system

Abstract

The system according to this embodiment aims to analyze the availability and pricing structure of parking lots around a destination in real time and suggest an appropriate parking lot. [Solution] The system according to this embodiment comprises a reception unit, a suggestion unit, and an interlocking unit. The reception unit receives input of the destination and length of stay. The suggestion unit analyzes real-time availability and fee structure based on the information received by the reception unit and suggests an appropriate parking lot. The interlocking unit makes reservations and payments for the parking lots suggested by the suggestion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the vacancy status and fee system of parking lots around the destination in real time and find an appropriate parking lot.

[0005] The system according to the embodiment aims to analyze the vacancy status and fee system of parking lots around the destination in real time and propose an appropriate parking lot.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, and an interlocking unit. The reception unit receives input of the destination and length of stay. The suggestion unit analyzes real-time availability and pricing based on the information received by the reception unit and suggests an appropriate parking lot. The interlocking unit handles reservations and payments for the parking lots suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the availability and pricing structure of parking lots around the destination in real time and suggest a suitable parking lot. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The parking availability search and reservation system according to an embodiment of the present invention is a system that suggests available parking spaces based on the destination and length of stay, even when a user is visiting a new place by car. This system suggests the optimal parking space based on factors such as distance from the destination and usage fees. Furthermore, by linking with an in-vehicle system, it can handle reservations and payments. Parking lot owners can also provide real-time availability information, efficiently guiding users who are looking for parking spaces. For example, a user inputs their destination and length of stay. Next, the system suggests the optimal parking space based on factors such as distance from the destination and usage fees. This suggestion takes into account real-time availability and fee structures based on time of day. In addition, by linking with an in-vehicle system, users can easily make reservations and payments for parking spaces. Parking lot owners can also provide real-time availability information, efficiently guiding users who are looking for parking spaces. This improves parking lot utilization rates and maximizes revenue. This system utilizes technologies such as AI, IoT, and BI via mobile data communication. Specifically, AI analyzes real-time parking availability and fee structures based on time of day to suggest the optimal parking space. Furthermore, by using an electronic payment system for parking fee payments, it is expected that opportunities for use will be created. For example, if a user is looking for parking in a place they are visiting for the first time, the system will suggest the most suitable parking lot simply by entering their destination and length of stay. The suggested parking lots are selected considering real-time availability and pricing, allowing users to use the parking lot with peace of mind. Furthermore, integration with in-car systems makes reservations and payments easy, improving convenience. Parking lot owners can also efficiently guide users seeking parking spaces by providing real-time availability information. This increases parking lot utilization and maximizes revenue. For instance, AI analyzes parking lot availability and pricing to suggest the most suitable parking lot, allowing users to find parking efficiently. Additionally, the use of electronic payment systems simplifies parking fee payments, creating opportunities for increased usage.This allows the parking availability search and reservation system to suggest the most suitable parking space to the user and handle reservations and payments as well.

[0029] The parking availability search and reservation system according to this embodiment comprises a reception unit, a suggestion unit, and an interlocking unit. The reception unit receives input of the destination and length of stay. For example, the reception unit provides an interface for the user to input the destination and length of stay. The suggestion unit analyzes real-time availability and fee structures based on the information received by the reception unit and suggests the most suitable parking lot. For example, the suggestion unit uses AI to suggest the most suitable parking lot based on the distance from the destination and the usage fee. The interlocking unit makes reservations and payments for the parking lots suggested by the suggestion unit. For example, the interlocking unit works in conjunction with an in-vehicle system to make reservations and payments for parking lots. As a result, the parking availability search and reservation system according to this embodiment can suggest the most suitable parking lot based on the destination and length of stay, and handle reservations and payments.

[0030] The reception desk accepts input of destinations and duration of stay. For example, the reception desk provides an interface for users to input their destinations and duration of stay. Specifically, the reception desk is designed to allow users to easily input their destinations and duration of stay through a smartphone app or website. Users log in to the app or website, enter the destination address or landmarks, and select the planned duration of stay. The interface is designed to be intuitive and easy to use, ensuring that users can input information without confusion. Furthermore, a voice input function is included, allowing users to input their destinations and duration of stay by voice even while driving or when their hands are occupied. The reception desk immediately transmits the entered information to the system and prepares it for the next processing. It also has a function to simplify future input by saving and reusing a history of destinations and durations entered by the user in the past. In this way, the reception desk can support users in quickly and accurately entering destinations and durations of stay, improving the overall efficiency of the system.

[0031] The Proposal Department analyzes real-time availability and pricing based on information received by the Reception Department and proposes the most suitable parking lot. For example, the Proposal Department uses AI to suggest the best parking lot based on factors such as distance from the destination and usage fees. Specifically, the Proposal Department obtains real-time parking availability and pricing information from multiple data sources, integrates this data, and analyzes it. The AI ​​uses machine learning algorithms to select the best parking lot, taking into account the user's past parking history, current traffic conditions, and parking lot congestion. For example, the AI ​​prioritizes suggesting parking lots that are close to the destination and have low fees. It also takes into account parking lot security, facilities, and user reviews to select the most suitable parking lot overall. The Proposal Department presents the user with multiple options and provides detailed information about each parking lot. The user can choose the parking lot that best suits their needs from the proposed options. Furthermore, based on the user's selection, the Proposal Department updates parking availability in real time and confirms the reservation. This allows the Proposal Department to quickly and accurately suggest the best parking lot to the user, ensuring a smooth parking experience.

