system

Abstract

The system according to this embodiment aims to propose the optimal travel route by considering various pieces of information such as destination, current location, weather, and traffic conditions. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit receives destination input. The collection unit collects information such as current location, schedule, weather, and traffic conditions based on the destination information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal travel route considering multi-faceted information such as the destination, current location, climate, and traffic conditions has not been sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal travel route in consideration of multi-faceted information such as the destination, current location, climate, and traffic conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, and a proposal unit. The reception unit receives destination input. The data collection unit collects information such as current location, schedule, weather, and traffic conditions based on the destination information received by the reception unit. The analysis unit analyzes the information collected by the data collection unit and calculates the optimal travel route. The proposal unit proposes the optimal travel route calculated by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal travel route by considering various pieces of information such as destination, current location, weather, and traffic conditions. [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 manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the 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 optimal route guidance system according to an embodiment of the present invention is a system that uses a generating AI to analyze multifaceted information and propose the optimal travel route. The optimal route guidance system collects and analyzes information such as destination, current location, schedule, weather, and traffic conditions to propose the optimal route for all modes of transportation, including walking, cycling, public transport, and driving. First, the user inputs their destination. Next, the generating AI collects and analyzes information such as the current location, schedule, weather, and traffic conditions. Based on this information, the generating AI calculates the optimal travel route and proposes it to the user. For example, if walking is the optimal mode of transportation, the generating AI proposes a pedestrian-only route, and if traffic congestion is expected, it recommends using public transport. Furthermore, the generating AI proposes a route that takes into account travel time and arrival time based on the user's schedule. For example, if the user has a meeting to attend, the generating AI calculates the optimal route to arrive on time for the meeting. It can also consider weather information and prioritize routes with roofs in rainy weather. This system allows users to select the optimal route for all modes of transportation, thereby improving the efficiency of travel. For example, if cycling is the optimal mode of transport, the generating AI will suggest a dedicated cycling route, thus avoiding traffic congestion. Similarly, if using public transport, the generating AI will suggest the most efficient transfer route, reducing travel time. In this way, the optimal route guidance system uses generating AI to analyze multifaceted information and propose the most suitable travel route to the user, improving the efficiency and convenience of travel. Thus, the optimal route guidance system can streamline and improve the convenience of user travel.

[0029] The optimal route guidance system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, and a suggestion unit. The reception unit receives destination input. For example, the user can input the destination in text format. The reception unit can also accept voice input. For example, the user can input the destination by voice and convert it to text using voice recognition technology. Furthermore, the reception unit can also suggest destination candidates based on past destination history. For example, it can automatically display places the user has visited in the past as candidates. The data collection unit collects information such as current location, schedule, weather, and traffic conditions based on the destination information received by the reception unit. For example, the data collection unit obtains the current location using GPS. Furthermore, the data collection unit can obtain schedule information from calendar applications or schedule management systems. Furthermore, the data collection unit can obtain weather information based on meteorological data and weather forecasts. For example, the data collection unit obtains the latest weather information from weather databases on the internet. Regarding traffic conditions, the data collection unit can obtain traffic congestion information and the operating status of public transportation. For example, the data collection unit obtains real-time traffic conditions from traffic information provision services. The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. The analysis unit calculates the optimal route based on the collected information, for example, using a generation AI. The generation AI uses a text generation AI (e.g., LLM) to calculate the travel route. The analysis unit can also use a multimodal generation AI to integrate and analyze multiple pieces of information. For example, the analysis unit integrates information such as current location, schedule, weather, and traffic conditions to calculate the optimal route. The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user. The suggestion unit displays route guidance on the user's smartphone, for example. The suggestion unit can also provide route guidance by voice. For example, the suggestion unit uses speech synthesis technology to provide route guidance to the user by voice. Furthermore, the suggestion unit can propose a route that takes into account travel time and arrival time based on the user's schedule. For example, the suggestion unit calculates and proposes the optimal route to arrive on time for the start of a meeting.As a result, the optimal route guidance system according to the embodiment can consistently perform everything from inputting the destination to proposing the optimal travel route.

