system

Abstract

The system according to this embodiment aims to grasp the availability of parking spaces throughout a city in real time and manage them efficiently. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, and a proposal unit. The monitoring unit monitors parking lot data in real time. The detection unit detects available spaces based on the data monitored by the monitoring unit. The proposal unit proposes an optimal parking plan based on the available spaces detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the availability status of parking spaces in the whole city in real time and manage them efficiently.

[0005] The system according to the embodiment aims to grasp the availability status of parking spaces in the whole city in real time and manage them efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, and a suggestion unit. The monitoring unit monitors parking lot data in real time. The detection unit detects available parking spaces based on the data monitored by the monitoring unit. The suggestion unit proposes an optimal parking plan based on the available parking spaces detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the availability of parking spaces throughout a city in real time and manage them efficiently. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The parking management system according to an embodiment of the present invention is a system that efficiently manages parking spaces throughout a city and, by linking with a parking agent, can grasp the availability of spaces in real time. In this parking management system, drivers reserve parking spaces using an app, and the AI ​​suggests the optimal parking space. Specifically, it consists of the following steps: First, the driver reserves a parking space using the app. Next, the AI ​​suggests the optimal parking space. This system targets urban drivers, parking operators, local governments, and commercial facilities, and aims to solve problems such as a shortage of parking spaces, the time and stress associated with searching for parking, and inefficient parking. First, the AI ​​agent monitors parking data in real time and immediately detects available spaces. For example, it has characteristic information of parking spaces (location, fee, time of day) as its individuality, and stores and analyzes past parking data as memory. Next, it plans the optimal parking plan and proposes it to the driver. Drivers can reserve parking spaces in advance via the app. Furthermore, the reservation system is executed. This system reduces the stress of searching for parking and alleviates traffic congestion in urban areas. It can also provide parking operators with optimal management and improve operational efficiency. For example, with the increasing demand for smart parking systems due to urbanization and worsening traffic congestion, the advancement of AI technology has made it possible to collect and analyze real-time data, presenting an opportunity for market entry. This allows parking management systems to enable efficient use of parking spaces and improve convenience for drivers.

[0029] The parking management system according to this embodiment comprises a monitoring unit, a detection unit, and a suggestion unit. The monitoring unit monitors parking lot data in real time. For example, the monitoring unit monitors the usage status of the parking lot and checks for the presence or absence of vacant spaces. The monitoring unit can also monitor and collect data on the characteristic information of the parking lot (location, fee, time of day). For example, the monitoring unit monitors the usage status of the parking lot in real time and checks for the presence or absence of vacant spaces. Furthermore, the monitoring unit can also monitor and collect data on the characteristic information of the parking lot (location, fee, time of day). The detection unit detects vacant spaces based on the data monitored by the monitoring unit. For example, the detection unit analyzes the usage status data of the parking lot and detects vacant spaces. Furthermore, the detection unit can also detect vacant spaces by considering the characteristic information of the parking lot (location, fee, time of day). For example, the detection unit analyzes the usage status data of the parking lot and detects vacant spaces. Furthermore, the detection unit can also detect vacant spaces by considering the characteristic information of the parking lot (location, fee, time of day). The suggestion unit proposes an optimal parking plan based on the vacant spaces detected by the detection unit. The proposal department, for example, proposes the optimal parking plan by considering the characteristics of the parking lot (location, fees, time of day). Furthermore, the proposal department can also analyze past parking data to propose the optimal parking plan. This allows the parking management system to monitor parking lot data in real time, detect available spaces, and propose the optimal parking plan, thereby enabling efficient use of parking lots.

[0030] The monitoring unit monitors parking lot data in real time. For example, the monitoring unit monitors parking lot usage and checks for available spaces. Specifically, the monitoring unit uses cameras and sensors installed in the parking lot to grasp the occupancy status of each parking space in real time. Cameras photograph the entire parking lot and use image analysis technology to detect the presence or absence of vehicles. Sensors are installed in each parking space and identify available spaces by sensing the weight and presence of vehicles. The monitoring unit can also monitor and collect data on parking lot characteristics (location, fees, time of day). For example, parking lot location information is obtained using GPS data, and fee information is obtained from the parking lot management system. Time of day information is important for analyzing parking lot usage patterns, and the monitoring unit centrally manages this data. Furthermore, the monitoring unit monitors parking lot usage in real time and checks for available spaces. This allows the monitoring unit to always have up-to-date information on parking lot usage and achieve efficient parking lot management. The monitoring unit can send the collected data to a cloud server and link it with other systems and departments. For example, the monitoring unit provides parking lot usage data to the analysis unit, which is used to detect vacant spaces and propose optimal parking plans. Furthermore, the monitoring unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The detection unit detects available parking spaces based on data monitored by the monitoring unit. For example, the detection unit analyzes parking lot usage data to detect available spaces. Specifically, the detection unit uses AI to analyze image data and sensor data provided by the monitoring unit to identify available spaces. The AI ​​uses image recognition technology to analyze camera footage of the parking lot and determine the presence or absence of vehicles. It also analyzes sensor data to understand the occupancy status of each parking space. Furthermore, the detection unit can also detect available spaces by considering the characteristics of the parking lot (location, fees, time of day). For example, it can analyze parking lot usage patterns during specific time periods to predict times when available spaces are likely to occur. It can also identify the most economical available space for users by considering fee information. This allows the detection unit to quickly and accurately grasp the usage status of the parking lot and efficiently detect available spaces. Furthermore, the detection unit can also analyze long-term parking lot usage trends by utilizing past data and statistical information. For example, based on past parking data, it can predict the trend of available spaces occurring on specific days of the week and time periods, and formulate future parking lot operation plans. Furthermore, the detection unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data, enabling it to issue warnings early. This allows the detection unit to handle not only real-time vacant space detection but also long-term parking management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The suggestion unit proposes the optimal parking plan based on the available spaces detected by the detection unit. The suggestion unit proposes the optimal parking plan considering, for example, the characteristics of the parking lot (location, fees, time of day). Specifically, the suggestion unit uses AI to propose the optimal parking space based on the user's current location, destination, and parking lot characteristics. The AI ​​learns the user's past parking history and preferences to provide the optimal parking plan for each individual user. The suggestion unit can also analyze past parking data to propose the optimal parking plan. For example, based on past data, it analyzes parking lot usage trends at specific times and days of the week to propose the most convenient parking space for the user. Furthermore, the suggestion unit can continuously revise the optimal parking plan based on real-time updated parking lot usage data to adapt to the latest situation. For example, if the parking lot availability changes while the user is on their way to their destination, the suggestion unit immediately incorporates the new data and re-proposes the optimal parking space. The suggestion unit can also collect user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise parking plans and improve suggestions based on user feedback. This allows the proposal department to provide users with quick and accurate parking plans, supporting the efficient use of parking spaces. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This enables the proposal department to provide users with the optimal parking plan quickly and reliably, supporting the efficient use of parking spaces.

[0033] The parking management system includes a reservation unit for reserving parking spaces. The reservation unit allows drivers to reserve parking spaces using an app, for example. The reservation unit can also manage the reservation status of parking spaces and prevent duplicate reservations. Furthermore, the reservation unit can update the reservation status of parking spaces in real time, providing the latest information. This reduces the time and stress spent searching for parking by allowing drivers to reserve spaces in advance. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not. For example, the reservation unit can use AI to suggest the optimal parking space when a driver reserves a parking space using an app.