[0032] The integrated unit handles parking reservations and payments suggested by the suggestion unit. For example, the integrated unit works in conjunction with the in-vehicle system to make parking reservations and payments. Specifically, the integrated unit sends the reservation information for the parking lot selected by the user to the system and confirms the reservation. Once the reservation is confirmed, the user receives a confirmation email or notification, allowing them to check the reservation details. Furthermore, the integrated unit also handles payment processing, using the user's pre-registered credit card or electronic money to pay for parking. Once payment is complete, a receipt is issued to the user, which can be viewed on the app or website. The integrated unit also works in conjunction with the in-vehicle system, allowing users to make parking reservations and payments directly from inside the vehicle. For example, information on suggested parking lots can be displayed through the in-vehicle navigation system, and reservations and payments can be made. This allows users to safely make parking reservations and payments even while driving. In addition, the integrated unit works with the parking management system, updating reservation and payment information in real time and accurately understanding parking availability. This allows the interconnected unit to provide users with a smooth reservation and payment experience and efficiently manage parking lot usage.

[0033] The system includes a provision unit that allows parking lot owners to provide real-time availability information. This unit allows parking lot owners to provide real-time availability information. For example, the unit updates the availability status of parking lots in real time and provides it to users. The unit can also provide parking lot availability information via an API. For example, the unit periodically updates the availability information and provides it to users. Furthermore, the unit can acquire parking lot availability information in real time and provide it to users. For example, the unit acquires parking lot availability information in real time using sensors and provides it to users. This allows parking lot owners to provide real-time availability information, enabling efficient guidance of parking spaces to users.

[0034] The proposal department can analyze real-time availability and pricing structures to suggest the most suitable parking lot. For example, the proposal department can analyze real-time availability and suggest the most suitable parking lot. For example, the proposal department can use AI to analyze parking lot availability in real time and suggest the most suitable parking lot. The proposal department can also analyze pricing structures and suggest the most suitable parking lot. For example, the proposal department can use AI to analyze parking lot pricing structures and suggest the most suitable parking lot. Furthermore, the proposal department can combine real-time availability and pricing structures in its analysis to suggest the most suitable parking lot. For example, the proposal department can use AI to analyze parking lot availability and pricing structures in real time and suggest the most suitable parking lot. This improves user convenience by suggesting the most suitable parking lot based on real-time information.

[0035] The interconnected unit can work in conjunction with the in-vehicle system to make parking reservations and payments. For example, the interconnected unit can make parking reservations in conjunction with the in-vehicle system. For example, the interconnected unit can make parking reservations through the in-vehicle system. The interconnected unit can also make parking payments in conjunction with the in-vehicle system. For example, the interconnected unit can make parking payments through the in-vehicle system. Furthermore, the interconnected unit can make parking reservations and payments in a single operation in conjunction with the in-vehicle system. For example, the interconnected unit can make parking reservations and payments in a single operation through the in-vehicle system. This makes reservations and payments easy by connecting with the in-vehicle system.

[0036] The suggestion function can propose suitable parking lots based on factors such as distance from the destination and usage fees. For example, the suggestion function can propose suitable parking lots based on distance from the destination. For example, the suggestion function can use AI to propose suitable parking lots based on distance from the destination. The suggestion function can also propose suitable parking lots based on usage fees. For example, the suggestion function can use AI to propose suitable parking lots based on usage fees. Furthermore, the suggestion function can propose suitable parking lots by combining distance from the destination and usage fees. For example, the suggestion function can use AI to propose suitable parking lots based on distance from the destination and usage fees. This improves user convenience by suggesting the optimal parking lot considering distance from the destination and usage fees.

[0037] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display destinations and durations of stay that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations and durations of stay to be used during specific time periods based on the user's past input history. This improves user convenience by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0038] The reception desk can automatically complete the input content when the user enters their destination and length of stay, taking into account their current schedule and appointments. For example, the reception desk can refer to the user's calendar information and automatically set the destination and length of stay based on their appointments. The reception desk can also suggest locations related to specific events from the user's schedule as potential destinations. Furthermore, the reception desk can suggest the optimal length of stay to match the user's appointments. This reduces the effort required for input by automatically completing the input content while considering the user's schedule and appointments. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI perform automatic completion of the input content.

[0039] The reception unit can prioritize displaying highly relevant candidate locations by considering the user's geographical location information when the user inputs their destination and length of stay. For example, the reception unit can prioritize displaying candidate locations close to the user's current location. The reception unit can also suggest highly relevant candidate locations based on the user's past travel history. Furthermore, the reception unit can prioritize displaying candidate locations located between the user's current location and their destination. This allows the user to select an appropriate candidate location by displaying highly relevant locations while considering their geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI display highly relevant candidate locations.

[0040] The reception desk can analyze the user's social media activity when the user enters their destination and length of stay, and suggest relevant locations. For example, the reception desk can suggest places the user has checked in to on social media. The reception desk can also suggest relevant locations based on the user's social media posts. Furthermore, the reception desk can suggest places visited by the user's social media friends. This improves user convenience by suggesting relevant locations based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant locations.

[0041] The suggestion unit can adjust the level of detail of its suggestions based on the parking lot's congestion status. For example, if the parking lot is crowded, the suggestion unit can provide a detailed explanation of the availability of parking spaces. The suggestion unit can also provide a concise suggestion if there are available spaces. Furthermore, the suggestion unit can adjust the priority of its suggestions according to the parking lot's congestion status. By adjusting the level of detail of suggestions according to the parking lot's congestion status, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot congestion data into a generating AI and have the generating AI adjust the level of detail of its suggestions.

