system

Abstract

We provide the system. [Solution] A means of collecting information to obtain parking space availability information, weather information, and event information, A fee calculation means that automatically calculates parking fees based on the information acquired by the aforementioned information collection means, A notification means for notifying the user of the fee information calculated by the aforementioned fee calculation means, A system that includes this.

Description

Technical Field

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[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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0006] "Information gathering means" refers to the technological elements used to obtain information on parking lot availability, weather conditions, and surrounding events.

[0007] A "fee calculation method" is an element that has the function of automatically determining the optimal parking fee based on the demand for parking lot use, using the collected information.

[0008] A "notification method" refers to a technical element that has the function of sending calculated parking fee information as a push notification to the user's mobile device or other device.

[0009] A "system" is a technical mechanism that streamlines the operation and use of a parking lot through the coordinated operation of a series of components, including means of information gathering, means of fee calculation, and means of notification. [Brief explanation of the drawing]

[0010] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0011] 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.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0014] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0016] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0017] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] As shown in Figure 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.

[0021] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0022] 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.

[0023] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0024] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] The 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.

[0029] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0030] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0031] The present invention is a comprehensive system for achieving optimal pricing in parking lot operations, and includes information gathering means, pricing calculation means, and notification means. Specific embodiments using each of these means are described below.

[0032] First, the server obtains parking space availability information by utilizing vacancy sensors and cameras installed in the parking lot, as well as external databases connected to the network. In addition, it collects current weather forecasts and local event information through weather information APIs and event information APIs.

[0033] Next, the server integrates this information and inputs it into a generating AI model. The AI ​​model performs data analysis, including comparison with historical data, and calculates the optimal parking fee based on current demand forecasts. This fee is dynamically adjusted according to special conditions such as peak demand times and events.

[0034] The server transmits the calculated fee information to digital signage and the parking app. The parking app, acting as a terminal, notifies users of the received fee information via push notifications, promoting parking use based on real-time fee information. Furthermore, users can compare fees at other parking lots through the app, helping them make the best choice.

[0035] For example, if the server predicts twice the normal parking demand on a Saturday afternoon, the rates will be increased by 50%. This allows users who see the rate information in the push notification to take action, such as choosing a less crowded time slot or looking for alternative parking. In this way, the entire system works together to maximize parking lot occupancy and operational efficiency.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The server obtains parking space availability information from sensors and cameras installed in the parking lot. In addition, it obtains current weather data from a weather information API via the network, and also obtains information on nearby events from an event information API.

[0039] Step 2:

[0040] The server inputs acquired vacancy information, weather data, and event information into a generating AI model. Based on historical data, the AI ​​model predicts parking demand and calculates the optimal rate. This rate is dynamically adjusted according to the day of the week, time of day, and any specific events.

[0041] Step 3:

[0042] The server sends the calculated optimal price to the digital signage system and the parking app. The digital signage is installed at the parking lot entrance and on each floor, displaying real-time price information and parking availability.

[0043] Step 4:

[0044] The parking app, acting as a terminal, provides registered users with fee information received from the server via push notifications. These notifications can include information on changes in parking fees and special discounts.

[0045] Step 5:

[0046] Users check received notifications in the app. The app provides information to help them choose the best parking option by comparing current parking rates with those of other nearby parking lots.

[0047] Step 6:

[0048] Users can reserve parking spaces in advance through the app. By making a reservation, they can secure a designated parking space and park smoothly upon arrival.

[0049] Through these steps, the system optimizes parking lot usage and provides features that enhance convenience for users.

[0050] (Example 1)

[0051] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0052] In parking lot management, it is difficult to set optimal rates in real time in accordance with fluctuations in demand. Furthermore, there are currently limited means of providing users with instant information and encouraging efficient parking. As a result, there is a problem of declining parking lot occupancy rates and operational efficiency.

[0053] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0054] In this invention, the server includes data acquisition means for acquiring parking space availability information, weather information, and event information; data processing means for centralizing the data acquired by the data acquisition means and inputting the generated dataset into a generation AI model; and fee calculation means for dynamically calculating parking fees based on demand forecasts while referring to past data using the generation AI model. This enables fee setting that responds immediately to fluctuations in demand and real-time information provision to users.

[0055] "Data acquisition means" refers to means for acquiring information on parking space availability, weather information, and event information.

[0056] A "generative AI model" is an artificial intelligence model that predicts demand by referring to past data and derives the optimal parking fee through data analysis.

[0057] A "data processing method" is a means of centralizing acquired data and inputting the generated dataset into a generating AI model.

[0058] The "fee calculation method" is a method for dynamically calculating parking fees based on demand forecasts, while referencing historical data using a generative AI model.

[0059] "Information distribution means" refers to the means of distributing calculated fee information to users.

[0060] A "communication terminal" is an electronic device owned by a user and used to receive notifications such as parking fee information.

[0061] A "communication network" is a system of information transmission used to acquire and transmit data in real time.

[0062] This invention is a system that supports optimal pricing in parking lot operations and includes means for data acquisition, data processing, price calculation, and information distribution. Specific embodiments are described below.

[0063] The server collects real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The vacancy sensors detect the occupancy status of each parking space, and the cameras supplement this information through video analysis. The server also utilizes weather information APIs and event information APIs to obtain current weather forecasts and information on nearby events. This enables the collection of real-time and multifaceted data.

[0064] The collected data is centrally managed by a server and input into a generative AI model. The server uses this model to analyze the data and, referencing past data patterns, performs current and future demand forecasts. The generative AI model incorporates machine learning algorithms, enabling highly accurate predictions.

[0065] Based on demand forecasts, the server dynamically calculates parking fees. Pricing is adjusted to reflect fluctuations in demand, particularly during peak hours and events. This optimizes parking lot occupancy and maximizes revenue.

[0066] The calculated fee information is transmitted from the server to digital signage and the parking app, which acts as a terminal. The digital signage is installed at the entrance and exit of the parking lot and displays real-time fee information. The parking app provides this information to users via push notifications. In addition, this app, which acts as a terminal, also displays and compares the fees of other parking lots to help users make the best choice.

[0067] As a concrete example, consider a scenario where a server increases parking fees by 50% due to anticipated high demand on Saturday afternoons. Users who receive this information can then choose a less crowded time slot or look for alternative parking.

[0068] An example of input to the generating AI model would be a prompt message such as, "It's Saturday afternoon, the weather is sunny, and a large event is being held nearby. Please tell me the predicted parking demand and the optimal pricing." This prompt serves as the basis for the model to calculate appropriate pricing based on a specific scenario. In this way, the entire system works together to achieve efficient parking lot management.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server acquires real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The sensors detect the occupancy status of each parking space as a digital signal and transmit it to the server. The cameras capture video of the entire parking lot, and image analysis software identifies vacant spaces. The input data consists of sensor status data and camera video, and the output is integrated vacancy information.

[0072] Step 2:

[0073] The server obtains current weather forecasts and local event information through weather information APIs and event information APIs. The data obtained from the APIs concerns weather conditions and event dates and times. This information is integrated with seat availability information to form a dataset. The input is data from the APIs, and the output is the integrated dataset.

[0074] Step 3:

[0075] The server inputs the integrated dataset into a generative AI model. The generative AI model analyzes the data using a built-in machine learning algorithm based on historical parking usage data to forecast demand. In this process, the input data is transformed into a demand forecast. The output is a forecast result that predicts fluctuations in demand.

[0076] Step 4:

[0077] The server dynamically calculates parking fees using demand forecasts obtained from a generated AI model. The server applies an algorithm that changes the fee based on demand levels and specific event conditions. The input is the demand forecast, and the output is the optimal fee pattern.

[0078] Step 5:

[0079] The server transmits the calculated fee information to the digital signage and the parking app. The digital signage is a display device installed at the entrance of the parking lot, showing updated fees in real time. The parking app, acting as a terminal, sends this information to the user via push notification. The input is the fee information, and the output is the displayed fee notification.

[0080] Step 6:

[0081] Users check the fee information received via push notifications from the parking app on their device and make a parking decision. Users can also use the app to compare fees for different parking lots. Based on this information, users can choose the optimal parking lot and time. The input is the received fee information, and the output is the user's parking selection.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] Conventional parking management systems had the problem of being static, with static information on parking space availability and pricing, making them unable to respond to real-time demand fluctuations. Furthermore, there was a lack of information to compare multiple parking lots, making it difficult for users to efficiently choose a parking space.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] In this invention, the server includes an information gathering means for acquiring parking space availability information, weather-related information, and event-related information; a fee calculation means for automatically calculating parking fees based on the information acquired by the information gathering means and generating fee information that can be compared among multiple parking facilities; and a notification means for notifying the user of the fee information generated by the fee calculation means and assisting in making the optimal choice for parking. This enables real-time setting of parking fees in response to demand and allows users to make the optimal choice by comparing multiple parking facilities.

[0087] "Parking facility availability information" refers to information that shows the availability of parking spaces within a parking lot, providing real-time information on the number and location of available parking spaces.

[0088] "Weather-related information" refers to information about the weather, including data such as current weather, forecasts, temperature, and precipitation.

[0089] "Event-related information" refers to information about events and gatherings held in a specific region, and is used to predict the resulting changes in people's movements and traffic.

[0090] "Information gathering means" refers to devices and systems for acquiring the above-mentioned seat availability information, weather information, and event information, and includes means that utilize sensors, cameras, and network connections.

[0091] A "fee calculation method" is a means for calculating parking fees based on collected information and generating fee information that can be compared among multiple parking facilities.

[0092] "Notification means" refers to a means of informing users of charge information, and usually involves methods such as push notifications via mobile devices.

[0093] "Users" refer to individuals or companies that use the parking lot; they are the entities that utilize the parking facilities in search of a parking space.

[0094] A "communication network" is a network infrastructure for sending and receiving information in real time, and includes networks such as the internet and mobile networks.

[0095] To implement this invention, a system is required in which a server, a terminal, and a user work together. This system consists of the following elements.

[0096] The server is responsible for acquiring parking space availability information, weather-related information, and event-related information. This involves monitoring the status of parking facilities in real time using dedicated sensors and network cameras, and collecting relevant data using weather information APIs and local event information APIs. The internet is used as the communication network to collect and integrate this data.

[0097] The server uses the collected information to calculate parking fees. It utilizes the Python programming language and generative AI models such as Tensorflow (registered trademark) to analyze this data and predict demand patterns. Furthermore, it generates comparable parking fee information across multiple parking facilities. This process involves forecasting demand based on historical data, sending prompts to the generative AI model to determine the optimal pricing, and calculating the fee information. For example, the server might predict higher-than-usual parking demand on Saturday afternoons and dynamically adjust fees accordingly.

[0098] The terminal is responsible for notifying users of the generated fee information. Typically, a smartphone app is used to send fee information via push notifications to the user's mobile device. This allows users to obtain the most suitable parking information in real time using their smartphones and select the optimal parking lot.

[0099] Users receive notifications from the system via their terminals and make choices that suit their parking needs. This allows for more efficient use of parking facilities. An example of a prompt message might be: "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server collects parking space availability information. Specifically, it acquires data in real time from sensors and network cameras installed in the parking lot. The input is physical data from the sensors, which is converted into a digital format. The output is digital data indicating the availability of parking spaces. Based on this data, the server understands the current usage status of the parking spaces.

[0103] Step 2:

[0104] The server obtains weather-related and event-related information via APIs. Specifically, it accesses weather information APIs and local event information APIs to collect data on current weather, forecasts, and local events. The input is API requests, and the output is organized weather and event data. The server analyzes this input data to extract factors that influence parking demand.

[0105] Step 3:

[0106] The server integrates the collected data and uses a generative AI model to forecast demand. Specifically, it uses Python and TensorFlow to analyze real-time and historical data to predict fluctuations in parking demand. The inputs are vacancy information, weather data, and event data, and the output is the demand forecast result. The server uses the obtained forecast result to calculate the optimal parking fee.

