system

Abstract

The system according to this embodiment aims to improve the efficiency of parking lot utilization by confirming parking locations and suggesting optimal parking spaces. [Solution] The system according to the embodiment comprises an acquisition unit, a question reception unit, a suggestion unit, and a guidance unit. The acquisition unit recognizes the vehicle and driver and records accurate information. The question reception unit confirms the parking location based on the information recorded by the acquisition unit. The suggestion unit proposes the optimal parking space based on the information confirmed by the question reception unit. The guidance unit provides interactive guidance based on the parking space proposed 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 as a 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 are problems that it is easy to forget the parking location and it is difficult to select an appropriate parking space, and the efficiency of parking lot management is also poor.

[0005] The system according to the embodiment aims to improve the utilization efficiency of a parking lot by confirming the parking location and proposing an optimal parking space.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a question reception unit, a suggestion unit, and a guidance unit. The acquisition unit recognizes the vehicle and driver and records accurate information. The question reception unit confirms the parking location based on the information recorded by the acquisition unit. The suggestion unit proposes the optimal parking space based on the information confirmed by the question reception unit. The guidance unit provides interactive guidance based on the parking space proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the efficiency of parking lot utilization by confirming parking locations and suggesting optimal parking spaces. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database24 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 smart parking AI agent system according to an embodiment of the present invention is a system that assists in the management of parking lots. This smart parking AI agent system uses facial recognition and license plate recognition systems to recognize vehicles and drivers in parking lots and record accurate information. Next, it provides a function that allows users to easily ask "Where did I park?" using natural language processing. Furthermore, it has a function that suggests the optimal parking space considering the destination, luggage, and physical convenience. Finally, it provides interactive guidance through voice interaction. For example, upon arrival at a parking lot, the facial recognition and license plate recognition systems recognize the vehicle and driver and record accurate information in the parking lot management system. This allows for real-time monitoring of parking lot usage. Next, if the user forgets where they parked, they can easily ask "Where did I park?" using natural language processing. For example, if the user asks "Where did I park my car?", the system identifies the parking location and guides the user. Furthermore, upon arrival at a parking lot, it has a function that suggests the optimal parking space considering the destination, luggage, and physical convenience. For example, if the user has large luggage, it suggests a place that is easy to load the luggage. Also, if the user has a physical disability, it suggests a parking space that takes physical convenience into consideration. Finally, it provides interactive guidance through voice interaction. The system provides real-time guidance based on user voice commands. For example, if a user asks, "Where is the parking lot exit?", the system will provide voice guidance. This reduces the effort required to find a parking space and provides a smooth parking experience. It also improves the operational efficiency of the parking lot and streamlines parking lot management. As a result, the smart parking AI agent system can efficiently support parking lot management.

[0029] The smart parking AI agent system according to this embodiment comprises an acquisition unit, a question reception unit, a suggestion unit, and a guidance unit. The acquisition unit recognizes the vehicle and driver and records accurate information. The acquisition unit recognizes the vehicle and driver using, for example, facial recognition and a license plate recognition system. Facial recognition is performed using a facial recognition algorithm, and a high-resolution camera is used as the camera type. The license plate recognition system recognizes license plates using OCR technology, and a dedicated license plate camera is used as the camera type. The question reception unit confirms the parking location based on the information recorded by the acquisition unit. The question reception unit can ask, for example, "Where did I park?" using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The suggestion unit proposes the optimal parking space based on the information confirmed by the question reception unit. The suggestion unit proposes the optimal parking space considering, for example, the destination, luggage, and physical convenience. The destination, luggage, and physical convenience are considered based on criteria such as the distance to the destination, the size of the luggage, and the driver's physical condition. The guidance unit provides interactive guidance based on the parking space proposed by the suggestion unit. The guidance unit provides interactive guidance, for example, through voice interaction. Voice interaction is performed using voice recognition technology, voice synthesis technology, etc. As a result, the smart parking AI agent system according to this embodiment can recognize the vehicle and driver, confirm the parking location, suggest the optimal parking space, and provide interactive guidance.

[0030] The acquisition unit recognizes vehicles and drivers and records accurate information. For example, the acquisition unit uses facial recognition and license plate recognition systems to recognize vehicles and drivers. Facial recognition is performed using a facial recognition algorithm, and a high-resolution camera is used. The facial recognition algorithm utilizes deep learning technology and is trained on a dataset of millions of facial images. This allows the acquisition unit to recognize faces with high accuracy even under various lighting conditions and angles. The license plate recognition system recognizes license plates using OCR technology, and a dedicated license plate camera is used. OCR technology extracts characters from license plates using a character recognition algorithm and identifies vehicles by matching them with a database. Dedicated license plate cameras are equipped with infrared illumination and waterproofing to maintain high recognition accuracy even at night or in bad weather. By combining these technologies, the acquisition unit quickly and accurately acquires vehicle and driver information and records it in a database. Furthermore, the acquisition unit encrypts and stores the acquired information to ensure privacy protection. This allows the acquisition unit to improve the accuracy of vehicle and driver recognition and enhance the overall reliability and safety of the system.

[0031] The question reception unit verifies the parking location based on the information recorded by the information acquisition unit. For example, the question reception unit can ask "Where did I park?" using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides the input sentence into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of the sentence based on the order of words and grammatical rules. Semantic analysis understands the context and the meaning of words to accurately grasp the user's intent. By combining these techniques, the question reception unit can accurately understand the user's question and provide an appropriate answer. For example, if a user asks "Where did I park my car?", the question reception unit will identify the location of the user's vehicle based on the information recorded by the information acquisition unit and generate an answer. Furthermore, the question reception unit can learn the user's past question history and behavior patterns to provide more personalized answers. This allows the question reception unit to improve user convenience and enhance the overall user experience of the system.

