system

Abstract

The system according to this embodiment aims to optimize the user's travel plan and flexibly respond to changes in plans or problems during travel. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a support unit, and an update unit. The analysis unit analyzes the user's past behavior and current situation. The proposal unit proposes the optimal route and means of transportation based on the data analyzed by the analysis unit. The support unit provides conversational support utilizing generation AI. The update unit acquires real-time traffic information and updates the travel plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the user's travel plan is not sufficiently optimized and flexible responses to schedule changes and troubles during travel are not made.

[0005] The system according to the embodiment aims to optimize the user's travel plan and flexibly respond to schedule changes and troubles during travel.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a support unit, and an update unit. The analysis unit analyzes the user's past behavior and current situation. The proposal unit proposes the optimal route and mode of transportation based on the data analyzed by the analysis unit. The support unit provides conversational support utilizing generative AI. The update unit acquires real-time traffic information and updates the travel plan. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the user's travel plan and flexibly respond to changes in plans or problems during travel. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 travel plan provision system according to an embodiment of the present invention is a system that utilizes AI to provide users with the optimal travel plan and flexibly responds to changes in plans and problems during travel. This travel plan provision system generates a travel plan that optimizes time, cost, and comfort by combining multiple modes of transportation, and quickly proposes a new plan in case of changes in plans or problems during travel through conversational support using generation AI. Furthermore, it links with real-time traffic information and immediately obtains information such as congestion and delays to update the travel plan. For example, the travel plan provision system analyzes the user's past behavior and current situation to propose the optimal route and mode of transportation. For example, it generates an optimal travel plan based on the mode of transportation and travel time the user has used in the past. In this process, it optimizes time, cost, and comfort by combining multiple modes of transportation. Next, the travel plan provision system quickly responds to changes in plans and problems during travel through conversational support using generation AI. For example, if the user wants to change their plans during travel, the generation AI proposes a new travel plan. Also, if a problem occurs during travel, the generation AI will respond quickly and provide the optimal solution. It can answer the user's questions and requests in real time through a voice conversational assistant. Furthermore, the travel plan provision system integrates with real-time traffic information, instantly acquiring information such as congestion and delays to update travel plans. For example, if a user encounters congestion while traveling, the generating AI acquires real-time congestion information and suggests the optimal detour route. This allows users to respond flexibly to problems during their travels. This mechanism simplifies complex travel planning for users and suggests the optimal route and mode of transportation. It also improves the user's travel experience by enabling quick responses to changes in plans and problems during travel. For example, if a user traveling needs to make a sudden change of plans, the generating AI can quickly suggest a new travel plan to support a smooth journey. In this way, the travel plan provision system can improve the user's travel experience.

[0029] The travel plan provision system according to this embodiment comprises an analysis unit, a proposal unit, a support unit, and an update unit. The analysis unit analyzes the user's past behavior and current situation. The user's past behavior includes, but is not limited to, past travel history and previously selected modes of transportation. For example, the analysis unit retrieves the user's past travel history from a database and analyzes past travel patterns. The analysis unit can also acquire the user's current location information and current traffic conditions in real time and analyze the current situation. For example, the analysis unit uses GPS data to identify the user's current location and uses a traffic information API to obtain current traffic conditions. The proposal unit proposes the optimal route and mode of transportation based on the data analyzed by the analysis unit. The proposal unit selects the optimal route based on criteria such as shortest time, shortest distance, and lowest cost. The proposal unit can also generate an optimal travel plan by combining modes of transportation such as trains, buses, taxis, and bicycles. For example, the proposal unit proposes the optimal mode of transportation based on the user's past travel history. The support unit provides conversational support utilizing generation AI. The support unit answers user questions and requests, for example, through a voice-activated assistant. The generation AI generates the optimal answer to the user's question using a specific AI algorithm. The support unit can also respond quickly if the user wants to change their plans or encounters problems while traveling. For example, the support unit can grasp the user's current situation in real time and propose a new travel plan. The update unit updates the travel plan by acquiring real-time traffic information. The update unit acquires congestion and delay information using, for example, traffic information APIs or sensor data. The update unit updates the travel plan based on the acquired real-time traffic information and proposes the optimal route to the user. For example, if the user encounters congestion while traveling, the update unit acquires congestion information in real time and proposes the optimal detour route. In this way, the travel plan provision system according to the embodiment can analyze the user's past behavior and current situation and provide the optimal travel plan.

[0030] The analytics department conducts detailed analyses of users' past behavior and current circumstances. Past behavior includes past travel history, chosen modes of transportation, places visited, and times of day. This data is crucial for understanding users' travel patterns and preferences. For example, the analytics department identifies routes and modes of transportation frequently used by users in the past and analyzes their travel trends based on this information. Furthermore, to understand users' current circumstances, GPS data is used to determine their current location, and traffic information APIs are used to obtain current traffic conditions. This allows the analytics department to understand users' current situations in real time and collect foundational data to provide optimal travel plans. In addition, the analytics department utilizes external data such as weather and event information to comprehensively consider factors influencing user travel. For example, if a large-scale event is being held, traffic conditions in the surrounding area may be congested, allowing the analytics department to propose travel plans that take this into account. The analytics department integrates this diverse data to build a foundation for providing optimal travel plans for users.

[0031] The suggestion department proposes the optimal route and mode of transport to the user based on data analyzed by the analysis department. The suggestion department selects the best route considering criteria such as shortest travel time, shortest distance, lowest cost, and comfort, depending on the user's travel purpose and priorities. For example, if the user is in a hurry, it will suggest the route that arrives in the shortest time; if cost is a priority, it will suggest the cheapest route. The suggestion department can also combine multiple modes of transport, such as trains, buses, taxis, bicycles, and walking, to generate the most efficient travel plan for the user. For example, based on the user's past travel history, the suggestion department will prioritize suggesting routes that are less congested during specific times of the day and modes of transport preferred by the user. Furthermore, the suggestion department can dynamically adjust the travel plan considering the user's current situation and real-time traffic information. For example, if the user encounters traffic congestion while traveling, the suggestion department will immediately calculate a new route and notify the user. This allows the suggestion department to provide the user with the optimal travel plan, improving both the efficiency and comfort of their journey.

[0032] The support department provides interactive support utilizing generative AI. This generative AI uses natural language processing technology to generate optimal answers to user questions and requests. For example, if a user asks "Where is the nearest bus stop?" through a voice-activated assistant, the generative AI will identify the nearest bus stop based on the user's current location and provide a voice response. Furthermore, the support department can respond quickly if a user wants to change their plans or encounters problems during their journey. For example, if a user wants to change their destination during their journey, the support department will instantly calculate the optimal route to the new destination and propose it to the user. In addition, the support department can understand the user's current situation in real time and propose new travel plans as needed. For example, if a user encounters a traffic accident or natural disaster during their journey, the support department will instantly calculate a new route and provide a safe travel plan. This allows the support department to provide users with prompt and appropriate support, improving the safety and comfort of their journeys.

[0033] The update unit dynamically updates travel plans by acquiring real-time traffic information. The update unit utilizes traffic information APIs and sensor data to obtain real-time traffic information such as congestion, delays, accidents, and construction information. For example, the update unit acquires congestion information for major roads via traffic information APIs and monitors the operation status of public transport using sensor data. This allows the update unit to anticipate potential traffic disruptions that users may encounter during their travels and propose optimal alternative routes. Furthermore, the update unit continuously updates travel plans based on the acquired real-time traffic information, providing users with the latest information. For example, if a user encounters congestion during their travels, the update unit immediately calculates a new route and notifies the user. The update unit can also predict risks in specific time periods or areas based on the user's travel history and past trouble information, and issue warnings in advance. This allows the update unit to always provide users with the latest information, improving the efficiency and safety of travel.

[0034] The assistant unit can answer user questions and requests through a voice-interactive assistant. For example, the assistant unit can use speech recognition technology to convert the user's voice into text and natural language processing technology to generate the best possible answer to the user's question. For example, if the user asks, "Where is the nearest station?", the assistant unit will use speech recognition technology to convert the voice into text and natural language processing technology to provide information about the nearest station. The assistant unit can also suggest travel plans according to the user's requests. For example, if the user requests, "Please tell me the time of the next bus," the assistant unit will obtain real-time traffic information and provide the time of the next bus. This allows the assistant unit to answer user questions and requests in real time. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the user's voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0035] The trouble response unit can respond quickly to problems that occur while traveling. For example, in the event of a traffic accident or delay, the trouble response unit can quickly provide countermeasures. For example, in the event of a traffic accident, the trouble response unit can suggest the optimal detour route. Also, in the event of a delay, the trouble response unit can suggest the next available means of transportation. For example, the trouble response unit can obtain train delay information and provide information on the next available bus or taxi. This enables a quick response to problems while traveling. Some or all of the above processing in the trouble response unit may be performed using AI, for example, or not using AI. For example, the trouble response unit can input traffic accident and delay information into a generating AI and have the generating AI generate the optimal countermeasures.