[0107] Step 4:

[0108] The server uses a fee calculation method to calculate comparable parking fees across multiple parking facilities. Specifically, it sends a prompt message to the generating AI model stating, "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee." The input is the prompt message and the prediction result, and the output is dynamically adjusted fee information. The server generates the fee information and prepares it for provision to users.

[0109] Step 5:

[0110] The device receives billing information from the server and communicates it to the user via push notification. Specifically, billing information is sent to the mobile device via push notification and displayed. The input is billing information from the server, and the output is the notification information displayed on the user's device. This allows the user to check billing information in real time and make decisions accordingly.

[0111] Step 6:

[0112] The user selects the most suitable parking lot based on notification information displayed on their device. Specifically, it compares multiple parking fees and determines the most convenient parking lot based on the current situation. The input is the fee information displayed on the device, and the output is the user's parking lot selection. The user's decision is the final outcome of the system.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] This invention provides a system for optimizing parking lot operations that combines an emotion engine that recognizes user emotions. In addition to conventional information gathering, fee calculation, and notification methods, this system has a function that utilizes user emotion data to automatically improve parking services.

[0115] The server collects parking space availability information, weather information, and event information, and then analyzes the user's emotional state through an emotion engine. The emotion engine analyzes voice and text messages collected from the user's mobile device to identify their emotional state. For example, if it recognizes that the user is looking for parking under stressful circumstances, it can provide clearer navigation or offer special discounts.

[0116] Taking emotional data into consideration, the server adjusts parking fees using a fee calculation method. This adjustment can vary depending on a specific emotional state and is designed to provide the most suitable fee structure for the user.

[0117] The parking app, acting as a terminal, receives customized pricing and service information from a server based on the user's emotions and delivers it to the user as a push notification. This notification can include special services and discount information tailored to the user's emotions, thereby further enhancing user satisfaction.

[0118] As a concrete example, if the server analyzes the user's anxious voice data and determines that the user is urgently looking for parking, it will identify nearby available parking spaces and notify the user that they are available at a special rate. When this message is sent to the terminal, the user can make a quick decision, which can help reduce stress.

[0119] Thus, by combining an emotion engine, this invention makes it possible to provide advanced parking management that takes into account the user's emotional state and a customized user experience.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server collects current parking space availability information through sensors and cameras installed in the parking lot. In addition, it obtains current weather and nearby event information from weather information APIs and event information APIs.

[0123] Step 2:

[0124] The server receives user voice data and text messages through the terminal. This data is analyzed by an emotion engine to identify the user's emotional state (e.g., stressed, anxious, relaxed).

[0125] Step 3:

[0126] The server combines collected information on available parking spaces, weather, and events with the user's emotional state identified by the emotion engine, and uses a generative AI model to calculate parking fees. If the user is experiencing stress, special fee adjustments are made, such as applying a discount.

[0127] Step 4:

[0128] The latest pricing information and service details are sent from the server to the device. The parking app, acting as the device, provides this information to the user via push notifications, helping them to choose the best parking option in real time.

[0129] Step 5:

[0130] The user checks the notifications received in the app. These notifications include information on rates and special services customized to the user's emotional state. The user then uses this information to select the most suitable parking lot.

[0131] Step 6:

[0132] Users head to their chosen parking spot and park smoothly using the app. The app also collects appropriate feedback tailored to the user's emotions and uses this data to make further improvements for future use.

[0133] These steps allow a system that incorporates an emotion engine to provide a parking experience that takes the user's emotions into account.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0136] In the parking lot experience, the lack of service provision that considers the user's emotional state has led to situations where users experience stress. Conventional technology simply provides information on parking availability and fees, without customizing it to the individual user's situation or emotions. Therefore, it is necessary to improve user satisfaction by sensing the user's emotions and providing optimal fees and services based on those emotions.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes emotion analysis means for analyzing emotional data, fee adjustment means for adjusting parking fees based on the results obtained by the emotion analysis means, and customized notification means for transmitting customized fee information calculated by the fee adjustment means to the user. This makes it possible to provide a highly customized parking service that responds to the user's emotional state.

[0139] An "emotion analysis device" is a device that has the function of analyzing voice data and text messages collected from a user's individual device to identify the user's emotional state.

[0140] A "fee adjustment device" is a device that has the function of automatically calculating parking fees tailored to a specific user based on emotional state data obtained from an emotion analysis device.

[0141] A "customized notification device" is a device that has the function of instantly sending push notifications to the user's mobile device with individual charge information calculated by the charge adjustment device.

[0142] A "guidance provision device" is a device that has the function of generating guidance on special services or discounts according to the user's emotional state and sending it to the user at an appropriate time.

[0143] This invention is a system that optimizes parking services according to the user's emotional state. Specific embodiments of this system are described below.

[0144] The server uses sentiment analysis techniques to analyze voice data and text messages collected from the user's individual device, employing natural language processing techniques and machine learning algorithms. This allows for accurate identification of the user's emotional state. Specific software expected to utilize natural language processing libraries such as TensorFlow and PyTorch is anticipated.

[0145] Once the sentiment analysis is complete, the server uses a pricing adjustment mechanism to calculate the optimal parking fee based on the user's emotional state. For example, if the user is stressed, a discounted rate will be applied as part of a "stress reduction plan." Pricing adjustments are made considering parking availability, weather information, and event information, along with this sentiment data.

[0146] Subsequently, the server uses a customized notification method to send the adjusted billing information to the user's mobile device. This is done via push notification, allowing the information to be delivered to the user in real time. The device receives this notification and immediately alerts the user.

[0147] Furthermore, the server uses guidance delivery methods to generate special service guidance tailored to the user's emotions. In this process, a generative AI model is utilized to automatically generate guidance text that is most appropriate for the user. For example, by inputting a prompt such as "Please suggest a parking plan for a user experiencing high stress" into the generative AI model, specific guidance can be created.

[0148] With this configuration, servers, terminals, and users work together to enable flexible, user-centric parking management that leverages emotional data. This improves the convenience of using parking facilities and reduces user stress.

[0149] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0150] Step 1:

[0151] The server collects parking space availability information, weather information, and event information from its respective data sources. It takes data via the network as input, thereby obtaining basic data about the parking lot's operational status and the external environment. Its specific operations include API calls and periodic database queries.

[0152] Step 2:

[0153] Users send voice data and text messages to the system from their mobile devices. This data is input to a server and used to identify the user's emotional state through sentiment analysis. Specifically, this involves data transmission via a mobile application.

[0154] Step 3:

[0155] The server performs sentiment analysis. It receives user voice data and text messages as input and analyzes the data using natural language processing techniques and machine learning algorithms. This analysis outputs the user's emotional state (e.g., stress, feeling of security). Specific operations include the use of natural language processing models using TensorFlow and PyTorch.

[0156] Step 4:

[0157] The server adjusts parking fees based on the results of sentiment analysis. Using a fee adjustment mechanism, it calculates the optimal fee considering the input sentiment state and vacancy information. The output of this step is a fee plan customized for each user. The specific operation includes the execution of a fee calculation algorithm.

[0158] Step 5:

[0159] The server sends customized billing information to the device. Information is delivered to the user in real time via push notifications. This allows the device to display the billing information to the user. Specifically, this involves real-time message delivery using a notification system.

[0160] Step 6:

[0161] The device displays notifications sent to the user. The user can review these notifications and reserve a parking space if necessary. The output is the user's decision to reserve. Specific actions include displaying information on the UI and providing the reservation function.

[0162] (Application Example 2)

[0163] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0164] In modern parking systems, it is difficult to adequately alleviate the stress and dissatisfaction experienced by users. Specifically, there is a problem in that the provision of information regarding parking availability and fees is not optimized according to the user's situation and emotions, resulting in an unimproved user experience. Furthermore, there is a challenge in providing appropriate guidance that responds to the diverse emotions of users.

[0165] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0166] In this invention, the server includes information acquisition means for acquiring parking availability information, weather conditions, and event information; emotional analysis means for analyzing the user's emotional state; and guidance provision means for providing optimal guidance to the user. This enables the provision of flexible services that respond to the user's emotions and circumstances.

[0167] "Parking availability information" refers to information regarding the current availability and usability of parking spaces.

[0168] "Weather conditions" refer to information related to weather, such as climate, temperature, and precipitation in a particular region.

[0169] "Event information" refers to information about events and campaigns held in specific regions or facilities.

[0170] "Information acquisition means" refers to a method or apparatus for collecting and processing various types of information.

[0171] "Means of calculating fees" refers to a method or apparatus for calculating fees based on collected information.

[0172] "Emotional analysis means" refers to a method or device for analyzing a user's voice or text data to identify their emotional state.

[0173] "Information provision means" refers to a method or device for providing users with the most suitable information or services.

[0174] "Notification means" refers to a method or device for notifying or transmitting information to a user.

[0175] A "communication network" is a network that connects multiple devices in order to send and receive information.

[0176] The system for implementing this invention mainly consists of a server, a user terminal, and an emotion analysis engine.

[0177] The server acquires real-time information on parking availability, weather conditions, and event information via the network using information acquisition tools. In addition, it collects voice and text data from the user's smartphone or smart glasses and analyzes their emotional state through emotion analysis tools. For emotion analysis, the system uses Google® Cloud Speech-to-Text API to convert speech to text and IBM Watson® Tone Analyzer to identify emotions.

[0178] The server generates optimal parking directions based on the user's emotional state and notifies the user's terminal via a guidance delivery system. In this process, the Google Maps API is used to create the most suitable route directions for the user. Notifications are delivered instantly via a notification system, allowing users to receive information tailored to their situation in real time.

[0179] As a concrete example, the server analyzes the user's voice data, and if it determines that the user is tired, it guides them to nearby resting places or available cafes. This allows the user to reach their destination more comfortably.

[0180] An example of a prompt message might be: "Design an application that analyzes voice data for emotional content and provides optimal route guidance to the user. It should assess the user's fatigue level and suggest rest stops based on that."

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server collects real-time information on parking availability, weather conditions, and event information through various data acquisition methods. Input is data acquired via the network, and output is storage of this information in a database. Data is collected via API calls and integrated with existing information.

[0184] Step 2:

[0185] The user's device collects the user's voice data through a microphone. The input is voice data, which is converted into text data using speech recognition software. The output is text data, which is used for subsequent emotion analysis.

[0186] Step 3:

[0187] The server analyzes the converted text data using emotional analysis tools. The input is text data, and the emotional state is identified using APIs such as IBM Watson Tone Analyzer. The output is the result of the emotional state analysis, specifically the user's stress and fatigue levels, obtained as numerical values ​​or tags.

[0188] Step 4:

[0189] The server uses guidance mechanisms based on the obtained emotional state to generate services and routes suitable for the user. Input consists of the results of emotional analysis and real-time information, and the optimal route is calculated using the Google Maps API. Output is customized guidance information.

[0190] Step 5:

[0191] The device receives guidance information sent from the server and notifies the user. The input is guidance information from the server, which is then pushed to the user's smartphone or smart glasses. The output is the visual and audio guidance information that the user receives on the screen.

[0192] 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.

[0193] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] 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.

[0198] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0199] 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.

[0200] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0201] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0202] 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.

[0203] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0204] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The 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.

[0206] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0207] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0208] The present invention is a comprehensive system for achieving optimal pricing in parking lot operations, and includes information gathering means, pricing calculation means, and notification means. Specific embodiments using each of these means are described below.

[0209] First, the server obtains parking space availability information by utilizing vacancy sensors and cameras installed in the parking lot, as well as external databases connected to the network. In addition, it collects current weather forecasts and local event information through weather information APIs and event information APIs.

[0210] Next, the server integrates this information and inputs it into a generating AI model. The AI ​​model performs data analysis, including comparison with historical data, and calculates the optimal parking fee based on current demand forecasts. This fee is dynamically adjusted according to special conditions such as peak demand times and events.