[0032] The suggestion department proposes the optimal parking space based on information confirmed by the inquiry reception department. For example, the suggestion department proposes the optimal parking space considering the destination, luggage, and physical convenience. Destination, luggage, and physical convenience are considered based on criteria such as the distance to the destination, the size of the luggage, and the user's physical condition. Based on these criteria, the suggestion department identifies the most convenient parking space for the user. For example, if a user has heavy luggage, the suggestion department will propose the parking space closest to their destination. Also, for users with disabilities, barrier-free parking spaces will be prioritized. The suggestion department uses AI to analyze these criteria and quickly identify the optimal parking space. The AI ​​learns from past data and user behavior patterns to improve the accuracy of its suggestions. Furthermore, the suggestion department can make suggestions based on the latest situation, using real-time updated parking space availability information. This allows the suggestion department to provide the optimal parking space for the user and significantly improve the convenience of parking.

[0033] The guidance unit provides interactive directions based on the parking spaces suggested by the suggestion unit. For example, the guidance unit provides interactive directions through voice interaction. Voice interaction is performed using technologies such as speech recognition and speech synthesis. Speech recognition converts the user's voice into text in a format the system can understand. Speech synthesis converts the text into natural-sounding speech to provide directions to the user. By combining these technologies, the guidance unit can provide real-time, interactive directions to the user. For example, when a user arrives at the parking lot, the guidance unit will announce, "Welcome. Your parking space is A-12 on the 3rd floor." Also, if a user gets lost on their way to the parking space, the guidance unit can instruct them, "Turn right." Furthermore, the guidance unit can improve the directions based on user feedback, providing a more user-friendly system. This allows the guidance unit to provide users with quick and accurate directions, reducing the stress of parking.

[0034] The acquisition unit can recognize vehicles and drivers using facial recognition and license plate recognition systems. For example, the acquisition unit recognizes vehicles and drivers using facial recognition. Facial recognition is performed using a facial recognition algorithm, and a high-resolution camera is used. The acquisition unit can also recognize vehicles and drivers using a license plate recognition system. The license plate recognition system recognizes license plates using OCR technology, and a dedicated license plate camera is used. As a result, the accuracy of vehicle and driver recognition is improved by using both facial recognition and the license plate recognition system.

[0035] The question reception unit can ask "Where did I park?" using natural language processing. For example, the question reception unit uses natural language processing to make it easy for users to find their parking location. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, when a user asks "Where did I park my car?", the system identifies the parking location and guides the user. In this way, by using natural language processing, users can easily find their parking location.

[0036] The suggestion department can propose the optimal parking space considering the destination, luggage, and physical convenience. For example, the suggestion department will propose the optimal parking space considering the destination, luggage, and physical convenience. The destination, luggage, and physical convenience are considered based on criteria such as the distance to the destination, the size of the luggage, and the user's physical condition. For example, if the user has large luggage, the suggestion department will propose a location that is easy to load the luggage. If the user has a physical disability, the suggestion department can also propose a parking space that takes physical convenience into consideration. In this way, by considering the destination, luggage, and physical convenience, the suggestion department can propose the optimal parking space for the user.

[0037] The information unit can provide interactive guidance through voice interaction. For example, the information unit provides interactive guidance to the user through voice interaction. Voice interaction is performed using technologies such as speech recognition and speech synthesis. For instance, if a user asks, "Where is the parking lot exit?", the system will provide voice guidance. This allows the system to provide interactive guidance to the user through voice interaction.

[0038] The proposal department can suggest locations that are convenient for loading luggage if the customer has large items. For example, the proposal department can suggest locations that are convenient for loading luggage if the customer has large items. Large items include, but are not limited to, suitcases and furniture. This allows the proposal department to suggest locations that are convenient for loading luggage if the customer has large items.

[0039] The proposal department can propose parking spaces that take into consideration the convenience of people with physical disabilities. For example, the proposal department can propose parking spaces that take into consideration the convenience of people with physical disabilities. People with physical disabilities include, for example, wheelchair users and visually impaired people, but are not limited to these examples. This makes it possible to propose parking spaces that take into consideration the convenience of people with physical disabilities.

[0040] The acquisition unit can improve recognition accuracy by referring to past parking history when recognizing vehicles and drivers. For example, if the acquisition unit has a history of using the same parking lot in the past, it can improve recognition accuracy based on that information. The acquisition unit can also improve recognition accuracy by analyzing usage trends at specific time periods from past parking history. Based on past parking history, the acquisition unit can also prioritize the recognition of specific vehicle and driver combinations. In this way, recognition accuracy is improved by referring to past parking history.

[0041] The recognition unit can optimize its recognition algorithm according to weather and time of day when recognizing vehicles and drivers. For example, in rainy weather, the accuracy of facial recognition decreases, so the unit prioritizes license plate recognition. At night, the unit can also adjust the recognition algorithm according to lighting conditions to improve recognition accuracy. During congested daytime hours, the unit can speed up the recognition algorithm to improve processing speed. In this way, by optimizing the recognition algorithm according to weather and time of day, recognition accuracy is improved.

[0042] The question reception system can provide the best possible answer by referring to the user's past question history when a question is received. For example, the question reception system can provide the best answer based on the topics the user has frequently asked in the past. The question reception system can also analyze the user's past question history to determine when they tend to ask questions and provide the best answer based on that analysis. The question reception system can also suggest the best answer to a specific question based on the user's past question history. In this way, the system can provide the best possible answer by referring to the user's past question history.