[0036] The suggestion unit can generate an optimal travel plan based on the user's past behavioral data. For example, the suggestion unit can analyze the user's past travel history and propose the optimal route and mode of transportation. For example, the suggestion unit can generate an optimal travel plan based on the mode of transportation the user has frequently used in the past. The suggestion unit can also propose an optimal travel plan based on the user's past travel time, according to the time of day. For example, the suggestion unit can analyze the travel time the user has used in the past and propose a plan to travel at the optimal time of day. This allows the system to generate an optimal travel plan based on the user's past behavioral data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past travel history data into a generation AI and have the generation AI generate an optimal travel plan.

[0037] The update unit can acquire real-time traffic congestion and delay information and update travel plans. The update unit acquires real-time traffic congestion and delay information using, for example, traffic information APIs or sensor data. For example, the update unit can acquire the current traffic congestion situation using a traffic information API and propose the optimal detour route. The update unit can also acquire delay information using sensor data and propose the next available mode of transportation. For example, the update unit can acquire train delay information and provide information on the next available bus or taxi. This allows for real-time acquisition of traffic congestion and delay information and updating of travel plans. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input real-time acquired traffic information data into a generating AI and have the generating AI execute the travel plan update.

[0038] The analysis unit can analyze a user's past travel history in detail, extract specific patterns, and select the optimal analysis method. For example, the analysis unit can analyze the modes of transportation frequently used by the user in the past and propose the optimal travel plan. For example, the analysis unit can analyze a user's past travel times and propose the optimal travel plan according to the time of day. The analysis unit can also analyze a user's past travel patterns and propose the optimal travel plan for specific days of the week or time of day. For example, the analysis unit can extract specific patterns based on a user's past travel history and select the optimal analysis method. This allows for a detailed analysis of a user's past travel history, the extraction of specific patterns, and the selection of the optimal analysis method. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into a generating AI and have the generating AI perform the extraction of specific patterns and the selection of the optimal analysis method.

[0039] The analysis unit can suggest the most suitable mode of transportation considering the user's current health status and fatigue level. For example, if the user is tired, the analysis unit will suggest the most comfortable mode of transportation. For example, the analysis unit will analyze the user's vital data, such as heart rate and steps, to estimate their fatigue level. The analysis unit can also suggest modes of transportation such as walking or cycling if the user is seeking healthy exercise. For example, the analysis unit will evaluate the user's health status based on self-reported data and suggest the most suitable mode of transportation. The analysis unit can also suggest the quickest mode of transportation if the user is feeling unwell. For example, the analysis unit will analyze the user's sleep patterns, detect unwellness, and suggest the most suitable mode of transportation. This allows the analysis unit to suggest the most suitable mode of transportation considering the user's current health status and fatigue level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's vital data into a generating AI and have the generating AI perform the evaluation of health status and fatigue level and suggest the most suitable mode of transportation.

[0040] The analysis unit can analyze region-specific travel patterns by considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can analyze the transportation methods specific to that region. For example, the analysis unit can determine the user's current location using GPS data and analyze the transportation methods common in that region. The analysis unit can also analyze travel patterns specific to tourist destinations if the user is in a tourist area. For example, the analysis unit can obtain traffic information for the tourist area and propose the optimal travel plan. The analysis unit can also analyze travel patterns specific to urban areas if the user is in an urban area. For example, the analysis unit can analyze urban traffic volume data and propose the optimal transportation method. This allows for the analysis of region-specific travel patterns by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis of region-specific travel patterns.

[0041] The analysis unit can obtain relevant travel data by referring to the user's social media activity. For example, the analysis unit can suggest an optimal travel plan based on the locations where the user has checked in on social media. For example, the analysis unit can analyze the content of the user's social media posts and obtain relevant travel data. The analysis unit can also suggest an optimal travel plan by referring to the travel history of the user's social media friends. For example, the analysis unit can suggest an optimal travel plan based on the places the user's friends have visited in the past. This allows the analysis unit to obtain relevant travel data by referring to the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of relevant travel data.

[0042] The suggestion unit can select the optimal mode of transportation based on the user's past travel history. For example, the suggestion unit can suggest the optimal mode of transportation based on the mode of transport the user has used in the past. For example, the suggestion unit can suggest the optimal mode of transportation for a given time of day based on the user's past travel time. The suggestion unit can also suggest the optimal mode of transportation for a specific day of the week or time of day based on the user's past travel patterns. For example, the suggestion unit can select the optimal mode of transportation based on the user's past travel history. This allows the optimal mode of transportation to be selected based on the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI select the optimal mode of transportation.

[0043] The suggestion unit can generate an optimal travel plan considering the user's current schedule and appointments. For example, the suggestion unit can refer to the user's calendar information and propose an optimal travel plan based on the appointments. For example, the suggestion unit can obtain data from the user's calendar app and generate an optimal travel plan based on the appointments. The suggestion unit can also propose the optimal mode of transportation according to the user's schedule. For example, the suggestion unit can propose the optimal mode of transportation based on the user's calendar. The suggestion unit can also propose a plan that optimizes travel time according to the user's appointments. For example, the suggestion unit can generate an optimal travel plan considering the user's meeting schedules and event attendance plans. This allows the suggestion unit to generate an optimal travel plan considering the user's current schedule and appointments. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's schedule data into a generation AI and have the generation AI execute the generation of an optimal travel plan.

[0044] The suggestion unit can propose region-specific modes of transportation, taking into account the user's geographical location. For example, if the user is in a specific region, the suggestion unit can propose transportation methods specific to that region. For example, the suggestion unit can determine the user's current location using GPS data and propose common modes of transportation in that region. The suggestion unit can also propose transportation methods specific to tourist destinations if the user is in a tourist destination. For example, the suggestion unit can obtain traffic information for the tourist destination and propose the most suitable mode of transportation. The suggestion unit can also propose transportation methods specific to urban areas if the user is in an urban area. For example, the suggestion unit can analyze urban traffic volume data and propose the most suitable mode of transportation. This allows the suggestion unit to propose region-specific modes of transportation, taking into account the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI execute the suggestion of region-specific modes of transportation.

[0045] The suggestion unit can propose relevant travel plans by referring to the user's social media activity. For example, the suggestion unit can propose the optimal travel plan based on the locations the user has checked into on social media. For example, the suggestion unit can analyze the content of the user's social media posts and propose relevant travel plans. The suggestion unit can also propose the optimal travel plan by referring to the travel history of the user's social media friends. For example, the suggestion unit can propose the optimal travel plan based on the places the user's friends have visited in the past. This allows the suggestion unit to propose relevant travel plans by referring to the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of relevant travel plans.

[0046] The support unit can select the optimal support method by referring to the user's past conversation history. For example, the support unit provides the optimal support based on the support methods the user has used in the past. For example, the support unit analyzes the user's past conversation history and selects the optimal support method. The support unit can also propose the optimal support method for a specific problem based on the user's past conversation history. For example, the support unit proposes the optimal support method based on problems the user has experienced in the past. This allows the support unit to select the optimal support method by referring to the user's past conversation history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past conversation history data into a generating AI and have the generating AI select the optimal support method.

[0047] The support unit can provide optimal dialogue content considering the user's current situation and environment. For example, if the user is on the move, the support unit can provide concise and quick dialogue. For example, the support unit can acquire the user's current location information and detect that the user is on the move. The support unit can also provide detailed dialogue content if the user is relaxed. For example, the support unit can detect the noise level around the user using sensors and determine that the user is relaxed. The support unit can also provide dialogue content that offers a quick solution if the user is in a hurry. For example, the support unit can analyze the speed of the user's voice and determine that the user is in a hurry. This allows the support unit to provide optimal dialogue content considering the user's current situation and environment. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current situation data into a generating AI and have the generating AI perform the task of providing optimal dialogue content.