[0211] The server transmits the calculated fee information to digital signage and the parking app. The parking app, acting as a terminal, notifies users of the received fee information via push notifications, promoting parking use based on real-time fee information. Furthermore, users can compare fees at other parking lots through the app, helping them make the best choice.

[0212] For example, if the server predicts twice the normal parking demand on a Saturday afternoon, the rates will be increased by 50%. This allows users who see the rate information in the push notification to take action, such as choosing a less crowded time slot or looking for alternative parking. In this way, the entire system works together to maximize parking lot occupancy and operational efficiency.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The server obtains parking space availability information from sensors and cameras installed in the parking lot. In addition, it obtains current weather data from a weather information API via the network, and also obtains information on nearby events from an event information API.

[0216] Step 2:

[0217] The server inputs acquired vacancy information, weather data, and event information into a generating AI model. Based on historical data, the AI ​​model predicts parking demand and calculates the optimal rate. This rate is dynamically adjusted according to the day of the week, time of day, and any specific events.

[0218] Step 3:

[0219] The server sends the calculated optimal price to the digital signage system and the parking app. The digital signage is installed at the parking lot entrance and on each floor, displaying real-time price information and parking availability.

[0220] Step 4:

[0221] The parking app, acting as a terminal, provides registered users with fee information received from the server via push notifications. These notifications can include information on changes in parking fees and special discounts.

[0222] Step 5:

[0223] Users check received notifications in the app. The app provides information to help them choose the best parking option by comparing current parking rates with those of other nearby parking lots.

[0224] Step 6:

[0225] Users can reserve parking spaces in advance through the app. By making a reservation, they can secure a designated parking space and park smoothly upon arrival.

[0226] Through these steps, the system optimizes parking lot usage and provides features that enhance convenience for users.

[0227] (Example 1)

[0228] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0229] In parking lot management, it is difficult to set optimal rates in real time in accordance with fluctuations in demand. Furthermore, there are currently limited means of providing users with instant information and encouraging efficient parking. As a result, there is a problem of declining parking lot occupancy rates and operational efficiency.

[0230] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0231] In this invention, the server includes data acquisition means for acquiring parking space availability information, weather information, and event information; data processing means for centralizing the data acquired by the data acquisition means and inputting the generated dataset into a generation AI model; and fee calculation means for dynamically calculating parking fees based on demand forecasts while referring to past data using the generation AI model. This enables fee setting that responds immediately to fluctuations in demand and real-time information provision to users.

[0232] "Data acquisition means" refers to means for acquiring information on parking space availability, weather information, and event information.

[0233] A "generative AI model" is an artificial intelligence model that predicts demand by referring to past data and derives the optimal parking fee through data analysis.

[0234] A "data processing method" is a means of centralizing acquired data and inputting the generated dataset into a generating AI model.

[0235] The "fee calculation method" is a method for dynamically calculating parking fees based on demand forecasts, while referencing historical data using a generative AI model.

[0236] "Information distribution means" refers to the means of distributing calculated fee information to users.

[0237] A "communication terminal" is an electronic device owned by a user and used to receive notifications such as parking fee information.

[0238] A "communication network" is a system of information transmission used to acquire and transmit data in real time.

[0239] This invention is a system that supports optimal pricing in parking lot operations and includes means for data acquisition, data processing, price calculation, and information distribution. Specific embodiments are described below.

[0240] The server collects real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The vacancy sensors detect the occupancy status of each parking space, and the cameras supplement this information through video analysis. The server also utilizes weather information APIs and event information APIs to obtain current weather forecasts and information on nearby events. This enables the collection of real-time and multifaceted data.

[0241] The collected data is centrally managed by a server and input into a generative AI model. The server uses this model to analyze the data and, referencing past data patterns, performs current and future demand forecasts. The generative AI model incorporates machine learning algorithms, enabling highly accurate predictions.

[0242] Based on demand forecasts, the server dynamically calculates parking fees. Pricing is adjusted to reflect fluctuations in demand, particularly during peak hours and events. This optimizes parking lot occupancy and maximizes revenue.

[0243] The calculated fee information is transmitted from the server to digital signage and the parking app, which acts as a terminal. The digital signage is installed at the entrance and exit of the parking lot and displays real-time fee information. The parking app provides this information to users via push notifications. In addition, this app, which acts as a terminal, also displays and compares the fees of other parking lots to help users make the best choice.

[0244] As a concrete example, consider a scenario where a server increases parking fees by 50% due to anticipated high demand on Saturday afternoons. Users who receive this information can then choose a less crowded time slot or look for alternative parking.

[0245] An example of input to the generating AI model would be a prompt message such as, "It's Saturday afternoon, the weather is sunny, and a large event is being held nearby. Please tell me the predicted parking demand and the optimal pricing." This prompt serves as the basis for the model to calculate appropriate pricing based on a specific scenario. In this way, the entire system works together to achieve efficient parking lot management.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The server acquires real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The sensors detect the occupancy status of each parking space as a digital signal and transmit it to the server. The cameras capture video of the entire parking lot, and image analysis software identifies vacant spaces. The input data consists of sensor status data and camera video, and the output is integrated vacancy information.

[0249] Step 2:

[0250] The server obtains current weather forecasts and local event information through weather information APIs and event information APIs. The data obtained from the APIs concerns weather conditions and event dates and times. This information is integrated with seat availability information to form a dataset. The input is data from the APIs, and the output is the integrated dataset.

[0251] Step 3:

[0252] The server inputs the integrated dataset into a generative AI model. The generative AI model analyzes the data using a built-in machine learning algorithm based on historical parking usage data to forecast demand. In this process, the input data is transformed into a demand forecast. The output is a forecast result that predicts fluctuations in demand.

[0253] Step 4:

[0254] The server dynamically calculates parking fees using demand forecasts obtained from a generated AI model. The server applies an algorithm that changes the fee based on demand levels and specific event conditions. The input is the demand forecast, and the output is the optimal fee pattern.

[0255] Step 5:

[0256] The server transmits the calculated fee information to the digital signage and the parking app. The digital signage is a display device installed at the entrance of the parking lot, showing updated fees in real time. The parking app, acting as a terminal, sends this information to the user via push notification. The input is the fee information, and the output is the displayed fee notification.

[0257] Step 6:

[0258] Users check the fee information received via push notifications from the parking app on their device and make a parking decision. Users can also use the app to compare fees for different parking lots. Based on this information, users can choose the optimal parking lot and time. The input is the received fee information, and the output is the user's parking selection.

[0259] (Application Example 1)

[0260] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] Conventional parking management systems had the problem of being static, with static information on parking space availability and pricing, making them unable to respond to real-time demand fluctuations. Furthermore, there was a lack of information to compare multiple parking lots, making it difficult for users to efficiently choose a parking space.

[0262] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0263] In this invention, the server includes an information gathering means for acquiring parking space availability information, weather-related information, and event-related information; a fee calculation means for automatically calculating parking fees based on the information acquired by the information gathering means and generating fee information that can be compared among multiple parking facilities; and a notification means for notifying the user of the fee information generated by the fee calculation means and assisting in making the optimal choice for parking. This enables real-time setting of parking fees in response to demand and allows users to make the optimal choice by comparing multiple parking facilities.

[0264] "Parking facility availability information" refers to information that shows the availability of parking spaces within a parking lot, providing real-time information on the number and location of available parking spaces.

[0265] "Weather-related information" refers to information about the weather, including data such as current weather, forecasts, temperature, and precipitation.

[0266] "Event-related information" refers to information about events and gatherings held in a specific region, and is used to predict the resulting changes in people's movements and traffic.

[0267] "Information gathering means" refers to devices and systems for acquiring the above-mentioned seat availability information, weather information, and event information, and includes means that utilize sensors, cameras, and network connections.

[0268] A "fee calculation method" is a means for calculating parking fees based on collected information and generating fee information that can be compared among multiple parking facilities.

[0269] "Notification means" refers to a means of informing users of charge information, and usually involves methods such as push notifications via mobile devices.

[0270] "Users" refer to individuals or companies that use the parking lot; they are the entities that utilize the parking facilities in search of a parking space.

[0271] A "communication network" is a network infrastructure for sending and receiving information in real time, and includes networks such as the internet and mobile networks.

[0272] To implement this invention, a system is required in which a server, a terminal, and a user work together. This system consists of the following elements.

[0273] The server is responsible for acquiring parking space availability information, weather-related information, and event-related information. This involves monitoring the status of parking facilities in real time using dedicated sensors and network cameras, and collecting relevant data using weather information APIs and local event information APIs. The internet is used as the communication network to collect and integrate this data.

[0274] The server uses the collected information to calculate parking fees. It utilizes the Python programming language and generative AI models such as TensorFlow to analyze this data and predict demand patterns. Furthermore, it generates comparable parking fee information across multiple parking facilities. This process involves forecasting demand based on historical data, sending prompts to the generative AI model to determine the optimal pricing, and calculating the fee information. For example, the server might predict higher-than-usual parking demand on Saturday afternoons and dynamically adjust fees accordingly.

[0275] The terminal is responsible for notifying users of the generated fee information. Typically, a smartphone app is used to send fee information via push notifications to the user's mobile device. This allows users to obtain the most suitable parking information in real time using their smartphones and select the optimal parking lot.

[0276] Users receive notifications from the system via their terminals and make choices that suit their parking needs. This allows for more efficient use of parking facilities. An example of a prompt message might be: "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee."

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] The server collects parking space availability information. Specifically, it acquires data in real time from sensors and network cameras installed in the parking lot. The input is physical data from the sensors, which is converted into a digital format. The output is digital data indicating the availability of parking spaces. Based on this data, the server understands the current usage status of the parking spaces.

[0280] Step 2:

[0281] The server obtains weather-related and event-related information via APIs. Specifically, it accesses weather information APIs and local event information APIs to collect data on current weather, forecasts, and local events. The input is API requests, and the output is organized weather and event data. The server analyzes this input data to extract factors that influence parking demand.

[0282] Step 3:

[0283] The server integrates the data collected and performs demand prediction using a generative AI model. Specifically, using Python and TensorFlow, it analyzes real-time data and past data to predict the fluctuations in parking demand. The inputs are vacancy information, weather data, and event data, and the output is the demand prediction result. The server calculates the optimal parking fee using the obtained prediction result.

[0284] Step 4:

[0285] The server uses a fee calculation means to calculate comparable parking fees among multiple parking facilities. Specifically, it sends a prompt sentence "Please predict the future parking demand based on the current vacancy situation of the parking lot, event information in the area, and weather information, and calculate the optimal fee." to the generative AI model. The inputs are the prompt sentence and the prediction result, and the output is dynamically adjusted fee information. The server generates the fee information and prepares to provide it to the user.

[0286] Step 5:

[0287] The terminal receives the fee information provided by the server and conveys it to the user through a push notification. Specifically, it sends and displays the fee information to the mobile information terminal via a push notification. The input is the fee information from the server, and the output is the notification information displayed on the user's terminal. Thereby, the user can confirm the fee information in real time and make a decision.

[0288] Step 6:

[0289] The user selects the optimal parking lot based on the notification information displayed on the terminal. Specifically, it compares multiple parking fees and determines the most convenient parking lot according to the current situation. The input is the fee information displayed on the terminal, and the output is the user's parking lot selection. The user's decision becomes the final outcome of the system.

[0290] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0291] This invention provides a system for optimizing parking lot operations that combines an emotion engine that recognizes user emotions. In addition to conventional information gathering, fee calculation, and notification methods, this system has a function that utilizes user emotion data to automatically improve parking services.

[0292] The server collects parking space availability information, weather information, and event information, and then analyzes the user's emotional state through an emotion engine. The emotion engine analyzes voice and text messages collected from the user's mobile device to identify their emotional state. For example, if it recognizes that the user is looking for parking under stressful circumstances, it can provide clearer navigation or offer special discounts.