[0043] The question reception system can prioritize questions based on the user's current situation when they are received. For example, if the user is in a hurry, the system will prioritize high-priority questions. If the user is relaxed, the system can also prioritize detailed questions. The system can also dynamically adjust the priority of questions based on the user's current situation. This streamlines the question reception process by prioritizing questions according to the user's current situation.

[0044] The question reception system can provide the most appropriate answer by considering the user's geographical location when a question is submitted. For example, if the user is in a specific area, the system can provide the most appropriate answer by considering the characteristics of that area. If the user is using a specific parking lot, the system can also provide the most appropriate answer by considering the characteristics of that parking lot. If the user is using a specific area at a specific time of day, the system can provide the most appropriate answer based on that information. In this way, the system can provide the most appropriate answer by considering the user's geographical location.

[0045] The question reception department can predict relevant questions by analyzing the user's social media activity when a question is received. For example, the question reception department can understand the user's current interests from their social media posts and predict relevant questions. The question reception department can also predict relevant questions based on information about the user's participation in specific events from their social media activity. The question reception department can also predict relevant questions by analyzing the user's tendency to visit specific places at specific times from their social media activity. In this way, relevant questions can be predicted by analyzing the user's social media activity.

[0046] The suggestion unit can propose the optimal parking space by referring to the user's past parking history. For example, the suggestion unit can propose the optimal parking space based on the parking spaces the user has used in the past. The suggestion unit can also propose parking spaces that avoid congestion based on the user's past parking history. The suggestion unit can also analyze the user's past parking history and propose the most efficient parking space. In this way, the optimal parking space can be proposed by referring to past parking history.

[0047] The suggestion system can prioritize suggestions based on the user's current situation. For example, if the user is in a hurry, the suggestion system will prioritize providing high-priority suggestions. If the user is relaxed, the suggestion system may also prioritize providing detailed suggestions. The suggestion system can also dynamically adjust the priority of suggestions based on the user's current situation. This improves the accuracy of suggestions by prioritizing them according to the user's current situation.

[0048] The suggestion function can propose the optimal parking space by considering the user's geographical location information. For example, if the user is in a specific area, the suggestion function will propose the optimal parking space by considering the characteristics of that area. If the user is using a specific parking lot, the suggestion function can also propose the optimal parking space by considering the characteristics of that parking lot. If the user is using a specific area at a specific time of day, the suggestion function can also propose the optimal parking space based on that information. In this way, the optimal parking space can be proposed by considering the user's geographical location information.

[0049] The proposal department can analyze users' social media activity and make relevant suggestions when making proposals. For example, the proposal department can understand users' current interests from their social media posts and make relevant suggestions. The proposal department can also make relevant suggestions based on information from users' social media activity about their participation in specific events. The proposal department can also analyze users' social media activity about their tendency to visit specific places at specific times and make relevant suggestions. In this way, relevant suggestions can be made by analyzing users' social media activity.

[0050] The guidance system can provide optimal guidance by referring to the user's past guidance history. For example, the guidance system can provide optimal guidance based on the guidance methods the user has used in the past. The guidance system can also analyze the user's past guidance history to determine usage patterns at specific times and provide optimal guidance. The guidance system can also suggest the optimal method for specific guidance based on the user's past guidance history. In this way, the guidance system can provide optimal guidance by referring to past guidance history.

[0051] The guidance system can determine the priority of guidance based on the user's current situation. For example, if the user is in a hurry, the guidance system will prioritize providing high-priority guidance. If the user is relaxed, the guidance system may also prioritize providing detailed guidance. The guidance system can also dynamically adjust the priority of guidance based on the user's current situation. This improves the accuracy of guidance by determining the priority of guidance according to the user's current situation.

[0052] The guidance system can provide optimal directions by considering the user's geographical location. For example, if the user is in a specific area, the guidance system can provide optimal directions by considering the characteristics of that area. If the user is using a specific parking lot, the guidance system can also provide optimal directions by considering the characteristics of that parking lot. If the user is using a specific area at a specific time of day, the guidance system can provide optimal directions based on that information. In this way, by considering the user's geographical location, optimal directions can be provided.

[0053] The guidance department can analyze users' social media activity and provide relevant guidance when providing information. For example, the guidance department can understand users' current interests from their social media posts and provide relevant guidance. The guidance department can also provide relevant guidance based on information from users' social media activity about their participation in specific events. The guidance department can also analyze users' social media activity about their tendency to visit specific places at specific times and provide relevant guidance. In this way, relevant guidance can be provided by analyzing users' social media activity.

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

[0055] The acquisition unit can improve recognition accuracy by referring to the user's driving history when recognizing a vehicle and driver. For example, if the acquisition unit has a history of using the same parking lot in the past, it can improve recognition accuracy based on that information. Furthermore, the acquisition unit can also improve recognition accuracy by analyzing usage trends at specific time periods from past driving history. In this way, recognition accuracy is improved by referring to past driving history.

[0056] The suggestion unit can propose the optimal parking space by referring to the user's past parking history. For example, it can suggest the optimal parking space based on the parking spaces the user has used in the past. Furthermore, the suggestion unit can also suggest parking spaces that avoid congestion based on the user's past parking history. In this way, the optimal parking space can be suggested by referring to past parking history.

[0057] The acquisition unit can optimize its recognition algorithm according to weather and time of day when recognizing vehicles and drivers. For example, in rainy weather, facial recognition accuracy decreases, so license plate recognition is prioritized. At night, the recognition algorithm can also be adjusted according to lighting conditions to improve recognition accuracy. In this way, the accuracy of recognition is improved by optimizing the recognition algorithm according to weather and time of day.