[0048] The support unit can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, the support unit can provide region-specific information. For example, the support unit can use the user's GPS data to determine the current location and provide general information for that region. The support unit can also provide tourist destination-specific information if the user is in a tourist destination. For example, the support unit can provide event information and traffic information for the tourist destination. The support unit can also provide urban area-specific information if the user is in an urban area. For example, the support unit can provide urban traffic volume data and event information. This allows the support unit to provide region-specific information by taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0049] The support unit can provide relevant information by referring to the user's social media activity. For example, the support unit can provide relevant information based on the locations the user has checked into on social media. For example, the support unit can analyze the content of the user's social media posts and provide relevant information. The support unit can also provide relevant information by referring to information about the user's social media friends. For example, the support unit can provide relevant information based on places the user's friends have visited in the past. This allows the support unit to provide relevant information by referring to the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant information.

[0050] The update unit can select the optimal update method by referring to the user's past travel history. For example, the update unit can propose the optimal update method based on routes the user has used in the past. For example, the update unit can analyze the user's past travel history and propose an update method to avoid congestion. The update unit can also propose the most efficient update method from the user's past travel history. For example, the update unit can select the optimal update method based on the user's past travel routes. This allows the update unit to select the optimal update method by referring to the user's past travel history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's past travel history data into a generating AI and have the generating AI select the optimal update method.

[0051] The update unit can provide optimal updates considering the user's current situation and environment. For example, if the user is on the move, the update unit can provide real-time updates to deliver the latest information. For example, the update unit can acquire the user's current location information and detect that the user is on the move. The update unit can also provide detailed updates if the user is relaxed. For example, the update unit can detect the noise level around the user using sensors and determine that the user is relaxed. The update unit can also provide rapid updates to deliver the latest information if the user is in a hurry. For example, the update unit can analyze the speed of the user's speech and determine that the user is in a hurry. This allows the update unit to provide optimal updates considering the user's current situation and environment. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's current situation data into a generating AI and have the generating AI execute the provision of optimal updates.

[0052] The update unit can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, the update unit can provide region-specific information. For example, the update unit can use the user's GPS data to determine the current location and provide general information for that region. The update unit can also provide tourist destination-specific information if the user is in a tourist destination. For example, the update unit can provide event information and traffic information for the tourist destination. The update unit can also provide urban area-specific information if the user is in an urban area. For example, the update unit can provide urban traffic volume data and event information. This allows the update unit to provide region-specific information by taking into account the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0053] The update unit can provide relevant information by referring to the user's social media activity. For example, the update unit can provide relevant information based on places the user has checked in to on social media. For example, the update unit can analyze the content of the user's social media posts and provide relevant information. The update unit can also provide relevant information by referring to information about the user's social media friends. For example, the update unit can provide relevant information based on places the user's friends have visited in the past. This allows the update unit to provide relevant information by referring to the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant information.

[0054] The assistant unit can select the optimal assistance method by referring to the user's past dialogue history. For example, the assistant unit provides the optimal assistance based on the assistance methods the user has used in the past. For example, the assistant unit analyzes the user's past dialogue history and selects the optimal assistance method. The assistant unit can also propose the optimal assistance method for a specific problem based on the user's past dialogue history. For example, the assistant unit proposes the optimal assistance method based on problems the user has experienced in the past. This allows the assistant unit to select the optimal assistance method by referring to the user's past dialogue history. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the user's past dialogue history data into a generating AI and have the generating AI perform the selection of the optimal assistance method.

[0055] The assistant unit can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, the assistant unit can provide region-specific information. For example, the assistant unit can use the user's GPS data to determine the current location and provide general information for that region. The assistant unit can also provide tourist-specific information if the user is in a tourist area. For example, the assistant unit can provide event information and traffic information for the tourist area. The assistant unit can also provide urban-specific information if the user is in an urban area. For example, the assistant unit can provide urban traffic volume data and event information. This allows the assistant unit to provide region-specific information by taking into account the user's geographical location. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0056] The troubleshooting unit can select the optimal solution by referring to the user's past troubleshooting history. For example, the troubleshooting unit can propose the optimal solution based on the user's past troubleshooting experiences. For example, the troubleshooting unit can analyze the user's past troubleshooting history and select the optimal solution. The troubleshooting unit can also propose the optimal solution for a specific problem based on the user's past troubleshooting history. For example, the troubleshooting unit can propose the optimal solution based on the user's past troubleshooting experiences. This allows the system to select the optimal solution by referring to the user's past troubleshooting history. Some or all of the above processing in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's past troubleshooting history data into a generating AI and have the generating AI select the optimal solution.

[0057] The troubleshooting unit can provide region-specific solutions by considering the user's geographical location. For example, if the user is in a specific region, the troubleshooting unit can provide region-specific solutions. For instance, the troubleshooting unit can use the user's GPS data to determine their current location and provide general solutions for that region. Furthermore, if the user is in a tourist area, the troubleshooting unit can provide tourist area-specific solutions. For example, the troubleshooting unit can provide event and traffic information for the tourist area. Also, if the user is in an urban area, the troubleshooting unit can provide urban area-specific solutions. For example, the troubleshooting unit can provide urban traffic volume data and event information. This allows the system to provide region-specific solutions by considering the user's geographical location. Some or all of the above processing in the troubleshooting unit may be performed using AI, or not. For example, the troubleshooting unit can input the user's geographical location data into a generating AI and have the generating AI provide region-specific solutions.

[0058] The troubleshooting unit can refer to the user's social media activity and provide relevant solutions. For example, the troubleshooting unit can suggest the best solution based on problems reported by the user on social media. For example, the troubleshooting unit can analyze the content of the user's social media posts and provide relevant solutions. The troubleshooting unit can also refer to the trouble history of the user's social media friends and suggest the best solution. For example, the troubleshooting unit can provide relevant solutions based on problems experienced by the user's friends in the past. This allows the troubleshooting unit to refer to the user's social media activity and provide relevant solutions. Some or all of the above processing in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant solutions.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The travel plan provision system can also include a health management unit that monitors the user's health condition and provides the optimal travel plan. The health management unit acquires vital data such as the user's heart rate, blood pressure, and steps in real time and evaluates the user's health condition. For example, if the user is tired, the health management unit can suggest the most comfortable mode of transportation. If the user is seeking healthy exercise, it can also suggest modes of transportation such as walking or cycling. Furthermore, if the user is feeling unwell, it can suggest the quickest way to travel. This allows the system to provide the optimal travel plan tailored to the user's health condition.

[0061] The travel plan provision system may also include a social media integration unit that references the user's social media activity and proposes relevant travel plans. The social media integration unit analyzes locations and posts the user has made on social media and proposes relevant travel plans. For example, if a user wants to go to the same place as a friend, the social media integration unit can propose the optimal travel plan based on the friend's travel history. It can also propose relevant travel plans based on places the user has visited in the past. This allows the system to provide optimal travel plans based on the user's social media activity.

[0062] The travel plan provision system may further include a schedule integration unit that generates an optimal travel plan considering the user's current schedule and appointments. The schedule integration unit refers to the user's calendar information and proposes the optimal travel plan based on their appointments. For example, it can generate an optimal travel plan considering the user's meeting schedules and event attendance plans. It can also propose the most suitable mode of transportation according to the user's schedule. This allows the system to provide an optimal travel plan that takes into account the user's current schedule and appointments.

[0063] The travel plan provision system may further include a region-specific unit that proposes region-specific modes of transportation, taking into account the user's geographical location. The region-specific unit proposes transportation methods specific to a particular region if the user is in that area. For example, if the user is in a tourist area, it can propose transportation methods specific to that area. Similarly, if the user is in an urban area, it can propose transportation methods specific to that urban area. This allows the system to provide region-specific transportation options that take the user's geographical location into account.

[0064] The mobility plan provision system may also include a dialogue history unit that selects the optimal support method by referring to the user's past dialogue history. The dialogue history unit provides optimal support based on the support methods the user has used in the past. For example, it can analyze the user's past dialogue history and propose the optimal support method for a specific problem. It can also propose the optimal support method based on problems the user has experienced in the past. This allows for the provision of optimal support that references the user's past dialogue history.

[0065] The travel plan provision system may further include a regional information unit that provides region-specific information considering the user's geographical location. The regional information unit provides region-specific information when the user is in a specific area. For example, if the user is in a tourist area, it can provide event information and traffic information for that area. Similarly, if the user is in an urban area, it can provide urban traffic volume data and event information. This allows for the provision of region-specific information that takes the user's geographical location into account.