[0293] Taking emotional data into consideration, the server adjusts parking fees using a fee calculation method. This adjustment can vary depending on a specific emotional state and is designed to provide the most suitable fee structure for the user.

[0294] The parking app, acting as a terminal, receives customized pricing and service information from a server based on the user's emotions and delivers it to the user as a push notification. This notification can include special services and discount information tailored to the user's emotions, thereby further enhancing user satisfaction.

[0295] As a concrete example, if the server analyzes the user's anxious voice data and determines that the user is urgently looking for parking, it will identify nearby available parking spaces and notify the user that they are available at a special rate. When this message is sent to the terminal, the user can make a quick decision, which can help reduce stress.

[0296] Thus, by combining an emotion engine, this invention makes it possible to provide advanced parking management that takes into account the user's emotional state and a customized user experience.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The server collects current parking space availability information through sensors and cameras installed in the parking lot. In addition, it obtains current weather and nearby event information from weather information APIs and event information APIs.

[0300] Step 2:

[0301] The server receives user voice data and text messages through the terminal. This data is analyzed by an emotion engine to identify the user's emotional state (e.g., stressed, anxious, relaxed).

[0302] Step 3:

[0303] The server combines collected information on available parking spaces, weather, and events with the user's emotional state identified by the emotion engine, and uses a generative AI model to calculate parking fees. If the user is experiencing stress, special fee adjustments are made, such as applying a discount.

[0304] Step 4:

[0305] The latest pricing information and service details are sent from the server to the device. The parking app, acting as the device, provides this information to the user via push notifications, helping them to choose the best parking option in real time.

[0306] Step 5:

[0307] The user checks the notifications received in the app. The notifications include fees customized according to the user's emotional state and information on special services. Based on this, the user selects the optimal parking lot.

[0308] Step 6:

[0309] The user goes to the selected parking lot and smoothly parks using the app. The app also collects appropriate feedback according to the user's emotions and utilizes the data for further improvement in subsequent uses.

[0310] Through these steps, the system combined with the emotion engine can provide a parking experience that takes into account the user's emotions.

[0311] (Example 2)

[0312] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] In parking lot usage, there is a lack of service provision considering the user's emotional state, leading to situations where users feel stressed. In the prior art, only the availability information and fee information of the parking lot are simply provided, and customization according to the individual situations and emotions of users cannot be achieved. Therefore, it is necessary to improve user satisfaction by sensing the user's emotions and providing optimal fees and services based on them.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0315] In this invention, the server includes emotion analysis means for analyzing emotional data, fee adjustment means for adjusting parking fees based on the results obtained by the emotion analysis means, and customized notification means for transmitting customized fee information calculated by the fee adjustment means to the user. This makes it possible to provide a highly customized parking service that responds to the user's emotional state.

[0316] An "emotion analysis device" is a device that has the function of analyzing voice data and text messages collected from a user's individual device to identify the user's emotional state.

[0317] A "fee adjustment device" is a device that has the function of automatically calculating parking fees tailored to a specific user based on emotional state data obtained from an emotion analysis device.

[0318] A "customized notification device" is a device that has the function of instantly sending push notifications to the user's mobile device with individual charge information calculated by the charge adjustment device.

[0319] A "guidance provision device" is a device that has the function of generating guidance on special services or discounts according to the user's emotional state and sending it to the user at an appropriate time.

[0320] This invention is a system that optimizes parking services according to the user's emotional state. Specific embodiments of this system are described below.

[0321] The server uses sentiment analysis techniques to analyze voice data and text messages collected from the user's individual device, employing natural language processing techniques and machine learning algorithms. This allows for accurate identification of the user's emotional state. Specific software expected to utilize natural language processing libraries such as TensorFlow and PyTorch is anticipated.

[0322] Once the sentiment analysis is complete, the server uses a pricing adjustment mechanism to calculate the optimal parking fee based on the user's emotional state. For example, if the user is stressed, a discounted rate will be applied as part of a "stress reduction plan." Pricing adjustments are made considering parking availability, weather information, and event information, along with this sentiment data.

[0323] Subsequently, the server uses a customized notification method to send the adjusted billing information to the user's mobile device. This is done via push notification, allowing the information to be delivered to the user in real time. The device receives this notification and immediately alerts the user.

[0324] Furthermore, the server uses guidance delivery methods to generate special service guidance tailored to the user's emotions. In this process, a generative AI model is utilized to automatically generate guidance text that is most appropriate for the user. For example, by inputting a prompt such as "Please suggest a parking plan for a user experiencing high stress" into the generative AI model, specific guidance can be created.

[0325] With this configuration, servers, terminals, and users work together to enable flexible, user-centric parking management that leverages emotional data. This improves the convenience of using parking facilities and reduces user stress.

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The server collects parking space availability information, weather information, and event information from its respective data sources. It takes data via the network as input, thereby obtaining basic data about the parking lot's operational status and the external environment. Its specific operations include API calls and periodic database queries.

[0329] Step 2:

[0330] Users send voice data and text messages to the system from their mobile devices. This data is input to a server and used to identify the user's emotional state through sentiment analysis. Specifically, this involves data transmission via a mobile application.

[0331] Step 3:

[0332] The server performs sentiment analysis. It receives user voice data and text messages as input and analyzes the data using natural language processing techniques and machine learning algorithms. This analysis outputs the user's emotional state (e.g., stress, feeling of security). Specific operations include the use of natural language processing models using TensorFlow and PyTorch.

[0333] Step 4:

[0334] The server adjusts parking fees based on the results of sentiment analysis. Using a fee adjustment mechanism, it calculates the optimal fee considering the input sentiment state and vacancy information. The output of this step is a fee plan customized for each user. The specific operation includes the execution of a fee calculation algorithm.

[0335] Step 5:

[0336] The server sends customized billing information to the device. Information is delivered to the user in real time via push notifications. This allows the device to display the billing information to the user. Specifically, this involves real-time message delivery using a notification system.

[0337] Step 6:

[0338] The device displays notifications sent to the user. The user can review these notifications and reserve a parking space if necessary. The output is the user's decision to reserve. Specific actions include displaying information on the UI and providing the reservation function.

[0339] (Application Example 2)

[0340] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0341] In modern parking systems, it is difficult to adequately alleviate the stress and dissatisfaction experienced by users. Specifically, there is a problem in that the provision of information regarding parking availability and fees is not optimized according to the user's situation and emotions, resulting in an unimproved user experience. Furthermore, there is a challenge in providing appropriate guidance that responds to the diverse emotions of users.

[0342] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0343] In this invention, the server includes information acquisition means for acquiring parking availability information, weather conditions, and event information; emotional analysis means for analyzing the user's emotional state; and guidance provision means for providing optimal guidance to the user. This enables the provision of flexible services that respond to the user's emotions and circumstances.

[0344] "Parking availability information" refers to information regarding the current availability and usability of parking spaces.

[0345] "Weather conditions" refer to information related to weather, such as climate, temperature, and precipitation in a particular region.

[0346] "Event information" refers to information about events and campaigns held in specific regions or facilities.

[0347] "Information acquisition means" refers to a method or apparatus for collecting and processing various types of information.

[0348] "Means of calculating fees" refers to a method or apparatus for calculating fees based on collected information.

[0349] "Emotional analysis means" refers to a method or device for analyzing a user's voice or text data to identify their emotional state.

[0350] "Information provision means" refers to a method or device for providing users with the most suitable information or services.

[0351] "Notification means" refers to a method or device for notifying or transmitting information to a user.

[0352] A "communication network" is a network that connects multiple devices in order to send and receive information.

[0353] The system for implementing this invention mainly consists of a server, a user terminal, and an emotion analysis engine.

[0354] The server acquires real-time information on parking availability, weather conditions, and event information via the network using information acquisition tools. In addition, it collects voice and text data from users' devices such as smartphones and smart glasses, and analyzes their emotional state through emotion analysis tools. For emotion analysis, the Google Cloud Speech-to-Text API is used to convert speech to text, and IBM Watson Tone Analyzer is used to identify emotions.

[0355] The server generates optimal parking directions based on the user's emotional state and notifies the user's terminal via a guidance delivery system. In this process, the Google Maps API is used to create the most suitable route directions for the user. Notifications are delivered instantly via a notification system, allowing users to receive information tailored to their situation in real time.

[0356] As a concrete example, the server analyzes the user's voice data, and if it determines that the user is tired, it guides them to nearby resting places or available cafes. This allows the user to reach their destination more comfortably.

[0357] An example of a prompt message might be: "Design an application that analyzes voice data for emotional content and provides optimal route guidance to the user. It should assess the user's fatigue level and suggest rest stops based on that."

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The server collects real-time information on parking availability, weather conditions, and event information through various data acquisition methods. Input is data acquired via the network, and output is storage of this information in a database. Data is collected via API calls and integrated with existing information.

[0361] Step 2:

[0362] The user's device collects the user's voice data through a microphone. The input is voice data, which is converted into text data using speech recognition software. The output is text data, which is used for subsequent emotion analysis.

[0363] Step 3:

[0364] The server analyzes the converted text data using emotional analysis tools. The input is text data, and the emotional state is identified using APIs such as IBM Watson Tone Analyzer. The output is the result of the emotional state analysis, specifically the user's stress and fatigue levels, obtained as numerical values ​​or tags.

[0365] Step 4:

[0366] The server uses guidance mechanisms based on the obtained emotional state to generate services and routes suitable for the user. Input consists of the results of emotional analysis and real-time information, and the optimal route is calculated using the Google Maps API. Output is customized guidance information.

[0367] Step 5:

[0368] The device receives guidance information sent from the server and notifies the user. The input is guidance information from the server, which is then pushed to the user's smartphone or smart glasses. The output is the visual and audio guidance information that the user receives on the screen.

[0369] 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.

[0370] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0371] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0372] [Third Embodiment]

[0373] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0374] 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.

[0375] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0376] 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.

[0377] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0378] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0379] 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.

[0380] 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.

[0381] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0382] The 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.

[0383] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0384] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0385] The present invention is a comprehensive system for achieving optimal pricing in parking lot operations, and includes information gathering means, pricing calculation means, and notification means. Specific embodiments using each of these means are described below.

[0386] First, the server obtains parking space availability information by utilizing vacancy sensors and cameras installed in the parking lot, as well as external databases connected to the network. In addition, it collects current weather forecasts and local event information through weather information APIs and event information APIs.

[0387] Next, the server integrates this information and inputs it into a generating AI model. The AI ​​model performs data analysis, including comparison with historical data, and calculates the optimal parking fee based on current demand forecasts. This fee is dynamically adjusted according to special conditions such as peak demand times and events.

[0388] The server transmits the calculated fee information to digital signage and the parking app. The parking app, acting as a terminal, notifies users of the received fee information via push notifications, promoting parking use based on real-time fee information. Furthermore, users can compare fees at other parking lots through the app, helping them make the best choice.

[0389] For example, if the server predicts twice the normal parking demand on a Saturday afternoon, the rates will be increased by 50%. This allows users who see the rate information in the push notification to take action, such as choosing a less crowded time slot or looking for alternative parking. In this way, the entire system works together to maximize parking lot occupancy and operational efficiency.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The server obtains parking space availability information from sensors and cameras installed in the parking lot. In addition, it obtains current weather data from a weather information API via the network, and also obtains information on nearby events from an event information API.

[0393] Step 2:

[0394] The server inputs acquired vacancy information, weather data, and event information into a generating AI model. Based on historical data, the AI ​​model predicts parking demand and calculates the optimal rate. This rate is dynamically adjusted according to the day of the week, time of day, and any specific events.

[0395] Step 3:

[0396] The server sends the calculated optimal price to the digital signage system and the parking app. The digital signage is installed at the parking lot entrance and on each floor, displaying real-time price information and parking availability.