[0030] The reception desk accepts destination input. For example, users can input their destination in text format. The reception desk can also accept voice input. For example, a user can input their destination by voice, and speech recognition technology can convert it to text. Furthermore, the reception desk can suggest destinations based on past destination history. For example, it can automatically display places the user has visited in the past as suggestions. The reception desk provides various means for accepting destination input through the user interface. For text input, users can input their destination using a keyboard or touchscreen. For voice input, voice is collected through a microphone, and a speech recognition engine converts the voice to text. The speech recognition engine uses natural language processing technology to accurately understand the user's speech and convert it into appropriate text. Furthermore, the reception desk has a function that analyzes the user's past behavior history and automatically suggests destinations based on frequently visited places and previously entered destinations. This allows users to select destinations quickly and easily. For example, it can automatically display the addresses of restaurants or offices the user has visited in the past as suggestions, allowing for one-click selection. This significantly improves user convenience.

[0031] The data collection unit collects information such as current location, schedule, weather, and traffic conditions based on destination information received by the reception unit. For example, the data collection unit obtains the current location using GPS. It can also obtain schedule information from calendar apps and schedule management systems. Furthermore, the data collection unit can obtain weather information based on meteorological data and weather forecasts. For example, the data collection unit obtains the latest weather information from online weather databases. Regarding traffic conditions, the data collection unit can obtain traffic congestion information and public transport operating status. For example, the data collection unit obtains real-time traffic conditions from traffic information services. The data collection unit has an interface for centrally managing this information and providing it to the analysis unit. Obtaining the current location using GPS is done using location information services from smartphones and in-car navigation systems. This allows for real-time tracking of the user's precise current location. Obtaining schedule information from calendar apps and schedule management systems is done with the user's permission, and detailed information such as the start and end times and location of the schedule is collected. Weather data is obtained from online weather databases and weather forecasting services, taking into account weather conditions that may affect the travel route. The collection of traffic conditions involves obtaining real-time traffic congestion information and public transport operation status from traffic information services, which are used as crucial data for calculating the optimal travel route. This allows the collection unit to efficiently collect diverse information related to the user's travel and provide it to the analysis unit.

[0032] The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. For example, the analysis unit uses a generative AI to calculate the optimal route based on the collected information. The generative AI uses a text generation AI (e.g., LLM) to calculate the travel route. The analysis unit can also use a multimodal generative AI to integrate and analyze multiple pieces of information. For example, the analysis unit integrates information such as current location, schedule, weather, and traffic conditions to calculate the optimal route. The generative AI learns from a vast dataset and uses advanced algorithms to generate the optimal route. Specifically, the text generation AI calculates the optimal route from the user's current location to their destination and generates a detailed description of that route. The multimodal generative AI can integrate and analyze multiple data sources, including not only text data but also image data and sensor data. This allows the analysis unit to provide more accurate and reliable route guidance. For example, the analysis unit integrates GPS data of the current location, calendar information of the schedule, weather data, and traffic congestion information to calculate the optimal travel route. Furthermore, the analysis unit can analyze past travel history and user travel patterns to propose routes optimized for individual users. This allows the analysis unit to provide customized route guidance tailored to the user's needs, improving the efficiency and comfort of travel.

[0033] The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user. The suggestion unit, for example, displays route guidance on the user's smartphone. The suggestion unit can also provide route guidance via voice. For example, it uses speech synthesis technology to provide route guidance to the user verbally. Furthermore, the suggestion unit can propose routes that take into account travel and arrival times based on the user's schedule. For example, it calculates and proposes the optimal route to ensure the user arrives on time for a meeting. The suggestion unit has various means of providing route guidance visually and audibly through the user interface. The route is displayed on a map on the smartphone screen, allowing the user to check their current location and progress toward their destination in real time. Voice guidance uses speech synthesis technology to inform the user of the next turn and direction of travel. This allows the user to rely on voice guidance even in situations where visual confirmation is difficult. Furthermore, the suggestion unit optimizes travel and arrival times by considering the user's schedule information. For example, it proposes the optimal departure time and travel route to ensure the user arrives on time for a meeting. This allows users to travel with ample time and reduce stress. The suggestion department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This enables the suggestion department to provide users with the optimal travel route, improving the efficiency and comfort of their journey.