[0042] The proposal unit can apply different proposal algorithms depending on the parking lot category when making a proposal. For example, in the case of a commercial facility parking lot, the proposal unit will make proposals that prioritize usage fees and distance. For example, in the case of a residential area parking lot, the proposal unit can also make proposals that prioritize safety and convenience. For example, in the case of a residential area parking lot, the proposal unit can make proposals that prioritize safety and convenience. Furthermore, in the case of a tourist area parking lot, the proposal unit can make proposals that prioritize accessibility and fees. For example, in the case of a tourist area parking lot, the proposal unit can make proposals that prioritize accessibility and fees. By applying a proposal algorithm according to the parking lot category, the optimal proposal can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input parking lot category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0043] The suggestion unit can determine the priority of suggestions based on the user's parking usage history. For example, the suggestion unit can prioritize suggesting parking lots that the user has used in the past. The suggestion unit can also suggest parking lots that avoid congestion based on the user's past usage history. Furthermore, the suggestion unit can analyze the user's past usage history and suggest the most efficient parking lot. This improves user convenience by determining the priority of suggestions based on parking usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's usage history data into a generating AI and have the generating AI determine the priority of suggestions.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of the parking lots. For example, the suggestion unit can prioritize suggesting parking lots that are close to the destination. The suggestion unit can also prioritize suggesting parking lots with low usage fees. Furthermore, the suggestion unit can suggest parking lots in the optimal order based on their availability. By adjusting the order of suggestions based on the relevance of the parking lots, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0045] The integrated unit can analyze the user's past usage history and propose the optimal procedure when making a reservation or payment. For example, the integrated unit can prioritize suggesting payment methods the user has used in the past. The integrated unit can also propose the optimal reservation method based on the user's past usage history. Furthermore, the integrated unit can analyze the user's past usage history and propose the most efficient procedure. This improves user convenience by proposing the optimal procedure based on past usage history. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's usage history data into a generating AI and have the generating AI execute the proposal of the optimal procedure.

[0046] The integrated unit can customize procedures based on the user's current situation when making reservations or payments. For example, if the user is in a hurry, the integrated unit can provide a quick procedure. For example, if the user is relaxed, the integrated unit can provide a procedure that includes detailed explanations. For example, if the user is excited, the integrated unit can provide a procedure that includes visually stimulating effects. This improves user convenience by customizing procedures based on the user's current situation. Some or all of the above processing in the integrated unit may be performed using AI, for example, or not using AI. For example, the integrated unit can input the user's current situation data into a generating AI and have the generating AI perform the procedure customization.

[0047] The integrated unit can propose the most suitable procedure when making a reservation or payment, taking into account the user's geographical location. For example, the integrated unit can prioritize suggesting parking lots close to the user's current location. The integrated unit can also propose highly relevant parking lots based on the user's past travel history. Furthermore, the integrated unit can prioritize suggesting parking lots located between the user's current location and their destination. This improves convenience by proposing the most suitable procedure, taking into account the user's geographical location. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's geographical location information into a generating AI and have the generating AI execute the proposal of the most suitable procedure.

[0048] The integrated unit can analyze the user's social media activity and suggest procedures when making reservations or payments. For example, the integrated unit can suggest locations where the user has checked in on social media as potential locations. The integrated unit can also suggest relevant parking lots based on the content of the user's social media posts. Furthermore, the integrated unit can suggest parking lots used by the user's social media friends as potential locations. This improves convenience by suggesting procedures based on the user's social media activity. Some or all of the above processing in the integrated unit may be performed using AI, for example, or not. For example, the integrated unit can input the user's social media data into a generating AI and have the generating AI execute the procedure suggestions.

[0049] The service provider can select the optimal service method by referring to past service history when providing availability information. For example, the service provider can prioritize service methods that users have preferred in the past. The service provider can also select the most effective service method from past service history. Furthermore, the service provider can analyze past service history and select a service method based on user trends. This improves convenience by selecting the optimal service method based on past service history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past service history data into a generating AI and have the generating AI select the optimal service method.

[0050] The service provider can customize the content of its information provision based on the parking lot's congestion status. For example, if the parking lot is crowded, the service provider can provide detailed information about available spaces. The service provider can also provide concise information about available spaces if there are spaces available. Furthermore, the service provider can adjust the content of its information provision according to the parking lot's congestion status. This improves convenience by customizing the content of the information provision according to the parking lot's congestion status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input parking lot congestion data into a generating AI and have the generating AI perform the customization of the content of its information provision.

[0051] The service provider can select the optimal service method when providing vacancy information, taking into account the geographical distribution of parking spaces. For example, the service provider can select the optimal service method based on the geographical distribution of parking spaces. The service provider can also analyze the geographical distribution of parking spaces and select the most convenient service method for the user. Furthermore, the service provider can adjust the service content considering the geographical distribution of parking spaces. For example, the service provider can adjust the service content considering the geographical distribution of parking spaces. This improves convenience by selecting the optimal service method considering the geographical distribution of parking spaces. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical distribution data of parking spaces into a generating AI and have the generating AI select the optimal service method.

[0052] The service provider can improve the content of its offerings by referring to relevant literature on parking lots when providing vacancy information. For example, the service provider can improve the content of its offerings by referring to relevant literature on parking lots. For example, the service provider can improve the content of its offerings by referring to relevant literature on parking lots. The service provider can also analyze relevant literature on parking lots and select the optimal method of offering. For example, the service provider can analyze relevant literature on parking lots and select the optimal method of offering. Furthermore, the service provider can customize the content of its offerings based on relevant literature on parking lots. For example, the service provider can customize the content of its offerings based on relevant literature on parking lots. This improves convenience by improving the content of the offerings based on relevant literature on parking lots. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input relevant literature data on parking lots into a generating AI and have the generating AI perform improvements to the content of its offerings.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The suggestion unit can adjust the level of detail in its suggestions based on the parking lot's congestion status. For example, if the parking lot is crowded, it can provide a detailed explanation of the availability. If the parking lot is empty, it can provide a concise suggestion. Furthermore, it can adjust the priority of suggestions according to the parking lot's congestion status. This improves user convenience by adjusting the level of detail in suggestions according to the parking lot's congestion status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot congestion data into a generating AI and have the generating AI perform the adjustment of the level of detail in its suggestions.

[0055] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display destinations and durations of stay that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest destinations and durations of stay to be used during specific time periods based on the user's past input history. This improves user convenience by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0056] The reception desk can automatically complete the input content when the user enters their destination and length of stay, taking into account their current schedule and appointments. For example, it can refer to the user's calendar information and automatically set the destination and length of stay based on their appointments. It can also suggest locations related to specific events from the user's schedule as candidate locations. Furthermore, it can suggest the optimal length of stay to match the user's appointments. This reduces the effort required for input by automatically completing the input content while considering the user's schedule and appointments. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI perform automatic completion of the input content.