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

[0067] Step 1: The analysis unit analyzes the user's past behavior and current situation. The user's past behavior includes past travel history and previously selected modes of transportation. The analysis unit retrieves the user's past travel history from the database and analyzes past travel patterns. The analysis unit also obtains the user's current location information and current traffic conditions in real time and analyzes the current situation. For example, it uses GPS data to determine the user's current location and a traffic information API to obtain current traffic conditions. Step 2: The proposal department proposes the optimal route and mode of transportation based on the data analyzed by the analysis department. The proposal department selects the optimal route based on criteria such as shortest time, shortest distance, and lowest cost. The proposal department also generates the optimal travel plan by combining modes of transportation such as trains, buses, taxis, and bicycles. For example, it may propose the optimal mode of transportation based on the user's past travel history. Step 3: The support department provides interactive support utilizing generative AI. The support department answers user questions and requests through a voice-activated assistant. The generative AI uses a specific AI algorithm to generate the best possible answers to user questions. The support department also responds quickly if the user wants to change their plans or encounters problems while traveling. For example, it can grasp the user's current situation in real time and propose a new travel plan. Step 4: The update unit acquires real-time traffic information and updates the travel plan. The update unit acquires congestion and delay information using traffic information APIs and sensor data. Based on the acquired real-time traffic information, it updates the travel plan and suggests the optimal route to the user. For example, if the user encounters congestion while traveling, it acquires real-time congestion information and suggests the optimal detour route.

[0068] (Example of form 2) The travel plan provision system according to an embodiment of the present invention is a system that utilizes AI to provide users with the optimal travel plan and flexibly responds to changes in plans and problems during travel. This travel plan provision system generates a travel plan that optimizes time, cost, and comfort by combining multiple modes of transportation, and quickly proposes a new plan in case of changes in plans or problems during travel through conversational support using generation AI. Furthermore, it links with real-time traffic information and immediately obtains information such as congestion and delays to update the travel plan. For example, the travel plan provision system analyzes the user's past behavior and current situation to propose the optimal route and mode of transportation. For example, it generates an optimal travel plan based on the mode of transportation and travel time the user has used in the past. In this process, it optimizes time, cost, and comfort by combining multiple modes of transportation. Next, the travel plan provision system quickly responds to changes in plans and problems during travel through conversational support using generation AI. For example, if the user wants to change their plans during travel, the generation AI proposes a new travel plan. Also, if a problem occurs during travel, the generation AI will respond quickly and provide the optimal solution. It can answer the user's questions and requests in real time through a voice conversational assistant. Furthermore, the travel plan provision system integrates with real-time traffic information, instantly acquiring information such as congestion and delays to update travel plans. For example, if a user encounters congestion while traveling, the generating AI acquires real-time congestion information and suggests the optimal detour route. This allows users to respond flexibly to problems during their travels. This mechanism simplifies complex travel planning for users and suggests the optimal route and mode of transportation. It also improves the user's travel experience by enabling quick responses to changes in plans and problems during travel. For example, if a user traveling needs to make a sudden change of plans, the generating AI can quickly suggest a new travel plan to support a smooth journey. In this way, the travel plan provision system can improve the user's travel experience.

[0069] The travel plan provision system according to this embodiment comprises an analysis unit, a proposal unit, a support unit, and an update unit. The analysis unit analyzes the user's past behavior and current situation. The user's past behavior includes, but is not limited to, past travel history and previously selected modes of transportation. For example, the analysis unit retrieves the user's past travel history from a database and analyzes past travel patterns. The analysis unit can also acquire the user's current location information and current traffic conditions in real time and analyze the current situation. For example, the analysis unit uses GPS data to identify the user's current location and uses a traffic information API to obtain current traffic conditions. The proposal unit proposes the optimal route and mode of transportation based on the data analyzed by the analysis unit. The proposal unit selects the optimal route based on criteria such as shortest time, shortest distance, and lowest cost. The proposal unit can also generate an optimal travel plan by combining modes of transportation such as trains, buses, taxis, and bicycles. For example, the proposal unit proposes the optimal mode of transportation based on the user's past travel history. The support unit provides conversational support utilizing generation AI. The support unit answers user questions and requests, for example, through a voice-activated assistant. The generation AI generates the optimal answer to the user's question using a specific AI algorithm. The support unit can also respond quickly if the user wants to change their plans or encounters problems while traveling. For example, the support unit can grasp the user's current situation in real time and propose a new travel plan. The update unit updates the travel plan by acquiring real-time traffic information. The update unit acquires congestion and delay information using, for example, traffic information APIs or sensor data. The update unit updates the travel plan based on the acquired real-time traffic information and proposes the optimal route to the user. For example, if the user encounters congestion while traveling, the update unit acquires congestion information in real time and proposes the optimal detour route. In this way, the travel plan provision system according to the embodiment can analyze the user's past behavior and current situation and provide the optimal travel plan.

[0070] The analytics department conducts detailed analyses of users' past behavior and current circumstances. Past behavior includes past travel history, chosen modes of transportation, places visited, and times of day. This data is crucial for understanding users' travel patterns and preferences. For example, the analytics department identifies routes and modes of transportation frequently used by users in the past and analyzes their travel trends based on this information. Furthermore, to understand users' current circumstances, GPS data is used to determine their current location, and traffic information APIs are used to obtain current traffic conditions. This allows the analytics department to understand users' current situations in real time and collect foundational data to provide optimal travel plans. In addition, the analytics department utilizes external data such as weather and event information to comprehensively consider factors influencing user travel. For example, if a large-scale event is being held, traffic conditions in the surrounding area may be congested, allowing the analytics department to propose travel plans that take this into account. The analytics department integrates this diverse data to build a foundation for providing optimal travel plans for users.

[0071] The suggestion department proposes the optimal route and mode of transport to the user based on data analyzed by the analysis department. The suggestion department selects the best route considering criteria such as shortest travel time, shortest distance, lowest cost, and comfort, depending on the user's travel purpose and priorities. For example, if the user is in a hurry, it will suggest the route that arrives in the shortest time; if cost is a priority, it will suggest the cheapest route. The suggestion department can also combine multiple modes of transport, such as trains, buses, taxis, bicycles, and walking, to generate the most efficient travel plan for the user. For example, based on the user's past travel history, the suggestion department will prioritize suggesting routes that are less congested during specific times of the day and modes of transport preferred by the user. Furthermore, the suggestion department can dynamically adjust the travel plan considering the user's current situation and real-time traffic information. For example, if the user encounters traffic congestion while traveling, the suggestion department will immediately calculate a new route and notify the user. This allows the suggestion department to provide the user with the optimal travel plan, improving both the efficiency and comfort of their journey.

[0072] The support department provides interactive support utilizing generative AI. This generative AI uses natural language processing technology to generate optimal answers to user questions and requests. For example, if a user asks "Where is the nearest bus stop?" through a voice-activated assistant, the generative AI will identify the nearest bus stop based on the user's current location and provide a voice response. Furthermore, the support department can respond quickly if a user wants to change their plans or encounters problems during their journey. For example, if a user wants to change their destination during their journey, the support department will instantly calculate the optimal route to the new destination and propose it to the user. In addition, the support department can understand the user's current situation in real time and propose new travel plans as needed. For example, if a user encounters a traffic accident or natural disaster during their journey, the support department will instantly calculate a new route and provide a safe travel plan. This allows the support department to provide users with prompt and appropriate support, improving the safety and comfort of their journeys.

[0073] The update unit dynamically updates travel plans by acquiring real-time traffic information. The update unit utilizes traffic information APIs and sensor data to obtain real-time traffic information such as congestion, delays, accidents, and construction information. For example, the update unit acquires congestion information for major roads via traffic information APIs and monitors the operation status of public transport using sensor data. This allows the update unit to anticipate potential traffic disruptions that users may encounter during their travels and propose optimal alternative routes. Furthermore, the update unit continuously updates travel plans based on the acquired real-time traffic information, providing users with the latest information. For example, if a user encounters congestion during their travels, the update unit immediately calculates a new route and notifies the user. The update unit can also predict risks in specific time periods or areas based on the user's travel history and past trouble information, and issue warnings in advance. This allows the update unit to always provide users with the latest information, improving the efficiency and safety of travel.

