system

Abstract

The system according to this embodiment aims to reduce the hassle and stress involved in using public facilities and to provide a smooth and personalized experience. [Solution] The system according to the embodiment comprises an installation unit, a collaboration unit, and a provision unit. The installation unit installs a personal AI agent on an individual's smart device or wearable device. The collaboration unit collaborates in real time with a public AI agent installed in a public facility. The provision unit provides the user with the most suitable service based on the information shared by the collaboration unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have problems such as the trouble and time loss associated with using public facilities, stress caused by crowds and waiting in line, and insufficient information guidance and support.

[0005] The system according to the embodiment aims to reduce the trouble and stress in using public facilities and provide a smooth and personalized experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an installation unit, a collaboration unit, and a service provision unit. The installation unit installs a personal AI agent on an individual's smart device or wearable device. The collaboration unit collaborates in real time with a public AI agent installed in a public facility. The service provision unit provides the user with the most suitable service based on the information shared by the collaboration unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the hassle and stress involved in using public facilities and provide a smooth and personalized experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 system according to an embodiment of the present invention is a system that removes various daily barriers and provides a smoother and more personalized experience by having each individual have their own personal AI agent and cooperating with public AI agents in public facilities and services. For example, this system provides a mechanism in which the public agent in the station premises and the individual's personal agent cooperate to automatically settle fares, instead of using ticket gates at train stations. This solves problems such as the hassle and time loss associated with using public facilities such as ticket gates and payment terminals, the stress caused by crowds and waiting in line, and the lack of information guidance and support at public facilities. Specifically, it consists of the following steps: First, the personal AI agent is installed in the individual's smart device or wearable device. Next, it cooperates in real time with the public AI agent installed in the public facility. This allows the individual's personal AI agent to share information with the public AI agent in the public facility and provide the user with the most suitable service. For example, by providing a mechanism in which the public agent in the station premises and the individual's personal agent cooperate to automatically settle fares, it becomes possible to use the station without going through ticket gates. Furthermore, the AI ​​agent integrates personality, memory, planning, and behavior, providing an interface through natural language processing (NLP). It also utilizes RFID / NFC and location-based services to analyze user behavior patterns using machine learning. This system can remove all barriers in people's daily lives, creating a smooth and stress-free social infrastructure. This allows individuals to focus on truly valuable activities, improving their quality of life. The system can then provide optimal services to users by equipping personal AI agents on individual smart devices and wearable devices, and by collaborating in real-time with public AI agents installed in public facilities.

[0029] The system according to this embodiment comprises an installation unit, a collaboration unit, and a service provision unit. The installation unit installs a personal AI agent into an individual's smart device or wearable device. For example, the installation unit installs the AI ​​agent into a device such as a smartphone or smartwatch. The installation unit can also automatically adjust the device settings to ensure the AI ​​agent operates optimally. For example, the installation unit optimizes the operation of the AI ​​agent by considering the device's battery level and storage capacity. The collaboration unit collaborates in real time with a public AI agent installed in a public facility. The collaboration unit exchanges data with the public AI agent using communication technologies such as Wi-Fi or Bluetooth®. The collaboration unit can also synchronize data in real time and provide services tailored to the user's situation. For example, when a user arrives at a station, the collaboration unit collaborates with the public AI agent to automatically settle the fare. The service provision unit provides the user with the most suitable service based on the information shared by the collaboration unit. For example, the service provision unit analyzes the user's behavior patterns and past usage history to propose the most suitable service. The service provision unit can also customize the service in real time according to the user's current situation. For example, the service provider will guide users to the shortest route if they are in a hurry, and provide tourist information if they are relaxed. This allows the system, according to the embodiment, to provide users with optimal services by equipping personal AI agents on individual smart devices or wearable devices and coordinating in real time with public AI agents installed in public facilities.

[0030] The integration unit will install a personal AI agent into individual smart devices and wearable devices. Specifically, it will install the AI ​​agent into devices such as smartphones and smartwatches. This will allow users to utilize the AI ​​agent's functions through devices they use daily. The integration unit can also automatically adjust device settings to ensure the AI ​​agent operates optimally. For example, it will optimize the AI ​​agent's operation by considering the device's battery level and storage capacity. This allows the AI ​​agent to efficiently use device resources and always provide the user with optimal performance. Furthermore, the integration unit can enhance the device's security settings to ensure the AI ​​agent operates securely. For example, it will adjust the device's firewall settings to protect the AI ​​agent from external attacks. It can also adjust the device's privacy settings to ensure the user's personal information is properly protected. In this way, the integration unit can provide an environment in which the AI ​​agent operates safely and efficiently, offering users high reliability and convenience.

[0031] The collaboration unit interacts in real time with public AI agents installed in public facilities. Specifically, it exchanges data with public AI agents using communication technologies such as Wi-Fi and Bluetooth. This allows personal AI agents installed in users' smart devices and wearable devices to seamlessly interact with public AI agents installed in public facilities and provide services tailored to the user's situation. The collaboration unit can also synchronize data in real time and provide services tailored to the user's situation. For example, when a user arrives at a station, the collaboration unit interacts with a public AI agent to automatically settle the fare. This allows users to use public transportation smoothly through their smart devices. The collaboration unit can also interact with a public AI agent to provide store information and sales information when a user arrives at a shopping mall. This allows users to enjoy shopping efficiently through their smart devices. Furthermore, when a user arrives at a hospital, the collaboration unit can interact with a public AI agent to provide appointment information and waiting time information. This allows users to receive medical services smoothly through their smart devices. Through these functions, the collaboration unit can provide users with a high level of convenience and comfort.

[0032] The service provider department provides users with the most suitable services based on information shared by the collaboration department. Specifically, it analyzes users' behavior patterns and past usage history to propose the most suitable services. For example, the service provider department can understand the places users frequently visit and the services they use, and make personalized suggestions based on that. This allows users to receive the most suitable services for them. The service provider department can also customize services in real time according to the user's current situation. For example, if the user is in a hurry, the service provider department will guide them to the shortest route, and if they are relaxed, it will provide tourist information. This allows users to receive the most suitable services according to their situation at any given time. Furthermore, the service provider department can collect user feedback and continuously improve the quality of its services. For example, the service provider department reviews and improves its services based on user evaluations and opinions. This allows the service provider department to always provide users with high-quality services. Through these functions, the service provider department can provide users with high satisfaction and convenience.

[0033] The service provider allows users to pay fares without passing through ticket gates within the station. For example, when a user arrives at a station, the service provider works in conjunction with a public AI agent to automatically pay the fare. The service provider can also pay fares using methods such as electronic money, credit cards, and QR code payments. For example, the service provider can pay fares using an electronic money app installed on the user's smartphone. The service provider can also register the user's credit card information in advance and automatically deduct the fare. Furthermore, the service provider can pay fares by scanning QR codes. For example, the service provider can use a QR code reader installed in the station to scan the QR code displayed on the user's smartphone and pay the fare. This improves user convenience by allowing fare payment without passing through ticket gates within the station.

[0034] The service provider's AI agent can integrate personality, memory, planning, and behavior. For example, it can provide optimal services based on the user's personality and past behavioral history. It can also customize services considering the user's schedule and plans. For example, it can integrate with the user's calendar app to provide services according to their schedule. It can also analyze the user's behavioral patterns and provide services at the optimal time. For example, it can suggest music that will help the user relax during their commute. In this way, the AI ​​agent can provide the most optimal service to the user by integrating personality, memory, planning, and behavior.