[0034] The climate unit takes climate information into consideration. For example, it obtains the latest weather information from a weather database. The climate unit uses generative AI to analyze weather data and identify climate conditions that affect travel routes. For example, the climate unit prioritizes suggesting routes with roofs during rainy weather. It can also suggest comfortable travel routes based on information such as temperature and wind speed. For example, on hot days, the climate unit suggests routes with plenty of shade. This allows for the suggestion of optimal travel routes that take climate information into account.

[0035] The Ministry of Transportation takes traffic conditions into consideration. For example, it obtains real-time traffic information from traffic information services. The Ministry of Transportation uses generative AI to analyze traffic congestion information and the operating status of public transportation and calculates the optimal travel route. For example, the Ministry of Transportation recommends using public transportation when traffic congestion is expected. The Ministry of Transportation can also suggest detour routes, taking into account traffic accidents and construction information. For example, if a traffic accident occurs, the Ministry of Transportation will suggest a route that avoids its impact. This allows for the suggestion of the optimal travel route that takes traffic conditions into account.

[0036] The scheduling function considers travel and arrival times based on the user's schedule. For example, it retrieves schedule information from calendar apps or scheduling management systems. Using a generative AI, it analyzes the schedule information and calculates the optimal route considering travel and arrival times. For example, it calculates and proposes the best route to ensure the user arrives on time for a meeting. The scheduling function can also select a mode of transportation based on the user's schedule. For example, it might recommend using a taxi if the user needs to travel quickly. This allows the system to propose the optimal travel route based on the user's schedule.

[0037] The suggestion function proposes the optimal route based on the mode of transportation, such as walking, cycling, public transport, or driving. For example, if walking is the optimal mode of transport, the suggestion function will propose a pedestrian-only route. The suggestion function uses generative AI to calculate and propose a pedestrian-only route. The suggestion function can also propose a bicycle-only route if cycling is the optimal mode of transport. For example, the suggestion function will prioritize suggesting bicycle paths. Furthermore, if using public transport, the suggestion function can propose the optimal transfer route. For example, the suggestion function will calculate and propose the optimal route based on bus and train transfer information. This allows the system to propose the optimal route according to the mode of transport.

[0038] The reception desk analyzes the user's past destination history and suggests the optimal input method. For example, the reception desk automatically displays destinations that the user has frequently entered in the past as suggestions. The reception desk uses generative AI to analyze past destination history. For example, the generative AI analyzes the user's destination history data and identifies places that the user frequently visits. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest destinations that the user will use at specific times of day based on their past destination history. For example, the reception desk can predict places the user visits on specific days of the week and display them as destination suggestions. This allows the system to provide the optimal input method based on past destination history.

[0039] The reception desk, when a user enters a destination, presents input suggestions based on the user's current activities and areas of interest. For example, the reception desk may suggest destinations related to an event the user is currently attending. The reception desk uses generative AI to analyze the user's current activities. For example, the generative AI estimates the user's activities based on smartphone sensor information. The reception desk can also prioritize displaying relevant destinations based on the user's areas of interest. For example, the reception desk may analyze the user's social media posts and suggest places of interest. Furthermore, the reception desk can suggest relevant destinations based on the user's current activities (e.g., shopping). For example, if the user is in a shopping mall, the reception desk may suggest nearby restaurants or cafes. This allows the system to provide destination input suggestions based on the user's current activities and areas of interest.

[0040] The reception desk prioritizes and displays highly relevant destinations when the user enters a destination, taking into account the user's geographical location. For example, the reception desk prioritizes displaying destinations close to the user's current location. The reception desk uses generative AI to analyze the user's geographical location. For example, the generative AI identifies the user's current location based on GPS data. The reception desk can also suggest destinations with convenient transportation based on the user's current location. For example, if the reception desk is near a train station, it will prioritize displaying destinations around the station. Furthermore, the reception desk can also prioritize displaying popular tourist destinations based on the user's current location. For example, if the reception desk is in a tourist destination, it will suggest nearby tourist spots. This allows the system to provide destination input candidates based on the user's geographical location.