[0057] The suggestion unit can apply different suggestion algorithms depending on the parking lot category. For example, for parking lots in commercial facilities, it can make suggestions that prioritize usage fees and distance. For parking lots in residential areas, it can make suggestions that prioritize safety and convenience. Furthermore, for parking lots in tourist areas, it can make suggestions that prioritize accessibility and fees. By applying a suggestion algorithm appropriate to the parking lot category, the optimal suggestion can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0058] The integrated unit can analyze a user's past usage history and propose the optimal procedure when making a reservation or payment. For example, it can prioritize suggesting payment methods the user has used in the past. It can also suggest the optimal reservation method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and propose the most efficient procedure. This improves user convenience by suggesting the optimal procedure based on past usage history. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's usage history data into a generating AI and have the generating AI execute a proposal for the optimal procedure.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk accepts input of destination and length of stay. For example, the reception desk provides an interface for the user to input their destination and length of stay. Step 2: Based on the information received by the reception department, the proposal department analyzes real-time availability and pricing structures to suggest the most suitable parking lot. For example, the proposal department uses AI to suggest the best parking lot based on factors such as distance from the destination and usage fees. Step 3: The interlocking unit makes parking reservations and payments as proposed by the suggestion unit. For example, the interlocking unit works in conjunction with the in-vehicle system to make parking reservations and payments.

[0061] (Example of form 2) The parking availability search and reservation system according to an embodiment of the present invention is a system that suggests available parking spaces based on the destination and length of stay, even when a user is visiting a new place by car. This system suggests the optimal parking space based on factors such as distance from the destination and usage fees. Furthermore, by linking with an in-vehicle system, it can handle reservations and payments. Parking lot owners can also provide real-time availability information, efficiently guiding users who are looking for parking spaces. For example, a user inputs their destination and length of stay. Next, the system suggests the optimal parking space based on factors such as distance from the destination and usage fees. This suggestion takes into account real-time availability and fee structures based on time of day. In addition, by linking with an in-vehicle system, users can easily make reservations and payments for parking spaces. Parking lot owners can also provide real-time availability information, efficiently guiding users who are looking for parking spaces. This improves parking lot utilization rates and maximizes revenue. This system utilizes technologies such as AI, IoT, and BI via mobile data communication. Specifically, AI analyzes real-time parking availability and fee structures based on time of day to suggest the optimal parking space. Furthermore, by using an electronic payment system for parking fee payments, it is expected that opportunities for use will be created. For example, if a user is looking for parking in a place they are visiting for the first time, the system will suggest the most suitable parking lot simply by entering their destination and length of stay. The suggested parking lots are selected considering real-time availability and pricing, allowing users to use the parking lot with peace of mind. Furthermore, integration with in-car systems makes reservations and payments easy, improving convenience. Parking lot owners can also efficiently guide users seeking parking spaces by providing real-time availability information. This increases parking lot utilization and maximizes revenue. For instance, AI analyzes parking lot availability and pricing to suggest the most suitable parking lot, allowing users to find parking efficiently. Additionally, the use of electronic payment systems simplifies parking fee payments, creating opportunities for increased usage.This allows the parking availability search and reservation system to suggest the most suitable parking space to the user and handle reservations and payments as well.

[0062] The parking availability search and reservation system according to this embodiment comprises a reception unit, a suggestion unit, and an interlocking unit. The reception unit receives input of the destination and length of stay. For example, the reception unit provides an interface for the user to input the destination and length of stay. The suggestion unit analyzes real-time availability and fee structures based on the information received by the reception unit and suggests the most suitable parking lot. For example, the suggestion unit uses AI to suggest the most suitable parking lot based on the distance from the destination and the usage fee. The interlocking unit makes reservations and payments for the parking lots suggested by the suggestion unit. For example, the interlocking unit works in conjunction with an in-vehicle system to make reservations and payments for parking lots. As a result, the parking availability search and reservation system according to this embodiment can suggest the most suitable parking lot based on the destination and length of stay, and handle reservations and payments.

[0063] The reception desk accepts input of destinations and duration of stay. For example, the reception desk provides an interface for users to input their destinations and duration of stay. Specifically, the reception desk is designed to allow users to easily input their destinations and duration of stay through a smartphone app or website. Users log in to the app or website, enter the destination address or landmarks, and select the planned duration of stay. The interface is designed to be intuitive and easy to use, ensuring that users can input information without confusion. Furthermore, a voice input function is included, allowing users to input their destinations and duration of stay by voice even while driving or when their hands are occupied. The reception desk immediately transmits the entered information to the system and prepares it for the next processing. It also has a function to simplify future input by saving and reusing a history of destinations and durations entered by the user in the past. In this way, the reception desk can support users in quickly and accurately entering destinations and durations of stay, improving the overall efficiency of the system.

[0064] The Proposal Department analyzes real-time availability and pricing based on information received by the Reception Department and proposes the most suitable parking lot. For example, the Proposal Department uses AI to suggest the best parking lot based on factors such as distance from the destination and usage fees. Specifically, the Proposal Department obtains real-time parking availability and pricing information from multiple data sources, integrates this data, and analyzes it. The AI ​​uses machine learning algorithms to select the best parking lot, taking into account the user's past parking history, current traffic conditions, and parking lot congestion. For example, the AI ​​prioritizes suggesting parking lots that are close to the destination and have low fees. It also takes into account parking lot security, facilities, and user reviews to select the most suitable parking lot overall. The Proposal Department presents the user with multiple options and provides detailed information about each parking lot. The user can choose the parking lot that best suits their needs from the proposed options. Furthermore, based on the user's selection, the Proposal Department updates parking availability in real time and confirms the reservation. This allows the Proposal Department to quickly and accurately suggest the best parking lot to the user, ensuring a smooth parking experience.