[0074] The assistant unit can answer user questions and requests through a voice-interactive assistant. For example, the assistant unit can use speech recognition technology to convert the user's voice into text and natural language processing technology to generate the best possible answer to the user's question. For example, if the user asks, "Where is the nearest station?", the assistant unit will use speech recognition technology to convert the voice into text and natural language processing technology to provide information about the nearest station. The assistant unit can also suggest travel plans according to the user's requests. For example, if the user requests, "Please tell me the time of the next bus," the assistant unit will obtain real-time traffic information and provide the time of the next bus. This allows the assistant unit to answer user questions and requests in real time. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the user's voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0075] The trouble response unit can respond quickly to problems that occur while traveling. For example, in the event of a traffic accident or delay, the trouble response unit can quickly provide countermeasures. For example, in the event of a traffic accident, the trouble response unit can suggest the optimal detour route. Also, in the event of a delay, the trouble response unit can suggest the next available means of transportation. For example, the trouble response unit can obtain train delay information and provide information on the next available bus or taxi. This enables a quick response to problems while traveling. Some or all of the above processing in the trouble response unit may be performed using AI, for example, or not using AI. For example, the trouble response unit can input traffic accident and delay information into a generating AI and have the generating AI generate the optimal countermeasures.

[0076] The suggestion unit can generate an optimal travel plan based on the user's past behavioral data. For example, the suggestion unit can analyze the user's past travel history and propose the optimal route and mode of transportation. For example, the suggestion unit can generate an optimal travel plan based on the mode of transportation the user has frequently used in the past. The suggestion unit can also propose an optimal travel plan based on the user's past travel time, according to the time of day. For example, the suggestion unit can analyze the travel time the user has used in the past and propose a plan to travel at the optimal time of day. This allows the system to generate an optimal travel plan based on the user's past behavioral data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past travel history data into a generation AI and have the generation AI generate an optimal travel plan.

[0077] The update unit can acquire real-time traffic congestion and delay information and update travel plans. The update unit acquires real-time traffic congestion and delay information using, for example, traffic information APIs or sensor data. For example, the update unit can acquire the current traffic congestion situation using a traffic information API and propose the optimal detour route. The update unit can also acquire delay information using sensor data and propose the next available mode of transportation. For example, the update unit can acquire train delay information and provide information on the next available bus or taxi. This allows for real-time acquisition of traffic congestion and delay information and updating of travel plans. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input real-time acquired traffic information data into a generating AI and have the generating AI execute the travel plan update.

[0078] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can improve the accuracy of the analysis to provide a more accurate travel plan. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also adjust the accuracy of the analysis to provide a more flexible travel plan if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also perform a rapid analysis and provide a travel plan that allows for the shortest possible travel time if the user is in a hurry. For example, the analysis unit can analyze the user's text messages and estimate their emotions. This allows the accuracy of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform adjustments to the accuracy of the emotion-based analysis.

[0079] The analysis unit can analyze a user's past travel history in detail, extract specific patterns, and select the optimal analysis method. For example, the analysis unit can analyze the modes of transportation frequently used by the user in the past and propose the optimal travel plan. For example, the analysis unit can analyze a user's past travel times and propose the optimal travel plan according to the time of day. The analysis unit can also analyze a user's past travel patterns and propose the optimal travel plan for specific days of the week or time of day. For example, the analysis unit can extract specific patterns based on a user's past travel history and select the optimal analysis method. This allows for a detailed analysis of a user's past travel history, the extraction of specific patterns, and the selection of the optimal analysis method. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into a generating AI and have the generating AI perform the extraction of specific patterns and the selection of the optimal analysis method.

[0080] The analysis unit can suggest the most suitable mode of transportation considering the user's current health status and fatigue level. For example, if the user is tired, the analysis unit will suggest the most comfortable mode of transportation. For example, the analysis unit will analyze the user's vital data, such as heart rate and steps, to estimate their fatigue level. The analysis unit can also suggest modes of transportation such as walking or cycling if the user is seeking healthy exercise. For example, the analysis unit will evaluate the user's health status based on self-reported data and suggest the most suitable mode of transportation. The analysis unit can also suggest the quickest mode of transportation if the user is feeling unwell. For example, the analysis unit will analyze the user's sleep patterns, detect unwellness, and suggest the most suitable mode of transportation. This allows the analysis unit to suggest the most suitable mode of transportation considering the user's current health status and fatigue level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's vital data into a generating AI and have the generating AI perform the evaluation of health status and fatigue level and suggest the most suitable mode of transportation.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also provide a concise display method if the user is in a hurry. For example, the analysis unit can analyze the user's text message and estimate the emotion. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the AI ​​adjust the display method based on that emotion.

[0082] The analysis unit can analyze region-specific travel patterns by considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can analyze the transportation methods specific to that region. For example, the analysis unit can determine the user's current location using GPS data and analyze the transportation methods common in that region. The analysis unit can also analyze travel patterns specific to tourist destinations if the user is in a tourist area. For example, the analysis unit can obtain traffic information for the tourist area and propose the optimal travel plan. The analysis unit can also analyze travel patterns specific to urban areas if the user is in an urban area. For example, the analysis unit can analyze urban traffic volume data and propose the optimal transportation method. This allows for the analysis of region-specific travel patterns by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis of region-specific travel patterns.

[0083] The analysis unit can obtain relevant travel data by referring to the user's social media activity. For example, the analysis unit can suggest an optimal travel plan based on the locations where the user has checked in on social media. For example, the analysis unit can analyze the content of the user's social media posts and obtain relevant travel data. The analysis unit can also suggest an optimal travel plan by referring to the travel history of the user's social media friends. For example, the analysis unit can suggest an optimal travel plan based on the places the user's friends have visited in the past. This allows the analysis unit to obtain relevant travel data by referring to the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of relevant travel data.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can present suggestions at a relaxed pace. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is in a hurry, the suggestion unit can also present suggestions that emphasize the shortest route. For example, it can record the user's voice and estimate their emotions using voice analysis technology. If the user is excited, the suggestion unit can also present suggestions with visually stimulating effects. For example, it can analyze the user's text messages and estimate their emotions. This allows the suggestion unit to adjust the way it presents its suggestions 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. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way proposals are expressed based on those emotions.

[0085] The suggestion unit can select the optimal mode of transportation based on the user's past travel history. For example, the suggestion unit can suggest the optimal mode of transportation based on the mode of transport the user has used in the past. For example, the suggestion unit can suggest the optimal mode of transportation for a given time of day based on the user's past travel time. The suggestion unit can also suggest the optimal mode of transportation for a specific day of the week or time of day based on the user's past travel patterns. For example, the suggestion unit can select the optimal mode of transportation based on the user's past travel history. This allows the optimal mode of transportation to be selected based on the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI select the optimal mode of transportation.

[0086] The suggestion unit can generate an optimal travel plan considering the user's current schedule and appointments. For example, the suggestion unit can refer to the user's calendar information and propose an optimal travel plan based on the appointments. For example, the suggestion unit can obtain data from the user's calendar app and generate an optimal travel plan based on the appointments. The suggestion unit can also propose the optimal mode of transportation according to the user's schedule. For example, the suggestion unit can propose the optimal mode of transportation based on the user's calendar. The suggestion unit can also propose a plan that optimizes travel time according to the user's appointments. For example, the suggestion unit can generate an optimal travel plan considering the user's meeting schedules and event attendance plans. This allows the suggestion unit to generate an optimal travel plan considering the user's current schedule and appointments. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's schedule data into a generation AI and have the generation AI execute the generation of an optimal travel plan.

[0087] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is nervous, the suggestion unit will prioritize the most important suggestions. For example, the suggestion unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit may also prioritize detailed suggestions if the user is relaxed. For example, the suggestion unit may record the user's voice and estimate their emotions using voice analysis technology. The suggestion unit may also prioritize suggestions that can be acted upon quickly if the user is in a hurry. For example, the suggestion unit may analyze the user's text messages and estimate their emotions. This allows the suggestion unit to prioritize suggestions 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. Some or all of the above-described processes in the suggestion unit may be performed using AI or not using AI. For example, the proposal department can input user emotion data into a generating AI and have the AI ​​determine the priority of proposals based on those emotions.