[0041] The reception desk analyzes the user's social media activity when they enter their destination and suggests relevant destinations. For example, the reception desk suggests relevant destinations based on places the user has checked in to on social media. The reception desk uses generative AI to analyze the user's social media activity. For example, the generative AI analyzes the content of social media posts to identify the user's interests. The reception desk can also analyze the content of the user's social media posts and suggest destinations that they might be interested in. For example, if the reception desk posts a lot about food, it will suggest restaurants and cafes. Furthermore, the reception desk can suggest relevant destinations based on places the user's friends have visited. For example, it will suggest tourist spots that the user's friends have visited. This allows the system to provide destination input suggestions based on the user's social media activity.

[0042] The data collection unit analyzes the user's past information gathering history and selects the optimal collection method. For example, the unit prioritizes collecting information that the user has frequently collected in the past. The data collection unit uses generative AI to analyze past information gathering history. For example, the generative AI analyzes the user's information gathering history data and identifies information that is frequently collected. The data collection unit can also prioritize the use of information gathering methods (APIs, websites, etc.) that the user has used in the past. For example, if the data collection unit has frequently used a particular API in the past, it will prioritize the use of that API. Furthermore, the data collection unit can predict and collect information that the user will collect at specific times of day based on their past information gathering history. For example, the data collection unit predicts and prioritizes the collection of information that the user will collect on specific days of the week. This allows the system to provide the optimal data collection method based on past information gathering history.

[0043] The data collection unit filters information based on the user's current lifestyle and areas of interest. For example, it prioritizes collecting relevant information based on the user's current lifestyle (e.g., at work). The data collection unit analyzes the user's lifestyle using generative AI. For example, the generative AI estimates the user's lifestyle based on data from smart home devices. The data collection unit can also filter relevant information based on the user's areas of interest. For example, it analyzes the user's social media posts and prioritizes collecting information of interest. Furthermore, the data collection unit can collect relevant information based on the user's current activities (e.g., traveling). For example, if the user is traveling, it prioritizes collecting tourist information about the travel destination. This allows for information collection based on the user's current lifestyle and areas of interest.

[0044] The data collection unit prioritizes collecting highly relevant information, taking into account the user's geographical location. For example, it prioritizes collecting information close to the user's current location. The data collection unit analyzes the user's geographical location using generative AI. For example, the generative AI identifies the user's current location based on GPS data. The data collection unit can also collect information on convenient transportation based on the user's current location. For example, if the user is near a train station, the data collection unit prioritizes collecting transportation information around the station. Furthermore, the data collection unit can prioritize collecting information on popular tourist destinations based on the user's current location. For example, if the user is in a tourist destination, the data collection unit collects information on nearby tourist spots. This enables the collection of information based on the user's geographical location.

[0045] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the unit collects information related to places the user has checked in to on social media. The data collection unit uses generative AI to analyze the user's social media activity. For example, the generative AI analyzes the content of social media posts to identify the user's interests. The data collection unit can also analyze the content of the user's social media posts and collect information that they might be interested in. For example, if the user posts a lot about food, the data collection unit will collect information about restaurants and cafes. Furthermore, the data collection unit can also collect information related to places the user's friends have visited. For example, the data collection unit will collect information about tourist destinations visited by the user's friends. This allows for data collection based on the user's social media activity.

[0046] The analysis unit improves the accuracy of the analysis by considering the interrelationships of information during the analysis. For example, the analysis unit performs analysis considering the interrelationships between traffic information and climate information. The analysis unit uses a generative AI to analyze the interrelationships of information. For example, the generative AI performs a correlation analysis between traffic information and climate information. The analysis unit can also perform analysis considering the interrelationships between planned information and current location information. For example, the analysis unit performs a network analysis of planned information and current location information. Furthermore, the analysis unit can also perform analysis considering the interrelationships between destination information and past travel history. For example, the analysis unit performs a correlation analysis between destination information and past travel history. This enables the provision of analysis that takes into account the interrelationships of information.