[0397] Step 4:

[0398] The parking app, acting as a terminal, provides registered users with fee information received from the server via push notifications. These notifications can include information on changes in parking fees and special discounts.

[0399] Step 5:

[0400] Users check received notifications in the app. The app provides information to help them choose the best parking option by comparing current parking rates with those of other nearby parking lots.

[0401] Step 6:

[0402] Users can reserve parking spaces in advance through the app. By making a reservation, they can secure a designated parking space and park smoothly upon arrival.

[0403] Through these steps, the system optimizes parking lot usage and provides features that enhance convenience for users.

[0404] (Example 1)

[0405] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0406] In parking lot management, it is difficult to set optimal rates in real time in accordance with fluctuations in demand. Furthermore, there are currently limited means of providing users with instant information and encouraging efficient parking. As a result, there is a problem of declining parking lot occupancy rates and operational efficiency.

[0407] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0408] In this invention, the server includes data acquisition means for acquiring parking space availability information, weather information, and event information; data processing means for centralizing the data acquired by the data acquisition means and inputting the generated dataset into a generation AI model; and fee calculation means for dynamically calculating parking fees based on demand forecasts while referring to past data using the generation AI model. This enables fee setting that responds immediately to fluctuations in demand and real-time information provision to users.

[0409] "Data acquisition means" refers to means for acquiring information on parking space availability, weather information, and event information.

[0410] A "generative AI model" is an artificial intelligence model that predicts demand by referring to past data and derives the optimal parking fee through data analysis.

[0411] A "data processing method" is a means of centralizing acquired data and inputting the generated dataset into a generating AI model.

[0412] The "fee calculation method" is a method for dynamically calculating parking fees based on demand forecasts, while referencing historical data using a generative AI model.

[0413] "Information distribution means" refers to the means of distributing calculated fee information to users.

[0414] A "communication terminal" is an electronic device owned by a user and used to receive notifications such as parking fee information.

[0415] A "communication network" is a system of information transmission used to acquire and transmit data in real time.

[0416] This invention is a system that supports optimal pricing in parking lot operations and includes means for data acquisition, data processing, price calculation, and information distribution. Specific embodiments are described below.

[0417] The server collects real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The vacancy sensors detect the occupancy status of each parking space, and the cameras supplement this information through video analysis. The server also utilizes weather information APIs and event information APIs to obtain current weather forecasts and information on nearby events. This enables the collection of real-time and multifaceted data.

[0418] The collected data is centrally managed by a server and input into a generative AI model. The server uses this model to analyze the data and, referencing past data patterns, performs current and future demand forecasts. The generative AI model incorporates machine learning algorithms, enabling highly accurate predictions.

[0419] Based on demand forecasts, the server dynamically calculates parking fees. Pricing is adjusted to reflect fluctuations in demand, particularly during peak hours and events. This optimizes parking lot occupancy and maximizes revenue.

[0420] The calculated fee information is transmitted from the server to digital signage and the parking app, which acts as a terminal. The digital signage is installed at the entrance and exit of the parking lot and displays real-time fee information. The parking app provides this information to users via push notifications. In addition, this app, which acts as a terminal, also displays and compares the fees of other parking lots to help users make the best choice.

[0421] As a concrete example, consider a scenario where a server increases parking fees by 50% due to anticipated high demand on Saturday afternoons. Users who receive this information can then choose a less crowded time slot or look for alternative parking.

[0422] An example of input to the generating AI model would be a prompt message such as, "It's Saturday afternoon, the weather is sunny, and a large event is being held nearby. Please tell me the predicted parking demand and the optimal pricing." This prompt serves as the basis for the model to calculate appropriate pricing based on a specific scenario. In this way, the entire system works together to achieve efficient parking lot management.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The server acquires real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The sensors detect the occupancy status of each parking space as a digital signal and transmit it to the server. The cameras capture video of the entire parking lot, and image analysis software identifies vacant spaces. The input data consists of sensor status data and camera video, and the output is integrated vacancy information.

[0426] Step 2:

[0427] The server obtains current weather forecasts and local event information through weather information APIs and event information APIs. The data obtained from the APIs concerns weather conditions and event dates and times. This information is integrated with seat availability information to form a dataset. The input is data from the APIs, and the output is the integrated dataset.

[0428] Step 3:

[0429] The server inputs the integrated dataset into a generative AI model. The generative AI model analyzes the data using a built-in machine learning algorithm based on historical parking usage data to forecast demand. In this process, the input data is transformed into a demand forecast. The output is a forecast result that predicts fluctuations in demand.

[0430] Step 4:

[0431] The server dynamically calculates parking fees using demand forecasts obtained from a generated AI model. The server applies an algorithm that changes the fee based on demand levels and specific event conditions. The input is the demand forecast, and the output is the optimal fee pattern.

[0432] Step 5:

[0433] The server transmits the calculated fee information to the digital signage and the parking app. The digital signage is a display device installed at the entrance of the parking lot, showing updated fees in real time. The parking app, acting as a terminal, sends this information to the user via push notification. The input is the fee information, and the output is the displayed fee notification.

[0434] Step 6:

[0435] Users check the fee information received via push notifications from the parking app on their device and make a parking decision. Users can also use the app to compare fees for different parking lots. Based on this information, users can choose the optimal parking lot and time. The input is the received fee information, and the output is the user's parking selection.

[0436] (Application Example 1)

[0437] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0438] Conventional parking management systems had the problem of being static, with static information on parking space availability and pricing, making them unable to respond to real-time demand fluctuations. Furthermore, there was a lack of information to compare multiple parking lots, making it difficult for users to efficiently choose a parking space.

[0439] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0440] In this invention, the server includes an information gathering means for acquiring parking space availability information, weather-related information, and event-related information; a fee calculation means for automatically calculating parking fees based on the information acquired by the information gathering means and generating fee information that can be compared among multiple parking facilities; and a notification means for notifying the user of the fee information generated by the fee calculation means and assisting in making the optimal choice for parking. This enables real-time setting of parking fees in response to demand and allows users to make the optimal choice by comparing multiple parking facilities.

[0441] "Parking facility availability information" refers to information that shows the availability of parking spaces within a parking lot, providing real-time information on the number and location of available parking spaces.

[0442] "Weather-related information" refers to information about the weather, including data such as current weather, forecasts, temperature, and precipitation.

[0443] "Event-related information" refers to information about events and gatherings held in a specific region, and is used to predict the resulting changes in people's movements and traffic.

[0444] "Information gathering means" refers to devices and systems for acquiring the above-mentioned seat availability information, weather information, and event information, and includes means that utilize sensors, cameras, and network connections.

[0445] A "fee calculation method" is a means for calculating parking fees based on collected information and generating fee information that can be compared among multiple parking facilities.

[0446] "Notification means" refers to a means of informing users of charge information, and usually involves methods such as push notifications via mobile devices.

[0447] "Users" refer to individuals or companies that use the parking lot; they are the entities that utilize the parking facilities in search of a parking space.

[0448] A "communication network" is a network infrastructure for sending and receiving information in real time, and includes networks such as the internet and mobile networks.

[0449] To implement this invention, a system is required in which a server, a terminal, and a user work together. This system consists of the following elements.

[0450] The server is responsible for acquiring parking space availability information, weather-related information, and event-related information. This involves monitoring the status of parking facilities in real time using dedicated sensors and network cameras, and collecting relevant data using weather information APIs and local event information APIs. The internet is used as the communication network to collect and integrate this data.

[0451] The server uses the collected information to calculate parking fees. It utilizes the Python programming language and generative AI models such as TensorFlow to analyze this data and predict demand patterns. Furthermore, it generates comparable parking fee information across multiple parking facilities. This process involves forecasting demand based on historical data, sending prompts to the generative AI model to determine the optimal pricing, and calculating the fee information. For example, the server might predict higher-than-usual parking demand on Saturday afternoons and dynamically adjust fees accordingly.

[0452] The terminal is responsible for notifying users of the generated fee information. Typically, a smartphone app is used to send fee information via push notifications to the user's mobile device. This allows users to obtain the most suitable parking information in real time using their smartphones and select the optimal parking lot.

[0453] Users receive notifications from the system via their terminals and make choices that suit their parking needs. This allows for more efficient use of parking facilities. An example of a prompt message might be: "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee."

[0454] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0455] Step 1:

[0456] The server collects parking space availability information. Specifically, it acquires data in real time from sensors and network cameras installed in the parking lot. The input is physical data from the sensors, which is converted into a digital format. The output is digital data indicating the availability of parking spaces. Based on this data, the server understands the current usage status of the parking spaces.

[0457] Step 2:

[0458] The server obtains weather-related and event-related information via APIs. Specifically, it accesses weather information APIs and local event information APIs to collect data on current weather, forecasts, and local events. The input is API requests, and the output is organized weather and event data. The server analyzes this input data to extract factors that influence parking demand.

[0459] Step 3:

[0460] The server integrates the collected data and uses a generative AI model to forecast demand. Specifically, it uses Python and TensorFlow to analyze real-time and historical data to predict fluctuations in parking demand. The inputs are vacancy information, weather data, and event data, and the output is the demand forecast result. The server uses the obtained forecast result to calculate the optimal parking fee.

[0461] Step 4:

[0462] The server uses a fee calculation method to calculate comparable parking fees across multiple parking facilities. Specifically, it sends a prompt message to the generating AI model stating, "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee." The input is the prompt message and the prediction result, and the output is dynamically adjusted fee information. The server generates the fee information and prepares it for provision to users.

[0463] Step 5:

[0464] The device receives billing information from the server and communicates it to the user via push notification. Specifically, billing information is sent to the mobile device via push notification and displayed. The input is billing information from the server, and the output is the notification information displayed on the user's device. This allows the user to check billing information in real time and make decisions accordingly.

[0465] Step 6:

[0466] The user selects the most suitable parking lot based on notification information displayed on their device. Specifically, it compares multiple parking fees and determines the most convenient parking lot based on the current situation. The input is the fee information displayed on the device, and the output is the user's parking lot selection. The user's decision is the final outcome of the system.

[0467] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0468] This invention provides a system for optimizing parking lot operations that combines an emotion engine that recognizes user emotions. In addition to conventional information gathering, fee calculation, and notification methods, this system has a function that utilizes user emotion data to automatically improve parking services.

[0469] The server collects parking space availability information, weather information, and event information, and then analyzes the user's emotional state through an emotion engine. The emotion engine analyzes voice and text messages collected from the user's mobile device to identify their emotional state. For example, if it recognizes that the user is looking for parking under stressful circumstances, it can provide clearer navigation or offer special discounts.

[0470] Taking emotional data into consideration, the server adjusts parking fees using a fee calculation method. This adjustment can vary depending on a specific emotional state and is designed to provide the most suitable fee structure for the user.

[0471] The parking app, acting as a terminal, receives customized pricing and service information from a server based on the user's emotions and delivers it to the user as a push notification. This notification can include special services and discount information tailored to the user's emotions, thereby further enhancing user satisfaction.

[0472] As a concrete example, if the server analyzes the user's anxious voice data and determines that the user is urgently looking for parking, it will identify nearby available parking spaces and notify the user that they are available at a special rate. When this message is sent to the terminal, the user can make a quick decision, which can help reduce stress.

[0473] Thus, by combining an emotion engine, this invention makes it possible to provide advanced parking management that takes into account the user's emotional state and a customized user experience.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The server collects current parking space availability information through sensors and cameras installed in the parking lot. In addition, it obtains current weather and nearby event information from weather information APIs and event information APIs.

[0477] Step 2:

[0478] The server receives user voice data and text messages through the terminal. This data is analyzed by an emotion engine to identify the user's emotional state (e.g., stressed, anxious, relaxed).