[0035] The service provider can provide an interface based on natural language processing. For example, it can analyze the user's voice using speech recognition technology to enable natural dialogue. It can also analyze user input using text analysis technology to generate appropriate responses. For example, it can analyze text entered by the user on their smartphone and suggest the most suitable service. Furthermore, it can enable natural dialogue with the user using a dialogue system. For example, it can provide appropriate answers to the user's questions. By providing an interface based on natural language processing, users can intuitively operate the system.

[0036] The service provider can utilize RFID / NFC and location information service integration technologies. For example, the service provider can obtain a user's location information by reading an RFID tag. The service provider can also exchange data between devices using NFC communication. For example, the service provider can synchronize data between a user's smartphone and a terminal in a public facility using NFC communication. Furthermore, the service provider can obtain a user's location information using GPS or Wi-Fi location services. For example, the service provider can determine the user's current location using GPS data from the user's smartphone. By utilizing RFID / NFC and location information service integration technologies, the service provider can provide the user with the most optimal service.

[0037] The service provider can perform user behavior pattern analysis using machine learning. For example, the service provider can analyze behavior patterns based on a user's past behavior history. Furthermore, the service provider can analyze behavior patterns in real time according to the user's current situation. For example, the service provider can suggest the optimal route based on the user's travel history. The service provider can also provide health management services based on the user's activity records. For example, the service provider can analyze the user's exercise level and suggest an appropriate exercise plan. In this way, by performing user behavior pattern analysis using machine learning, the service provider can provide the most suitable service to the user.

[0038] The integration unit can analyze the user's past device usage history and select the optimal integration method. For example, the integration unit can customize the integration method of the personal AI agent based on the applications the user has frequently used in the past. The integration unit can also analyze the user's device usage time and integrate the personal AI agent at the optimal time. Furthermore, the integration unit can analyze the user's device usage patterns and propose the most efficient integration method. For example, the integration unit optimizes the functions of the personal AI agent based on the user's application usage history. This allows the optimal integration method to be selected by analyzing the user's past device usage history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's device usage history into a generating AI and have the generating AI select the optimal integration method.

[0039] The onboard unit can customize the personal AI agent based on the user's current device settings and usage when it is installed. For example, the onboard unit can automatically set the language of the personal AI agent based on the language settings of the user's device. The onboard unit can also install the personal AI agent with optimal performance, taking into account the battery level of the user's device. Furthermore, the onboard unit can check the storage status of the user's device and efficiently manage the necessary data. For example, the onboard unit can automatically adjust the settings of the user's device to ensure that the personal AI agent operates optimally. This allows the onboard unit to provide the optimal personal AI agent by customizing it based on the user's current device settings and usage. Some or all of the above processes in the onboard unit may be performed using AI, or not. For example, the onboard unit can input the user's device settings and usage into a generating AI and have the generating AI perform the customization.

[0040] The integrated unit can perform optimal settings when installing a personal AI agent, taking into account the user's geographical location information. For example, if the user is at home, the integrated unit can prioritize settings for support functions within the home. If the user is out, the integrated unit can also prioritize settings for support functions while on the go. Furthermore, if the user is traveling, the integrated unit can also prioritize settings for providing information about the travel destination. For example, the integrated unit can use the user's GPS data to perform optimal settings according to the current location. This allows the integrated unit to provide the user with the best possible service by performing optimal settings that take into account the user's geographical location information. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's geographical location information into a generating AI and have the generating AI execute the optimal settings.

[0041] The integrated unit can analyze the user's social media activity and add relevant functions when a personal AI agent is integrated. For example, the integrated unit can add a notification function based on the social media platforms the user frequently uses. It can also add a content suggestion function based on the user's interests on social media. Furthermore, it can add a communication support function based on the user's social media friendships. For example, the integrated unit can analyze the content of the user's social media posts and add relevant functions. In this way, relevant functions can be added by analyzing the user's social media activity. Some or all of the above processing in the integrated unit may be performed using AI, for example, or not using AI. For example, the integrated unit can input the user's social media activity into a generating AI and have the generating AI perform the addition of relevant functions.

[0042] The integration unit can analyze the usage history of public facilities and select the optimal integration method during integration. For example, the integration unit can propose the optimal integration method based on the public facilities that the user has frequently used in the past. The integration unit can also analyze the user's public facility usage times and perform integration at the optimal timing. Furthermore, the integration unit can analyze the user's public facility usage patterns and propose the most efficient integration method. For example, the integration unit can select the optimal integration method based on the user's entry and exit records. In this way, the optimal integration method can be selected by analyzing the usage history of public facilities. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the usage history of public facilities into a generating AI and have the generating AI select the optimal integration method.

[0043] The integration unit can determine the priority of integration based on the current status and congestion level of public facilities during integration. For example, if a public facility is crowded, the integration unit can lower the priority of integration to reduce user stress. Conversely, if a public facility is not crowded, the integration unit can raise the priority of integration to provide a faster service. Furthermore, the integration unit can also propose the optimal integration method depending on the status of the public facility. For example, the integration unit can evaluate the congestion level of public facilities based on sensor information and camera images and determine the priority of integration. This reduces user stress by determining the priority of integration based on the current status and congestion level of public facilities. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the current status and congestion level of public facilities into a generating AI and have the generating AI perform the determination of the integration priority.

[0044] The integration unit can select the optimal integration method when integrating, taking into account the geographical location information of public facilities. For example, if the public facility is nearby, the integration unit will select a rapid integration method. If the public facility is far away, the integration unit can also adjust the timing of the integration and select an efficient integration method. Furthermore, the integration unit can propose the optimal integration method based on the geographical location information of public facilities. For example, the integration unit can use GPS data and map information to identify the location of public facilities and select the optimal integration method. By selecting the optimal integration method while considering the geographical location information of public facilities, the integration unit can provide the user with the best possible service. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the geographical location information of public facilities into a generating AI and have the generating AI select the optimal integration method.

[0045] The collaboration unit can analyze the social media activities of public facilities and link relevant information during the collaboration process. For example, the collaboration unit can select information to link based on event information on the public facilities' social media. The collaboration unit can also analyze user feedback on the public facilities' social media and propose the optimal collaboration method. Furthermore, the collaboration unit can select information to link based on trend information on the public facilities' social media. For example, the collaboration unit can analyze the content of social media posts from public facilities and link relevant information. In this way, relevant information can be linked by analyzing the social media activities of public facilities. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the social media activities of public facilities into a generating AI and have the generating AI perform the linking of relevant information.

[0046] The service provider can analyze the user's past service usage history to select the most suitable service at the time of service provision. For example, the service provider can propose the most suitable service based on the services the user has used in the past. The service provider can also analyze the user's service usage time and provide the service at the optimal time. Furthermore, the service provider can analyze the user's service usage patterns and propose the most efficient service. For example, the service provider can select the most suitable service based on the user's usage frequency and usage time. In this way, the service provider can select the most suitable service by analyzing the user's past service usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's service usage history into a generating AI and have the generating AI select the most suitable service.

[0047] The service provider can customize services based on the user's current living situation and areas of interest at the time of delivery. For example, if the user is at home, the service provider will provide support services within the home. The service provider can also provide support services while the user is out. Furthermore, if the user is traveling, the service provider can provide information services about their destination. For example, the service provider can customize the optimal service based on the user's lifestyle and hobbies. This allows for the provision of more appropriate services by customizing them based on the user's current living situation and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation and areas of interest into a generating AI and have the generating AI perform the service customization.

[0048] The service provider can provide the most suitable service by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide in-home support services. Furthermore, if the user is out, the service provider can provide support services while on the move. Additionally, if the user is traveling, the service provider can provide information services about their travel destination. For example, the service provider can use the user's GPS data to provide the most suitable service based on their current location. This allows the service provider to provide the most suitable service by considering the user's geographical location information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of the most suitable service.