[0088] The suggestion unit can propose region-specific modes of transportation, taking into account the user's geographical location. For example, if the user is in a specific region, the suggestion unit can propose transportation methods specific to that region. For example, the suggestion unit can determine the user's current location using GPS data and propose common modes of transportation in that region. The suggestion unit can also propose transportation methods specific to tourist destinations if the user is in a tourist destination. For example, the suggestion unit can obtain traffic information for the tourist destination and propose the most suitable mode of transportation. The suggestion unit can also propose transportation methods specific to urban areas if the user is in an urban area. For example, the suggestion unit can analyze urban traffic volume data and propose the most suitable mode of transportation. This allows the suggestion unit to propose region-specific modes of transportation, taking into account the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI execute the suggestion of region-specific modes of transportation.

[0089] The suggestion unit can propose relevant travel plans by referring to the user's social media activity. For example, the suggestion unit can propose the optimal travel plan based on the locations the user has checked into on social media. For example, the suggestion unit can analyze the content of the user's social media posts and propose relevant travel plans. The suggestion unit can also propose the optimal travel plan by referring to the travel history of the user's social media friends. For example, the suggestion unit can propose the optimal travel plan based on the places the user's friends have visited in the past. This allows the suggestion unit to propose relevant travel plans by referring to the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of relevant travel plans.

[0090] The support unit can estimate the user's emotions and adjust the tone and content of the conversation based on the estimated emotions. For example, if the user is nervous, the support unit can conduct the conversation in a calm tone. For example, the support unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit can also conduct the conversation in a cheerful tone if the user is relaxed. For example, the support unit can record the user's voice and estimate their emotions using voice analysis technology. The support unit can also conduct a quick and concise conversation if the user is in a hurry. For example, the support unit can analyze the user's text messages and estimate their emotions. This allows the tone and content of the conversation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​adjust the tone and content of the dialogue based on those emotions.

[0091] The support unit can select the optimal support method by referring to the user's past conversation history. For example, the support unit provides the optimal support based on the support methods the user has used in the past. For example, the support unit analyzes the user's past conversation history and selects the optimal support method. The support unit can also propose the optimal support method for a specific problem based on the user's past conversation history. For example, the support unit proposes the optimal support method based on problems the user has experienced in the past. This allows the support unit to select the optimal support method by referring to the user's past conversation history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past conversation history data into a generating AI and have the generating AI select the optimal support method.

[0092] The support unit can provide optimal dialogue content considering the user's current situation and environment. For example, if the user is on the move, the support unit can provide concise and quick dialogue. For example, the support unit can acquire the user's current location information and detect that the user is on the move. The support unit can also provide detailed dialogue content if the user is relaxed. For example, the support unit can detect the noise level around the user using sensors and determine that the user is relaxed. The support unit can also provide dialogue content that offers a quick solution if the user is in a hurry. For example, the support unit can analyze the speed of the user's voice and determine that the user is in a hurry. This allows the support unit to provide optimal dialogue content considering the user's current situation and environment. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current situation data into a generating AI and have the generating AI perform the task of providing optimal dialogue content.

[0093] The support unit can estimate the user's emotions and determine the priority of conversations based on the estimated emotions. For example, if the user is nervous, the support unit will prioritize the most important conversations. For example, the support unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit may also prioritize detailed conversations if the user is relaxed. For example, the support unit may record the user's voice and estimate their emotions using voice analysis technology. The support unit may also prioritize conversations that provide quick solutions if the user is in a hurry. For example, the support unit may analyze the user's text messages and estimate their emotions. This allows the support unit to determine the priority of conversations 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. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generative AI and have the AI ​​determine the priority of dialogue based on those emotions.

[0094] The support unit can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, the support unit can provide region-specific information. For example, the support unit can use the user's GPS data to determine the current location and provide general information for that region. The support unit can also provide tourist destination-specific information if the user is in a tourist destination. For example, the support unit can provide event information and traffic information for the tourist destination. The support unit can also provide urban area-specific information if the user is in an urban area. For example, the support unit can provide urban traffic volume data and event information. This allows the support unit to provide region-specific information by taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0095] The support unit can provide relevant information by referring to the user's social media activity. For example, the support unit can provide relevant information based on the locations the user has checked into on social media. For example, the support unit can analyze the content of the user's social media posts and provide relevant information. The support unit can also provide relevant information by referring to information about the user's social media friends. For example, the support unit can provide relevant information based on places the user's friends have visited in the past. This allows the support unit to provide relevant information by referring to the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant information.

[0096] The update unit can estimate the user's emotions and adjust the update frequency based on the estimated emotions. For example, if the user is stressed, the update unit can increase the update frequency to provide the latest information. For example, the update unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The update unit can also adjust the update frequency to provide flexible information if the user is relaxed. For example, the update unit can record the user's voice and estimate their emotions using voice analysis technology. The update unit can also provide quick updates to provide the latest information if the user is in a hurry. For example, the update unit can analyze the user's text messages and estimate their emotions. This allows the update frequency to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user emotion data into a generating AI and have the generating AI adjust the update frequency based on that emotion.

[0097] The update unit can select the optimal update method by referring to the user's past travel history. For example, the update unit can propose the optimal update method based on routes the user has used in the past. For example, the update unit can analyze the user's past travel history and propose an update method to avoid congestion. The update unit can also propose the most efficient update method from the user's past travel history. For example, the update unit can select the optimal update method based on the user's past travel routes. This allows the update unit to select the optimal update method by referring to the user's past travel history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's past travel history data into a generating AI and have the generating AI select the optimal update method.

[0098] The update unit can provide optimal updates considering the user's current situation and environment. For example, if the user is on the move, the update unit can provide real-time updates to deliver the latest information. For example, the update unit can acquire the user's current location information and detect that the user is on the move. The update unit can also provide detailed updates if the user is relaxed. For example, the update unit can detect the noise level around the user using sensors and determine that the user is relaxed. The update unit can also provide rapid updates to deliver the latest information if the user is in a hurry. For example, the update unit can analyze the speed of the user's speech and determine that the user is in a hurry. This allows the update unit to provide optimal updates considering the user's current situation and environment. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's current situation data into a generating AI and have the generating AI execute the provision of optimal updates.

[0099] The update unit can estimate the user's emotions and determine update priorities based on the estimated emotions. For example, if the user is tense, the update unit will prioritize updating the most important information. For example, the update unit may capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The update unit may also prioritize updating detailed information if the user is relaxed. For example, the update unit may record the user's voice and estimate the emotion using voice analysis technology. The update unit may also prioritize updating information that can be quickly processed if the user is in a hurry. For example, the update unit may analyze the user's text message and estimate the emotion. This allows the update unit to determine update priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user emotion data into a generating AI, which can then perform the task of determining the priority of updates based on that emotion.

[0100] The update unit can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, the update unit can provide region-specific information. For example, the update unit can use the user's GPS data to determine the current location and provide general information for that region. The update unit can also provide tourist destination-specific information if the user is in a tourist destination. For example, the update unit can provide event information and traffic information for the tourist destination. The update unit can also provide urban area-specific information if the user is in an urban area. For example, the update unit can provide urban traffic volume data and event information. This allows the update unit to provide region-specific information by taking into account the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0101] The update unit can provide relevant information by referring to the user's social media activity. For example, the update unit can provide relevant information based on places the user has checked in to on social media. For example, the update unit can analyze the content of the user's social media posts and provide relevant information. The update unit can also provide relevant information by referring to information about the user's social media friends. For example, the update unit can provide relevant information based on places the user's friends have visited in the past. This allows the update unit to provide relevant information by referring to the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant information.

[0102] The assistant unit can estimate the user's emotions and adjust the content of its dialogue based on the estimated emotions. For example, if the user is nervous, the assistant unit will converse in a calm tone. For example, the assistant unit may capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The assistant unit can also converse in a cheerful tone if the user is relaxed. For example, the assistant unit may record the user's voice and estimate the emotion using voice analysis technology. The assistant unit can also converse quickly and concisely if the user is in a hurry. For example, the assistant unit may analyze the user's text message and estimate the emotion. This allows the content of the assistant's dialogue to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input user emotion data into a generating AI and have the AI ​​adjust the dialogue content based on those emotions.

[0103] The assistant unit can select the optimal assistance method by referring to the user's past dialogue history. For example, the assistant unit provides the optimal assistance based on the assistance methods the user has used in the past. For example, the assistant unit analyzes the user's past dialogue history and selects the optimal assistance method. The assistant unit can also propose the optimal assistance method for a specific problem based on the user's past dialogue history. For example, the assistant unit proposes the optimal assistance method based on problems the user has experienced in the past. This allows the assistant unit to select the optimal assistance method by referring to the user's past dialogue history. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the user's past dialogue history data into a generating AI and have the generating AI perform the selection of the optimal assistance method.