[0479] Step 3:

[0480] The server combines collected information on available parking spaces, weather, and events with the user's emotional state identified by the emotion engine, and uses a generative AI model to calculate parking fees. If the user is experiencing stress, special fee adjustments are made, such as applying a discount.

[0481] Step 4:

[0482] The latest pricing information and service details are sent from the server to the device. The parking app, acting as the device, provides this information to the user via push notifications, helping them to choose the best parking option in real time.

[0483] Step 5:

[0484] The user checks the notifications received in the app. These notifications include information on rates and special services customized to the user's emotional state. The user then uses this information to select the most suitable parking lot.

[0485] Step 6:

[0486] Users head to their chosen parking spot and park smoothly using the app. The app also collects appropriate feedback tailored to the user's emotions and uses this data to make further improvements for future use.

[0487] These steps allow a system that incorporates an emotion engine to provide a parking experience that takes the user's emotions into account.

[0488] (Example 2)

[0489] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] In the parking lot experience, the lack of service provision that considers the user's emotional state has led to situations where users experience stress. Conventional technology simply provides information on parking availability and fees, without customizing it to the individual user's situation or emotions. Therefore, it is necessary to improve user satisfaction by sensing the user's emotions and providing optimal fees and services based on those emotions.

[0491] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0492] In this invention, the server includes emotion analysis means for analyzing emotional data, fee adjustment means for adjusting parking fees based on the results obtained by the emotion analysis means, and customized notification means for transmitting customized fee information calculated by the fee adjustment means to the user. This makes it possible to provide a highly customized parking service that responds to the user's emotional state.

[0493] An "emotion analysis device" is a device that has the function of analyzing voice data and text messages collected from a user's individual device to identify the user's emotional state.

[0494] A "fee adjustment device" is a device that has the function of automatically calculating parking fees tailored to a specific user based on emotional state data obtained from an emotion analysis device.

[0495] A "customized notification device" is a device that has the function of instantly sending push notifications to the user's mobile device with individual charge information calculated by the charge adjustment device.

[0496] A "guidance provision device" is a device that has the function of generating guidance on special services or discounts according to the user's emotional state and sending it to the user at an appropriate time.

[0497] This invention is a system that optimizes parking services according to the user's emotional state. Specific embodiments of this system are described below.

[0498] The server uses sentiment analysis techniques to analyze voice data and text messages collected from the user's individual device, employing natural language processing techniques and machine learning algorithms. This allows for accurate identification of the user's emotional state. Specific software expected to utilize natural language processing libraries such as TensorFlow and PyTorch is anticipated.

[0499] Once the sentiment analysis is complete, the server uses a pricing adjustment mechanism to calculate the optimal parking fee based on the user's emotional state. For example, if the user is stressed, a discounted rate will be applied as part of a "stress reduction plan." Pricing adjustments are made considering parking availability, weather information, and event information, along with this sentiment data.

[0500] Subsequently, the server uses a customized notification method to send the adjusted billing information to the user's mobile device. This is done via push notification, allowing the information to be delivered to the user in real time. The device receives this notification and immediately alerts the user.

[0501] Furthermore, the server uses guidance delivery methods to generate special service guidance tailored to the user's emotions. In this process, a generative AI model is utilized to automatically generate guidance text that is most appropriate for the user. For example, by inputting a prompt such as "Please suggest a parking plan for a user experiencing high stress" into the generative AI model, specific guidance can be created.

[0502] With this configuration, servers, terminals, and users work together to enable flexible, user-centric parking management that leverages emotional data. This improves the convenience of using parking facilities and reduces user stress.

[0503] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0504] Step 1:

[0505] The server collects parking space availability information, weather information, and event information from its respective data sources. It takes data via the network as input, thereby obtaining basic data about the parking lot's operational status and the external environment. Its specific operations include API calls and periodic database queries.

[0506] Step 2:

[0507] Users send voice data and text messages to the system from their mobile devices. This data is input to a server and used to identify the user's emotional state through sentiment analysis. Specifically, this involves data transmission via a mobile application.

[0508] Step 3:

[0509] The server performs sentiment analysis. It receives user voice data and text messages as input and analyzes the data using natural language processing techniques and machine learning algorithms. This analysis outputs the user's emotional state (e.g., stress, feeling of security). Specific operations include the use of natural language processing models using TensorFlow and PyTorch.

[0510] Step 4:

[0511] The server adjusts parking fees based on the results of sentiment analysis. Using a fee adjustment mechanism, it calculates the optimal fee considering the input sentiment state and vacancy information. The output of this step is a fee plan customized for each user. The specific operation includes the execution of a fee calculation algorithm.

[0512] Step 5:

[0513] The server sends customized billing information to the device. Information is delivered to the user in real time via push notifications. This allows the device to display the billing information to the user. Specifically, this involves real-time message delivery using a notification system.

[0514] Step 6:

[0515] The device displays notifications sent to the user. The user can review these notifications and reserve a parking space if necessary. The output is the user's decision to reserve. Specific actions include displaying information on the UI and providing the reservation function.

[0516] (Application Example 2)

[0517] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] In modern parking systems, it is difficult to adequately alleviate the stress and dissatisfaction experienced by users. Specifically, there is a problem in that the provision of information regarding parking availability and fees is not optimized according to the user's situation and emotions, resulting in an unimproved user experience. Furthermore, there is a challenge in providing appropriate guidance that responds to the diverse emotions of users.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0520] In this invention, the server includes information acquisition means for acquiring parking availability information, weather conditions, and event information; emotional analysis means for analyzing the user's emotional state; and guidance provision means for providing optimal guidance to the user. This enables the provision of flexible services that respond to the user's emotions and circumstances.

[0521] "Parking availability information" refers to information regarding the current availability and usability of parking spaces.

[0522] "Weather conditions" refer to information related to weather, such as climate, temperature, and precipitation in a particular region.

[0523] "Event information" refers to information about events and campaigns held in specific regions or facilities.

[0524] "Information acquisition means" refers to a method or apparatus for collecting and processing various types of information.

[0525] "Means of calculating fees" refers to a method or apparatus for calculating fees based on collected information.

[0526] "Emotional analysis means" refers to a method or device for analyzing a user's voice or text data to identify their emotional state.

[0527] "Information provision means" refers to a method or device for providing users with the most suitable information or services.

[0528] "Notification means" refers to a method or device for notifying or transmitting information to a user.

[0529] A "communication network" is a network that connects multiple devices in order to send and receive information.

[0530] The system for implementing this invention mainly consists of a server, a user terminal, and an emotion analysis engine.

[0531] The server acquires real-time information on parking availability, weather conditions, and event information via the network using information acquisition tools. In addition, it collects voice and text data from users' devices such as smartphones and smart glasses, and analyzes their emotional state through emotion analysis tools. For emotion analysis, the Google Cloud Speech-to-Text API is used to convert speech to text, and IBM Watson Tone Analyzer is used to identify emotions.

[0532] The server generates optimal parking directions based on the user's emotional state and notifies the user's terminal via a guidance delivery system. In this process, the Google Maps API is used to create the most suitable route directions for the user. Notifications are delivered instantly via a notification system, allowing users to receive information tailored to their situation in real time.

[0533] As a concrete example, the server analyzes the user's voice data, and if it determines that the user is tired, it guides them to nearby resting places or available cafes. This allows the user to reach their destination more comfortably.

[0534] An example of a prompt message might be: "Design an application that analyzes voice data for emotional content and provides optimal route guidance to the user. It should assess the user's fatigue level and suggest rest stops based on that."

[0535] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0536] Step 1:

[0537] The server collects real-time information on parking availability, weather conditions, and event information through various data acquisition methods. Input is data acquired via the network, and output is storage of this information in a database. Data is collected via API calls and integrated with existing information.

[0538] Step 2:

[0539] The user's device collects the user's voice data through a microphone. The input is voice data, which is converted into text data using speech recognition software. The output is text data, which is used for subsequent emotion analysis.

[0540] Step 3:

[0541] The server analyzes the converted text data using emotional analysis tools. The input is text data, and the emotional state is identified using APIs such as IBM Watson Tone Analyzer. The output is the result of the emotional state analysis, specifically the user's stress and fatigue levels, obtained as numerical values ​​or tags.

[0542] Step 4:

[0543] The server uses guidance mechanisms based on the obtained emotional state to generate services and routes suitable for the user. Input consists of the results of emotional analysis and real-time information, and the optimal route is calculated using the Google Maps API. Output is customized guidance information.

[0544] Step 5:

[0545] The device receives guidance information sent from the server and notifies the user. The input is guidance information from the server, which is then pushed to the user's smartphone or smart glasses. The output is the visual and audio guidance information that the user receives on the screen.

[0546] 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.

[0547] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0548] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0549] [Fourth Embodiment]

[0550] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0551] 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.

[0552] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0553] 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.

[0554] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0555] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0556] 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.

[0557] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0558] 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.

[0559] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0560] The 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.

[0561] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0562] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0563] The present invention is a comprehensive system for achieving optimal pricing in parking lot operations, and includes information gathering means, pricing calculation means, and notification means. Specific embodiments using each of these means are described below.

[0564] First, the server obtains parking space availability information by utilizing vacancy sensors and cameras installed in the parking lot, as well as external databases connected to the network. In addition, it collects current weather forecasts and local event information through weather information APIs and event information APIs.

[0565] Next, the server integrates this information and inputs it into a generating AI model. The AI ​​model performs data analysis, including comparison with historical data, and calculates the optimal parking fee based on current demand forecasts. This fee is dynamically adjusted according to special conditions such as peak demand times and events.

[0566] The server transmits the calculated fee information to digital signage and the parking app. The parking app, acting as a terminal, notifies users of the received fee information via push notifications, promoting parking use based on real-time fee information. Furthermore, users can compare fees at other parking lots through the app, helping them make the best choice.

[0567] For example, if the server predicts twice the normal parking demand on a Saturday afternoon, the rates will be increased by 50%. This allows users who see the rate information in the push notification to take action, such as choosing a less crowded time slot or looking for alternative parking. In this way, the entire system works together to maximize parking lot occupancy and operational efficiency.

[0568] The following describes the processing flow.

[0569] Step 1:

[0570] The server obtains parking space availability information from sensors and cameras installed in the parking lot. In addition, it obtains current weather data from a weather information API via the network, and also obtains information on nearby events from an event information API.

[0571] Step 2:

[0572] The server inputs acquired vacancy information, weather data, and event information into a generating AI model. Based on historical data, the AI ​​model predicts parking demand and calculates the optimal rate. This rate is dynamically adjusted according to the day of the week, time of day, and any specific events.

[0573] Step 3:

[0574] The server sends the calculated optimal price to the digital signage system and the parking app. The digital signage is installed at the parking lot entrance and on each floor, displaying real-time price information and parking availability.

[0575] Step 4:

[0576] The parking app, acting as a terminal, provides registered users with fee information received from the server via push notifications. These notifications can include information on changes in parking fees and special discounts.

[0577] Step 5:

[0578] Users check received notifications in the app. The app provides information to help them choose the best parking option by comparing current parking rates with those of other nearby parking lots.

[0579] Step 6:

[0580] Users can reserve parking spaces in advance through the app. By making a reservation, they can secure a designated parking space and park smoothly upon arrival.

[0581] Through these steps, the system optimizes parking lot usage and provides features that enhance convenience for users.

[0582] (Example 1)

[0583] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] In parking lot management, it is difficult to set optimal rates in real time in accordance with fluctuations in demand. Furthermore, there are currently limited means of providing users with instant information and encouraging efficient parking. As a result, there is a problem of declining parking lot occupancy rates and operational efficiency.

[0585] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0586] In this invention, the server includes data acquisition means for acquiring parking space availability information, weather information, and event information; data processing means for centralizing the data acquired by the data acquisition means and inputting the generated dataset into a generation AI model; and fee calculation means for dynamically calculating parking fees based on demand forecasts while referring to past data using the generation AI model. This enables fee setting that responds immediately to fluctuations in demand and real-time information provision to users.

