way, the analysis unit can improve its analysis accuracy by referring to past data.

[0069] The analysis unit can analyze congestion levels by considering the user attributes of the bicycle parking area. For example, the analysis unit can analyze congestion levels by considering the age group of the users of the bicycle parking area. It can also analyze congestion levels by considering the gender of the users of the bicycle parking area. Furthermore, the analysis unit can analyze congestion levels by considering the purpose of use of the bicycle parking area (commuting, shopping, etc.). In this way, the analysis unit can improve the accuracy of its congestion analysis by considering the user attributes of the bicycle parking area. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user attribute data into AI and have the AI ​​perform the congestion analysis. In this way, the analysis unit can improve the accuracy of its congestion analysis by considering the user attributes of the bicycle parking area.

[0070] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can determine the priority of analysis results according to the user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying information about the least crowded bicycle parking spaces. If the user is relaxed, the analysis unit can display information about normal congestion levels. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying information about the nearest bicycle parking spaces. In this way, the analysis unit can determine the priority of analysis results according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's facial expression data into a generating AI, which can then perform emotion estimation. This allows the analysis unit to prioritize the analysis results according to the user's emotions and provide more appropriate information.

[0071] The analysis unit can predict congestion by considering surrounding event information during analysis. For example, the analysis unit can predict congestion in the bicycle parking area if a large-scale event is held nearby. The analysis unit can also predict congestion in the bicycle parking area by considering event information at nearby schools. Furthermore, the analysis unit can predict congestion in the bicycle parking area based on sales information from nearby commercial facilities. In this way, the analysis unit can improve the accuracy of congestion prediction by considering surrounding event information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input event information data into AI and have the AI ​​perform bicycle parking area congestion prediction. In this way, the analysis unit can improve the accuracy of congestion prediction by considering surrounding event information.

[0072] The analysis unit can predict congestion by considering the facilities of the bicycle parking area during analysis. For example, the analysis unit can predict congestion by considering the facilities of the bicycle parking area (e.g., covered, equipped with surveillance cameras). The analysis unit can also predict congestion based on the number of bicycles that can be accommodated in the bicycle parking area. Furthermore, the analysis unit can predict congestion by considering the location conditions of the bicycle parking area (e.g., near a train station, near a commercial facility). In this way, the analysis unit can improve the accuracy of congestion prediction by considering the facilities of the bicycle parking area. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input facility status data into AI and have the AI ​​perform bicycle parking area congestion prediction. In this way, the analysis unit can improve the accuracy of congestion prediction by considering the facilities of the bicycle parking area.

[0073] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The service provider can adjust the level of detail of the information provided according to the user's emotions. For example, if the user is tense, the service provider can provide simple and easily understandable information. If the user is relaxed, the service provider can provide detailed information. Furthermore, if the user is in a hurry, the service provider can provide concise information. In this way, the service provider can adjust the level of detail of the information according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation. This allows the service provider to adjust the level of detail in the information according to the user's emotions, thereby providing more appropriate information.

[0074] The service provider can provide optimal information by referring to the user's past usage history at the time of provision. For example, the service provider can prioritize providing information on bicycle parking lots that the user has used in the past. It can also provide information on bicycle parking lots that are less crowded based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and provide information on the most efficient bicycle parking lots. In this way, the service provider can provide more appropriate information by referring to past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history data into AI and have the AI ​​select the optimal information. In this way, the service provider can provide more appropriate information by referring to past usage history.

[0075] The service provider can suggest the most suitable bicycle parking area by considering the user's current location information at the time of service provision. For example, the service provider can provide information on the bicycle parking area closest to the user's current location. It can also suggest the most suitable bicycle parking area by considering the distance from the user's current location. Furthermore, the service provider can provide information on bicycle parking areas that are easily accessible from the user's current location. In this way, the service provider can suggest the most suitable bicycle parking area to the user by considering the user's current location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input current location data into AI and have the AI ​​select the most suitable bicycle parking area. In this way, the service provider can suggest the most suitable bicycle parking area to the user by considering the user's current location information.