[0034] The parking management system includes an execution unit that runs a reservation system. The execution unit manages the reservation status of parking spaces and executes reservations. The execution unit can also update the reservation status of parking spaces in real time and provide the latest information. Furthermore, the execution unit can monitor the reservation status of parking spaces and manage cancellations and changes to reservations. This makes parking management more efficient by running the reservation system. Some or all of the above processes in the execution unit may be performed using AI, for example, or not. For example, when managing the reservation status of parking spaces, the execution unit can use AI to suggest the optimal reservation method.

[0035] The suggestion unit can suggest the most suitable parking lot for the driver. For example, the suggestion unit can suggest the most suitable parking lot by considering the characteristics of the parking lot (location, fee, time of day). The suggestion unit can also suggest the most suitable parking lot by analyzing past parking data. For example, the suggestion unit can suggest the most suitable parking lot by analyzing past parking data. Furthermore, the suggestion unit can suggest the most suitable parking lot by considering the driver's current location. For example, the suggestion unit can suggest the most suitable parking lot by considering the driver's current location. This reduces the time and stress of searching for parking by suggesting the most suitable parking lot for the driver. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot characteristics and past parking data into AI and have the AI ​​suggest the most suitable parking lot.

[0036] The proposal unit can acquire parking lot characteristic information as its unique feature and store and analyze past parking data as its memory. For example, the proposal unit can acquire parking lot characteristic information (location, fee, time of day) as its unique feature and store and analyze past parking data as its memory. The proposal unit can acquire parking lot characteristic information (location, fee, time of day) as its unique feature and store and analyze past parking data as its memory. Furthermore, the proposal unit can propose the optimal parking plan based on the parking lot characteristic information and past parking data. For example, the proposal unit proposes the optimal parking plan based on the parking lot characteristic information and past parking data. In addition, the proposal unit can analyze parking lot usage trends based on the parking lot characteristic information and past parking data. For example, the proposal unit analyzes parking lot usage trends based on the parking lot characteristic information and past parking data. By analyzing parking lot characteristic information and past parking data, it becomes possible to make more accurate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input parking lot characteristic information and past parking data into AI and have the AI ​​propose the optimal parking plan.

[0037] The suggestion unit can plan and propose an optimal parking plan to the driver. For example, the suggestion unit can plan and propose an optimal parking plan by considering the characteristics of the parking lot (location, fee, time of day). The suggestion unit can also plan and propose an optimal parking plan by analyzing past parking data. For example, the suggestion unit can plan and propose an optimal parking plan by analyzing past parking data. Furthermore, the suggestion unit can plan and propose an optimal parking plan by considering the driver's current location information. For example, the suggestion unit can plan and propose an optimal parking plan by considering the driver's current location information. This improves the efficiency of parking lot utilization by planning and proposing an optimal parking plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot characteristics information and past parking data into AI and have the AI ​​plan and propose an optimal parking plan.

[0038] The monitoring unit can monitor the usage status of the parking lot in real time and detect abnormal patterns. For example, the monitoring unit can monitor the usage status of the parking lot and detect abnormally long-term parking. The monitoring unit can also monitor the usage status of the parking lot and detect sudden increases in usage during specific time periods. For example, the monitoring unit can monitor the usage status of the parking lot and detect sudden increases in usage during specific time periods. Furthermore, the monitoring unit can monitor the usage status of the parking lot and detect illegal parking. For example, the monitoring unit can monitor the usage status of the parking lot and detect illegal parking. In this way, illegal parking can be prevented by monitoring the usage status of the parking lot in real time and detecting abnormal patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input parking lot usage data into a generating AI and have the generating AI perform the detection of abnormal patterns.

[0039] The monitoring unit can monitor environmental data of the parking lot and analyze parking lot usage trends. For example, the monitoring unit can monitor weather data and analyze parking lot usage trends during rainy weather. The monitoring unit can also monitor traffic volume data and analyze parking lot usage trends during traffic congestion. Furthermore, the monitoring unit can monitor information on surrounding events and analyze parking lot usage trends during events. By monitoring environmental data of the parking lot and analyzing usage trends, the efficiency of parking lot utilization can be improved. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input weather data and traffic volume data into a generating AI and have the generating AI perform the usage trend analysis.

[0040] The monitoring unit can change its monitoring focus based on surrounding event information. For example, if a large-scale event is being held nearby, the monitoring unit will focus its monitoring on parking lots in that area. The monitoring unit can also focus its monitoring on parking lots affected by traffic restrictions in the surrounding area. For example, if traffic restrictions are being implemented nearby, the monitoring unit will focus its monitoring on parking lots affected by those restrictions. Furthermore, if congestion is expected during a specific time period in the surrounding area, the monitoring unit can focus its monitoring on that time period. For example, if congestion is expected during a specific time period in the surrounding area, the monitoring unit will focus its monitoring on that time period. This improves the efficiency of parking lot utilization by changing the monitoring focus based on surrounding event information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input surrounding event information into a generating AI and have the generating AI execute the change in monitoring focus.

[0041] The monitoring unit can monitor the security status of the parking lot and evaluate its safety. For example, the monitoring unit can monitor the video feed from the parking lot's surveillance cameras in real time and detect suspicious activity. The monitoring unit can also monitor the lighting conditions of the parking lot and evaluate the safety of dark areas. Furthermore, the monitoring unit can monitor the conditions at the entrances and exits of the parking lot and detect unauthorized entry. In this way, monitoring the security status of the parking lot and evaluating its safety improves the safety of the parking lot. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data from the parking lot's surveillance cameras into a generating AI and have the generating AI perform the detection of suspicious activity and the evaluation of safety.

[0042] The detection unit can improve detection accuracy based on parking lot usage history. For example, the detection unit can prioritize detecting parking lots with many vacancies during specific time periods based on past usage history. The detection unit can also prioritize detecting parking lots with many vacancies on specific days of the week based on past usage history. For example, the detection unit can prioritize detecting parking lots with many vacancies on specific days of the week based on past usage history. Furthermore, the detection unit can prioritize detecting parking lots with many vacancies during specific events based on past usage history. For example, the detection unit can prioritize detecting parking lots with many vacancies during specific events based on past usage history. By improving detection accuracy by considering parking lot usage history, it becomes possible to detect vacant spaces more accurately. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past usage history data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0043] The detection unit can perform detection based on the characteristic information of the parking lot. For example, the detection unit can consider the location information of the parking lot and detect the available space closest to the destination. The detection unit can also consider the parking fee information and detect the cheapest available space. Furthermore, the detection unit can consider the time-of-day information of the parking lot and detect spaces that are available during available time slots. In this way, by performing detection while considering the characteristic information of the parking lot, it becomes possible to provide more appropriate parking spaces. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the characteristic information of the parking lot into a generating AI and have the generating AI perform the detection of available spaces.