[0058] The question reception system can provide the most appropriate answer by referring to the user's past question history when a question is received. For example, it can provide the best answer based on the types of questions the user has frequently asked in the past. Furthermore, the question reception system can analyze the user's past question history to determine when they tend to ask questions and provide the best answer based on that analysis. In this way, the system can provide the best answer by referring to the user's past question history.

[0059] The guidance system can provide optimal guidance by referring to the user's past guidance history. For example, it can provide optimal guidance based on the guidance methods the user has used in the past. Furthermore, the guidance system can analyze the user's past guidance history to determine usage patterns at specific times of day and provide optimal guidance accordingly. In this way, it can provide optimal guidance by referring to past guidance history.

[0060] The suggestion function can propose the optimal parking space by considering the user's geographical location. For example, if the user is in a specific area, it will propose the optimal parking space by considering the characteristics of that area. Furthermore, if the user is using a specific parking lot, the suggestion function can also propose the optimal parking space by considering the characteristics of that parking lot. In this way, the optimal parking space can be proposed by considering the user's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The acquisition unit recognizes the vehicle and driver and records accurate information. The acquisition unit recognizes the vehicle and driver using facial recognition and a license plate recognition system. Facial recognition is performed using a facial recognition algorithm and a high-resolution camera is used. The license plate recognition system recognizes the license plate using OCR technology and a dedicated license plate camera is used. Step 2: The question reception unit verifies the parking location based on the information recorded by the acquisition unit. The question reception unit can ask "where was it parked?" using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The proposal department will suggest the optimal parking space based on the information confirmed by the inquiry department. The proposal department will suggest the optimal parking space considering the destination, luggage, and physical convenience. This will be based on criteria such as the distance to the destination, the size of the luggage, and the physical condition of the user. Step 4: The guidance unit provides interactive guidance based on the parking space proposed by the suggestion unit. The guidance unit provides interactive guidance through voice interaction. Voice interaction is performed using voice recognition technology, speech synthesis technology, etc.

[0063] (Example of form 2) The smart parking AI agent system according to an embodiment of the present invention is a system that assists in the management of parking lots. This smart parking AI agent system uses facial recognition and license plate recognition systems to recognize vehicles and drivers in parking lots and record accurate information. Next, it provides a function that allows users to easily ask "Where did I park?" using natural language processing. Furthermore, it has a function that suggests the optimal parking space considering the destination, luggage, and physical convenience. Finally, it provides interactive guidance through voice interaction. For example, upon arrival at a parking lot, the facial recognition and license plate recognition systems recognize the vehicle and driver and record accurate information in the parking lot management system. This allows for real-time monitoring of parking lot usage. Next, if the user forgets where they parked, they can easily ask "Where did I park?" using natural language processing. For example, if the user asks "Where did I park my car?", the system identifies the parking location and guides the user. Furthermore, upon arrival at a parking lot, it has a function that suggests the optimal parking space considering the destination, luggage, and physical convenience. For example, if the user has large luggage, it suggests a place that is easy to load the luggage. Also, if the user has a physical disability, it suggests a parking space that takes physical convenience into consideration. Finally, it provides interactive guidance through voice interaction. The system provides real-time guidance based on user voice commands. For example, if a user asks, "Where is the parking lot exit?", the system will provide voice guidance. This reduces the effort required to find a parking space and provides a smooth parking experience. It also improves the operational efficiency of the parking lot and streamlines parking lot management. As a result, the smart parking AI agent system can efficiently support parking lot management.

[0064] The smart parking AI agent system according to this embodiment comprises an acquisition unit, a question reception unit, a suggestion unit, and a guidance unit. The acquisition unit recognizes the vehicle and driver and records accurate information. The acquisition unit recognizes the vehicle and driver using, for example, facial recognition and a license plate recognition system. Facial recognition is performed using a facial recognition algorithm, and a high-resolution camera is used as the camera type. The license plate recognition system recognizes license plates using OCR technology, and a dedicated license plate camera is used as the camera type. The question reception unit confirms the parking location based on the information recorded by the acquisition unit. The question reception unit can ask, for example, "Where did I park?" using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The suggestion unit proposes the optimal parking space based on the information confirmed by the question reception unit. The suggestion unit proposes the optimal parking space considering, for example, the destination, luggage, and physical convenience. The destination, luggage, and physical convenience are considered based on criteria such as the distance to the destination, the size of the luggage, and the driver's physical condition. The guidance unit provides interactive guidance based on the parking space proposed by the suggestion unit. The guidance unit provides interactive guidance, for example, through voice interaction. Voice interaction is performed using voice recognition technology, voice synthesis technology, etc. As a result, the smart parking AI agent system according to this embodiment can recognize the vehicle and driver, confirm the parking location, suggest the optimal parking space, and provide interactive guidance.

[0065] The acquisition unit recognizes vehicles and drivers and records accurate information. For example, the acquisition unit uses facial recognition and license plate recognition systems to recognize vehicles and drivers. Facial recognition is performed using a facial recognition algorithm, and a high-resolution camera is used. The facial recognition algorithm utilizes deep learning technology and is trained on a dataset of millions of facial images. This allows the acquisition unit to recognize faces with high accuracy even under various lighting conditions and angles. The license plate recognition system recognizes license plates using OCR technology, and a dedicated license plate camera is used. OCR technology extracts characters from license plates using a character recognition algorithm and identifies vehicles by matching them with a database. Dedicated license plate cameras are equipped with infrared illumination and waterproofing to maintain high recognition accuracy even at night or in bad weather. By combining these technologies, the acquisition unit quickly and accurately acquires vehicle and driver information and records it in a database. Furthermore, the acquisition unit encrypts and stores the acquired information to ensure privacy protection. This allows the acquisition unit to improve the accuracy of vehicle and driver recognition and enhance the overall reliability and safety of the system.