[0065] The integrated unit handles parking reservations and payments suggested by the suggestion unit. For example, the integrated unit works in conjunction with the in-vehicle system to make parking reservations and payments. Specifically, the integrated unit sends the reservation information for the parking lot selected by the user to the system and confirms the reservation. Once the reservation is confirmed, the user receives a confirmation email or notification, allowing them to check the reservation details. Furthermore, the integrated unit also handles payment processing, using the user's pre-registered credit card or electronic money to pay for parking. Once payment is complete, a receipt is issued to the user, which can be viewed on the app or website. The integrated unit also works in conjunction with the in-vehicle system, allowing users to make parking reservations and payments directly from inside the vehicle. For example, information on suggested parking lots can be displayed through the in-vehicle navigation system, and reservations and payments can be made. This allows users to safely make parking reservations and payments even while driving. In addition, the integrated unit works with the parking management system, updating reservation and payment information in real time and accurately understanding parking availability. This allows the interconnected unit to provide users with a smooth reservation and payment experience and efficiently manage parking lot usage.

[0066] The system includes a provision unit that allows parking lot owners to provide real-time availability information. This unit allows parking lot owners to provide real-time availability information. For example, the unit updates the availability status of parking lots in real time and provides it to users. The unit can also provide parking lot availability information via an API. For example, the unit periodically updates the availability information and provides it to users. Furthermore, the unit can acquire parking lot availability information in real time and provide it to users. For example, the unit acquires parking lot availability information in real time using sensors and provides it to users. This allows parking lot owners to provide real-time availability information, enabling efficient guidance of parking spaces to users.

[0067] The proposal department can analyze real-time availability and pricing structures to suggest the most suitable parking lot. For example, the proposal department can analyze real-time availability and suggest the most suitable parking lot. For example, the proposal department can use AI to analyze parking lot availability in real time and suggest the most suitable parking lot. The proposal department can also analyze pricing structures and suggest the most suitable parking lot. For example, the proposal department can use AI to analyze parking lot pricing structures and suggest the most suitable parking lot. Furthermore, the proposal department can combine real-time availability and pricing structures in its analysis to suggest the most suitable parking lot. For example, the proposal department can use AI to analyze parking lot availability and pricing structures in real time and suggest the most suitable parking lot. This improves user convenience by suggesting the most suitable parking lot based on real-time information.

[0068] The interconnected unit can work in conjunction with the in-vehicle system to make parking reservations and payments. For example, the interconnected unit can make parking reservations in conjunction with the in-vehicle system. For example, the interconnected unit can make parking reservations through the in-vehicle system. The interconnected unit can also make parking payments in conjunction with the in-vehicle system. For example, the interconnected unit can make parking payments through the in-vehicle system. Furthermore, the interconnected unit can make parking reservations and payments in a single operation in conjunction with the in-vehicle system. For example, the interconnected unit can make parking reservations and payments in a single operation through the in-vehicle system. This makes reservations and payments easy by connecting with the in-vehicle system.

[0069] The suggestion function can propose suitable parking lots based on factors such as distance from the destination and usage fees. For example, the suggestion function can propose suitable parking lots based on distance from the destination. For example, the suggestion function can use AI to propose suitable parking lots based on distance from the destination. The suggestion function can also propose suitable parking lots based on usage fees. For example, the suggestion function can use AI to propose suitable parking lots based on usage fees. Furthermore, the suggestion function can propose suitable parking lots by combining distance from the destination and usage fees. For example, the suggestion function can use AI to propose suitable parking lots based on distance from the destination and usage fees. This improves user convenience by suggesting the optimal parking lot considering distance from the destination and usage fees.

[0070] The reception desk can estimate the user's emotions and adjust the input interface for destination and duration of stay based on the estimated emotions. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk will provide detailed input options and suggest a customizable input method. For example, if the user is relaxed, the reception desk will provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick input of destination and duration of stay. For example, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick input of destination and duration of stay. This improves user convenience by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's facial expression data into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0071] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display destinations and durations of stay that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations and durations of stay to be used during specific time periods based on the user's past input history. This improves user convenience by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0072] The reception desk can automatically complete the input content when the user enters their destination and length of stay, taking into account their current schedule and appointments. For example, the reception desk can refer to the user's calendar information and automatically set the destination and length of stay based on their appointments. The reception desk can also suggest locations related to specific events from the user's schedule as potential destinations. Furthermore, the reception desk can suggest the optimal length of stay to match the user's appointments. This reduces the effort required for input by automatically completing the input content while considering the user's schedule and appointments. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI perform automatic completion of the input content.

[0073] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize displaying important input items. Furthermore, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Additionally, if the user is in a hurry, the reception desk can prioritize displaying the most important input items to allow for quick input. This allows for the priority of input content based on the user's emotions, ensuring that important information is entered first. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's facial expression data into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0074] The reception unit can prioritize displaying highly relevant candidate locations by considering the user's geographical location information when the user inputs their destination and length of stay. For example, the reception unit can prioritize displaying candidate locations close to the user's current location. The reception unit can also suggest highly relevant candidate locations based on the user's past travel history. Furthermore, the reception unit can prioritize displaying candidate locations located between the user's current location and their destination. This allows the user to select an appropriate candidate location by displaying highly relevant locations while considering their geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI display highly relevant candidate locations.

[0075] The reception desk can analyze the user's social media activity when the user enters their destination and length of stay, and suggest relevant locations. For example, the reception desk can suggest places the user has checked in to on social media. The reception desk can also suggest relevant locations based on the user's social media posts. Furthermore, the reception desk can suggest places visited by the user's social media friends. This improves user convenience by suggesting relevant locations based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant locations.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can offer suggestions that include detailed explanations. It can also offer concise and to-the-point suggestions if the user is in a hurry. Furthermore, if the user is excited, the suggestion unit can offer suggestions with visually stimulating effects. This allows for a deeper understanding of the user by adjusting the presentation of suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the proposal department can input user facial expression data into a generation AI and have the generation AI adjust the way the proposal is presented.