[0044] The detection unit can perform detection based on surrounding traffic conditions. For example, the detection unit can consider surrounding traffic congestion information and detect available spaces on routes that avoid congestion. The detection unit can also consider surrounding traffic regulation information and detect available spaces on routes that avoid restrictions. Furthermore, the detection unit can consider surrounding traffic volume information and detect available spaces on routes with low traffic volume. By performing detection while considering surrounding traffic conditions, it becomes possible to provide more appropriate parking spaces. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input surrounding traffic condition data into a generating AI and have the generating AI perform the detection of available spaces.

[0045] The detection unit can perform detection based on the security status of the parking lot. For example, the detection unit may consider the presence or absence of surveillance cameras in the parking lot and prioritize vacant spaces where surveillance cameras are installed. The detection unit may also consider the lighting conditions of the parking lot and prioritize vacant spaces with sufficient lighting. Furthermore, the detection unit may consider the conditions of the parking lot entrances and prioritize vacant spaces near secure entrances. For example, the detection unit may consider the conditions of the parking lot entrances and prioritize vacant spaces near secure entrances. By performing detection while considering the security status of the parking lot, it becomes possible to provide safer parking spaces. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input parking lot security status data into a generating AI and have the generating AI perform the detection of vacant spaces.

[0046] The suggestion unit can propose the optimal parking plan based on the characteristics of the parking lot. For example, the suggestion unit can consider the location information of the parking lot and propose the parking plan closest to the destination. The suggestion unit can also consider the parking fee information and propose the cheapest parking plan. Furthermore, the suggestion unit can consider the time of day information of the parking lot and propose the optimal parking plan for the available time slots. In this way, by proposing the optimal parking plan considering the characteristics of the parking lot, the efficiency of parking lot utilization is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the characteristics of the parking lot into a generating AI and have the generating AI propose the optimal parking plan.

[0047] The suggestion unit can analyze past parking data and propose the optimal parking plan. For example, the suggestion unit can propose a parking plan with more available spaces during specific time periods based on past parking data. The suggestion unit can also propose a parking plan with more available spaces on specific days of the week based on past parking data. Furthermore, the suggestion unit can propose a parking plan with more available spaces during specific events based on past parking data. In this way, by analyzing past parking data and proposing the optimal parking plan, the efficiency of parking lot utilization is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past parking data into a generating AI and have the generating AI propose the optimal parking plan.

[0048] The suggestion unit can propose the optimal parking plan based on the driver's current destination information. For example, the suggestion unit can propose the parking lot closest to the driver's destination. The suggestion unit can also propose a parking lot that is conveniently located for travel from the driver's destination. Furthermore, the suggestion unit can also consider the availability of parking lots around the driver's destination when making suggestions. This improves the efficiency of parking lot utilization by proposing the optimal parking plan while considering the driver's current destination information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the driver's destination information into a generating AI and have the generating AI propose the optimal parking plan.

[0049] The suggestion unit can propose the optimal parking plan based on the parking lot congestion status. For example, the suggestion unit can monitor the parking lot congestion status in real time and propose parking lots with plenty of available spaces. The suggestion unit can also propose a parking plan that avoids expected congestion times based on parking lot congestion forecasts. Furthermore, the suggestion unit can analyze the parking lot congestion status and propose parking lots with less congestion. In this way, by proposing the optimal parking plan considering the parking lot congestion status, the efficiency of parking lot use is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot congestion data into a generating AI and have the generating AI propose the optimal parking plan.

[0050] The reservation unit can select the optimal reservation method based on the characteristics of the parking lot. For example, the reservation unit can consider the location information of the parking lot and reserve the parking lot closest to the destination. The reservation unit can also consider the parking fee information and reserve the cheapest parking lot. Furthermore, the reservation unit can consider the time of day information of the parking lot and reserve the parking lot best suited to the available time slot. By selecting the optimal reservation method based on the characteristics of the parking lot, the efficiency of parking lot utilization is improved. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the characteristics of the parking lot into a generating AI and have the generating AI select the optimal reservation method.

[0051] The reservation unit can select the optimal reservation method based on the driver's current schedule information. For example, the reservation unit can reserve a parking space that suits the driver's schedule, taking into account the driver's schedule information. The reservation unit can also reserve a parking space that is available during a specific time period, based on the driver's schedule information. Furthermore, the reservation unit can suggest and reserve the most suitable parking space based on the driver's schedule information. This improves the efficiency of parking space utilization by selecting the optimal reservation method while considering the driver's current schedule information. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the driver's schedule information into a generating AI and have the generating AI select the optimal reservation method.

[0052] The execution unit can select the optimal execution method based on the characteristics information of the parking lot. For example, the execution unit can consider the location information of the parking lot and select the parking lot closest to the destination. The execution unit can also consider the parking fee information and select the cheapest parking lot. For example, the execution unit can consider the parking fee information and select the cheapest parking lot. Furthermore, the execution unit can consider the time of day information of the parking lot and select the parking lot that is best suited to the available time. For example, the execution unit can consider the time of day information of the parking lot and select the parking lot that is best suited to the available time. By selecting the optimal execution method while considering the characteristics information of the parking lot, the efficiency of parking lot utilization is improved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the characteristics information of the parking lot into a generating AI and have the generating AI select the optimal execution method.

[0053] The execution unit can select the optimal execution method based on the driver's current schedule information. For example, the execution unit can select a parking lot that matches the driver's schedule, taking into account the driver's schedule information. The execution unit can also select a parking lot that is available during a specific time period based on the driver's schedule information. Furthermore, the execution unit can propose and execute the optimal parking lot based on the driver's schedule information. This improves the efficiency of parking lot utilization by selecting the optimal execution method while considering the driver's current schedule information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the driver's schedule information into a generating AI and have the generating AI select the optimal execution method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] A parking management system can monitor parking lot usage and detect abnormal patterns. For example, it can monitor parking lot usage and detect vehicles parked for unusually long periods. It can also detect sudden increases in usage during specific time periods. Furthermore, it can detect illegal parking. This allows for real-time monitoring of parking lot usage and the detection of abnormal patterns, thereby preventing illegal parking.

[0056] A parking management system can monitor environmental data of a parking lot and analyze parking lot usage trends. For example, it can monitor weather data to analyze parking lot usage trends during rainy weather. It can also monitor traffic volume data to analyze parking lot usage trends during traffic congestion. Furthermore, it can monitor information on surrounding events to analyze parking lot usage trends during events. By monitoring environmental data of a parking lot and analyzing usage trends, the efficiency of parking lot utilization can be improved.

[0057] A parking management system can monitor the security status of a parking lot and assess its safety. For example, it can monitor footage from parking lot surveillance cameras in real time and detect suspicious activity. It can also monitor the lighting conditions of the parking lot and assess the safety of dark areas. Furthermore, it can monitor the conditions of parking lot entrances and exits to detect unauthorized entry. By monitoring the security status of the parking lot and assessing its safety, the safety of the parking lot can be improved.

[0058] Parking management systems can improve detection accuracy based on parking lot usage history. For example, they can prioritize detecting parking lots with many vacancies during specific time periods based on past usage history. They can also prioritize detecting parking lots with many vacancies on specific days of the week. Furthermore, they can prioritize detecting parking lots with many vacancies during specific events. By improving detection accuracy by considering parking lot usage history, it becomes possible to detect vacant spaces more accurately.