[0104] The assistant unit can estimate the user's emotions and determine the priority of assistants based on the estimated emotions. For example, if the user is nervous, the assistant unit will prioritize the most important assistant. For example, the assistant unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The assistant unit may also prioritize detailed assistants if the user is relaxed. For example, the assistant unit may record the user's voice and estimate their emotions using voice analysis technology. The assistant unit may also prioritize assistants that can provide quick solutions if the user is in a hurry. For example, the assistant unit may analyze the user's text messages and estimate their emotions. This allows the assistant unit to determine the priority of assistants based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input user emotion data into a generating AI, which can then perform the task of prioritizing the assistant based on those emotions.

[0105] The assistant unit can provide region-specific information by taking into account the user's geographical location. For example, if the user is in a specific region, the assistant unit can provide region-specific information. For example, the assistant unit can use the user's GPS data to determine the current location and provide general information for that region. The assistant unit can also provide tourist-specific information if the user is in a tourist area. For example, the assistant unit can provide event information and traffic information for the tourist area. The assistant unit can also provide urban-specific information if the user is in an urban area. For example, the assistant unit can provide urban traffic volume data and event information. This allows the assistant unit to provide region-specific information by taking into account the user's geographical location. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0106] The troubleshooting unit can estimate the user's emotions and adjust its troubleshooting method based on the estimated emotions. For example, if the user is tense, the troubleshooting unit will troubleshoot in a calm tone. For example, the troubleshooting unit may capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The troubleshooting unit can also troubleshoot in a cheerful tone if the user is relaxed. For example, the troubleshooting unit may record the user's voice and estimate their emotions using voice analysis technology. The troubleshooting unit can also troubleshoot quickly and concisely if the user is in a hurry. For example, the troubleshooting unit may analyze the user's text message and estimate their emotions. This allows the troubleshooting method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input user emotion data into a generating AI and have the AI ​​adjust the troubleshooting method based on those emotions.

[0107] The troubleshooting unit can select the optimal solution by referring to the user's past troubleshooting history. For example, the troubleshooting unit can propose the optimal solution based on the user's past troubleshooting experiences. For example, the troubleshooting unit can analyze the user's past troubleshooting history and select the optimal solution. The troubleshooting unit can also propose the optimal solution for a specific problem based on the user's past troubleshooting history. For example, the troubleshooting unit can propose the optimal solution based on the user's past troubleshooting experiences. This allows the system to select the optimal solution by referring to the user's past troubleshooting history. Some or all of the above processing in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's past troubleshooting history data into a generating AI and have the generating AI select the optimal solution.

[0108] The troubleshooting unit can estimate the user's emotions and determine the priority of troubleshooting based on the estimated emotions. For example, if the user is tense, the troubleshooting unit will prioritize the most important troubleshooting. For example, the troubleshooting unit may capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The troubleshooting unit may also prioritize detailed troubleshooting if the user is relaxed. For example, the troubleshooting unit may record the user's voice and estimate their emotions using voice analysis technology. The troubleshooting unit may also prioritize troubleshooting that provides a quick solution if the user is in a hurry. For example, the troubleshooting unit may analyze the user's text messages and estimate their emotions. This allows the troubleshooting unit to determine the priority of troubleshooting based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input user emotion data into a generating AI, which can then perform the task of determining the priority of troubleshooting based on those emotions.

[0109] The troubleshooting unit can provide region-specific solutions by considering the user's geographical location. For example, if the user is in a specific region, the troubleshooting unit can provide region-specific solutions. For instance, the troubleshooting unit can use the user's GPS data to determine their current location and provide general solutions for that region. Furthermore, if the user is in a tourist area, the troubleshooting unit can provide tourist area-specific solutions. For example, the troubleshooting unit can provide event and traffic information for the tourist area. Also, if the user is in an urban area, the troubleshooting unit can provide urban area-specific solutions. For example, the troubleshooting unit can provide urban traffic volume data and event information. This allows the system to provide region-specific solutions by considering the user's geographical location. Some or all of the above processing in the troubleshooting unit may be performed using AI, or not. For example, the troubleshooting unit can input the user's geographical location data into a generating AI and have the generating AI provide region-specific solutions.

[0110] The troubleshooting unit can refer to the user's social media activity and provide relevant solutions. For example, the troubleshooting unit can suggest the best solution based on problems reported by the user on social media. For example, the troubleshooting unit can analyze the content of the user's social media posts and provide relevant solutions. The troubleshooting unit can also refer to the trouble history of the user's social media friends and suggest the best solution. For example, the troubleshooting unit can provide relevant solutions based on problems experienced by the user's friends in the past. This allows the troubleshooting unit to refer to the user's social media activity and provide relevant solutions. Some or all of the above processing in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant solutions.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The travel plan provision system can also include a health management unit that monitors the user's health condition and provides the optimal travel plan. The health management unit acquires vital data such as the user's heart rate, blood pressure, and steps in real time and evaluates the user's health condition. For example, if the user is tired, the health management unit can suggest the most comfortable mode of transportation. If the user is seeking healthy exercise, it can also suggest modes of transportation such as walking or cycling. Furthermore, if the user is feeling unwell, it can suggest the quickest way to travel. This allows the system to provide the optimal travel plan tailored to the user's health condition.

[0113] The travel plan provision system may also include an emotion adjustment unit that estimates the user's emotions and adjusts the travel plan based on those emotions. The emotion adjustment unit analyzes the user's facial expressions and voice to estimate their emotions. For example, if the user is feeling stressed, the emotion adjustment unit can suggest a relaxing mode of transportation. If the user is relaxed, the emotion adjustment unit can also suggest an efficient mode of transportation. Furthermore, if the user is in a hurry, the emotion adjustment unit can suggest a mode of transportation that will get them there in the shortest time. This allows the system to provide an optimal travel plan tailored to the user's emotions.

[0114] The travel plan provision system may also include a social media integration unit that references the user's social media activity and proposes relevant travel plans. The social media integration unit analyzes locations and posts the user has made on social media and proposes relevant travel plans. For example, if a user wants to go to the same place as a friend, the social media integration unit can propose the optimal travel plan based on the friend's travel history. It can also propose relevant travel plans based on places the user has visited in the past. This allows the system to provide optimal travel plans based on the user's social media activity.

[0115] The travel plan provision system may further include a schedule integration unit that generates an optimal travel plan considering the user's current schedule and appointments. The schedule integration unit refers to the user's calendar information and proposes the optimal travel plan based on their appointments. For example, it can generate an optimal travel plan considering the user's meeting schedules and event attendance plans. It can also propose the most suitable mode of transportation according to the user's schedule. This allows the system to provide an optimal travel plan that takes into account the user's current schedule and appointments.

[0116] The travel plan provision system may further include a region-specific unit that proposes region-specific modes of transportation, taking into account the user's geographical location. The region-specific unit proposes transportation methods specific to a particular region if the user is in that area. For example, if the user is in a tourist area, it can propose transportation methods specific to that area. Similarly, if the user is in an urban area, it can propose transportation methods specific to that urban area. This allows the system to provide region-specific transportation options that take the user's geographical location into account.

[0117] The travel plan provision system may also include a dialogue adjustment unit that estimates the user's emotions and adjusts the tone and content of the conversation based on those estimated emotions. The dialogue adjustment unit analyzes the user's facial expressions and voice to estimate their emotions. For example, if the user is nervous, the dialogue adjustment unit can conduct the conversation in a calm tone. If the user is relaxed, it can conduct the conversation in a cheerful tone. Furthermore, if the user is in a hurry, it can conduct a quick and concise conversation. This allows the system to provide the most appropriate conversation based on the user's emotions.

[0118] The mobility plan provision system may also include a dialogue history unit that selects the optimal support method by referring to the user's past dialogue history. The dialogue history unit provides optimal support based on the support methods the user has used in the past. For example, it can analyze the user's past dialogue history and propose the optimal support method for a specific problem. It can also propose the optimal support method based on problems the user has experienced in the past. This allows for the provision of optimal support that references the user's past dialogue history.

[0119] The travel plan provision system may further include a priority adjustment unit that estimates the user's emotions and determines the priority of suggestions based on those emotions. The priority adjustment unit analyzes the user's facial expressions and voice to estimate their emotions. For example, if the user is tense, the most important suggestions can be prioritized. If the user is relaxed, detailed suggestions can be prioritized. Furthermore, if the user is in a hurry, suggestions that can be implemented quickly can be prioritized. This allows the system to provide an optimal priority of suggestions based on the user's emotions.