[0587] "Data acquisition means" refers to means for acquiring information on parking space availability, weather information, and event information.

[0588] A "generative AI model" is an artificial intelligence model that predicts demand by referring to past data and derives the optimal parking fee through data analysis.

[0589] A "data processing method" is a means of centralizing acquired data and inputting the generated dataset into a generating AI model.

[0590] The "fee calculation method" is a method for dynamically calculating parking fees based on demand forecasts, while referencing historical data using a generative AI model.

[0591] "Information distribution means" refers to the means of distributing calculated fee information to users.

[0592] A "communication terminal" is an electronic device owned by a user and used to receive notifications such as parking fee information.

[0593] A "communication network" is a system of information transmission used to acquire and transmit data in real time.

[0594] This invention is a system that supports optimal pricing in parking lot operations and includes means for data acquisition, data processing, price calculation, and information distribution. Specific embodiments are described below.

[0595] The server collects real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The vacancy sensors detect the occupancy status of each parking space, and the cameras supplement this information through video analysis. The server also utilizes weather information APIs and event information APIs to obtain current weather forecasts and information on nearby events. This enables the collection of real-time and multifaceted data.

[0596] The collected data is centrally managed by a server and input into a generative AI model. The server uses this model to analyze the data and, referencing past data patterns, performs current and future demand forecasts. The generative AI model incorporates machine learning algorithms, enabling highly accurate predictions.

[0597] Based on demand forecasts, the server dynamically calculates parking fees. Pricing is adjusted to reflect fluctuations in demand, particularly during peak hours and events. This optimizes parking lot occupancy and maximizes revenue.

[0598] The calculated fee information is transmitted from the server to digital signage and the parking app, which acts as a terminal. The digital signage is installed at the entrance and exit of the parking lot and displays real-time fee information. The parking app provides this information to users via push notifications. In addition, this app, which acts as a terminal, also displays and compares the fees of other parking lots to help users make the best choice.

[0599] As a concrete example, consider a scenario where a server increases parking fees by 50% due to anticipated high demand on Saturday afternoons. Users who receive this information can then choose a less crowded time slot or look for alternative parking.

[0600] An example of input to the generating AI model would be a prompt message such as, "It's Saturday afternoon, the weather is sunny, and a large event is being held nearby. Please tell me the predicted parking demand and the optimal pricing." This prompt serves as the basis for the model to calculate appropriate pricing based on a specific scenario. In this way, the entire system works together to achieve efficient parking lot management.

[0601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0602] Step 1:

[0603] The server acquires real-time parking space availability information using vacancy sensors and cameras installed in the parking lot. The sensors detect the occupancy status of each parking space as a digital signal and transmit it to the server. The cameras capture video of the entire parking lot, and image analysis software identifies vacant spaces. The input data consists of sensor status data and camera video, and the output is integrated vacancy information.

[0604] Step 2:

[0605] The server obtains current weather forecasts and local event information through weather information APIs and event information APIs. The data obtained from the APIs concerns weather conditions and event dates and times. This information is integrated with seat availability information to form a dataset. The input is data from the APIs, and the output is the integrated dataset.

[0606] Step 3:

[0607] The server inputs the integrated dataset into a generative AI model. The generative AI model analyzes the data using a built-in machine learning algorithm based on historical parking usage data to forecast demand. In this process, the input data is transformed into a demand forecast. The output is a forecast result that predicts fluctuations in demand.

[0608] Step 4:

[0609] The server dynamically calculates parking fees using demand forecasts obtained from a generated AI model. The server applies an algorithm that changes the fee based on demand levels and specific event conditions. The input is the demand forecast, and the output is the optimal fee pattern.

[0610] Step 5:

[0611] The server transmits the calculated fee information to the digital signage and the parking app. The digital signage is a display device installed at the entrance of the parking lot, showing updated fees in real time. The parking app, acting as a terminal, sends this information to the user via push notification. The input is the fee information, and the output is the displayed fee notification.

[0612] Step 6:

[0613] Users check the fee information received via push notifications from the parking app on their device and make a parking decision. Users can also use the app to compare fees for different parking lots. Based on this information, users can choose the optimal parking lot and time. The input is the received fee information, and the output is the user's parking selection.

[0614] (Application Example 1)

[0615] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] Conventional parking management systems had the problem of being static, with static information on parking space availability and pricing, making them unable to respond to real-time demand fluctuations. Furthermore, there was a lack of information to compare multiple parking lots, making it difficult for users to efficiently choose a parking space.

[0617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0618] In this invention, the server includes an information gathering means for acquiring parking space availability information, weather-related information, and event-related information; a fee calculation means for automatically calculating parking fees based on the information acquired by the information gathering means and generating fee information that can be compared among multiple parking facilities; and a notification means for notifying the user of the fee information generated by the fee calculation means and assisting in making the optimal choice for parking. This enables real-time setting of parking fees in response to demand and allows users to make the optimal choice by comparing multiple parking facilities.

[0619] "Parking facility availability information" refers to information that shows the availability of parking spaces within a parking lot, providing real-time information on the number and location of available parking spaces.

[0620] "Weather-related information" refers to information about the weather, including data such as current weather, forecasts, temperature, and precipitation.

[0621] "Event-related information" refers to information about events and gatherings held in a specific region, and is used to predict the resulting changes in people's movements and traffic.

[0622] "Information gathering means" refers to devices and systems for acquiring the above-mentioned seat availability information, weather information, and event information, and includes means that utilize sensors, cameras, and network connections.

[0623] A "fee calculation method" is a means for calculating parking fees based on collected information and generating fee information that can be compared among multiple parking facilities.

[0624] "Notification means" refers to a means of informing users of charge information, and usually involves methods such as push notifications via mobile devices.

[0625] "Users" refer to individuals or companies that use the parking lot; they are the entities that utilize the parking facilities in search of a parking space.

[0626] A "communication network" is a network infrastructure for sending and receiving information in real time, and includes networks such as the internet and mobile networks.

[0627] To implement this invention, a system is required in which a server, a terminal, and a user work together. This system consists of the following elements.

[0628] The server is responsible for acquiring parking space availability information, weather-related information, and event-related information. This involves monitoring the status of parking facilities in real time using dedicated sensors and network cameras, and collecting relevant data using weather information APIs and local event information APIs. The internet is used as the communication network to collect and integrate this data.

[0629] The server uses the collected information to calculate parking fees. It utilizes the Python programming language and generative AI models such as TensorFlow to analyze this data and predict demand patterns. Furthermore, it generates comparable parking fee information across multiple parking facilities. This process involves forecasting demand based on historical data, sending prompts to the generative AI model to determine the optimal pricing, and calculating the fee information. For example, the server might predict higher-than-usual parking demand on Saturday afternoons and dynamically adjust fees accordingly.

[0630] The terminal is responsible for notifying users of the generated fee information. Typically, a smartphone app is used to send fee information via push notifications to the user's mobile device. This allows users to obtain the most suitable parking information in real time using their smartphones and select the optimal parking lot.

[0631] Users receive notifications from the system via their terminals and make choices that suit their parking needs. This allows for more efficient use of parking facilities. An example of a prompt message might be: "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee."

[0632] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0633] Step 1:

[0634] The server collects parking space availability information. Specifically, it acquires data in real time from sensors and network cameras installed in the parking lot. The input is physical data from the sensors, which is converted into a digital format. The output is digital data indicating the availability of parking spaces. Based on this data, the server understands the current usage status of the parking spaces.

[0635] Step 2:

[0636] The server obtains weather-related and event-related information via APIs. Specifically, it accesses weather information APIs and local event information APIs to collect data on current weather, forecasts, and local events. The input is API requests, and the output is organized weather and event data. The server analyzes this input data to extract factors that influence parking demand.

[0637] Step 3:

[0638] The server integrates the collected data and uses a generative AI model to forecast demand. Specifically, it uses Python and TensorFlow to analyze real-time and historical data to predict fluctuations in parking demand. The inputs are vacancy information, weather data, and event data, and the output is the demand forecast result. The server uses the obtained forecast result to calculate the optimal parking fee.

[0639] Step 4:

[0640] The server uses a fee calculation method to calculate comparable parking fees across multiple parking facilities. Specifically, it sends a prompt message to the generating AI model stating, "Based on the current availability of parking spaces, local event information, and weather information, predict future parking demand and calculate the optimal fee." The input is the prompt message and the prediction result, and the output is dynamically adjusted fee information. The server generates the fee information and prepares it for provision to users.

[0641] Step 5:

[0642] The device receives billing information from the server and communicates it to the user via push notification. Specifically, billing information is sent to the mobile device via push notification and displayed. The input is billing information from the server, and the output is the notification information displayed on the user's device. This allows the user to check billing information in real time and make decisions accordingly.

[0643] Step 6:

[0644] The user selects the most suitable parking lot based on notification information displayed on their device. Specifically, it compares multiple parking fees and determines the most convenient parking lot based on the current situation. The input is the fee information displayed on the device, and the output is the user's parking lot selection. The user's decision is the final outcome of the system.

[0645] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0646] This invention provides a system for optimizing parking lot operations that combines an emotion engine that recognizes user emotions. In addition to conventional information gathering, fee calculation, and notification methods, this system has a function that utilizes user emotion data to automatically improve parking services.

[0647] The server collects parking space availability information, weather information, and event information, and then analyzes the user's emotional state through an emotion engine. The emotion engine analyzes voice and text messages collected from the user's mobile device to identify their emotional state. For example, if it recognizes that the user is looking for parking under stressful circumstances, it can provide clearer navigation or offer special discounts.

[0648] Taking emotional data into consideration, the server adjusts parking fees using a fee calculation method. This adjustment can vary depending on a specific emotional state and is designed to provide the most suitable fee structure for the user.

[0649] The parking app, acting as a terminal, receives customized pricing and service information from a server based on the user's emotions and delivers it to the user as a push notification. This notification can include special services and discount information tailored to the user's emotions, thereby further enhancing user satisfaction.

[0650] As a concrete example, if the server analyzes the user's anxious voice data and determines that the user is urgently looking for parking, it will identify nearby available parking spaces and notify the user that they are available at a special rate. When this message is sent to the terminal, the user can make a quick decision, which can help reduce stress.

[0651] Thus, by combining an emotion engine, this invention makes it possible to provide advanced parking management that takes into account the user's emotional state and a customized user experience.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The server collects current parking space availability information through sensors and cameras installed in the parking lot. In addition, it obtains current weather and nearby event information from weather information APIs and event information APIs.

[0655] Step 2:

[0656] The server receives user voice data and text messages through the terminal. This data is analyzed by an emotion engine to identify the user's emotional state (e.g., stressed, anxious, relaxed).

[0657] Step 3:

[0658] The server combines collected information on available parking spaces, weather, and events with the user's emotional state identified by the emotion engine, and uses a generative AI model to calculate parking fees. If the user is experiencing stress, special fee adjustments are made, such as applying a discount.

[0659] Step 4:

[0660] The latest pricing information and service details are sent from the server to the device. The parking app, acting as the device, provides this information to the user via push notifications, helping them to choose the best parking option in real time.

[0661] Step 5:

[0662] The user checks the notifications received in the app. These notifications include information on rates and special services customized to the user's emotional state. The user then uses this information to select the most suitable parking lot.

[0663] Step 6:

[0664] Users head to their chosen parking spot and park smoothly using the app. The app also collects appropriate feedback tailored to the user's emotions and uses this data to make further improvements for future use.

[0665] These steps allow a system that incorporates an emotion engine to provide a parking experience that takes the user's emotions into account.

[0666] (Example 2)

[0667] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] In the parking lot experience, the lack of service provision that considers the user's emotional state has led to situations where users experience stress. Conventional technology simply provides information on parking availability and fees, without customizing it to the individual user's situation or emotions. Therefore, it is necessary to improve user satisfaction by sensing the user's emotions and providing optimal fees and services based on those emotions.