[0077] The suggestion unit can adjust the level of detail of its suggestions based on the parking lot's congestion status. For example, if the parking lot is crowded, the suggestion unit can provide a detailed explanation of the availability of parking spaces. The suggestion unit can also provide a concise suggestion if there are available spaces. Furthermore, the suggestion unit can adjust the priority of its suggestions according to the parking lot's congestion status. By adjusting the level of detail of suggestions according to the parking lot's congestion status, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot congestion data into a generating AI and have the generating AI adjust the level of detail of its suggestions.

[0078] The proposal unit can apply different proposal algorithms depending on the parking lot category when making a proposal. For example, in the case of a commercial facility parking lot, the proposal unit will make proposals that prioritize usage fees and distance. For example, in the case of a residential area parking lot, the proposal unit can also make proposals that prioritize safety and convenience. For example, in the case of a residential area parking lot, the proposal unit can make proposals that prioritize safety and convenience. Furthermore, in the case of a tourist area parking lot, the proposal unit can make proposals that prioritize accessibility and fees. For example, in the case of a tourist area parking lot, the proposal unit can make proposals that prioritize accessibility and fees. By applying a proposal algorithm according to the parking lot category, the optimal proposal can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input parking lot category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0079] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion section will provide a short, to-the-point suggestion. For example, if the user is in a hurry, the suggestion section will provide a short, to-the-point suggestion. For example, if the user is relaxed, the suggestion section will provide a longer suggestion that includes detailed explanations. For example, if the user is excited, the suggestion section will provide a longer suggestion that includes detailed explanations. Furthermore, if the user is excited, the suggestion section will provide a suggestion that includes visually stimulating effects. For example, if the user is excited, the suggestion section will provide a suggestion that includes visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the user's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generation AI and have the generation AI adjust the length of the proposal.

[0080] The suggestion unit can determine the priority of suggestions based on the user's parking usage history. For example, the suggestion unit can prioritize suggesting parking lots that the user has used in the past. The suggestion unit can also suggest parking lots that avoid congestion based on the user's past usage history. Furthermore, the suggestion unit can analyze the user's past usage history and suggest the most efficient parking lot. This improves user convenience by determining the priority of suggestions based on parking usage history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's usage history data into a generating AI and have the generating AI determine the priority of suggestions.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of the parking lots. For example, the suggestion unit can prioritize suggesting parking lots that are close to the destination. The suggestion unit can also prioritize suggesting parking lots with low usage fees. Furthermore, the suggestion unit can suggest parking lots in the optimal order based on their availability. By adjusting the order of suggestions based on the relevance of the parking lots, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0082] The interaction unit can estimate the user's emotions and adjust booking and payment procedures based on the estimated emotions. For example, if the user is relaxed, the interaction unit can provide procedures that include detailed explanations. For example, if the user is relaxed, the interaction unit can provide procedures that include detailed explanations. The interaction unit can also provide concise and quick procedures if the user is in a hurry. For example, if the user is excited, the interaction unit can provide procedures that include concise and quick procedures. Furthermore, if the user is excited, the interaction unit can provide procedures that include visually stimulating effects. For example, if the user is excited, the interaction unit can provide procedures that include visually stimulating effects. This improves user convenience by adjusting booking and payment procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the linked unit can input user facial expression data into a generating AI, and have the generating AI perform adjustments to reservation and payment procedures.

[0083] The integrated unit can analyze the user's past usage history and propose the optimal procedure when making a reservation or payment. For example, the integrated unit can prioritize suggesting payment methods the user has used in the past. The integrated unit can also propose the optimal reservation method based on the user's past usage history. Furthermore, the integrated unit can analyze the user's past usage history and propose the most efficient procedure. This improves user convenience by proposing the optimal procedure based on past usage history. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's usage history data into a generating AI and have the generating AI execute the proposal of the optimal procedure.

[0084] The integrated unit can customize procedures based on the user's current situation when making reservations or payments. For example, if the user is in a hurry, the integrated unit can provide a quick procedure. For example, if the user is relaxed, the integrated unit can provide a procedure that includes detailed explanations. For example, if the user is excited, the integrated unit can provide a procedure that includes visually stimulating effects. This improves user convenience by customizing procedures based on the user's current situation. Some or all of the above processing in the integrated unit may be performed using AI, for example, or not using AI. For example, the integrated unit can input the user's current situation data into a generating AI and have the generating AI perform the procedure customization.

[0085] The interaction unit can estimate the user's emotions and determine the priority of reservations and payments based on the estimated emotions. For example, if the user is nervous, the interaction unit will prioritize displaying important procedures. For example, if the user is nervous, the interaction unit will prioritize displaying important procedures. For example, if the user is relaxed, the interaction unit will provide detailed procedures. For example, if the user is relaxed, the interaction unit will provide detailed procedures. Furthermore, if the user is in a hurry, the interaction unit can prioritize displaying the most important procedures and allow them to complete the procedures quickly. For example, if the user is in a hurry, the interaction unit will prioritize displaying the most important procedures and allow them to complete the procedures quickly. This allows important procedures to be prioritized by determining the priority of reservations and payments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the linked unit can input user facial expression data into a generating AI, which can then perform tasks such as determining the priority of reservations and payments.

[0086] The integrated unit can propose the most suitable procedure when making a reservation or payment, taking into account the user's geographical location. For example, the integrated unit can prioritize suggesting parking lots close to the user's current location. The integrated unit can also propose highly relevant parking lots based on the user's past travel history. Furthermore, the integrated unit can prioritize suggesting parking lots located between the user's current location and their destination. This improves convenience by proposing the most suitable procedure, taking into account the user's geographical location. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's geographical location information into a generating AI and have the generating AI execute the proposal of the most suitable procedure.