[0049] The service provider can analyze the user's social media activity and provide relevant services at the time of service delivery. For example, the service provider can provide notification services based on the social media platforms the user frequently uses. The service provider can also provide content suggestion services based on the user's interests on social media. Furthermore, the service provider can provide communication support services based on the user's social media friendships. For example, the service provider can analyze the content of the user's social media posts and provide relevant services. In this way, relevant services can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI execute the provision of relevant services.

[0050] The service provider can analyze the user's past transportation usage history to select the optimal payment method when settling fares. For example, the service provider can propose the optimal payment method based on the transportation methods the user has frequently used in the past. The service provider can also analyze the user's transportation usage time and perform fare settlement at the optimal time. Furthermore, the service provider can analyze the user's transportation usage patterns and propose the most efficient payment method. For example, the service provider can select the optimal payment method based on the user's ride history. In this way, the optimal payment method can be selected by analyzing the user's past transportation usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's transportation usage history into a generating AI and have the generating AI perform the selection of the optimal payment method.

[0051] The service provider can select the optimal fare settlement method when settling fares, taking into account the user's geographical location information. For example, if the user is close to a station, the service provider can select a quick fare settlement method. If the user is far from a station, the service provider can also adjust the timing of fare settlement and select an efficient settlement method. Furthermore, the service provider can propose the optimal fare settlement method based on the user's geographical location information. For example, the service provider can use GPS data or map information to identify the user's location and select the optimal settlement method. By selecting the optimal settlement method considering the user's geographical location information, the service provider can provide the user with the most suitable fare settlement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal settlement method.

[0052] The service provider can analyze the user's past behavior history during integration to select the optimal integration method. For example, the service provider can propose the optimal integration method based on actions the user has frequently performed in the past. The service provider can also analyze the user's behavior history and integrate information at the optimal timing. Furthermore, the service provider can analyze the user's behavior patterns and propose the most efficient integration method. For example, the service provider can select the optimal integration method based on the user's movement history. This allows the service provider to select the optimal integration method by analyzing the user's past behavior history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's behavior history into a generating AI and have the generating AI select the optimal integration method.

[0053] The service provider can select the optimal integration method during integration, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize integrating information within the home. If the user is out, the service provider can also prioritize integrating information taken while traveling. Furthermore, if the user is traveling, the service provider can prioritize integrating information taken at the travel destination. For example, the service provider can use GPS data and map information to determine the user's location and select the optimal integration method. By selecting the optimal integration method considering the user's geographical location information, the service provider can provide the user with the most relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal integration method.

[0054] The service provider can analyze the user's past dialogue history and select the optimal expression method when providing an interface. For example, the service provider can provide the optimal interface based on the expression methods the user has preferred to use in the past. The service provider can also analyze the user's dialogue history and provide the interface at the optimal timing. Furthermore, the service provider can analyze the user's dialogue patterns and propose the most efficient interface. For example, the service provider can select the optimal expression method based on the content of the user's past conversations. This allows the service provider to select the optimal expression method by analyzing the user's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's dialogue history into a generating AI and have the generating AI select the optimal expression method.

[0055] The service provider can select the optimal presentation method when providing an interface, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize providing information relevant to the user's home. Similarly, if the user is out, the service provider can prioritize providing information relevant to the user's travel destination. Furthermore, if the user is traveling, the service provider can prioritize providing information relevant to the user's travel destination. For example, the service provider can use GPS data and map information to determine the user's location and select the optimal presentation method. This allows the service provider to provide the user with an optimal interface by selecting the most suitable presentation method while considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal presentation method.

[0056] The service provider can select the optimal technology by analyzing the user's past usage history when utilizing linked technologies. For example, the service provider can propose the optimal technology based on the linked technologies the user has frequently used in the past. The service provider can also analyze the user's usage history and use linked technologies at the optimal timing. Furthermore, the service provider can analyze the user's usage patterns and propose the most efficient linked technology. For example, the service provider can select the optimal technology based on the user's usage frequency and duration. In this way, the optimal technology can be selected by analyzing the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history into a generating AI and have the generating AI select the optimal technology.

[0057] The service provider can select the optimal technology when utilizing collaborative technologies, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize using collaborative technologies suitable for use within the home. Furthermore, if the user is out, the service provider can prioritize using collaborative technologies suitable for use while traveling. Additionally, if the user is traveling, the service provider can prioritize using collaborative technologies suitable for the travel destination. For example, the service provider can use GPS data and map information to determine the user's location and select the optimal technology. This allows the service provider to provide the user with the most suitable technology by selecting the optimal technology while considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal technology.

[0058] The service provider can analyze the user's past behavioral history to select the optimal analysis method when analyzing behavioral patterns. For example, the service provider can propose the optimal analysis method based on actions the user has frequently performed in the past. The service provider can also analyze the user's behavioral history and analyze behavioral patterns at the optimal timing. Furthermore, the service provider can analyze the user's behavioral patterns and propose the most efficient analysis method. For example, the service provider can select the optimal analysis method based on the user's movement history. This allows the service provider to select the optimal analysis method by analyzing the user's past behavioral history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's behavioral history into a generating AI and have the generating AI select the optimal analysis method.

[0059] The service provider can select the optimal analysis method when analyzing behavioral patterns, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize analyzing behavioral patterns within the home. If the user is out, the service provider can also prioritize analyzing behavioral patterns while traveling. Furthermore, if the user is traveling, the service provider can prioritize analyzing behavioral patterns at the travel destination. For example, the service provider can use GPS data and map information to identify the user's location and select the optimal analysis method. By selecting the optimal analysis method while considering the user's geographical location information, the service provider can provide the user with the most optimal analysis. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The service provider can analyze a user's past service usage history and provide the most suitable service. For example, the service provider can suggest the most suitable service based on the services the user has used in the past. The service provider can also analyze the user's service usage time and provide the service at the optimal time. Furthermore, the service provider can analyze the user's service usage patterns and suggest the most efficient service. For example, the service provider can select the most suitable service based on the user's usage frequency and usage time. This allows the service provider to select the most suitable service by analyzing the user's past service usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's service usage history into a generating AI and have the generating AI select the most suitable service.

[0062] The service provider can provide optimal services by taking into account the user's geographical location information. For example, if the user is at home, the service provider can provide in-home support services. Furthermore, if the user is out, the service provider can provide support services while on the go. Additionally, if the user is traveling, the service provider can provide information about their travel destination. For example, the service provider can use the user's GPS data to provide optimal services based on their current location. This allows the service provider to provide the best possible service by considering the user's geographical location information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of optimal services.

[0063] The service provider can analyze a user's social media activity and provide related services. For example, the service provider can provide notification services based on the social media platforms the user frequently uses. The service provider can also provide content suggestion services based on the user's interests on social media. Furthermore, the service provider can provide communication support services based on the user's social media friendships. For example, the service provider can analyze the content of a user's social media posts and provide related services. In this way, it can provide related services by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI perform the provision of related services.

[0064] The service provider can analyze the user's past transportation usage history to select the optimal payment method when settling fares. For example, the service provider can propose the optimal payment method based on the transportation methods the user has frequently used in the past. The service provider can also analyze the user's transportation usage time and perform fare settlement at the optimal time. Furthermore, the service provider can analyze the user's transportation usage patterns and propose the most efficient payment method. For example, the service provider can select the optimal payment method based on the user's ride history. In this way, the optimal payment method can be selected by analyzing the user's past transportation usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's transportation usage history into a generating AI and have the generating AI perform the selection of the optimal payment method.