[0059] A parking management system can suggest the optimal parking plan based on the characteristics of the parking lot. For example, it can consider the location of the parking lot and suggest the parking plan closest to the destination. It can also consider parking fee information and suggest the cheapest parking plan. Furthermore, it can consider the time of day information of the parking lot and suggest the optimal parking plan for the available time slot. In this way, by suggesting the optimal parking plan based on the characteristics of the parking lot, the efficiency of parking lot utilization is improved.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The monitoring unit monitors parking lot data in real time. The monitoring unit monitors the usage status of the parking lot and checks for available spaces. It can also monitor and collect data on parking lot characteristics (location, fees, time of day). Step 2: The detection unit detects available spaces based on data monitored by the monitoring unit. The detection unit analyzes parking lot usage data to detect available spaces. It can also detect available spaces by considering parking lot characteristics (location, fees, time of day). Step 3: The suggestion unit proposes the optimal parking plan based on the available spaces detected by the detection unit. The suggestion unit proposes the optimal parking plan considering the characteristics of the parking lot (location, fees, time of day). It can also analyze past parking data to propose the optimal parking plan.

[0062] (Example of form 2) The parking management system according to an embodiment of the present invention is a system that efficiently manages parking spaces throughout a city and, by linking with a parking agent, can grasp the availability of spaces in real time. In this parking management system, drivers reserve parking spaces using an app, and the AI ​​suggests the optimal parking space. Specifically, it consists of the following steps: First, the driver reserves a parking space using the app. Next, the AI ​​suggests the optimal parking space. This system targets urban drivers, parking operators, local governments, and commercial facilities, and aims to solve problems such as a shortage of parking spaces, the time and stress associated with searching for parking, and inefficient parking. First, the AI ​​agent monitors parking data in real time and immediately detects available spaces. For example, it has characteristic information of parking spaces (location, fee, time of day) as its individuality, and stores and analyzes past parking data as memory. Next, it plans the optimal parking plan and proposes it to the driver. Drivers can reserve parking spaces in advance via the app. Furthermore, the reservation system is executed. This system reduces the stress of searching for parking and alleviates traffic congestion in urban areas. It can also provide parking operators with optimal management and improve operational efficiency. For example, with the increasing demand for smart parking systems due to urbanization and worsening traffic congestion, the advancement of AI technology has made it possible to collect and analyze real-time data, presenting an opportunity for market entry. This allows parking management systems to enable efficient use of parking spaces and improve convenience for drivers.

[0063] The parking management system according to this embodiment comprises a monitoring unit, a detection unit, and a suggestion unit. The monitoring unit monitors parking lot data in real time. For example, the monitoring unit monitors the usage status of the parking lot and checks for the presence or absence of vacant spaces. The monitoring unit can also monitor and collect data on the characteristic information of the parking lot (location, fee, time of day). For example, the monitoring unit monitors the usage status of the parking lot in real time and checks for the presence or absence of vacant spaces. Furthermore, the monitoring unit can also monitor and collect data on the characteristic information of the parking lot (location, fee, time of day). The detection unit detects vacant spaces based on the data monitored by the monitoring unit. For example, the detection unit analyzes the usage status data of the parking lot and detects vacant spaces. Furthermore, the detection unit can also detect vacant spaces by considering the characteristic information of the parking lot (location, fee, time of day). For example, the detection unit analyzes the usage status data of the parking lot and detects vacant spaces. Furthermore, the detection unit can also detect vacant spaces by considering the characteristic information of the parking lot (location, fee, time of day). The suggestion unit proposes an optimal parking plan based on the vacant spaces detected by the detection unit. The proposal department, for example, proposes the optimal parking plan by considering the characteristics of the parking lot (location, fees, time of day). Furthermore, the proposal department can also analyze past parking data to propose the optimal parking plan. This allows the parking management system to monitor parking lot data in real time, detect available spaces, and propose the optimal parking plan, thereby enabling efficient use of parking lots.

[0064] The monitoring unit monitors parking lot data in real time. For example, the monitoring unit monitors parking lot usage and checks for available spaces. Specifically, the monitoring unit uses cameras and sensors installed in the parking lot to grasp the occupancy status of each parking space in real time. Cameras photograph the entire parking lot and use image analysis technology to detect the presence or absence of vehicles. Sensors are installed in each parking space and identify available spaces by sensing the weight and presence of vehicles. The monitoring unit can also monitor and collect data on parking lot characteristics (location, fees, time of day). For example, parking lot location information is obtained using GPS data, and fee information is obtained from the parking lot management system. Time of day information is important for analyzing parking lot usage patterns, and the monitoring unit centrally manages this data. Furthermore, the monitoring unit monitors parking lot usage in real time and checks for available spaces. This allows the monitoring unit to always have up-to-date information on parking lot usage and achieve efficient parking lot management. The monitoring unit can send the collected data to a cloud server and link it with other systems and departments. For example, the monitoring unit provides parking lot usage data to the analysis unit, which is used to detect vacant spaces and propose optimal parking plans. Furthermore, the monitoring unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The detection unit detects available parking spaces based on data monitored by the monitoring unit. For example, the detection unit analyzes parking lot usage data to detect available spaces. Specifically, the detection unit uses AI to analyze image data and sensor data provided by the monitoring unit to identify available spaces. The AI ​​uses image recognition technology to analyze camera footage of the parking lot and determine the presence or absence of vehicles. It also analyzes sensor data to understand the occupancy status of each parking space. Furthermore, the detection unit can also detect available spaces by considering the characteristics of the parking lot (location, fees, time of day). For example, it can analyze parking lot usage patterns during specific time periods to predict times when available spaces are likely to occur. It can also identify the most economical available space for users by considering fee information. This allows the detection unit to quickly and accurately grasp the usage status of the parking lot and efficiently detect available spaces. Furthermore, the detection unit can also analyze long-term parking lot usage trends by utilizing past data and statistical information. For example, based on past parking data, it can predict the trend of available spaces occurring on specific days of the week and time periods, and formulate future parking lot operation plans. Furthermore, the detection unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data, enabling it to issue warnings early. This allows the detection unit to handle not only real-time vacant space detection but also long-term parking management and anomaly detection, improving the overall reliability and safety of the system.

[0066] The suggestion unit proposes the optimal parking plan based on the available spaces detected by the detection unit. The suggestion unit proposes the optimal parking plan considering, for example, the characteristics of the parking lot (location, fees, time of day). Specifically, the suggestion unit uses AI to propose the optimal parking space based on the user's current location, destination, and parking lot characteristics. The AI ​​learns the user's past parking history and preferences to provide the optimal parking plan for each individual user. The suggestion unit can also analyze past parking data to propose the optimal parking plan. For example, based on past data, it analyzes parking lot usage trends at specific times and days of the week to propose the most convenient parking space for the user. Furthermore, the suggestion unit can continuously revise the optimal parking plan based on real-time updated parking lot usage data to adapt to the latest situation. For example, if the parking lot availability changes while the user is on their way to their destination, the suggestion unit immediately incorporates the new data and re-proposes the optimal parking space. The suggestion unit can also collect user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise parking plans and improve suggestions based on user feedback. This allows the proposal department to provide users with quick and accurate parking plans, supporting the efficient use of parking spaces. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This enables the proposal department to provide users with the optimal parking plan quickly and reliably, supporting the efficient use of parking spaces.