[0087] The integrated unit can analyze the user's social media activity and suggest procedures when making reservations or payments. For example, the integrated unit can suggest locations where the user has checked in on social media as potential locations. The integrated unit can also suggest relevant parking lots based on the content of the user's social media posts. Furthermore, the integrated unit can suggest parking lots used by the user's social media friends as potential locations. This improves convenience by suggesting procedures based on the user's social media activity. Some or all of the above processing in the integrated unit may be performed using AI, for example, or not. For example, the integrated unit can input the user's social media data into a generating AI and have the generating AI execute the procedure suggestions.

[0088] The service provider can estimate the user's emotions and adjust the way it provides available information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide available information that includes detailed explanations. For example, if the user is relaxed, the service provider can provide available information that includes detailed explanations. The service provider can also provide available information that is concise and to the point if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide available information that is concise and to the point if the user is in a hurry. Furthermore, if the user is excited, the service provider can provide available information with visually stimulating effects. For example, if the user is excited, the service provider can provide available information with visually stimulating effects. This improves usability by adjusting the way available information is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the AI ​​adjust the method of providing availability information.

[0089] The service provider can select the optimal service method by referring to past service history when providing availability information. For example, the service provider can prioritize service methods that users have preferred in the past. The service provider can also select the most effective service method from past service history. Furthermore, the service provider can analyze past service history and select a service method based on user trends. This improves convenience by selecting the optimal service method based on past service history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past service history data into a generating AI and have the generating AI select the optimal service method.

[0090] The service provider can customize the content of its information provision based on the parking lot's congestion status. For example, if the parking lot is crowded, the service provider can provide detailed information about available spaces. The service provider can also provide concise information about available spaces if there are spaces available. Furthermore, the service provider can adjust the content of its information provision according to the parking lot's congestion status. This improves convenience by customizing the content of the information provision according to the parking lot's congestion status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input parking lot congestion data into a generating AI and have the generating AI perform the customization of the content of its information provision.

[0091] The service provider can estimate the user's emotions and prioritize available information based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize displaying important available information. The service provider can also provide detailed available information if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can prioritize displaying and quickly providing the most important available information. This allows for the priority provision of important information by prioritizing available information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the AI ​​determine the priority of available information.

[0092] The service provider can select the optimal service method when providing vacancy information, taking into account the geographical distribution of parking spaces. For example, the service provider can select the optimal service method based on the geographical distribution of parking spaces. The service provider can also analyze the geographical distribution of parking spaces and select the most convenient service method for the user. Furthermore, the service provider can adjust the service content considering the geographical distribution of parking spaces. For example, the service provider can adjust the service content considering the geographical distribution of parking spaces. This improves convenience by selecting the optimal service method considering the geographical distribution of parking spaces. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical distribution data of parking spaces into a generating AI and have the generating AI select the optimal service method.

[0093] The service provider can improve the content of its offerings by referring to relevant literature on parking lots when providing vacancy information. For example, the service provider can improve the content of its offerings by referring to relevant literature on parking lots. For example, the service provider can improve the content of its offerings by referring to relevant literature on parking lots. The service provider can also analyze relevant literature on parking lots and select the optimal method of offering. For example, the service provider can analyze relevant literature on parking lots and select the optimal method of offering. Furthermore, the service provider can customize the content of its offerings based on relevant literature on parking lots. For example, the service provider can customize the content of its offerings based on relevant literature on parking lots. This improves convenience by improving the content of the offerings based on relevant literature on parking lots. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input relevant literature data on parking lots into a generating AI and have the generating AI perform improvements to the content of its offerings.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can offer suggestions that include detailed explanations. If the user is in a hurry, it can offer concise and to-the-point suggestions. Furthermore, if the user is excited, it can offer suggestions with visually stimulating effects. By adjusting the way suggestions are presented according to the user's emotions, the user's understanding is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0096] The service provider can estimate the user's emotions and adjust how available information is provided based on the estimated emotions. For example, if the user is relaxed, it can provide available information that includes detailed explanations. If the user is in a hurry, it can provide concise and to-the-point available information. Furthermore, if the user is excited, it can provide available information with visually stimulating effects. This improves usability by adjusting how available information is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI adjust how available information is provided.

[0097] The linked unit can estimate the user's emotions and adjust booking and payment procedures based on the estimated emotions. For example, if the user is relaxed, it can provide procedures with detailed explanations. If the user is in a hurry, it can provide concise and quick procedures. Furthermore, if the user is excited, it can provide procedures with visually stimulating effects. This improves user convenience by adjusting booking and payment procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the linked unit may be performed using AI or not using AI. For example, the linked unit can input user facial expression data into a generative AI and have the generative AI perform adjustments to booking and payment procedures.

[0098] The reception desk can estimate the user's emotions and adjust the input interface for destination and duration of stay based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of destination and duration of stay. This improves user convenience by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, concise suggestion. If the user is relaxed, it can provide a longer suggestion that includes detailed explanations. Furthermore, if the user is excited, it can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the user's understanding is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into the generative AI and have the generative AI adjust the length of the suggestion.

[0100] The suggestion unit can adjust the level of detail in its suggestions based on the parking lot's congestion status. For example, if the parking lot is crowded, it can provide a detailed explanation of the availability. If the parking lot is empty, it can provide a concise suggestion. Furthermore, it can adjust the priority of suggestions according to the parking lot's congestion status. This improves user convenience by adjusting the level of detail in suggestions according to the parking lot's congestion status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot congestion data into a generating AI and have the generating AI perform the adjustment of the level of detail in its suggestions.