[0066] The question reception unit verifies the parking location based on the information recorded by the information acquisition unit. For example, the question reception unit can ask "Where did I park?" using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides the input sentence into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of the sentence based on the order of words and grammatical rules. Semantic analysis understands the context and the meaning of words to accurately grasp the user's intent. By combining these techniques, the question reception unit can accurately understand the user's question and provide an appropriate answer. For example, if a user asks "Where did I park my car?", the question reception unit will identify the location of the user's vehicle based on the information recorded by the information acquisition unit and generate an answer. Furthermore, the question reception unit can learn the user's past question history and behavior patterns to provide more personalized answers. This allows the question reception unit to improve user convenience and enhance the overall user experience of the system.

[0067] The suggestion department proposes the optimal parking space based on information confirmed by the inquiry reception department. For example, the suggestion department proposes the optimal parking space considering the destination, luggage, and physical convenience. Destination, luggage, and physical convenience are considered based on criteria such as the distance to the destination, the size of the luggage, and the user's physical condition. Based on these criteria, the suggestion department identifies the most convenient parking space for the user. For example, if a user has heavy luggage, the suggestion department will propose the parking space closest to their destination. Also, for users with disabilities, barrier-free parking spaces will be prioritized. The suggestion department uses AI to analyze these criteria and quickly identify the optimal parking space. The AI ​​learns from past data and user behavior patterns to improve the accuracy of its suggestions. Furthermore, the suggestion department can make suggestions based on the latest situation, using real-time updated parking space availability information. This allows the suggestion department to provide the optimal parking space for the user and significantly improve the convenience of parking.

[0068] The guidance unit provides interactive directions based on the parking spaces suggested by the suggestion unit. For example, the guidance unit provides interactive directions through voice interaction. Voice interaction is performed using technologies such as speech recognition and speech synthesis. Speech recognition converts the user's voice into text in a format the system can understand. Speech synthesis converts the text into natural-sounding speech to provide directions to the user. By combining these technologies, the guidance unit can provide real-time, interactive directions to the user. For example, when a user arrives at the parking lot, the guidance unit will announce, "Welcome. Your parking space is A-12 on the 3rd floor." Also, if a user gets lost on their way to the parking space, the guidance unit can instruct them, "Turn right." Furthermore, the guidance unit can improve the directions based on user feedback, providing a more user-friendly system. This allows the guidance unit to provide users with quick and accurate directions, reducing the stress of parking.

[0069] The acquisition unit can recognize vehicles and drivers using facial recognition and license plate recognition systems. For example, the acquisition unit recognizes vehicles and drivers using facial recognition. Facial recognition is performed using a facial recognition algorithm, and a high-resolution camera is used. The acquisition unit can also recognize vehicles and drivers using a license plate recognition system. The license plate recognition system recognizes license plates using OCR technology, and a dedicated license plate camera is used. As a result, the accuracy of vehicle and driver recognition is improved by using both facial recognition and the license plate recognition system.

[0070] The question reception unit can ask "Where did I park?" using natural language processing. For example, the question reception unit uses natural language processing to make it easy for users to find their parking location. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, when a user asks "Where did I park my car?", the system identifies the parking location and guides the user. In this way, by using natural language processing, users can easily find their parking location.

[0071] The suggestion department can propose the optimal parking space considering the destination, luggage, and physical convenience. For example, the suggestion department will propose the optimal parking space considering the destination, luggage, and physical convenience. The destination, luggage, and physical convenience are considered based on criteria such as the distance to the destination, the size of the luggage, and the user's physical condition. For example, if the user has large luggage, the suggestion department will propose a location that is easy to load the luggage. If the user has a physical disability, the suggestion department can also propose a parking space that takes physical convenience into consideration. In this way, by considering the destination, luggage, and physical convenience, the suggestion department can propose the optimal parking space for the user.

[0072] The information unit can provide interactive guidance through voice interaction. For example, the information unit provides interactive guidance to the user through voice interaction. Voice interaction is performed using technologies such as speech recognition and speech synthesis. For instance, if a user asks, "Where is the parking lot exit?", the system will provide voice guidance. This allows the system to provide interactive guidance to the user through voice interaction.

[0073] The proposal department can suggest locations that are convenient for loading luggage if the customer has large items. For example, the proposal department can suggest locations that are convenient for loading luggage if the customer has large items. Large items include, but are not limited to, suitcases and furniture. This allows the proposal department to suggest locations that are convenient for loading luggage if the customer has large items.

[0074] The proposal department can propose parking spaces that take into consideration the convenience of people with physical disabilities. For example, the proposal department can propose parking spaces that take into consideration the convenience of people with physical disabilities. People with physical disabilities include, for example, wheelchair users and visually impaired people, but are not limited to these examples. This makes it possible to propose parking spaces that take into consideration the convenience of people with physical disabilities.

[0075] The acquisition unit can estimate the user's emotions and adjust the vehicle and driver recognition accuracy based on the estimated emotions. For example, if the user is stressed, the acquisition unit can use both facial recognition and license plate recognition to improve recognition accuracy. If the user is relaxed, the acquisition unit can prioritize processing speed by performing recognition using only facial recognition. If the user is in a hurry, the acquisition unit can perform rapid recognition using only license plate recognition. This improves recognition accuracy by adjusting the recognition accuracy 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.