[0065] The service provider can select the optimal fare settlement method when settling fares, taking into account the user's geographical location information. For example, if the user is close to a station, the service provider can select a quick fare settlement method. If the user is far from a station, the service provider can also adjust the timing of fare settlement and select an efficient settlement method. Furthermore, the service provider can propose the optimal fare settlement method based on the user's geographical location information. For example, the service provider can use GPS data or map information to identify the user's location and select the optimal settlement method. By selecting the optimal settlement method considering the user's geographical location information, the service provider can provide the user with the most suitable fare settlement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal settlement method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The implementation unit installs a personal AI agent into individual smart devices and wearable devices. For example, it installs the AI ​​agent on devices such as smartphones and smartwatches, and automatically adjusts the device settings to ensure the AI ​​agent operates optimally. This includes optimizing the AI ​​agent's operation by considering the device's battery level and storage capacity. Step 2: The integration unit interacts in real time with public AI agents installed in public facilities. For example, it exchanges data with public AI agents using communication technologies such as Wi-Fi and Bluetooth, and synchronizes the data in real time. This enables the provision of services tailored to the user's situation. For example, when a user arrives at a station, it interacts with the public AI agent to automatically settle the fare. Step 3: The service provider provides the user with the most suitable service based on the information shared by the collaboration department. For example, it analyzes the user's behavior patterns and past usage history to suggest the most suitable service. It can also customize the service in real time according to the user's current situation. For example, if the user is in a hurry, it will guide them to the shortest route, and if they are relaxed, it will provide tourist information.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that removes various daily barriers and provides a smoother and more personalized experience by having each individual have their own personal AI agent and cooperating with public AI agents in public facilities and services. For example, this system provides a mechanism in which the public agent in the station premises and the individual's personal agent cooperate to automatically settle fares, instead of using ticket gates at train stations. This solves problems such as the hassle and time loss associated with using public facilities such as ticket gates and payment terminals, the stress caused by crowds and waiting in line, and the lack of information guidance and support at public facilities. Specifically, it consists of the following steps: First, the personal AI agent is installed in the individual's smart device or wearable device. Next, it cooperates in real time with the public AI agent installed in the public facility. This allows the individual's personal AI agent to share information with the public AI agent in the public facility and provide the user with the most suitable service. For example, by providing a mechanism in which the public agent in the station premises and the individual's personal agent cooperate to automatically settle fares, it becomes possible to use the station without going through ticket gates. Furthermore, the AI ​​agent integrates personality, memory, planning, and behavior, providing an interface through natural language processing (NLP). It also utilizes RFID / NFC and location-based services to analyze user behavior patterns using machine learning. This system can remove all barriers in people's daily lives, creating a smooth and stress-free social infrastructure. This allows individuals to focus on truly valuable activities, improving their quality of life. The system can then provide optimal services to users by equipping personal AI agents on individual smart devices and wearable devices, and by collaborating in real-time with public AI agents installed in public facilities.

[0069] The system according to this embodiment comprises an installation unit, a collaboration unit, and a service provision unit. The installation unit installs a personal AI agent into an individual's smart device or wearable device. For example, the installation unit installs the AI ​​agent into a device such as a smartphone or smartwatch. The installation unit can also automatically adjust the device settings to ensure the AI ​​agent operates optimally. For example, the installation unit optimizes the operation of the AI ​​agent by considering the device's battery level and storage capacity. The collaboration unit collaborates in real time with a public AI agent installed in a public facility. The collaboration unit exchanges data with the public AI agent using communication technologies such as Wi-Fi or Bluetooth. The collaboration unit can also synchronize data in real time and provide services tailored to the user's situation. For example, when a user arrives at a station, the collaboration unit collaborates with the public AI agent to automatically settle the fare. The service provision unit provides the user with the most suitable service based on the information shared by the collaboration unit. For example, the service provision unit analyzes the user's behavior patterns and past usage history to propose the most suitable service. The service provision unit can also customize the service in real time according to the user's current situation. For example, the service provider will guide users to the shortest route if they are in a hurry, and provide tourist information if they are relaxed. This allows the system, according to the embodiment, to provide users with optimal services by equipping personal AI agents on individual smart devices or wearable devices and coordinating in real time with public AI agents installed in public facilities.

[0070] The integration unit will install a personal AI agent into individual smart devices and wearable devices. Specifically, it will install the AI ​​agent into devices such as smartphones and smartwatches. This will allow users to utilize the AI ​​agent's functions through devices they use daily. The integration unit can also automatically adjust device settings to ensure the AI ​​agent operates optimally. For example, it will optimize the AI ​​agent's operation by considering the device's battery level and storage capacity. This allows the AI ​​agent to efficiently use device resources and always provide the user with optimal performance. Furthermore, the integration unit can enhance the device's security settings to ensure the AI ​​agent operates securely. For example, it will adjust the device's firewall settings to protect the AI ​​agent from external attacks. It can also adjust the device's privacy settings to ensure the user's personal information is properly protected. In this way, the integration unit can provide an environment in which the AI ​​agent operates safely and efficiently, offering users high reliability and convenience.

[0071] The collaboration unit interacts in real time with public AI agents installed in public facilities. Specifically, it exchanges data with public AI agents using communication technologies such as Wi-Fi and Bluetooth. This allows personal AI agents installed in users' smart devices and wearable devices to seamlessly interact with public AI agents installed in public facilities and provide services tailored to the user's situation. The collaboration unit can also synchronize data in real time and provide services tailored to the user's situation. For example, when a user arrives at a station, the collaboration unit interacts with a public AI agent to automatically settle the fare. This allows users to use public transportation smoothly through their smart devices. The collaboration unit can also interact with a public AI agent to provide store information and sales information when a user arrives at a shopping mall. This allows users to enjoy shopping efficiently through their smart devices. Furthermore, when a user arrives at a hospital, the collaboration unit can interact with a public AI agent to provide appointment information and waiting time information. This allows users to receive medical services smoothly through their smart devices. Through these functions, the collaboration unit can provide users with a high level of convenience and comfort.

[0072] The service provider department provides users with the most suitable services based on information shared by the collaboration department. Specifically, it analyzes users' behavior patterns and past usage history to propose the most suitable services. For example, the service provider department can understand the places users frequently visit and the services they use, and make personalized suggestions based on that. This allows users to receive the most suitable services for them. The service provider department can also customize services in real time according to the user's current situation. For example, if the user is in a hurry, the service provider department will guide them to the shortest route, and if they are relaxed, it will provide tourist information. This allows users to receive the most suitable services according to their situation at any given time. Furthermore, the service provider department can collect user feedback and continuously improve the quality of its services. For example, the service provider department reviews and improves its services based on user evaluations and opinions. This allows the service provider department to always provide users with high-quality services. Through these functions, the service provider department can provide users with high satisfaction and convenience.

[0073] The service provider allows users to pay fares without passing through ticket gates within the station. For example, when a user arrives at a station, the service provider works in conjunction with a public AI agent to automatically pay the fare. The service provider can also pay fares using methods such as electronic money, credit cards, and QR code payments. For example, the service provider can pay fares using an electronic money app installed on the user's smartphone. The service provider can also register the user's credit card information in advance and automatically deduct the fare. Furthermore, the service provider can pay fares by scanning QR codes. For example, the service provider can use a QR code reader installed in the station to scan the QR code displayed on the user's smartphone and pay the fare. This improves user convenience by allowing fare payment without passing through ticket gates within the station.