[0047] The analysis unit considers the attribute information of the information provider during the analysis. For example, the analysis unit considers the reliability of the traffic information provider during the analysis. The analysis unit uses a generation AI to analyze the attribute information of the information provider. For example, the generation AI evaluates the reliability of the traffic information provider. The analysis unit can also consider the reliability of the climate information provider during the analysis. For example, the analysis unit evaluates the reliability of the climate information provider. Furthermore, the analysis unit can also consider the reliability of the schedule information provider during the analysis. For example, the analysis unit evaluates the reliability of the schedule information provider. This allows for analysis that takes into account the attribute information of the information provider.

[0048] The analysis unit considers the geographical distribution of information during analysis. For example, the analysis unit considers the geographical distribution of traffic information. The analysis unit uses a generation AI to analyze the geographical distribution of information. For example, the generation AI identifies the geographical distribution of traffic information based on map data. The analysis unit can also consider the geographical distribution of climate information. For example, the analysis unit analyzes climate information by region. Furthermore, the analysis unit can also consider the geographical distribution of scheduled information. For example, the analysis unit analyzes scheduled information by region. This allows for analysis that takes the geographical distribution of information into account.

[0049] The analysis unit improves the accuracy of its analysis by referring to relevant literature during the analysis process. For example, when analyzing traffic information, the analysis unit refers to relevant research papers. The analysis unit uses a generative AI to analyze the relevant literature. For example, the generative AI retrieves relevant research papers from academic paper databases. The analysis unit can also refer to relevant meteorological data when analyzing climate information. For example, the analysis unit refers to data from the Japan Meteorological Agency. Furthermore, the analysis unit can also refer to relevant calendar data when analyzing schedule information. For example, the analysis unit refers to data from a calendar application. In this way, the accuracy of the analysis can be improved by referring to relevant literature.

[0050] The suggestion function adjusts the level of detail in its suggestions based on the importance of the mode of transportation. For example, if the user is walking, the suggestion function provides detailed route guidance. The suggestion function uses generative AI to analyze the importance of the mode of transportation. For example, the generative AI evaluates the importance of the mode of transportation. In addition, if the user is cycling, the suggestion function can also provide suggestions that include traffic safety information. For example, the suggestion function can suggest dedicated bicycle paths and safe routes. Furthermore, if the user is using public transport, the suggestion function can also provide suggestions that include transfer information. For example, the suggestion function can suggest the optimal route based on bus and train transfer information. This allows for a level of detail in suggestions that corresponds to the importance of the mode of transportation.

[0051] The suggestion function applies different suggestion algorithms depending on the mode of transport during the suggestion process. For example, for walking, the suggestion function applies an algorithm that suggests pedestrian-only routes. The suggestion function uses generative AI to analyze the mode of transport categories. For example, the generative AI classifies the modes of transport. The suggestion function can also apply an algorithm that suggests bicycle-only routes for cycling. For example, the suggestion function applies an algorithm that suggests bicycle-only paths and safe routes. Furthermore, for public transport, the suggestion function can apply an algorithm that suggests routes including transfer information. For example, the suggestion function applies an algorithm that suggests the optimal route based on bus and train transfer information. This allows the system to provide the optimal suggestion algorithm according to the mode of transport category.

[0052] The suggestion function prioritizes suggestions based on the timing of travel use. For example, it might prioritize suggesting the most recent mode of transport. The suggestion function uses generative AI to analyze the timing of travel use. For example, the generative AI identifies the timing of travel use based on the user's schedule data. The suggestion function can also suggest the most suitable mode of transport based on the user's schedule. For example, it suggests the optimal mode of transport to match the user's schedule. Furthermore, the suggestion function can suggest the optimal mode of transport based on the user's past travel history. For example, it might prioritize suggesting modes of transport that the user has frequently used in the past. This allows for prioritizing suggestions based on the timing of travel use.