[0076] The acquisition unit can improve recognition accuracy by referring to past parking history when recognizing vehicles and drivers. For example, if the acquisition unit has a history of using the same parking lot in the past, it can improve recognition accuracy based on that information. The acquisition unit can also improve recognition accuracy by analyzing usage trends at specific time periods from past parking history. Based on past parking history, the acquisition unit can also prioritize the recognition of specific vehicle and driver combinations. In this way, recognition accuracy is improved by referring to past parking history.

[0077] The recognition unit can optimize its recognition algorithm according to weather and time of day when recognizing vehicles and drivers. For example, in rainy weather, the accuracy of facial recognition decreases, so the unit prioritizes license plate recognition. At night, the unit can also adjust the recognition algorithm according to lighting conditions to improve recognition accuracy. During congested daytime hours, the unit can speed up the recognition algorithm to improve processing speed. In this way, by optimizing the recognition algorithm according to weather and time of day, recognition accuracy is improved.

[0078] The question reception unit can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, if the user is stressed, the question reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the question reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the question reception unit can prioritize voice input and quickly receive the question. This makes question reception smoother by adjusting the question reception method 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.

[0079] The question reception system can provide the best possible answer by referring to the user's past question history when a question is received. For example, the question reception system can provide the best answer based on the topics the user has frequently asked in the past. The question reception system can also analyze the user's past question history to determine when they tend to ask questions and provide the best answer based on that analysis. The question reception system can also suggest the best answer to a specific question based on the user's past question history. In this way, the system can provide the best possible answer by referring to the user's past question history.

[0080] The question reception system can prioritize questions based on the user's current situation when they are received. For example, if the user is in a hurry, the system will prioritize high-priority questions. If the user is relaxed, the system can also prioritize detailed questions. The system can also dynamically adjust the priority of questions based on the user's current situation. This streamlines the question reception process by prioritizing questions according to the user's current situation.

[0081] The question reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, if the user is nervous, the question reception unit can delay the timing of question reception to help them relax. If the user is relaxed, the question reception unit can also speed up the timing of question reception to respond quickly. If the user is in a hurry, the question reception unit can optimize the timing of question reception to respond quickly. This makes question reception smoother by adjusting the timing of question reception 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.

[0082] The question reception system can provide the most appropriate answer by considering the user's geographical location when a question is submitted. For example, if the user is in a specific area, the system can provide the most appropriate answer by considering the characteristics of that area. If the user is using a specific parking lot, the system can also provide the most appropriate answer by considering the characteristics of that parking lot. If the user is using a specific area at a specific time of day, the system can provide the most appropriate answer based on that information. In this way, the system can provide the most appropriate answer by considering the user's geographical location.

[0083] The question reception department can predict relevant questions by analyzing the user's social media activity when a question is received. For example, the question reception department can understand the user's current interests from their social media posts and predict relevant questions. The question reception department can also predict relevant questions based on information about the user's participation in specific events from their social media activity. The question reception department can also predict relevant questions by analyzing the user's tendency to visit specific places at specific times from their social media activity. In this way, relevant questions can be predicted by analyzing the user's social media activity.

[0084] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. If the user is in a hurry, the suggestion function can also provide concise and to-the-point suggestions. If the user is excited, the suggestion function can also provide visually stimulating suggestions. By adjusting the content of suggestions according to the user's emotions, the accuracy of the suggestions is improved. 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.

[0085] The suggestion unit can propose the optimal parking space by referring to the user's past parking history. For example, the suggestion unit can propose the optimal parking space based on the parking spaces the user has used in the past. The suggestion unit can also propose parking spaces that avoid congestion based on the user's past parking history. The suggestion unit can also analyze the user's past parking history and propose the most efficient parking space. In this way, the optimal parking space can be proposed by referring to past parking history.

[0086] The suggestion system can prioritize suggestions based on the user's current situation. For example, if the user is in a hurry, the suggestion system will prioritize providing high-priority suggestions. If the user is relaxed, the suggestion system may also prioritize providing detailed suggestions. The suggestion system can also dynamically adjust the priority of suggestions based on the user's current situation. This improves the accuracy of suggestions by prioritizing them according to the user's current situation.

[0087] The suggestion section can estimate the user's emotions and adjust how suggestions are displayed based on those emotions. For example, if the user is stressed, the suggestion section can provide a simple and highly visible display. If the user is relaxed, the suggestion section can also provide a display that includes detailed information. If the user is in a hurry, the suggestion section can also provide a concise display. By adjusting how suggestions are displayed according to the user's emotions, the visibility of suggestions is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The suggestion function can propose the optimal parking space by considering the user's geographical location information. For example, if the user is in a specific area, the suggestion function will propose the optimal parking space by considering the characteristics of that area. If the user is using a specific parking lot, the suggestion function can also propose the optimal parking space by considering the characteristics of that parking lot. If the user is using a specific area at a specific time of day, the suggestion function can also propose the optimal parking space based on that information. In this way, the optimal parking space can be proposed by considering the user's geographical location information.

[0089] The proposal department can analyze users' social media activity and make relevant suggestions when making proposals. For example, the proposal department can understand users' current interests from their social media posts and make relevant suggestions. The proposal department can also make relevant suggestions based on information from users' social media activity about their participation in specific events. The proposal department can also analyze users' social media activity about their tendency to visit specific places at specific times and make relevant suggestions. In this way, relevant suggestions can be made by analyzing users' social media activity.