[0067] The parking management system includes a reservation unit for reserving parking spaces. The reservation unit allows drivers to reserve parking spaces using an app, for example. The reservation unit can also manage the reservation status of parking spaces and prevent duplicate reservations. Furthermore, the reservation unit can update the reservation status of parking spaces in real time, providing the latest information. This reduces the time and stress spent searching for parking by allowing drivers to reserve spaces in advance. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not. For example, the reservation unit can use AI to suggest the optimal parking space when a driver reserves a parking space using an app.

[0068] The parking management system includes an execution unit that runs a reservation system. The execution unit manages the reservation status of parking spaces and executes reservations. The execution unit can also update the reservation status of parking spaces in real time and provide the latest information. Furthermore, the execution unit can monitor the reservation status of parking spaces and manage cancellations and changes to reservations. This makes parking management more efficient by running the reservation system. Some or all of the above processes in the execution unit may be performed using AI, for example, or not. For example, when managing the reservation status of parking spaces, the execution unit can use AI to suggest the optimal reservation method.

[0069] The suggestion unit can suggest the most suitable parking lot for the driver. For example, the suggestion unit can suggest the most suitable parking lot by considering the characteristics of the parking lot (location, fee, time of day). The suggestion unit can also suggest the most suitable parking lot by analyzing past parking data. For example, the suggestion unit can suggest the most suitable parking lot by analyzing past parking data. Furthermore, the suggestion unit can suggest the most suitable parking lot by considering the driver's current location. For example, the suggestion unit can suggest the most suitable parking lot by considering the driver's current location. This reduces the time and stress of searching for parking by suggesting the most suitable parking lot for the driver. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot characteristics and past parking data into AI and have the AI ​​suggest the most suitable parking lot.

[0070] The proposal unit can acquire parking lot characteristic information as its unique feature and store and analyze past parking data as its memory. For example, the proposal unit can acquire parking lot characteristic information (location, fee, time of day) as its unique feature and store and analyze past parking data as its memory. The proposal unit can acquire parking lot characteristic information (location, fee, time of day) as its unique feature and store and analyze past parking data as its memory. Furthermore, the proposal unit can propose the optimal parking plan based on the parking lot characteristic information and past parking data. For example, the proposal unit proposes the optimal parking plan based on the parking lot characteristic information and past parking data. In addition, the proposal unit can analyze parking lot usage trends based on the parking lot characteristic information and past parking data. For example, the proposal unit analyzes parking lot usage trends based on the parking lot characteristic information and past parking data. By analyzing parking lot characteristic information and past parking data, it becomes possible to make more accurate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input parking lot characteristic information and past parking data into AI and have the AI ​​propose the optimal parking plan.

[0071] The suggestion unit can plan and propose an optimal parking plan to the driver. For example, the suggestion unit can plan and propose an optimal parking plan by considering the characteristics of the parking lot (location, fee, time of day). The suggestion unit can also plan and propose an optimal parking plan by analyzing past parking data. For example, the suggestion unit can plan and propose an optimal parking plan by analyzing past parking data. Furthermore, the suggestion unit can plan and propose an optimal parking plan by considering the driver's current location information. For example, the suggestion unit can plan and propose an optimal parking plan by considering the driver's current location information. This improves the efficiency of parking lot utilization by planning and proposing an optimal parking plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot characteristics information and past parking data into AI and have the AI ​​plan and propose an optimal parking plan.

[0072] The monitoring unit can estimate the driver's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the driver is stressed, the monitoring unit can increase the monitoring frequency to provide available spaces in real time. The monitoring unit can also maintain a normal monitoring frequency and provide necessary information if the driver is relaxed. For example, if the driver is relaxed, the monitoring unit can maintain a normal monitoring frequency and provide necessary information. Furthermore, if the driver is in a hurry, the monitoring unit can maximize the monitoring frequency to quickly provide available spaces. For example, if the driver is in a hurry, the monitoring unit can maximize the monitoring frequency to quickly provide available spaces. This makes it possible to provide more appropriate parking spaces by adjusting the monitoring frequency according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input driver emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of monitoring frequency.

[0073] The monitoring unit can monitor the usage status of the parking lot in real time and detect abnormal patterns. For example, the monitoring unit can monitor the usage status of the parking lot and detect abnormally long-term parking. The monitoring unit can also monitor the usage status of the parking lot and detect sudden increases in usage during specific time periods. For example, the monitoring unit can monitor the usage status of the parking lot and detect sudden increases in usage during specific time periods. Furthermore, the monitoring unit can monitor the usage status of the parking lot and detect illegal parking. For example, the monitoring unit can monitor the usage status of the parking lot and detect illegal parking. In this way, illegal parking can be prevented by monitoring the usage status of the parking lot in real time and detecting abnormal patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input parking lot usage data into a generating AI and have the generating AI perform the detection of abnormal patterns.

[0074] The monitoring unit can monitor environmental data of the parking lot and analyze parking lot usage trends. For example, the monitoring unit can monitor weather data and analyze parking lot usage trends during rainy weather. The monitoring unit can also monitor traffic volume data and analyze parking lot usage trends during traffic congestion. Furthermore, the monitoring unit can monitor information on surrounding events and analyze parking lot usage trends during events. By monitoring environmental data of the parking lot and analyzing usage trends, the efficiency of parking lot utilization can be improved. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input weather data and traffic volume data into a generating AI and have the generating AI perform the usage trend analysis.

[0075] The monitoring unit can estimate the driver's emotions and determine the priority of parking lots to monitor based on the estimated emotions. For example, if the driver is stressed, the monitoring unit will prioritize monitoring nearby parking lots. The monitoring unit can also maintain the normal monitoring priority if the driver is relaxed. Furthermore, if the driver is in a hurry, the monitoring unit can prioritize monitoring the parking lot closest to the destination. This makes it possible to provide more appropriate parking spaces by determining the priority of parking lots to monitor according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input driver emotion data into a generating AI, which can then perform emotion estimation and determine monitoring priorities.

[0076] The monitoring unit can change its monitoring focus based on surrounding event information. For example, if a large-scale event is being held nearby, the monitoring unit will focus its monitoring on parking lots in that area. The monitoring unit can also focus its monitoring on parking lots affected by traffic restrictions in the surrounding area. For example, if traffic restrictions are being implemented nearby, the monitoring unit will focus its monitoring on parking lots affected by those restrictions. Furthermore, if congestion is expected during a specific time period in the surrounding area, the monitoring unit can focus its monitoring on that time period. For example, if congestion is expected during a specific time period in the surrounding area, the monitoring unit will focus its monitoring on that time period. This improves the efficiency of parking lot utilization by changing the monitoring focus based on surrounding event information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input surrounding event information into a generating AI and have the generating AI execute the change in monitoring focus.

[0077] The monitoring unit can monitor the security status of the parking lot and evaluate its safety. For example, the monitoring unit can monitor the video feed from the parking lot's surveillance cameras in real time and detect suspicious activity. The monitoring unit can also monitor the lighting conditions of the parking lot and evaluate the safety of dark areas. Furthermore, the monitoring unit can monitor the conditions at the entrances and exits of the parking lot and detect unauthorized entry. In this way, monitoring the security status of the parking lot and evaluating its safety improves the safety of the parking lot. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data from the parking lot's surveillance cameras into a generating AI and have the generating AI perform the detection of suspicious activity and the evaluation of safety.