[0669] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0670] In this invention, the server includes emotion analysis means for analyzing emotional data, fee adjustment means for adjusting parking fees based on the results obtained by the emotion analysis means, and customized notification means for transmitting customized fee information calculated by the fee adjustment means to the user. This makes it possible to provide a highly customized parking service that responds to the user's emotional state.

[0671] An "emotion analysis device" is a device that has the function of analyzing voice data and text messages collected from a user's individual device to identify the user's emotional state.

[0672] A "fee adjustment device" is a device that has the function of automatically calculating parking fees tailored to a specific user based on emotional state data obtained from an emotion analysis device.

[0673] A "customized notification device" is a device that has the function of instantly sending push notifications to the user's mobile device with individual charge information calculated by the charge adjustment device.

[0674] A "guidance provision device" is a device that has the function of generating guidance on special services or discounts according to the user's emotional state and sending it to the user at an appropriate time.

[0675] This invention is a system that optimizes parking services according to the user's emotional state. Specific embodiments of this system are described below.

[0676] The server uses sentiment analysis techniques to analyze voice data and text messages collected from the user's individual device, employing natural language processing techniques and machine learning algorithms. This allows for accurate identification of the user's emotional state. Specific software expected to utilize natural language processing libraries such as TensorFlow and PyTorch is anticipated.

[0677] Once the sentiment analysis is complete, the server uses a pricing adjustment mechanism to calculate the optimal parking fee based on the user's emotional state. For example, if the user is stressed, a discounted rate will be applied as part of a "stress reduction plan." Pricing adjustments are made considering parking availability, weather information, and event information, along with this sentiment data.

[0678] Subsequently, the server uses a customized notification method to send the adjusted billing information to the user's mobile device. This is done via push notification, allowing the information to be delivered to the user in real time. The device receives this notification and immediately alerts the user.

[0679] Furthermore, the server uses guidance delivery methods to generate special service guidance tailored to the user's emotions. In this process, a generative AI model is utilized to automatically generate guidance text that is most appropriate for the user. For example, by inputting a prompt such as "Please suggest a parking plan for a user experiencing high stress" into the generative AI model, specific guidance can be created.

[0680] With this configuration, servers, terminals, and users work together to enable flexible, user-centric parking management that leverages emotional data. This improves the convenience of using parking facilities and reduces user stress.

[0681] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0682] Step 1:

[0683] The server collects parking space availability information, weather information, and event information from its respective data sources. It takes data via the network as input, thereby obtaining basic data about the parking lot's operational status and the external environment. Its specific operations include API calls and periodic database queries.

[0684] Step 2:

[0685] Users send voice data and text messages to the system from their mobile devices. This data is input to a server and used to identify the user's emotional state through sentiment analysis. Specifically, this involves data transmission via a mobile application.

[0686] Step 3:

[0687] The server performs sentiment analysis. It receives user voice data and text messages as input and analyzes the data using natural language processing techniques and machine learning algorithms. This analysis outputs the user's emotional state (e.g., stress, feeling of security). Specific operations include the use of natural language processing models using TensorFlow and PyTorch.

[0688] Step 4:

[0689] The server adjusts parking fees based on the results of sentiment analysis. Using a fee adjustment mechanism, it calculates the optimal fee considering the input sentiment state and vacancy information. The output of this step is a fee plan customized for each user. The specific operation includes the execution of a fee calculation algorithm.

[0690] Step 5:

[0691] The server sends customized billing information to the device. Information is delivered to the user in real time via push notifications. This allows the device to display the billing information to the user. Specifically, this involves real-time message delivery using a notification system.

[0692] Step 6:

[0693] The device displays notifications sent to the user. The user can review these notifications and reserve a parking space if necessary. The output is the user's decision to reserve. Specific actions include displaying information on the UI and providing the reservation function.

[0694] (Application Example 2)

[0695] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] In modern parking systems, it is difficult to adequately alleviate the stress and dissatisfaction experienced by users. Specifically, there is a problem in that the provision of information regarding parking availability and fees is not optimized according to the user's situation and emotions, resulting in an unimproved user experience. Furthermore, there is a challenge in providing appropriate guidance that responds to the diverse emotions of users.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0698] In this invention, the server includes information acquisition means for acquiring parking availability information, weather conditions, and event information; emotional analysis means for analyzing the user's emotional state; and guidance provision means for providing optimal guidance to the user. This enables the provision of flexible services that respond to the user's emotions and circumstances.

[0699] "Parking availability information" refers to information regarding the current availability and usability of parking spaces.

[0700] "Weather conditions" refer to information related to weather, such as climate, temperature, and precipitation in a particular region.

[0701] "Event information" refers to information about events and campaigns held in specific regions or facilities.

[0702] "Information acquisition means" refers to a method or apparatus for collecting and processing various types of information.

[0703] "Means of calculating fees" refers to a method or apparatus for calculating fees based on collected information.

[0704] "Emotional analysis means" refers to a method or device for analyzing a user's voice or text data to identify their emotional state.

[0705] "Information provision means" refers to a method or device for providing users with the most suitable information or services.

[0706] "Notification means" refers to a method or device for notifying or transmitting information to a user.

[0707] A "communication network" is a network that connects multiple devices in order to send and receive information.

[0708] The system for implementing this invention mainly consists of a server, a user terminal, and an emotion analysis engine.

[0709] The server acquires real-time information on parking availability, weather conditions, and event information via the network using information acquisition tools. In addition, it collects voice and text data from users' devices such as smartphones and smart glasses, and analyzes their emotional state through emotion analysis tools. For emotion analysis, the Google Cloud Speech-to-Text API is used to convert speech to text, and IBM Watson Tone Analyzer is used to identify emotions.

[0710] The server generates optimal parking directions based on the user's emotional state and notifies the user's terminal via a guidance delivery system. In this process, the Google Maps API is used to create the most suitable route directions for the user. Notifications are delivered instantly via a notification system, allowing users to receive information tailored to their situation in real time.

[0711] As a concrete example, the server analyzes the user's voice data, and if it determines that the user is tired, it guides them to nearby resting places or available cafes. This allows the user to reach their destination more comfortably.

[0712] An example of a prompt message might be: "Design an application that analyzes voice data for emotional content and provides optimal route guidance to the user. It should assess the user's fatigue level and suggest rest stops based on that."

[0713] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0714] Step 1:

[0715] The server collects real-time information on parking availability, weather conditions, and event information through various data acquisition methods. Input is data acquired via the network, and output is storage of this information in a database. Data is collected via API calls and integrated with existing information.

[0716] Step 2:

[0717] The user's device collects the user's voice data through a microphone. The input is voice data, which is converted into text data using speech recognition software. The output is text data, which is used for subsequent emotion analysis.

[0718] Step 3:

[0719] The server analyzes the converted text data using emotional analysis tools. The input is text data, and the emotional state is identified using APIs such as IBM Watson Tone Analyzer. The output is the result of the emotional state analysis, specifically the user's stress and fatigue levels, obtained as numerical values ​​or tags.

[0720] Step 4:

[0721] The server uses guidance mechanisms based on the obtained emotional state to generate services and routes suitable for the user. Input consists of the results of emotional analysis and real-time information, and the optimal route is calculated using the Google Maps API. Output is customized guidance information.

[0722] Step 5:

[0723] The device receives guidance information sent from the server and notifies the user. The input is guidance information from the server, which is then pushed to the user's smartphone or smart glasses. The output is the visual and audio guidance information that the user receives on the screen.

[0724] 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.

[0725] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0726] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0727] 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.

[0728] Figure 9 shows an 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.

[0729] 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.

[0730] 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.

[0731] 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, motorcycles, etc., 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, for example, based 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.

[0732] 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."

[0733] 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.

[0734] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0735] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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 the like 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.

[0744] 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.

[0745] The following is further disclosed regarding the embodiments described above.

[0746] (Claim 1)

[0747] A means of collecting information to obtain parking space availability information, weather information, and event information,

[0748] A fee calculation means that automatically calculates parking fees based on the information acquired by the aforementioned information collection means,

[0749] A notification means for notifying the user of the fee information calculated by the aforementioned fee calculation means,

[0750] A system that includes this.

[0751] (Claim 2)

[0752] The system according to claim 1, wherein the notification means includes means for sending the charge information to the user's mobile terminal by push notification.

[0753] (Claim 3)

[0754] The system according to claim 1, wherein the information gathering means includes means for acquiring information in real time via a network.

[0755] "Example 1"

[0756] (Claim 1)

[0757] A data acquisition method for obtaining parking space availability information, weather information, and event information,

[0758] A data processing means that centralizes the data acquired by the aforementioned data acquisition means and inputs the generated dataset into the generating AI model,

[0759] A fee calculation method that dynamically calculates parking fees based on demand forecasts while referencing historical data using a generative AI model,

[0760] An information distribution means that distributes the fee information calculated by the aforementioned fee calculation means to the user,

[0761] A system that includes this.

[0762] (Claim 2)

[0763] The system according to claim 1, wherein the information distribution means includes means for sending the fee information to a user's communication terminal by push notification.

[0764] (Claim 3)

[0765] The system according to claim 1, wherein the data acquisition means includes means for acquiring data in real time via a communication network.

[0766] "Application Example 1"

[0767] (Claim 1)

[0768] A means of collecting information to obtain parking space availability information, weather-related information, and event-related information,

[0769] A fee calculation means that automatically calculates parking fees based on the information acquired by the aforementioned information gathering means and generates fee information that can be compared among multiple parking facilities,

[0770] A notification means that notifies the user of the fee information generated by the fee calculation means and assists in making the optimal choice for parking,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, wherein the notification means includes means for sending the charge information to the user's mobile information terminal by push notification.

[0774] (Claim 3)

[0775] The system according to claim 1, wherein the information gathering means includes means for acquiring information in real time via a communication network.

[0776] "Example 2 of combining an emotion engine"

[0777] (Claim 1)

[0778] A means of analyzing emotional data,

[0779] A fee adjustment means that adjusts parking fees based on the results obtained by the emotion analysis means,

[0780] A customized notification means that sends customized pricing information calculated by the aforementioned pricing adjustment means to the user,

[0781] The aforementioned customized notification means includes a guidance provision means that sends special guidance according to the emotional state,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the customized notification means includes means for sending the charge information to the user's mobile device by push notification.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein the emotion analysis means includes means for acquiring and analyzing voice data and text messages from the user's individual device.

[0787] "Application example 2 when combining with an emotional engine"

[0788] (Claim 1)

[0789] Information acquisition means for obtaining information on parking availability, weather conditions, and event information,

[0790] A fee calculation means that automatically calculates parking fees based on the information obtained by the aforementioned information acquisition means,

[0791] An emotional analysis method for analyzing the emotional state of a user,

[0792] A guidance provision means that provides optimal guidance to the user based on the results obtained by the aforementioned emotion analysis means,

[0793] A notification means for notifying the user of the fee information calculated by the aforementioned fee calculation means,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein the notification means includes means for immediately sending a notification to the user's information terminal regarding the fee information and the results of the emotional analysis.

[0797] (Claim 3)

[0798] The system according to claim 1, wherein the information acquisition means and the emotion analysis means include means for acquiring information immediately via a communication network. [Explanation of Symbols]

[0799] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information to obtain parking space availability information, weather information, and event information, A fee calculation means that automatically calculates parking fees based on the information acquired by the aforementioned information collection means, A notification means for notifying the user of the fee information calculated by the aforementioned fee calculation means, A system that includes this.

2. The system according to claim 1, wherein the notification means includes means for sending the charge information to the user's mobile terminal by push notification.

3. The system according to claim 1, wherein the information gathering means includes means for acquiring information in real time via a network.

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