[0076] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, the service provider can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The service provider can adjust the way the information is displayed according to the user's emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, the service provider can adjust the way the information is displayed according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generating AI and have the AI ​​perform emotion estimation. This allows the service provider to adjust how information is displayed according to the user's emotions, providing more appropriate information.

[0077] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the user with the optimal display method by considering device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information data into AI and have the AI ​​select the optimal display method. In this way, the service provider can provide the user with the optimal display method by considering device information.

[0078] The service provider can suggest the most suitable parking location by considering the user's schedule information at the time of service provision. For example, the service provider can suggest the most suitable parking location based on the user's schedule. Furthermore, the service provider can suggest parking locations related to specific events based on the user's schedule information. In addition, the service provider can suggest the most suitable route based on the user's schedule information. Thus, by considering the schedule information, the service provider can suggest the most suitable parking location to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input schedule information data into AI and have the AI ​​select the most suitable parking location. Thus, by considering the schedule information, the service provider can suggest the most suitable parking location to the user. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data from the bicycle parking area using the camera 42 and sensors of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the congestion status of the bicycle parking area using AI. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the analysis results to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from the bicycle parking area using the camera 42 and sensors of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the congestion status of the bicycle parking area using AI. The data provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the analysis results to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data from the bicycle parking area using the camera 42 and sensors of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the congestion status of the bicycle parking area using AI. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the analysis results to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data from the bicycle parking area using the camera 42 and sensors of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the congestion status of the bicycle parking area using AI. The data provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the analysis results to the user.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The data collection unit can collect user attribute data for bicycle parking areas and transmit it to the analysis unit. For example, the data collection unit can collect user age groups, genders, and purposes of use (commuting, shopping, etc.). This allows the analysis unit to analyze the congestion status of bicycle parking areas with greater accuracy based on user attributes. Furthermore, the data collection unit can collect user attribute data in real time and transmit it to the analysis unit. This allows the analysis unit to analyze congestion status based on the latest user attribute data. In addition, the data collection unit can collect user attribute data anonymized. This allows for the collection of user attribute data while protecting privacy.

[0081] The analysis unit can estimate the emotions of users in the bicycle parking area and analyze the congestion situation based on the estimated emotions. For example, the analysis unit can collect user facial expression data using a camera and estimate emotions using an emotion estimation algorithm. As a result, the analysis unit can determine that the congestion situation is worsening if users are feeling stressed. Conversely, if users are relaxed, the analysis unit can determine that the congestion situation is improving. Furthermore, if users are in a hurry, the analysis unit can determine that the congestion situation is temporarily worsening. In this way, the analysis unit can analyze the congestion situation based on users' emotions and provide more accurate information.

[0082] The service provider can suggest the most suitable parking location based on the user's past usage history. For example, the service provider can collect data on parking locations previously used by the user and transmit it to the analysis unit. This allows the analysis unit to suggest the most suitable parking location based on the user's past usage history. The service provider can also suggest parking locations that avoid congestion based on the user's past usage history. Furthermore, the service provider can suggest the most efficient parking location based on the user's past usage history. In short, the service provider can suggest the most suitable parking location based on the user's past usage history.

[0083] The information provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, the provider can collect the user's facial expression data with a camera and estimate the emotions using an emotion estimation algorithm. This allows the provider to provide simple, easily understandable information when the user is tense, detailed information when the user is relaxed, and concise information when the user is in a hurry. In addition, the provider can provide concise information when the user is in a hurry. This allows the provider to adjust the level of detail of the information according to the user's emotions and provide more appropriate information.

[0084] The data collection unit can analyze parking lot usage patterns and increase the frequency of data collection during peak times. For example, it can increase the frequency of data collection during weekday morning and evening commute hours. It can also increase the frequency of data collection at parking lots around shopping malls on weekends. Furthermore, it can increase the frequency of data collection at nearby parking lots when events are being held. By increasing the frequency of data collection during peak times, the data collection unit can improve the accuracy of understanding congestion levels.