[0074] The service provider's AI agent can integrate personality, memory, planning, and behavior. For example, it can provide optimal services based on the user's personality and past behavioral history. It can also customize services considering the user's schedule and plans. For example, it can integrate with the user's calendar app to provide services according to their schedule. It can also analyze the user's behavioral patterns and provide services at the optimal time. For example, it can suggest music that will help the user relax during their commute. In this way, the AI ​​agent can provide the most optimal service to the user by integrating personality, memory, planning, and behavior.

[0075] The service provider can provide an interface based on natural language processing. For example, it can analyze the user's voice using speech recognition technology to enable natural dialogue. It can also analyze user input using text analysis technology to generate appropriate responses. For example, it can analyze text entered by the user on their smartphone and suggest the most suitable service. Furthermore, it can enable natural dialogue with the user using a dialogue system. For example, it can provide appropriate answers to the user's questions. By providing an interface based on natural language processing, users can intuitively operate the system.

[0076] The service provider can utilize RFID / NFC and location information service integration technologies. For example, the service provider can obtain a user's location information by reading an RFID tag. The service provider can also exchange data between devices using NFC communication. For example, the service provider can synchronize data between a user's smartphone and a terminal in a public facility using NFC communication. Furthermore, the service provider can obtain a user's location information using GPS or Wi-Fi location services. For example, the service provider can determine the user's current location using GPS data from the user's smartphone. By utilizing RFID / NFC and location information service integration technologies, the service provider can provide the user with the most optimal service.

[0077] The service provider can perform user behavior pattern analysis using machine learning. For example, the service provider can analyze behavior patterns based on a user's past behavior history. Furthermore, the service provider can analyze behavior patterns in real time according to the user's current situation. For example, the service provider can suggest the optimal route based on the user's travel history. The service provider can also provide health management services based on the user's activity records. For example, the service provider can analyze the user's exercise level and suggest an appropriate exercise plan. In this way, by performing user behavior pattern analysis using machine learning, the service provider can provide the most suitable service to the user.

[0078] The device can estimate the user's emotions and adjust the activation timing of the personal AI agent based on the estimated emotions. For example, if the user is feeling stressed, the device can delay the activation of the personal AI agent to provide a relaxing environment. Conversely, if the user is in a hurry, the device can immediately activate the personal AI agent to provide rapid support. Furthermore, if the user is relaxed, the device can gradually activate the personal AI agent to begin support in a natural flow. For example, the device can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate timing of support by adjusting the activation timing of the personal AI agent based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The integration unit can analyze the user's past device usage history and select the optimal integration method. For example, the integration unit can customize the integration method of the personal AI agent based on the applications the user has frequently used in the past. The integration unit can also analyze the user's device usage time and integrate the personal AI agent at the optimal time. Furthermore, the integration unit can analyze the user's device usage patterns and propose the most efficient integration method. For example, the integration unit optimizes the functions of the personal AI agent based on the user's application usage history. This allows the optimal integration method to be selected by analyzing the user's past device usage history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's device usage history into a generating AI and have the generating AI select the optimal integration method.

[0080] The onboard unit can customize the personal AI agent based on the user's current device settings and usage when it is installed. For example, the onboard unit can automatically set the language of the personal AI agent based on the language settings of the user's device. The onboard unit can also install the personal AI agent with optimal performance, taking into account the battery level of the user's device. Furthermore, the onboard unit can check the storage status of the user's device and efficiently manage the necessary data. For example, the onboard unit can automatically adjust the settings of the user's device to ensure that the personal AI agent operates optimally. This allows the onboard unit to provide the optimal personal AI agent by customizing it based on the user's current device settings and usage. Some or all of the above processes in the onboard unit may be performed using AI, or not. For example, the onboard unit can input the user's device settings and usage into a generating AI and have the generating AI perform the customization.

[0081] The device can estimate the user's emotions and adjust the functions of the AI ​​agent based on the estimated emotions. For example, if the user is stressed, the device will prioritize functions that promote relaxation. It can also prioritize functions that provide quick support if the user is in a hurry. Furthermore, if the user is relaxed, it can prioritize functions that promote entertainment. For instance, the device can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the device to provide more appropriate functions by adjusting the functions of the AI ​​agent based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The integrated unit can perform optimal settings when installing a personal AI agent, taking into account the user's geographical location information. For example, if the user is at home, the integrated unit can prioritize settings for support functions within the home. If the user is out, the integrated unit can also prioritize settings for support functions while on the go. Furthermore, if the user is traveling, the integrated unit can also prioritize settings for providing information about the travel destination. For example, the integrated unit can use the user's GPS data to perform optimal settings according to the current location. This allows the integrated unit to provide the user with the best possible service by performing optimal settings that take into account the user's geographical location information. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input the user's geographical location information into a generating AI and have the generating AI execute the optimal settings.

[0083] The integrated unit can analyze the user's social media activity and add relevant functions when a personal AI agent is integrated. For example, the integrated unit can add a notification function based on the social media platforms the user frequently uses. It can also add a content suggestion function based on the user's interests on social media. Furthermore, it can add a communication support function based on the user's social media friendships. For example, the integrated unit can analyze the content of the user's social media posts and add relevant functions. In this way, relevant functions can be added by analyzing the user's social media activity. Some or all of the above processing in the integrated unit may be performed using AI, for example, or not using AI. For example, the integrated unit can input the user's social media activity into a generating AI and have the generating AI perform the addition of relevant functions.

[0084] The interaction unit can estimate the user's emotions and adjust the timing of interaction based on the estimated emotions. For example, if the user is stressed, the interaction unit can delay the interaction to provide a relaxing environment. Conversely, if the user is in a hurry, the interaction unit can provide immediate support. Furthermore, if the user is relaxed, the interaction unit can slow down the interaction to initiate support in a natural flow. For example, the interaction unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate timing of support by adjusting the interaction timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The integration unit can analyze the usage history of public facilities and select the optimal integration method during integration. For example, the integration unit can propose the optimal integration method based on the public facilities that the user has frequently used in the past. The integration unit can also analyze the user's public facility usage times and perform integration at the optimal timing. Furthermore, the integration unit can analyze the user's public facility usage patterns and propose the most efficient integration method. For example, the integration unit can select the optimal integration method based on the user's entry and exit records. In this way, the optimal integration method can be selected by analyzing the usage history of public facilities. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the usage history of public facilities into a generating AI and have the generating AI select the optimal integration method.

[0086] The integration unit can determine the priority of integration based on the current status and congestion level of public facilities during integration. For example, if a public facility is crowded, the integration unit can lower the priority of integration to reduce user stress. Conversely, if a public facility is not crowded, the integration unit can raise the priority of integration to provide a faster service. Furthermore, the integration unit can also propose the optimal integration method depending on the status of the public facility. For example, the integration unit can evaluate the congestion level of public facilities based on sensor information and camera images and determine the priority of integration. This reduces user stress by determining the priority of integration based on the current status and congestion level of public facilities. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the current status and congestion level of public facilities into a generating AI and have the generating AI perform the determination of the integration priority.

[0087] The integration unit can estimate the user's emotions and determine the priority of the information to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating important information. If the user is relaxed, the integration unit can also prioritize integrating entertainment information. Furthermore, if the user is in a hurry, the integration unit can prioritize integrating information that can be quickly addressed. For example, the integration unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows the integration unit to provide more appropriate information by prioritizing the information to integrate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The integration unit can select the optimal integration method when integrating, taking into account the geographical location information of public facilities. For example, if the public facility is nearby, the integration unit will select a rapid integration method. If the public facility is far away, the integration unit can also adjust the timing of the integration and select an efficient integration method. Furthermore, the integration unit can propose the optimal integration method based on the geographical location information of public facilities. For example, the integration unit can use GPS data and map information to identify the location of public facilities and select the optimal integration method. By selecting the optimal integration method while considering the geographical location information of public facilities, the integration unit can provide the user with the best possible service. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the geographical location information of public facilities into a generating AI and have the generating AI select the optimal integration method.