[0090] The guidance system can estimate the user's emotions and adjust its guidance method based on those emotions. For example, if the user is nervous, the guidance system can provide guidance in a calm voice. If the user is relaxed, the guidance system can provide guidance in a cheerful voice. If the user is in a hurry, the guidance system can provide quick and concise guidance. By adjusting the guidance method according to the user's emotions, the accuracy of the guidance is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The guidance system can provide optimal guidance by referring to the user's past guidance history. For example, the guidance system can provide optimal guidance based on the guidance methods the user has used in the past. The guidance system can also analyze the user's past guidance history to determine usage patterns at specific times and provide optimal guidance. The guidance system can also suggest the optimal method for specific guidance based on the user's past guidance history. In this way, the guidance system can provide optimal guidance by referring to past guidance history.

[0092] The guidance system can determine the priority of guidance based on the user's current situation. For example, if the user is in a hurry, the guidance system will prioritize providing high-priority guidance. If the user is relaxed, the guidance system may also prioritize providing detailed guidance. The guidance system can also dynamically adjust the priority of guidance based on the user's current situation. This improves the accuracy of guidance by determining the priority of guidance according to the user's current situation.

[0093] The guidance system can estimate the user's emotions and adjust the timing of guidance based on those emotions. For example, if the user is tense, the guidance system can delay the timing of guidance to help them relax. If the user is relaxed, the guidance system can also speed up the timing of guidance to provide a quicker response. If the user is in a hurry, the guidance system can also optimize the timing of guidance to provide a quicker response. This improves the accuracy of guidance by adjusting the timing of guidance 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The guidance system can provide optimal directions by considering the user's geographical location. For example, if the user is in a specific area, the guidance system can provide optimal directions by considering the characteristics of that area. If the user is using a specific parking lot, the guidance system can also provide optimal directions by considering the characteristics of that parking lot. If the user is using a specific area at a specific time of day, the guidance system can provide optimal directions based on that information. In this way, by considering the user's geographical location, optimal directions can be provided.

[0095] The guidance department can analyze users' social media activity and provide relevant guidance when providing information. For example, the guidance department can understand users' current interests from their social media posts and provide relevant guidance. The guidance department can also provide relevant guidance based on information from users' social media activity about their participation in specific events. The guidance department can also analyze users' social media activity about their tendency to visit specific places at specific times and provide relevant guidance. In this way, relevant guidance can be provided by analyzing users' social media activity.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The acquisition unit can improve recognition accuracy by referring to the user's driving history when recognizing a vehicle and driver. For example, if the acquisition unit has a history of using the same parking lot in the past, it can improve recognition accuracy based on that information. Furthermore, the acquisition unit can also improve recognition accuracy by analyzing usage trends at specific time periods from past driving history. In this way, recognition accuracy is improved by referring to past driving history.

[0098] The question reception unit can estimate the user's emotions and adjust the question reception method based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. This makes the question reception process smoother by adjusting the question reception method according to the user's emotions.

[0099] The suggestion unit can propose the optimal parking space by referring to the user's past parking history. For example, it can suggest the optimal parking space based on the parking spaces the user has used in the past. Furthermore, the suggestion unit can also suggest parking spaces that avoid congestion based on the user's past parking history. In this way, the optimal parking space can be suggested by referring to past parking history.

[0100] The guidance system can estimate the user's emotions and adjust its guidance method based on those estimates. For example, if the user is nervous, it can provide guidance in a calm voice. If the user is relaxed, it can provide guidance in a cheerful voice. By adjusting the guidance method according to the user's emotions, the accuracy of the guidance is improved.

[0101] The acquisition unit can optimize its recognition algorithm according to weather and time of day when recognizing vehicles and drivers. For example, in rainy weather, facial recognition accuracy decreases, so license plate recognition is prioritized. At night, the recognition algorithm can also be adjusted according to lighting conditions to improve recognition accuracy. In this way, the accuracy of recognition is improved by optimizing the recognition algorithm according to weather and time of day.

[0102] The question reception system can provide the most appropriate answer by referring to the user's past question history when a question is received. For example, it can provide the best answer based on the types of questions the user has frequently asked in the past. Furthermore, the question reception system can analyze the user's past question history to determine when they tend to ask questions and provide the best answer based on that analysis. In this way, the system can provide the best answer by referring to the user's past question history.

[0103] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise and to-the-point suggestions. By adjusting the content of suggestions according to the user's emotions, the accuracy of the suggestions is improved.

[0104] The guidance system can provide optimal guidance by referring to the user's past guidance history. For example, it can provide optimal guidance based on the guidance methods the user has used in the past. Furthermore, the guidance system can analyze the user's past guidance history to determine usage patterns at specific times of day and provide optimal guidance accordingly. In this way, it can provide optimal guidance by referring to past guidance history.

[0105] The suggestion function can propose the optimal parking space by considering the user's geographical location. For example, if the user is in a specific area, it will propose the optimal parking space by considering the characteristics of that area. Furthermore, if the user is using a specific parking lot, the suggestion function can also propose the optimal parking space by considering the characteristics of that parking lot. In this way, the optimal parking space can be proposed by considering the user's geographical location.