[0078] The detection unit can estimate the driver's emotions and adjust the method of detecting available spaces based on the estimated emotions. For example, if the driver is stressed, the detection unit will prioritize detecting the nearest available space. The detection unit can also maintain the normal detection method if the driver is relaxed. Furthermore, if the driver is in a hurry, the detection unit can prioritize detecting the available space that can be reached in the shortest amount of time. This allows for the provision of more appropriate parking spaces by adjusting the method of detecting available spaces according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input driver emotion data into a generating AI and have the generating AI perform emotion estimation and adjust the method for detecting empty spaces.

[0079] The detection unit can improve detection accuracy based on parking lot usage history. For example, the detection unit can prioritize detecting parking lots with many vacancies during specific time periods based on past usage history. The detection unit can also prioritize detecting parking lots with many vacancies on specific days of the week based on past usage history. For example, the detection unit can prioritize detecting parking lots with many vacancies on specific days of the week based on past usage history. Furthermore, the detection unit can prioritize detecting parking lots with many vacancies during specific events based on past usage history. For example, the detection unit can prioritize detecting parking lots with many vacancies during specific events based on past usage history. By improving detection accuracy by considering parking lot usage history, it becomes possible to detect vacant spaces more accurately. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past usage history data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0080] The detection unit can perform detection based on the characteristic information of the parking lot. For example, the detection unit can consider the location information of the parking lot and detect the available space closest to the destination. The detection unit can also consider the parking fee information and detect the cheapest available space. Furthermore, the detection unit can consider the time-of-day information of the parking lot and detect spaces that are available during available time slots. In this way, by performing detection while considering the characteristic information of the parking lot, it becomes possible to provide more appropriate parking spaces. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the characteristic information of the parking lot into a generating AI and have the generating AI perform the detection of available spaces.

[0081] The detection unit can estimate the driver's emotions and determine the priority of available parking spaces based on the estimated emotions. For example, if the driver is stressed, the detection unit will prioritize the nearest available parking space. The detection unit can also maintain the normal priority if the driver is relaxed. Furthermore, if the driver is in a hurry, the detection unit can prioritize the parking space that can be reached in the shortest amount of time. This makes it possible to provide more appropriate parking spaces by determining the priority of available parking spaces according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input driver emotion data into a generating AI, which can then perform emotion estimation and determine the priority of available spaces.

[0082] The detection unit can perform detection based on surrounding traffic conditions. For example, the detection unit can consider surrounding traffic congestion information and detect available spaces on routes that avoid congestion. The detection unit can also consider surrounding traffic regulation information and detect available spaces on routes that avoid restrictions. Furthermore, the detection unit can consider surrounding traffic volume information and detect available spaces on routes with low traffic volume. By performing detection while considering surrounding traffic conditions, it becomes possible to provide more appropriate parking spaces. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input surrounding traffic condition data into a generating AI and have the generating AI perform the detection of available spaces.

[0083] The detection unit can perform detection based on the security status of the parking lot. For example, the detection unit may consider the presence or absence of surveillance cameras in the parking lot and prioritize vacant spaces where surveillance cameras are installed. The detection unit may also consider the lighting conditions of the parking lot and prioritize vacant spaces with sufficient lighting. Furthermore, the detection unit may consider the conditions of the parking lot entrances and prioritize vacant spaces near secure entrances. For example, the detection unit may consider the conditions of the parking lot entrances and prioritize vacant spaces near secure entrances. By performing detection while considering the security status of the parking lot, it becomes possible to provide safer parking spaces. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input parking lot security status data into a generating AI and have the generating AI perform the detection of vacant spaces.

[0084] The suggestion unit can estimate the driver's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the driver is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. For example, if the driver is relaxed, the suggestion unit can provide suggestions that include detailed information. For example, if the driver is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the driver is in a hurry, the suggestion unit can provide quick and concise suggestions. For example, if the driver is in a hurry, the suggestion unit can provide quick and concise suggestions. By adjusting the way it presents its suggestions according to the driver's emotions, it becomes possible to suggest more appropriate parking plans. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input driver emotion data into a generation AI and have the generation AI perform emotion estimation and adjust the way the proposal is expressed.

[0085] The suggestion unit can propose the optimal parking plan based on the characteristics of the parking lot. For example, the suggestion unit can consider the location information of the parking lot and propose the parking plan closest to the destination. The suggestion unit can also consider the parking fee information and propose the cheapest parking plan. Furthermore, the suggestion unit can consider the time of day information of the parking lot and propose the optimal parking plan for the available time slots. In this way, by proposing the optimal parking plan considering the characteristics of the parking lot, the efficiency of parking lot utilization is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the characteristics of the parking lot into a generating AI and have the generating AI propose the optimal parking plan.

[0086] The suggestion unit can analyze past parking data and propose the optimal parking plan. For example, the suggestion unit can propose a parking plan with more available spaces during specific time periods based on past parking data. The suggestion unit can also propose a parking plan with more available spaces on specific days of the week based on past parking data. Furthermore, the suggestion unit can propose a parking plan with more available spaces during specific events based on past parking data. In this way, by analyzing past parking data and proposing the optimal parking plan, the efficiency of parking lot utilization is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past parking data into a generating AI and have the generating AI propose the optimal parking plan.

[0087] The suggestion unit can estimate the driver's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the driver is stressed, the suggestion unit will prioritize suggesting the nearest parking lot. The suggestion unit can also maintain the normal priority if the driver is relaxed. Furthermore, if the driver is in a hurry, the suggestion unit can prioritize suggesting the parking lot that can be reached in the shortest time. This allows for the suggestion of a more appropriate parking plan by determining the priority of suggestions according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 suggestion unit may be performed using AI or not. For example, the proposal unit can input driver emotion data into a generating AI, which can then perform emotion estimation and determine the priority of proposals.

[0088] The suggestion unit can propose the optimal parking plan based on the driver's current destination information. For example, the suggestion unit can propose the parking lot closest to the driver's destination. The suggestion unit can also propose a parking lot that is conveniently located for travel from the driver's destination. Furthermore, the suggestion unit can also consider the availability of parking lots around the driver's destination when making suggestions. This improves the efficiency of parking lot utilization by proposing the optimal parking plan while considering the driver's current destination information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the driver's destination information into a generating AI and have the generating AI propose the optimal parking plan.

[0089] The suggestion unit can propose the optimal parking plan based on the parking lot congestion status. For example, the suggestion unit can monitor the parking lot congestion status in real time and propose parking lots with plenty of available spaces. The suggestion unit can also propose a parking plan that avoids expected congestion times based on parking lot congestion forecasts. Furthermore, the suggestion unit can analyze the parking lot congestion status and propose parking lots with less congestion. In this way, by proposing the optimal parking plan considering the parking lot congestion status, the efficiency of parking lot use is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot congestion data into a generating AI and have the generating AI propose the optimal parking plan.