[0053] The suggestion unit adjusts the order of suggestions based on the relevance of the modes of transportation. For example, the suggestion unit prioritizes suggesting modes of transportation that the user has used in the past. The suggestion unit uses generative AI to analyze the relevance of modes of transportation. For example, the generative AI identifies the relevance of modes of transportation based on the user's travel history data. The suggestion unit can also prioritize suggesting the mode of transportation that is most suitable for the user's current situation. For example, the suggestion unit suggests the optimal mode of transportation based on the user's current location and situation. Furthermore, the suggestion unit can suggest the most efficient mode of transportation based on the user's schedule. For example, the suggestion unit suggests the most efficient mode of transportation according to the user's schedule. This allows for an order of suggestions that is tailored to the relevance of the modes of transportation.

[0054] The Climate Department predicts the current climate by referencing past climate data when collecting climate information. For example, the Climate Department predicts the current climate based on past climate data. The Climate Department analyzes past climate data using generative AI. For example, generative AI identifies past climate patterns based on data from the Japan Meteorological Agency. The Climate Department can also predict the current climate by analyzing past climate patterns. For example, the Climate Department predicts the current climate by comparing past and current climate data. Furthermore, the Climate Department can make more accurate climate predictions by integrating past and current climate data. For example, the Climate Department builds a climate prediction model by integrating past and current climate data. This allows it to predict the current climate by referring to past climate data.

[0055] The climate unit collects climate information while considering its geographical distribution. For example, it collects the most relevant climate information based on the user's current location. The climate unit analyzes geographical distribution using generative AI. For example, the generative AI identifies the geographical distribution of climate information based on map data. The climate unit can also collect climate information based on the user's destination. For example, it prioritizes collecting climate information for the user's destination. Furthermore, the climate unit can collect climate information based on the user's travel route. For example, it collects climate information along the user's travel route. This enables the collection of climate information that takes geographical distribution into consideration.

[0056] The Ministry of Transportation predicts current traffic conditions by referencing past traffic data when collecting traffic information. For example, the Ministry of Transportation predicts current traffic conditions based on past traffic data. The Ministry of Transportation analyzes past traffic data using generative AI. For example, generative AI identifies past traffic patterns based on traffic volume survey data. The Ministry of Transportation can also predict current traffic conditions by analyzing past traffic patterns. For example, the Ministry of Transportation predicts current traffic conditions by comparing past and current traffic data. Furthermore, the Ministry of Transportation can make more accurate traffic predictions by integrating past and current traffic data. For example, the Ministry of Transportation builds a traffic prediction model by integrating past and current traffic data. This allows it to predict current traffic conditions by referring to past traffic data.

[0057] The Transportation Department collects traffic information while considering its geographical distribution. For example, it collects the most relevant traffic information based on the user's current location. The Transportation Department uses generative AI to analyze geographical distribution. For example, the generative AI identifies the geographical distribution of traffic information based on map data. The Transportation Department can also collect traffic information based on the user's destination. For example, it prioritizes collecting traffic information for the user's destination. Furthermore, the Transportation Department can collect traffic information based on the user's travel route. For example, it collects traffic information along the user's travel route. This allows for the collection of traffic information that takes geographical distribution into consideration.

[0058] The scheduling unit predicts current schedules by referencing past schedule data when collecting schedule information. For example, the scheduling unit predicts current schedules based on past schedule data. The scheduling unit analyzes past schedule data using generative AI. For example, the generative AI identifies past schedule patterns based on data from a calendar application. The scheduling unit can also predict current schedules by analyzing past schedule patterns. For example, the scheduling unit predicts current schedules by comparing past and current schedule data. Furthermore, the scheduling unit can integrate past and current schedule data to make more accurate schedule predictions. For example, the scheduling unit integrates past and current schedule data to build a schedule prediction model. This allows it to predict current schedules by referencing past schedule data.

[0059] The planning unit collects planning information while considering its geographical distribution. For example, it collects the most relevant planning information based on the user's current location. The planning unit analyzes the geographical distribution using generative AI. For example, the generative AI identifies the geographical distribution of planning information based on map data. The planning unit can also collect planning information based on the user's destination. For example, it prioritizes collecting planning information for the user's destination. Furthermore, the planning unit can also collect planning information based on the user's travel route. For example, it collects planning information along the user's travel route. This allows for the collection of planning information that takes geographical distribution into consideration.