[0101] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display destinations and durations of stay that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest destinations and durations of stay to be used during specific time periods based on the user's past input history. This improves user convenience by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0102] The reception desk can automatically complete the input content when the user enters their destination and length of stay, taking into account their current schedule and appointments. For example, it can refer to the user's calendar information and automatically set the destination and length of stay based on their appointments. It can also suggest locations related to specific events from the user's schedule as candidate locations. Furthermore, it can suggest the optimal length of stay to match the user's appointments. This reduces the effort required for input by automatically completing the input content while considering the user's schedule and appointments. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI perform automatic completion of the input content.

[0103] The suggestion unit can apply different suggestion algorithms depending on the parking lot category. For example, for parking lots in commercial facilities, it can make suggestions that prioritize usage fees and distance. For parking lots in residential areas, it can make suggestions that prioritize safety and convenience. Furthermore, for parking lots in tourist areas, it can make suggestions that prioritize accessibility and fees. By applying a suggestion algorithm appropriate to the parking lot category, the optimal suggestion can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input parking lot category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0104] The integrated unit can analyze a user's past usage history and propose the optimal procedure when making a reservation or payment. For example, it can prioritize suggesting payment methods the user has used in the past. It can also suggest the optimal reservation method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and propose the most efficient procedure. This improves user convenience by suggesting the optimal procedure based on past usage history. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's usage history data into a generating AI and have the generating AI execute a proposal for the optimal procedure.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk accepts input of destination and length of stay. For example, the reception desk provides an interface for the user to input their destination and length of stay. Step 2: Based on the information received by the reception department, the proposal department analyzes real-time availability and pricing structures to suggest the most suitable parking lot. For example, the proposal department uses AI to suggest the best parking lot based on factors such as distance from the destination and usage fees. Step 3: The interlocking unit makes parking reservations and payments as proposed by the suggestion unit. For example, the interlocking unit works in conjunction with the in-vehicle system to make parking reservations and payments.

[0107] 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] For example, the reception unit is implemented by the reception device 38 of the smart device 14, providing an interface for the user to input their destination and length of stay. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, using AI to suggest the optimal parking lot based on factors such as distance from the destination and usage fees. The linkage unit is implemented by the control unit 46A of the smart device 14, working in conjunction with the in-vehicle system to reserve and pay for parking spaces. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, updating parking availability information in real time and providing it to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] 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.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input their destination and length of stay. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and uses AI to suggest the optimal parking lot based on the distance from the destination and the usage fee. The linkage unit is implemented by the control unit 46A of the smart glasses 214 and works in conjunction with the in-vehicle system to reserve and pay for parking. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and updates parking availability information in real time and provides it to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, providing an interface for the user to input their destination and length of stay. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, using AI to suggest the optimal parking lot based on factors such as distance from the destination and usage fees. The linkage unit is implemented by the control unit 46A of the headset terminal 314, working in conjunction with the in-vehicle system to reserve and pay for parking spaces. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, updating parking availability information in real time and providing it to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] For example, the reception unit is implemented by the microphone 238 of the robot 414, providing an interface for the user to input their destination and length of stay. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, using AI to suggest the optimal parking lot based on factors such as distance from the destination and usage fees. The linkage unit is implemented by the control unit 46A of the robot 414, working in conjunction with the in-vehicle system to reserve and pay for parking spaces. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, updating parking availability information in real time and providing it to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0160] 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.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] 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.

[0163] 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.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] 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.

[0178] (Note 1) A reception desk where you can enter your destination and length of stay, Based on the information received by the reception department, the proposal department analyzes real-time availability and pricing structures and proposes appropriate parking spaces. The system includes an interlocking unit that handles the reservation and payment of parking spaces proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The system includes a service that allows parking lot owners to provide real-time information on available spaces. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We analyze real-time availability and pricing structures to suggest the most suitable parking option. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned interlocking part is, The system works in conjunction with the vehicle's internal systems to allow for parking reservations and payments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will suggest a suitable parking lot based on factors such as distance from your destination and parking fees. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for destination and duration of stay based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering a destination and duration of stay, the system automatically completes the input, taking into account the user's current schedule and plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter their destination and length of stay, the system prioritizes displaying highly relevant locations based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their destination and length of stay, the system analyzes their social media activity and suggests relevant locations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the parking lot congestion status. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the parking lot category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we will prioritize the proposals based on the parking lot usage history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the parking area. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned interlocking part is, It estimates the user's emotions and adjusts booking and payment procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned interlocking part is, When making a reservation or payment, the system analyzes the user's past usage history to suggest the most suitable procedure. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned interlocking part is, Customize the booking and payment process based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned interlocking part is, It estimates the user's emotions and prioritizes reservations and payments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned interlocking part is, When making a reservation or payment, we suggest the most suitable procedure considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned interlocking part is, When making a reservation or payment, the system analyzes the user's social media activity and suggests appropriate procedures. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how availability information is provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing availability information, the optimal method of provision is selected by referring to past provision history. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing parking availability information, the content provided will be customized based on the parking lot's congestion status. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes availability based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information on available parking spaces, the optimal method of provision will be selected considering the geographical distribution of parking lots. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information on parking availability, we will improve the content of the information provided by referring to relevant literature on parking lots. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where you can enter your destination and length of stay, Based on the information received by the reception department, the proposal department analyzes real-time availability and pricing structures and proposes appropriate parking spaces. The system includes an interlocking unit that handles the reservation and payment of parking spaces proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The system includes a service that allows parking lot owners to provide real-time information on available spaces. The system according to feature 1.

3. The aforementioned proposal section is, We analyze real-time availability and pricing structures to suggest the most suitable parking option. The system according to feature 1.

4. The aforementioned interlocking part is, The system works in conjunction with the vehicle's internal systems to allow for parking reservations and payments. The system according to feature 1.

5. The aforementioned proposal section is, We will suggest a suitable parking lot based on factors such as distance from your destination and parking fees. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for destination and duration of stay based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering a destination and duration of stay, the system automatically completes the input, taking into account the user's current schedule and plans. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

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