[0090] The reservation system can estimate the driver's emotions and adjust the timing of the reservation based on the estimated emotions. For example, if the driver is feeling stressed, the reservation system can complete the reservation quickly. The reservation system can also perform the normal reservation procedure if the driver is relaxed. Furthermore, if the driver is in a hurry, the reservation system can complete the reservation in the shortest possible time. This allows for the reservation of a more appropriate parking space by adjusting the timing of the reservation according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 reservation system may be performed using AI, for example, or without AI. For example, the reservation unit can input driver emotion data into a generating AI, which can then perform emotion estimation and adjust the timing of reservations.

[0091] The reservation unit can select the optimal reservation method based on the characteristics of the parking lot. For example, the reservation unit can consider the location information of the parking lot and reserve the parking lot closest to the destination. The reservation unit can also consider the parking fee information and reserve the cheapest parking lot. Furthermore, the reservation unit can consider the time of day information of the parking lot and reserve the parking lot best suited to the available time slot. By selecting the optimal reservation method based on the characteristics of the parking lot, the efficiency of parking lot utilization is improved. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the characteristics of the parking lot into a generating AI and have the generating AI select the optimal reservation method.

[0092] The reservation system can estimate the driver's emotions and determine reservation priorities based on the estimated emotions. For example, if the driver is stressed, the reservation system will prioritize reserving the nearest parking space. The reservation system can also maintain normal priorities if the driver is relaxed. Furthermore, if the driver is in a hurry, the reservation system can prioritize reserving the parking space that can be reached in the shortest time. This allows for the reservation of a more appropriate parking space by determining reservation priorities according to the driver's emotions. 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 reservation system may be performed using AI, for example, or without AI. For example, the reservation unit can input driver emotion data into a generating AI, which can then perform emotion estimation and determine reservation priorities.

[0093] The reservation unit can select the optimal reservation method based on the driver's current schedule information. For example, the reservation unit can reserve a parking space that suits the driver's schedule, taking into account the driver's schedule information. The reservation unit can also reserve a parking space that is available during a specific time period, based on the driver's schedule information. Furthermore, the reservation unit can suggest and reserve the most suitable parking space based on the driver's schedule information. This improves the efficiency of parking space utilization by selecting the optimal reservation method while considering the driver's current schedule information. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the driver's schedule information into a generating AI and have the generating AI select the optimal reservation method.

[0094] The execution unit can estimate the driver's emotions and adjust the timing of execution based on the estimated emotions. For example, if the driver is stressed, the execution unit can start execution quickly. The execution unit can also start execution at the normal timing if the driver is relaxed. Furthermore, if the driver is in a hurry, the execution unit can complete execution in the shortest possible time. This allows for more appropriate use of parking spaces by adjusting the timing of execution according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the driver's emotional data into the generating AI, which can then perform emotion estimation and adjust the timing of execution.

[0095] The execution unit can select the optimal execution method based on the characteristics information of the parking lot. For example, the execution unit can consider the location information of the parking lot and select the parking lot closest to the destination. The execution unit can also consider the parking fee information and select the cheapest parking lot. For example, the execution unit can consider the parking fee information and select the cheapest parking lot. Furthermore, the execution unit can consider the time of day information of the parking lot and select the parking lot that is best suited to the available time. For example, the execution unit can consider the time of day information of the parking lot and select the parking lot that is best suited to the available time. By selecting the optimal execution method while considering the characteristics information of the parking lot, the efficiency of parking lot utilization is improved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the characteristics information of the parking lot into a generating AI and have the generating AI select the optimal execution method.

[0096] The execution unit can estimate the driver's emotions and determine execution priorities based on the estimated emotions. For example, if the driver is stressed, the execution unit will prioritize the nearest parking space. The execution unit can also maintain normal priorities if the driver is relaxed. Furthermore, if the driver is in a hurry, the execution unit can prioritize the parking space that can be reached in the shortest time. This allows for the use of more appropriate parking spaces by determining execution priorities according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input driver emotion data into a generating AI, which can then perform emotion estimation and determine execution priorities.

[0097] The execution unit can select the optimal execution method based on the driver's current schedule information. For example, the execution unit can select a parking lot that matches the driver's schedule, taking into account the driver's schedule information. The execution unit can also select a parking lot that is available during a specific time period based on the driver's schedule information. Furthermore, the execution unit can propose and execute the optimal parking lot based on the driver's schedule information. This improves the efficiency of parking lot utilization by selecting the optimal execution method while considering the driver's current schedule information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the driver's schedule information into a generating AI and have the generating AI select the optimal execution method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The parking management system can estimate the driver's emotions and notify them of parking availability based on those emotions. For example, if the driver is stressed, the system will prioritize notifying them of the nearest available parking space. If the driver is relaxed, the system can also suggest cheaper parking options. Furthermore, if the driver is in a hurry, the system can notify them of the parking space they can reach in the shortest time. This allows for optimal parking suggestions tailored to the driver's emotions.

[0100] A parking management system can monitor parking lot usage and detect abnormal patterns. For example, it can monitor parking lot usage and detect vehicles parked for unusually long periods. It can also detect sudden increases in usage during specific time periods. Furthermore, it can detect illegal parking. This allows for real-time monitoring of parking lot usage and the detection of abnormal patterns, thereby preventing illegal parking.

[0101] A parking management system can monitor environmental data of a parking lot and analyze parking lot usage trends. For example, it can monitor weather data to analyze parking lot usage trends during rainy weather. It can also monitor traffic volume data to analyze parking lot usage trends during traffic congestion. Furthermore, it can monitor information on surrounding events to analyze parking lot usage trends during events. By monitoring environmental data of a parking lot and analyzing usage trends, the efficiency of parking lot utilization can be improved.

[0102] The parking management system can estimate the driver's emotions and prioritize parking spaces based on those emotions. For example, if the driver is stressed, it will prioritize monitoring nearby parking spaces. If the driver is relaxed, it can maintain its normal monitoring priority. Furthermore, if the driver is in a hurry, it can prioritize monitoring the parking space closest to their destination. This allows for the provision of more appropriate parking spaces by prioritizing parking spaces according to the driver's emotions.

[0103] A parking management system can monitor the security status of a parking lot and assess its safety. For example, it can monitor footage from parking lot surveillance cameras in real time and detect suspicious activity. It can also monitor the lighting conditions of the parking lot and assess the safety of dark areas. Furthermore, it can monitor the conditions of parking lot entrances and exits to detect unauthorized entry. By monitoring the security status of the parking lot and assessing its safety, the safety of the parking lot can be improved.

[0104] The parking management system can estimate the driver's emotions and adjust its method of detecting available parking spaces based on those emotions. For example, if the driver is stressed, it will prioritize detecting the nearest available space. If the driver is relaxed, it can maintain the normal detection method. Furthermore, if the driver is in a hurry, it can prioritize detecting the available space that can be reached in the shortest time. This allows for the provision of more appropriate parking spaces by adjusting the detection method according to the driver's emotions.

[0105] Parking management systems can improve detection accuracy based on parking lot usage history. For example, they can prioritize detecting parking lots with many vacancies during specific time periods based on past usage history. They can also prioritize detecting parking lots with many vacancies on specific days of the week. Furthermore, they can prioritize detecting parking lots with many vacancies during specific events. By improving detection accuracy by considering parking lot usage history, it becomes possible to detect vacant spaces more accurately.