[0089] The collaboration unit can analyze the social media activities of public facilities and link relevant information during the collaboration process. For example, the collaboration unit can select information to link based on event information on the public facilities' social media. The collaboration unit can also analyze user feedback on the public facilities' social media and propose the optimal collaboration method. Furthermore, the collaboration unit can select information to link based on trend information on the public facilities' social media. For example, the collaboration unit can analyze the content of social media posts from public facilities and link relevant information. In this way, relevant information can be linked by analyzing the social media activities of public facilities. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the social media activities of public facilities into a generating AI and have the generating AI perform the linking of relevant information.

[0090] The service provider can estimate the user's emotions and adjust the content of the services offered based on those emotions. For example, if the user is feeling stressed, the service provider can offer relaxing services. It can also offer quick services if the user is in a hurry. Furthermore, if the user is relaxed, the service provider can offer entertainment services. For instance, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate service to be provided by adjusting the content of the services offered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The service provider can analyze the user's past service usage history to select the most suitable service at the time of service provision. For example, the service provider can propose the most suitable service based on the services the user has used in the past. The service provider can also analyze the user's service usage time and provide the service at the optimal time. Furthermore, the service provider can analyze the user's service usage patterns and propose the most efficient service. For example, the service provider can select the most suitable service based on the user's usage frequency and usage time. In this way, the service provider can select the most suitable service by analyzing the user's past service usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's service usage history into a generating AI and have the generating AI select the most suitable service.

[0092] The service provider can customize services based on the user's current living situation and areas of interest at the time of delivery. For example, if the user is at home, the service provider will provide support services within the home. The service provider can also provide support services while the user is out. Furthermore, if the user is traveling, the service provider can provide information services about their destination. For example, the service provider can customize the optimal service based on the user's lifestyle and hobbies. This allows for the provision of more appropriate services by customizing them based on the user's current living situation and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation and areas of interest into a generating AI and have the generating AI perform the service customization.

[0093] The service provider can estimate the user's emotions and prioritize the services to be provided based on those emotions. For example, if the user is stressed, the service provider will prioritize important services. If the user is relaxed, the service provider can also prioritize entertainment services. Furthermore, if the user is in a hurry, the service provider can prioritize services that can be handled quickly. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to prioritize services based on the user's emotions, thereby providing more appropriate services. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The service provider can provide the most suitable service by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide in-home support services. Furthermore, if the user is out, the service provider can provide support services while on the move. Additionally, if the user is traveling, the service provider can provide information services about their travel destination. For example, the service provider can use the user's GPS data to provide the most suitable service based on their current location. This allows the service provider to provide the most suitable service by considering the user's geographical location information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of the most suitable service.

[0095] The service provider can analyze the user's social media activity and provide relevant services at the time of service delivery. For example, the service provider can provide notification services based on the social media platforms the user frequently uses. The service provider can also provide content suggestion services based on the user's interests on social media. Furthermore, the service provider can provide communication support services based on the user's social media friendships. For example, the service provider can analyze the content of the user's social media posts and provide relevant services. In this way, relevant services can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI execute the provision of relevant services.

[0096] The service provider can estimate the user's emotions and adjust the timing of fare payment based on those emotions. For example, if the user is stressed, the service provider can delay the fare payment to provide a more relaxing environment. Alternatively, if the user is in a hurry, the service provider can process the fare payment immediately to provide prompt support. Furthermore, if the user is relaxed, the service provider can slow down the fare payment process to initiate support in a more natural way. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate fare payment timing by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The service provider can analyze the user's past transportation usage history to select the optimal payment method when settling fares. For example, the service provider can propose the optimal payment method based on the transportation methods the user has frequently used in the past. The service provider can also analyze the user's transportation usage time and perform fare settlement at the optimal time. Furthermore, the service provider can analyze the user's transportation usage patterns and propose the most efficient payment method. For example, the service provider can select the optimal payment method based on the user's ride history. In this way, the optimal payment method can be selected by analyzing the user's past transportation usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's transportation usage history into a generating AI and have the generating AI perform the selection of the optimal payment method.

[0098] The service provider can estimate the user's emotions and determine the priority of fare calculations based on those emotions. For example, if the user is stressed, the service provider will prioritize important fare calculations. If the user is relaxed, the service provider can also prioritize providing entertainment information. Furthermore, if the user is in a hurry, the service provider can prioritize fare calculations that can be handled quickly. For example, the service provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate fare calculations by determining the priority of fare calculations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The service provider can select the optimal fare settlement method when settling fares, taking into account the user's geographical location information. For example, if the user is close to a station, the service provider can select a quick fare settlement method. If the user is far from a station, the service provider can also adjust the timing of fare settlement and select an efficient settlement method. Furthermore, the service provider can propose the optimal fare settlement method based on the user's geographical location information. For example, the service provider can use GPS data or map information to identify the user's location and select the optimal settlement method. By selecting the optimal settlement method considering the user's geographical location information, the service provider can provide the user with the most suitable fare settlement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal settlement method.

[0100] The service provider can estimate the user's emotions and adjust the content of the information it integrates based on those emotions. For example, if the user is stressed, the service provider will prioritize integrating information that promotes relaxation. Similarly, if the user is in a hurry, the service provider can prioritize integrating information that allows for quick responses. Furthermore, if the user is relaxed, the service provider can prioritize integrating entertainment information. For instance, the service provider could capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide more appropriate information by adjusting the content of the information integrated based on the user's emotions. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The service provider can analyze the user's past behavior history during integration to select the optimal integration method. For example, the service provider can propose the optimal integration method based on actions the user has frequently performed in the past. The service provider can also analyze the user's behavior history and integrate information at the optimal timing. Furthermore, the service provider can analyze the user's behavior patterns and propose the most efficient integration method. For example, the service provider can select the optimal integration method based on the user's movement history. This allows the service provider to select the optimal integration method by analyzing the user's past behavior history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's behavior history into a generating AI and have the generating AI select the optimal integration method.

[0102] The service provider can estimate the user's emotions and determine the priority of information to integrate based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize integrating important information. If the user is relaxed, the service provider can also prioritize integrating entertainment information. Furthermore, if the user is in a hurry, the service provider can prioritize integrating information that allows for quick responses. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide more appropriate information by prioritizing the information to integrate based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The service provider can select the optimal integration method during integration, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize integrating information within the home. If the user is out, the service provider can also prioritize integrating information taken while traveling. Furthermore, if the user is traveling, the service provider can prioritize integrating information taken at the travel destination. For example, the service provider can use GPS data and map information to determine the user's location and select the optimal integration method. By selecting the optimal integration method considering the user's geographical location information, the service provider can provide the user with the most relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal integration method.

[0104] The service provider can estimate the user's emotions and adjust the interface's presentation based on those emotions. For example, if the user is nervous, the service provider can provide guidance in a calm voice. If the user is relaxed, the service provider can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the service provider can provide quick and concise voice guidance. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide a more appropriate interface by adjusting the interface's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The service provider can analyze the user's past dialogue history and select the optimal expression method when providing an interface. For example, the service provider can provide the optimal interface based on the expression methods the user has preferred to use in the past. The service provider can also analyze the user's dialogue history and provide the interface at the optimal timing. Furthermore, the service provider can analyze the user's dialogue patterns and propose the most efficient interface. For example, the service provider can select the optimal expression method based on the content of the user's past conversations. This allows the service provider to select the optimal expression method by analyzing the user's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's dialogue history into a generating AI and have the generating AI select the optimal expression method.