[0085] The analysis unit can improve its analysis accuracy by learning congestion patterns in bicycle parking lots by referring to past data. For example, the analysis unit can learn congestion patterns during specific time periods based on past data. It can also learn congestion patterns on specific days of the week based on past data. Furthermore, it can learn congestion patterns during specific events based on past data. In this way, the analysis unit can improve its analysis accuracy by referring to past data.

[0086] The information provider can estimate the user's emotions and adjust the way the information is displayed based on those estimated emotions. For example, the provider can collect the user's facial expression data using a camera and estimate their emotions using an emotion estimation algorithm. This allows the provider to provide a simple and highly visible display method when the user is tense. It can also provide a display method that includes detailed information when the user is relaxed. Furthermore, it can provide a concise display method when the user is in a hurry. This allows the provider to adjust the way information is displayed according to the user's emotions, providing more appropriate information.

[0087] The data collection unit can predict the usage status of bicycle parking lots by taking weather information into account during data collection. For example, during rainy weather, the data collection unit can prioritize collecting data on the usage status of covered bicycle parking lots. During sunny weather, the data collection unit can collect data on the usage status of all bicycle parking lots evenly. Furthermore, on snowy days, the data collection unit can collect data on the usage status of bicycle parking lots along less slippery routes. In this way, the data collection unit can improve the accuracy of predicting bicycle parking lot usage status by taking weather information into account.

[0088] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can collect the user's facial expression data using a camera and estimate emotions using an emotion estimation algorithm. This allows the analysis unit to prioritize displaying information about the least crowded parking spaces if the user is feeling stressed. If the user is relaxed, the analysis unit can display information about normal congestion levels. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying information about the nearest parking spaces. In this way, the analysis unit can determine the priority of analysis results according to the user's emotions and provide more appropriate information.

[0089] The service provider can suggest the most suitable parking location for the user, taking into account the user's schedule information. For example, the service provider can suggest the most suitable parking location based on the user's schedule. Furthermore, the service provider can suggest parking locations related to specific events based on the user's schedule information. In addition, the service provider can suggest the most suitable route based on the user's schedule information. Thus, by considering the user's schedule information, the service provider can suggest the most suitable parking location for the user.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The data collection unit collects data from sensors or cameras. For example, the data collection unit collects data from sensors or cameras installed in a bicycle parking area at a certain frequency and transmits it to the analysis unit. Data from sensors includes the number of available spaces and the number of bicycles parked in the bicycle parking area, while data from cameras includes image data of the bicycle parking area. The data collection unit can collect this data in real time and transmit it to the analysis unit. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit and determine the congestion level of the bicycle parking area. The analysis unit uses deep learning and machine learning algorithms to analyze the data and determines the congestion level based on information such as the percentage of available spaces and the number of bicycles parked in the bicycle parking area. Step 3: The service provider provides users with the congestion status determined by the analysis unit. The service provider can also provide the analysis results to users via a smartphone app or website, and can send them to users as notification messages. This allows users to check the availability of bicycle parking spaces in real time and use the parking spaces efficiently.

[0092] 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.

[0093] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.).

[0108] 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.

[0109] 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. 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.

[0110] 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.

[0111] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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. 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.

[0126] 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.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[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 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.

[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 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.

[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. 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 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.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 data collection unit that collects data from a sensor or camera, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the congestion status of the bicycle parking area, The system includes a provisioning unit that provides the user with the congestion status determined by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from sensors or cameras at regular intervals. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using AI to determine the congestion level of the bicycle parking area. The system according to feature 1.

4. The aforementioned supply unit is, Provide the analysis results to the user. The system according to feature 1.

5. The aforementioned supply unit is, Users can check the availability of parking spaces on their way to their destination and choose a different parking space if it is crowded. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze parking lot usage patterns and increase data collection frequency during peak hours. The system according to feature 1.

8. The aforementioned collection unit is Adjust the placement of sensors or cameras to improve the accuracy of data collection. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the user's emotions and selects the types of data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, weather information is taken into consideration to predict the usage status of bicycle parking areas. The system according to feature 1.

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