[0120] The travel plan provision system may further include a regional information unit that provides region-specific information considering the user's geographical location. The regional information unit provides region-specific information when the user is in a specific area. For example, if the user is in a tourist area, it can provide event information and traffic information for that area. Similarly, if the user is in an urban area, it can provide urban traffic volume data and event information. This allows for the provision of region-specific information that takes the user's geographical location into account.

[0121] The travel plan provision system may also include a trouble emotion adjustment unit that estimates the user's emotions and adjusts the trouble response method based on the estimated emotions. The trouble emotion adjustment unit analyzes the user's facial expressions and voice to estimate their emotions. For example, if the user is tense, the trouble response can be handled in a calm tone. If the user is relaxed, the trouble response can be handled in a cheerful tone. Furthermore, if the user is in a hurry, the trouble response can be handled quickly and concisely. This allows for the provision of optimal trouble response based on the user's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The analysis unit analyzes the user's past behavior and current situation. The user's past behavior includes past travel history and previously selected modes of transportation. The analysis unit retrieves the user's past travel history from the database and analyzes past travel patterns. The analysis unit also obtains the user's current location information and current traffic conditions in real time and analyzes the current situation. For example, it uses GPS data to determine the user's current location and a traffic information API to obtain current traffic conditions. Step 2: The proposal department proposes the optimal route and mode of transportation based on the data analyzed by the analysis department. The proposal department selects the optimal route based on criteria such as shortest time, shortest distance, and lowest cost. The proposal department also generates the optimal travel plan by combining modes of transportation such as trains, buses, taxis, and bicycles. For example, it may propose the optimal mode of transportation based on the user's past travel history. Step 3: The support department provides interactive support utilizing generative AI. The support department answers user questions and requests through a voice-activated assistant. The generative AI uses a specific AI algorithm to generate the best possible answers to user questions. The support department also responds quickly if the user wants to change their plans or encounters problems while traveling. For example, it can grasp the user's current situation in real time and propose a new travel plan. Step 4: The update unit acquires real-time traffic information and updates the travel plan. The update unit acquires congestion and delay information using traffic information APIs and sensor data. Based on the acquired real-time traffic information, it updates the travel plan and suggests the optimal route to the user. For example, if the user encounters congestion while traveling, it acquires real-time congestion information and suggests the optimal detour route.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the analysis unit, proposal unit, support unit, update unit, assistant unit, and troubleshooting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit can analyze the user's past behavior and current situation using the control unit 46A of the smart device 14. The proposal unit can propose the optimal route and means of transportation using the specific processing unit 290 of the data processing unit 12. The support unit can provide interactive support utilizing generated AI using the control unit 46A of the smart device 14. The update unit can acquire real-time traffic information and update the travel plan using the specific processing unit 290 of the data processing unit 12. The assistant unit can answer the user's questions and requests through a voice-activated assistant using the control unit 46A of the smart device 14. The troubleshooting unit can quickly respond to problems during travel using the specific processing unit 290 of the data processing unit 12. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the analysis unit, proposal unit, support unit, update unit, assistant unit, and troubleshooting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit can analyze the user's past behavior and current situation using the control unit 46A of the smart glasses 214. The proposal unit can propose the optimal route and means of transportation using the specific processing unit 290 of the data processing unit 12. The support unit can provide interactive support utilizing generated AI using the control unit 46A of the smart glasses 214. The update unit can acquire real-time traffic information and update the travel plan using the specific processing unit 290 of the data processing unit 12. The assistant unit can answer the user's questions and requests through a voice-activated assistant using the control unit 46A of the smart glasses 214. The troubleshooting unit can quickly respond to problems during travel using the specific processing unit 290 of the data processing unit 12. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

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

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

[0154] In 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.

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

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0158] The data processing system 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.

[0159] Each of the multiple elements described above, including the analysis unit, proposal unit, support unit, update unit, assistant unit, and troubleshooting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit can analyze the user's past behavior and current situation using the control unit 46A of the headset terminal 314. The proposal unit can propose the optimal route and means of transportation using the specific processing unit 290 of the data processing unit 12. The support unit can provide interactive support utilizing generated AI using the control unit 46A of the headset terminal 314. The update unit can acquire real-time traffic information and update the travel plan using the specific processing unit 290 of the data processing unit 12. The assistant unit can answer the user's questions and requests through a voice-activated assistant using the control unit 46A of the headset terminal 314. The troubleshooting unit can quickly respond to problems during travel using the specific processing unit 290 of the data processing unit 12. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the analysis unit, proposal unit, support unit, update unit, assistant unit, and troubleshooting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit can analyze the user's past actions and current situation using the control unit 46A of the robot 414. The proposal unit can propose the optimal route and means of transportation using the specific processing unit 290 of the data processing unit 12. The support unit can provide interactive support utilizing generated AI using the control unit 46A of the robot 414. The update unit can acquire real-time traffic information and update the travel plan using the specific processing unit 290 of the data processing unit 12. The assistant unit can answer the user's questions and requests through a voice-interactive assistant using the control unit 46A of the robot 414. The troubleshooting unit can quickly respond to problems during travel using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The analysis department analyzes the user's past behavior and current situation, Based on the data analyzed by the aforementioned analysis unit, the proposal unit proposes the optimal route and means of transportation. The support department provides interactive support using generation AI, It includes an update unit that acquires real-time traffic information and updates the travel plan. A system characterized by the following features. (Note 2) It features an assistant unit that answers user questions and requests through a voice-activated assistant. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a troubleshooting unit to quickly respond to problems during transit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The system generates the optimal travel plan based on the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned update unit is, Get real-time traffic and delay information and update your travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We analyze the user's past movement history in detail, extract specific patterns, and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The system suggests the most suitable mode of transportation, taking into account the user's current health condition and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is Analyze region-specific movement patterns by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is We retrieve relevant movement data by referring to the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, The system selects the optimal mode of transportation based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It generates the optimal travel plan considering the user's current schedule and plans. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It suggests region-specific transportation options, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, We suggest relevant travel plans based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is It estimates the user's emotions and adjusts the tone and content of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is The system selects the most suitable support method by referring to the user's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is Provides optimal dialogue content considering the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is Provide region-specific information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is Refer to the user's social media activity to provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is, It estimates user sentiment and adjusts the update frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is, The system selects the optimal update method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is, We provide the most suitable updates considering the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is, It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is, Provide region-specific information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is, Refer to the user's social media activity to provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned assistant section is It estimates the user's emotions and adjusts the assistant's dialogue based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned assistant section is The system selects the optimal assistant method by referring to the user's past conversation history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned assistant section is It estimates the user's emotions and determines the assistant's priority based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned assistant section is Provide region-specific information, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned troubleshooting unit is: It estimates the user's emotions and adjusts the troubleshooting method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned troubleshooting unit is: The optimal solution is selected by referring to the user's past trouble history. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned troubleshooting unit is: The system estimates the user's emotions and determines the priority of troubleshooting based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned troubleshooting unit is: Provide region-specific responses that take into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned troubleshooting unit is: Refer to the user's social media activity and provide relevant response methods. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0196] 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 analysis department analyzes the user's past behavior and current situation, Based on the data analyzed by the aforementioned analysis unit, the proposal unit proposes the optimal route and means of transportation. The support department provides interactive support using generative AI, It includes an update unit that acquires real-time traffic information and updates the travel plan. A system characterized by the following features.

2. It features an assistant unit that answers user questions and requests through a voice-activated assistant. The system according to feature 1.

3. Equipped with a troubleshooting unit to quickly respond to problems during transit. The system according to feature 1.

4. The aforementioned proposal section is, The system generates the optimal travel plan based on the user's past behavioral data. The system according to feature 1.

5. The aforementioned update unit is Get real-time traffic and delay information and update your travel plan. The system according to feature 1.

6. The aforementioned analysis unit is It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is We analyze the user's past movement history in detail, extract specific patterns, and select the optimal analysis method. The system according to feature 1.

8. The aforementioned analysis unit is The system suggests the most suitable mode of transportation, taking into account the user's current health condition and fatigue level. The system according to feature 1.

9. The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit is Analyze region-specific movement patterns by considering the user's geographical location. The system according to feature 1.

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