[0106] The service provider can estimate the user's emotions and prioritize the interface based on those emotions. For example, if the user is stressed, the service provider will prioritize providing important information. If the user is relaxed, the service provider can also prioritize providing entertainment information. Furthermore, if the user is in a hurry, the service provider can prioritize providing information that allows for quick responses. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide a more appropriate interface by prioritizing the interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The service provider can select the optimal presentation method when providing an interface, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize providing information relevant to the user's home. Similarly, if the user is out, the service provider can prioritize providing information relevant to the user's travel destination. Furthermore, if the user is traveling, the service provider can prioritize providing information relevant to the user's travel destination. For example, the service provider can use GPS data and map information to determine the user's location and select the optimal presentation method. This allows the service provider to provide the user with an optimal interface by selecting the most suitable presentation method while considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal presentation method.

[0108] The service provider can estimate the user's emotions and adjust the timing of the use of collaborative technology based on the estimated emotions. For example, if the user is feeling stressed, the service provider can delay the use of collaborative technology to provide a relaxing environment. Alternatively, if the user is in a hurry, the service provider can immediately use collaborative technology to provide rapid support. Furthermore, if the user is relaxed, the service provider can gradually introduce collaborative technology to begin support in a natural flow. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate timing of support by adjusting the timing of collaborative technology use based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The service provider can select the optimal technology by analyzing the user's past usage history when utilizing linked technologies. For example, the service provider can propose the optimal technology based on the linked technologies the user has frequently used in the past. The service provider can also analyze the user's usage history and use linked technologies at the optimal timing. Furthermore, the service provider can analyze the user's usage patterns and propose the most efficient linked technology. For example, the service provider can select the optimal technology based on the user's usage frequency and duration. In this way, the optimal technology can be selected by analyzing the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history into a generating AI and have the generating AI select the optimal technology.

[0110] The service provider can estimate the user's emotions and prioritize collaborative technologies based on those emotions. For example, if the user is stressed, the service provider will prioritize using important collaborative technologies. If the user is relaxed, the service provider can also prioritize providing entertainment information. Furthermore, if the user is in a hurry, the service provider can prioritize using collaborative technologies that can respond quickly. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide more appropriate technologies by prioritizing collaborative technologies based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The service provider can select the optimal technology when utilizing collaborative technologies, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize using collaborative technologies suitable for use within the home. Furthermore, if the user is out, the service provider can prioritize using collaborative technologies suitable for use while traveling. Additionally, if the user is traveling, the service provider can prioritize using collaborative technologies suitable for the travel destination. For example, the service provider can use GPS data and map information to determine the user's location and select the optimal technology. This allows the service provider to provide the user with the most suitable technology by selecting the optimal technology while considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal technology.

[0112] The service provider can estimate the user's emotions and adjust the content of the behavioral pattern analysis based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize analyzing behavioral patterns that promote relaxation. Similarly, if the user is in a hurry, the service provider can prioritize analyzing behavioral patterns that allow for quick responses. Furthermore, if the user is relaxed, the service provider can prioritize analyzing entertainment-related behavioral patterns. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis by adjusting the content of the behavioral pattern analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The service provider can analyze the user's past behavioral history to select the optimal analysis method when analyzing behavioral patterns. For example, the service provider can propose the optimal analysis method based on actions the user has frequently performed in the past. The service provider can also analyze the user's behavioral history and analyze behavioral patterns at the optimal timing. Furthermore, the service provider can analyze the user's behavioral patterns and propose the most efficient analysis method. For example, the service provider can select the optimal analysis method based on the user's movement history. This allows the service provider to select the optimal analysis method by analyzing the user's past behavioral history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's behavioral history into a generating AI and have the generating AI select the optimal analysis method.

[0114] The service provider can estimate the user's emotions and prioritize behavioral pattern analysis based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize analyzing important behavioral patterns. If the user is relaxed, the service provider can also prioritize analyzing entertainment behavioral patterns. Furthermore, if the user is in a hurry, the service provider can prioritize analyzing behavioral patterns that allow for quick responses. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis by prioritizing behavioral pattern analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The service provider can select the optimal analysis method when analyzing behavioral patterns, taking into account the user's geographical location information. For example, if the user is at home, the service provider will prioritize analyzing behavioral patterns within the home. If the user is out, the service provider can also prioritize analyzing behavioral patterns while traveling. Furthermore, if the user is traveling, the service provider can prioritize analyzing behavioral patterns at the travel destination. For example, the service provider can use GPS data and map information to identify the user's location and select the optimal analysis method. By selecting the optimal analysis method while considering the user's geographical location information, the service provider can provide the user with the most optimal analysis. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The service provider can estimate the user's emotions and adjust the content of the services offered based on those emotions. For example, if the user is feeling stressed, the service provider can offer relaxing services. It can also offer quick services if the user is in a hurry. Furthermore, if the user is relaxed, the service provider can offer entertainment services. For instance, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate service to be provided by adjusting the content of the services offered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The service provider can analyze a user's past service usage history and provide the most suitable service. For example, the service provider can suggest the most suitable service based on the services the user has used in the past. The service provider can also analyze the user's service usage time and provide the service at the optimal time. Furthermore, the service provider can analyze the user's service usage patterns and suggest the most efficient service. For example, the service provider can select the most suitable service based on the user's usage frequency and usage time. This allows the service provider to select the most suitable service by analyzing the user's past service usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's service usage history into a generating AI and have the generating AI select the most suitable service.

[0119] The service provider can estimate the user's emotions and prioritize the services to be provided based on those emotions. For example, if the user is stressed, the service provider will prioritize important services. If the user is relaxed, the service provider can also prioritize entertainment services. Furthermore, if the user is in a hurry, the service provider can prioritize services that can be handled quickly. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to prioritize services based on the user's emotions, thereby providing more appropriate services. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The service provider can provide optimal services by taking into account the user's geographical location information. For example, if the user is at home, the service provider can provide in-home support services. Furthermore, if the user is out, the service provider can provide support services while on the go. Additionally, if the user is traveling, the service provider can provide information about their travel destination. For example, the service provider can use the user's GPS data to provide optimal services based on their current location. This allows the service provider to provide the best possible service by considering the user's geographical location information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of optimal services.

[0121] The service provider can analyze a user's social media activity and provide related services. For example, the service provider can provide notification services based on the social media platforms the user frequently uses. The service provider can also provide content suggestion services based on the user's interests on social media. Furthermore, the service provider can provide communication support services based on the user's social media friendships. For example, the service provider can analyze the content of a user's social media posts and provide related services. In this way, it can provide related services by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI perform the provision of related services.

[0122] The service provider can estimate the user's emotions and adjust the timing of fare payment based on those emotions. For example, if the user is stressed, the service provider can delay the fare payment to provide a more relaxing environment. Alternatively, if the user is in a hurry, the service provider can process the fare payment immediately to provide prompt support. Furthermore, if the user is relaxed, the service provider can slow down the fare payment process to initiate support in a more natural way. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate fare payment timing by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The service provider can analyze the user's past transportation usage history to select the optimal payment method when settling fares. For example, the service provider can propose the optimal payment method based on the transportation methods the user has frequently used in the past. The service provider can also analyze the user's transportation usage time and perform fare settlement at the optimal time. Furthermore, the service provider can analyze the user's transportation usage patterns and propose the most efficient payment method. For example, the service provider can select the optimal payment method based on the user's ride history. In this way, the optimal payment method can be selected by analyzing the user's past transportation usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's transportation usage history into a generating AI and have the generating AI perform the selection of the optimal payment method.