[0106] The guidance system can estimate the user's emotions and adjust the timing of guidance based on those emotions. For example, if the user is nervous, the guidance can be delayed to help them relax. If the user is relaxed, the guidance can be advanced to provide a quicker response. By adjusting the timing of guidance according to the user's emotions, the accuracy of the guidance is improved.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The acquisition unit recognizes the vehicle and driver and records accurate information. The acquisition unit recognizes the vehicle and driver using facial recognition and a license plate recognition system. Facial recognition is performed using a facial recognition algorithm and a high-resolution camera is used. The license plate recognition system recognizes the license plate using OCR technology and a dedicated license plate camera is used. Step 2: The question reception unit verifies the parking location based on the information recorded by the acquisition unit. The question reception unit can ask "where was it parked?" using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The proposal department will suggest the optimal parking space based on the information confirmed by the inquiry department. The proposal department will suggest the optimal parking space considering the destination, luggage, and physical convenience. This will be based on criteria such as the distance to the destination, the size of the luggage, and the physical condition of the user. Step 4: The guidance unit provides interactive guidance based on the parking space proposed by the suggestion unit. The guidance unit provides interactive guidance through voice interaction. Voice interaction is performed using voice recognition technology, speech synthesis technology, etc.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the acquisition unit, question reception unit, suggestion unit, and guidance unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the camera 42 and control unit 46A of the smart device 14, and recognizes the vehicle and driver using facial recognition and a license plate recognition system. The question reception unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and confirms the parking location using natural language processing. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests the optimal parking space considering the destination, luggage, and physical convenience. The guidance unit is implemented, for example, by the control unit 46A of the smart device 14, and provides interactive guidance through voice interaction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the acquisition unit, question reception unit, suggestion unit, and guidance unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the camera 42 and control unit 46A of the smart glasses 214, and recognizes the vehicle and driver using facial recognition and a license plate recognition system. The question reception unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and confirms the parking location using natural language processing. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests the optimal parking space considering the destination, luggage, and physical convenience. The guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides interactive guidance through voice interaction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

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

[0132] The 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.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

[0136] Figure 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.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the 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.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the acquisition unit, question reception unit, suggestion unit, and guidance unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the camera 42 and control unit 46A of the headset terminal 314, and recognizes the vehicle and driver using facial recognition and a license plate recognition system. The question reception unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and confirms the parking location using natural language processing. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests the optimal parking space considering the destination, luggage, and physical convenience. The guidance unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides interactive guidance through voice interaction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the acquisition unit, question reception unit, suggestion unit, and guidance unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the camera 42 and control unit 46A of the robot 414, and recognizes the vehicle and driver using facial recognition and a license plate recognition system. The question reception unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and confirms the parking location using natural language processing. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests the optimal parking space considering the destination, luggage, and physical convenience. The guidance unit is implemented, for example, by the control unit 46A of the robot 414, and provides interactive guidance through voice interaction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) An acquisition unit that recognizes the vehicle and driver and records accurate information, A question reception unit that confirms the parking location based on the information recorded by the acquisition unit, Based on the information confirmed by the aforementioned question reception department, the proposal department proposes the most suitable parking space. The system includes a guidance unit that provides interactive guidance based on the parking space proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The acquisition unit is, Vehicles and drivers are identified using facial recognition and license plate recognition systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned question reception department, Using natural language processing, you can ask "Where did you park?" The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We will suggest the optimal parking space considering your destination, luggage, and physical convenience. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is Provides interactive guidance through voice interaction. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, If you have large luggage, we will suggest a location that makes loading easier. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, For those with physical disabilities, we propose parking spaces that take their physical convenience into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system estimates the user's emotions and adjusts the vehicle and driver recognition accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When recognizing a vehicle and driver, past parking history is referenced to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When recognizing a vehicle and driver, the recognition algorithm is optimized according to weather conditions and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned question reception department, The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned question reception department, When a question is submitted, the system provides the most appropriate answer by referring to the user's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question reception department, When a question is submitted, the priority of the question is determined based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question reception department, The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question reception department, When receiving a question, we will provide the most appropriate answer by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question reception department, When a question is submitted, the system analyzes the user's social media activity to predict relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a suggestion, the system will refer to the user's past parking history to suggest the most suitable parking space. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts how suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will suggest the optimal parking space considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is It estimates the user's emotions and adjusts the guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is When providing guidance, the system refers to the user's past guidance history to provide the most suitable guidance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is When providing guidance, the priority of the guidance is determined according to the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is It estimates the user's emotions and adjusts the timing of guidance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When providing directions, the system takes into account the user's geographical location to provide the most optimal guidance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is When providing guidance, we analyze the user's social media activity and provide relevant guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. An acquisition unit that recognizes the vehicle and driver and records accurate information, A question reception unit that confirms the parking location based on the information recorded by the acquisition unit, Based on the information confirmed by the aforementioned question reception department, the proposal department proposes the most suitable parking space. The system includes a guidance unit that provides interactive guidance based on the parking space proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The acquisition unit is, Vehicles and drivers are identified using facial recognition and license plate recognition systems. The system according to feature 1.

3. The aforementioned question reception department, Ask questions using natural language processing. The system according to feature 1.

4. The aforementioned proposal section is, We will suggest the optimal parking space considering your destination, luggage, and physical convenience. The system according to feature 1.

5. The aforementioned guide section is Provides interactive guidance through voice interaction. The system according to feature 1.

6. The aforementioned proposal section is, If you have large luggage, we will suggest a location that makes loading easier. The system according to feature 1.

7. The aforementioned proposal section is, For those with physical disabilities, we propose parking spaces that take their physical convenience into consideration. The system according to feature 1.

8. The acquisition unit is, The system estimates the user's emotions and adjusts the vehicle and driver recognition accuracy based on the estimated emotions. The system according to feature 1.

9. The acquisition unit is, When recognizing a vehicle and driver, past parking history is referenced to improve recognition accuracy. The system according to feature 1.

10. The acquisition unit is, When recognizing a vehicle and driver, the recognition algorithm is optimized according to weather conditions and time of day. The system according to feature 1.

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