[0106] The parking management system can estimate the driver's emotions and adjust the way suggestions are presented based on those emotions. For example, if the driver is stressed, it will offer simple and easy-to-understand suggestions. If the driver is relaxed, it can offer suggestions with more detailed information. Furthermore, if the driver is in a hurry, it can offer quick and concise suggestions. By adjusting the way suggestions are presented according to the driver's emotions, it becomes possible to offer more appropriate parking plans.

[0107] A parking management system can suggest the optimal parking plan based on the characteristics of the parking lot. For example, it can consider the location of the parking lot and suggest the parking plan closest to the destination. It can also consider parking fee information and suggest the cheapest parking plan. Furthermore, it can consider the time of day information of the parking lot and suggest the optimal parking plan for the available time slot. In this way, by suggesting the optimal parking plan based on the characteristics of the parking lot, the efficiency of parking lot utilization is improved.

[0108] The parking management system can estimate the driver's emotions and adjust the timing of reservations based on those emotions. For example, if the driver is stressed, the reservation can be completed quickly. If the driver is relaxed, the reservation process can proceed as usual. Furthermore, if the driver is in a hurry, the reservation can be completed in the shortest possible time. This allows for more appropriate parking space reservations by adjusting the timing of reservations according to the driver's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The monitoring unit monitors parking lot data in real time. The monitoring unit monitors the usage status of the parking lot and checks for available spaces. It can also monitor and collect data on parking lot characteristics (location, fees, time of day). Step 2: The detection unit detects available spaces based on data monitored by the monitoring unit. The detection unit analyzes parking lot usage data to detect available spaces. It can also detect available spaces by considering parking lot characteristics (location, fees, time of day). Step 3: The suggestion unit proposes the optimal parking plan based on the available spaces detected by the detection unit. The suggestion unit proposes the optimal parking plan considering the characteristics of the parking lot (location, fees, time of day). It can also analyze past parking data to propose the optimal parking plan.

[0111] 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] 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.

[0114] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, reservation unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the usage status of the parking lot using the camera 42 and sensors of the smart device 14 and collects data with the control unit 46A. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects available spaces by analyzing the data from the monitoring unit. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal parking plan based on the information from the detection unit. The reservation unit is implemented in the specific processing unit 46A of the smart device 14 and allows drivers to reserve a parking space using an app. The execution unit is implemented in the specific processing unit 290 of the data processing unit 12 and manages the reservation status and executes the reservation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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.

[0130] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, reservation unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the usage status of the parking lot using the camera 42 and sensors of the smart glasses 214 and collects data with the control unit 46A. The detection unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and detects available spaces by analyzing the data from the monitoring unit. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and proposes the optimal parking plan based on the information from the detection unit. The reservation unit is implemented, for example, in the control unit 46A of the smart glasses 214 and allows drivers to reserve a parking space using an app. The execution unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and manages the reservation status and executes the reservation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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.

[0146] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, reservation unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the usage status of the parking lot using the camera 42 and sensors of the headset terminal 314 and collects data with the control unit 46A. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects available spaces by analyzing the data from the monitoring unit. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal parking plan based on the information from the detection unit. The reservation unit is implemented in the specific processing unit 46A of the headset terminal 314 and allows drivers to reserve a parking space using an app. The execution unit is implemented in the specific processing unit 290 of the data processing unit 12 and manages the reservation status and executes the reservation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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.

[0163] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, reservation unit, and execution unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the parking lot usage status using the camera 42 and sensors of the robot 414 and collects data using the control unit 46A. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects available spaces by analyzing the data from the monitoring unit. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal parking plan based on the information from the detection unit. The reservation unit is implemented in the specific processing unit 46A of the robot 414 and allows drivers to reserve a parking space using an app. The execution unit is implemented in the specific processing unit 290 of the data processing unit 12 and manages the reservation status and executes the reservation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A monitoring unit that monitors parking lot data in real time, A detection unit that detects available space based on data monitored by the aforementioned monitoring unit, A proposal unit that proposes an optimal parking plan based on the available space detected by the detection unit, Equipped with A system characterized by the following features. (Note 2) It has a reservation department for reserving parking spaces. The system described in Appendix 1, characterized by the features described herein. (Note 3) Executable unit for running the reservation system The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest the best parking options for drivers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It incorporates the unique characteristics of each parking lot and stores and analyzes past parking data as a memory. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Plan and propose the optimal parking plan to the driver. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, The system estimates the driver's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, The system monitors parking lot usage in real time and detects abnormal patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, We monitor parking lot environmental data and analyze parking lot usage trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It estimates the driver's emotions and determines the priority of parking spaces to monitor based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, Shift monitoring focus based on surrounding event information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, Monitor the security status of the parking lot and evaluate its safety. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is The system estimates the driver's emotions and adjusts the method of detecting available parking spaces based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is Improving detection accuracy based on parking lot usage history The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is Detection is performed based on the characteristics information of the parking lot. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is The system estimates the driver's emotions and prioritizes available parking spaces based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is Detection is performed based on surrounding traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is Detection is performed based on the security status of the parking lot. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the driver's emotions and adjusts the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We propose the optimal parking plan based on the characteristics of the parking lot. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, over number (Note 22) The aforementioned proposal section is, The system estimates the driver's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We suggest the optimal parking plan based on the driver's current destination information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We will suggest the optimal parking plan based on the parking lot's congestion status. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, The system estimates the driver's emotions and adjusts the timing of reservations based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned reservation section is, Select the optimal reservation method based on the characteristics of the parking lot. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned reservation section is, The system estimates the driver's emotions and prioritizes bookings based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned reservation section is, The optimal booking method is selected based on the driver's current schedule information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The execution unit is, It estimates the driver's emotions and adjusts the timing of execution based on the estimated driver's emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The execution unit is, Select the optimal implementation method based on the characteristics of the parking lot. The system described in Appendix 3, characterized by the features described herein. (Note 31) The execution unit is, The system estimates the driver's emotions and determines the priority of actions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The execution unit is, The optimal execution method is selected based on the driver's current schedule information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0183] 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 monitoring unit that monitors parking lot data in real time, A detection unit that detects available space based on data monitored by the aforementioned monitoring unit, A proposal unit that proposes an optimal parking plan based on the available space detected by the detection unit, Equipped with A system characterized by the following features.

2. It has a reservation department for reserving parking spaces. The system according to feature 1.

3. Executable unit for running the reservation system The system according to feature 1.

4. The aforementioned proposal section is, We suggest the best parking options for drivers. The system according to feature 1.

5. The aforementioned proposal section is, It incorporates the unique characteristics of each parking lot and stores and analyzes past parking data as a memory. The system according to feature 1.

6. The aforementioned proposal section is, Plan and propose the optimal parking plan to the driver. The system according to feature 1.

7. The aforementioned monitoring unit, The system estimates the driver's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, The system monitors parking lot usage in real time and detects abnormal patterns. The system according to feature 1.

9. The aforementioned monitoring unit, We monitor parking lot environmental data and analyze parking lot usage trends. The system according to feature 1.

10. The aforementioned monitoring unit, It estimates the driver's emotions and determines the priority of parking spaces to monitor based on the estimated emotions. The system according to feature 1.

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