[0124] The service provider can estimate the user's emotions and determine the priority of fare calculations based on those emotions. For example, if the user is stressed, the service provider will prioritize important fare calculations. If the user is relaxed, the service provider can also prioritize providing entertainment information. Furthermore, if the user is in a hurry, the service provider can prioritize fare calculations that can be handled quickly. For example, the service provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate fare calculations by determining the priority of fare calculations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The service provider can select the optimal fare settlement method when settling fares, taking into account the user's geographical location information. For example, if the user is close to a station, the service provider can select a quick fare settlement method. If the user is far from a station, the service provider can also adjust the timing of fare settlement and select an efficient settlement method. Furthermore, the service provider can propose the optimal fare settlement method based on the user's geographical location information. For example, the service provider can use GPS data or map information to identify the user's location and select the optimal settlement method. By selecting the optimal settlement method considering the user's geographical location information, the service provider can provide the user with the most suitable fare settlement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal settlement method.

[0126] The service provider can estimate the user's emotions and adjust the content of the information it integrates based on those emotions. For example, if the user is stressed, the service provider will prioritize integrating information that promotes relaxation. Similarly, if the user is in a hurry, the service provider can prioritize integrating information that allows for quick responses. Furthermore, if the user is relaxed, the service provider can prioritize integrating entertainment information. For instance, the service provider could capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide more appropriate information by adjusting the content of the information integrated based on the user's emotions. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The implementation unit installs a personal AI agent into individual smart devices and wearable devices. For example, it installs the AI ​​agent on devices such as smartphones and smartwatches, and automatically adjusts the device settings to ensure the AI ​​agent operates optimally. This includes optimizing the AI ​​agent's operation by considering the device's battery level and storage capacity. Step 2: The integration unit interacts in real time with public AI agents installed in public facilities. For example, it exchanges data with public AI agents using communication technologies such as Wi-Fi and Bluetooth, and synchronizes the data in real time. This enables the provision of services tailored to the user's situation. For example, when a user arrives at a station, it interacts with the public AI agent to automatically settle the fare. Step 3: The service provider provides the user with the most suitable service based on the information shared by the collaboration department. For example, it analyzes the user's behavior patterns and past usage history to suggest the most suitable service. It can also customize the service in real time according to the user's current situation. For example, if the user is in a hurry, it will guide them to the shortest route, and if they are relaxed, it will provide tourist information.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the mounting unit, the collaboration unit, and the provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the mounting unit is implemented by the control unit 46A of the smart device 14 and installs a personal AI agent on a device such as a smartphone or smartwatch. The collaboration unit uses the communication I / F 44 of the smart device 14 to collaborate in real time with a public AI agent installed in a public facility. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with the most suitable service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the mounting unit, the collaboration unit, and the provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the mounting unit is implemented by the control unit 46A of the smart glasses 214, which installs a personal AI agent into the smart glasses. The collaboration unit uses the communication I / F 44 of the smart glasses 214 to collaborate in real time with a public AI agent installed in a public facility. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the user with the most suitable service. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the mounting unit, the collaboration unit, and the provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the mounting unit is implemented by the control unit 46A of the headset terminal 314, which installs a personal AI agent on the headset terminal. The collaboration unit uses the communication I / F 44 of the headset terminal 314 to collaborate in real time with a public AI agent installed in a public facility. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the user with the most suitable service. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the mounting unit, the collaboration unit, and the provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the mounting unit is implemented by the control unit 46A of the robot 414, which installs a personal AI agent on the robot. The collaboration unit uses the communication I / F 44 of the robot 414 to collaborate in real time with a public AI agent installed in a public facility. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the user with the most suitable service. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The unit for equipping personal AI agents into personal smart devices and wearable devices, A collaboration department that interacts in real time with public AI agents installed in public facilities, The system comprises a provisioning unit that provides the user with the most suitable service based on the information shared by the aforementioned linking unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Pay your fare without going through the ticket gate inside the station. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, AI agents integrate personality, memory, planning, and behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides an interface for natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Utilizing RFID / NFC and location service integration technologies The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We will perform user behavior pattern analysis using machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned mounting section is It estimates the user's emotions and adjusts the timing of the personal AI agent's activation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned mounting section is Analyze the user's past device usage history to select the optimal implementation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned mounting section is When a personal AI agent is installed, it is customized based on the user's current device settings and usage. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned mounting section is It estimates the user's emotions and adjusts the functions of the built-in AI agent based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned mounting section is When installing a personal AI agent, optimal settings are made considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned mounting section is When integrating a personal AI agent, it analyzes the user's social media activity and adds relevant features. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the timing of collaboration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned linkage unit is, During the integration process, the usage history of public facilities will be analyzed to select the most suitable integration method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned linkage unit is, When coordinating, priority is determined based on the current status and congestion level of public facilities. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned linkage unit is, It estimates the user's emotions and prioritizes the information to link based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned linkage unit is, When integrating, the optimal integration method will be selected considering the geographical location information of public facilities. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned linkage unit is, During the collaboration process, the social media activity of public facilities will be analyzed, and relevant information will be shared. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the content of the services we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing a service, the system analyzes the user's past service usage history to select the most suitable service. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we customize it based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the services to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we take the user's geographical location into consideration to provide the most suitable service. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant services. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of fare settlement based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When settling fares, the system analyzes the user's past transportation usage history to select the most suitable payment method. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of fare settlement based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When settling fares, the system selects the optimal payment method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the information to be integrated based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, During integration, the system analyzes the user's past behavior history to select the optimal integration method. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of information to integrate based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned supply unit is, During integration, the optimal integration method is selected, taking into account the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface's presentation based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the interface, the system analyzes the user's past dialogue history to select the most appropriate expression method. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned supply unit is, It estimates the user's emotions and determines interface priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the interface, the optimal representation method is selected considering the user's geographical location information. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of using collaborative technologies based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When utilizing integrated technologies, the system analyzes the user's past usage history to select the most suitable technology. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned supply unit is, It estimates user emotions and prioritizes collaborative technologies based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When utilizing collaborative technologies, the optimal technology is selected by considering the user's geographical location information. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the behavioral pattern analysis based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When analyzing behavioral patterns, the user's past behavioral history is analyzed to select the most suitable analysis method. The system described in Appendix 6, characterized by the features described herein. (Note 43) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes behavioral pattern analysis based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 44) The aforementioned supply unit is, When analyzing behavioral patterns, the optimal analysis method is selected by considering the user's geographical location information. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. The mounting unit for equipping personal AI agents into personal smart devices and wearable devices, A collaboration department that interacts in real time with public AI agents installed in public facilities, The system comprises a provisioning unit that provides the user with the most suitable service based on the information shared by the aforementioned linking unit. A system characterized by the following features.

2. The aforementioned supply unit is, Pay your fare without going through the ticket gate inside the station. The system according to feature 1.

3. The aforementioned supply unit is, AI agents integrate personality, memory, planning, and behavior. The system according to feature 1.

4. The aforementioned supply unit is, Provides an interface for natural language processing. The system according to feature 1.

5. The aforementioned supply unit is, Utilizing RFID / NFC and location service integration technologies The system according to feature 1.

6. The aforementioned supply unit is, We will perform user behavior pattern analysis using machine learning. The system according to feature 1.

7. The aforementioned mounting section is It estimates the user's emotions and adjusts the timing of the personal AI agent's activation based on the estimated emotions. The system according to feature 1.

8. The aforementioned mounting section is Analyze the user's past device usage history to select the optimal implementation method. The system according to feature 1.

Images