[0060] The scheduling function, when collecting scheduling information, refers to the user's calendar information to make suggestions based on the schedule. For example, the scheduling function refers to the schedules registered in the user's calendar and automatically sets the departure and destination points. The scheduling function analyzes the calendar information using generative AI. For example, the generative AI identifies scheduling information based on data from the calendar application. The scheduling function can also suggest locations related to specific events as candidate locations based on the user's calendar information. For example, the scheduling function suggests locations related to events registered in the user's calendar. Furthermore, the scheduling function can also suggest the optimal route based on the user's calendar information. For example, the scheduling function suggests the optimal travel route based on the user's calendar information. This allows the system to provide scheduling information suggestions based on the user's calendar information.

[0061] The scheduling unit analyzes the user's social media activity when collecting scheduling information and gathers relevant scheduling information. For example, the scheduling unit collects scheduling information related to events that the user has checked into on social media. The scheduling unit uses generative AI to analyze the user's social media activity. For example, the generative AI analyzes the content of social media posts to identify the user's interests. The scheduling unit can also analyze the content of the user's social media posts and collect scheduling information that is likely to interest them. For example, if the scheduling unit posts a lot about events, it will collect scheduling information related to those events. Furthermore, the scheduling unit can also collect scheduling information related to events that the user's friends are attending. For example, the scheduling unit collects scheduling information related to events that the user's friends are attending. This allows the system to provide scheduling information collection based on the user's social media activity.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk can also analyze a user's past destination history and suggest the most suitable input method. For example, it can automatically display destinations that the user has frequently entered in the past as suggestions. Generative AI can be used to analyze past destination history. For example, it can analyze the user's destination history data to identify places that the user frequently visits. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, if the user has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, it can predict and suggest destinations that the user will use at specific times of day based on their past destination history. For example, it can predict places the user visits on specific days of the week and display them as destination suggestions. This allows the system to provide the most suitable input method based on past destination history.

[0064] The reception desk can also suggest destinations based on the user's current activities and areas of interest when the user enters their destination. For example, it can suggest destinations related to events the user is currently attending. Generative AI can be used to analyze the user's current activities. For example, it can estimate the user's activities based on smartphone sensor information. It can also prioritize displaying destinations relevant to the user's areas of interest. For example, it can analyze social media posts and suggest places of interest. Furthermore, it can suggest relevant destinations based on the user's current activities (e.g., shopping). For example, if the user is in a shopping mall, it can suggest nearby restaurants and cafes. This allows for the provision of destination input suggestions based on the user's current activities and areas of interest.

[0065] The data collection unit can analyze a user's past information collection history and select the optimal collection method. For example, it can prioritize collecting information that the user has frequently collected in the past. Generative AI can be used to analyze past information collection history. For example, it can analyze the user's information collection history data to identify information that is frequently collected. It can also prioritize the use of information collection methods (APIs, websites, etc.) that the user has used in the past. For example, if a user has frequently used a particular API in the past, that API will be used preferentially. Furthermore, it can predict and collect information that a user will collect at specific times of the week based on their past information collection history. For example, it can predict and prioritize the collection of information that a user will collect on a specific day of the week. This allows for the provision of the optimal collection method based on past information collection history.

[0066] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between information during the analysis process. For example, it can perform analysis while considering the interrelationships between traffic information and climate information. It can also use a generation AI to analyze the interrelationships between information. For example, it can perform correlation analysis between traffic information and climate information. Furthermore, it can perform analysis while considering the interrelationships between planned information and current location information. For example, it can perform network analysis of planned information and current location information. In addition, it can perform analysis while considering the interrelationships between destination information and past travel history. For example, it can perform correlation analysis between destination information and past travel history. This allows for analysis that takes into account the interrelationships between information.

[0067] The suggestion function can adjust the level of detail in suggestions based on the importance of the mode of transportation. For example, for walking, it can provide detailed route guidance. Generative AI can be used to analyze the importance of the mode of transportation. For example, it can evaluate the importance of the mode of transportation. In the case of cycling, it can also provide suggestions that include traffic safety information. For example, it can suggest dedicated bicycle paths and safe routes. Furthermore, for public transport, it can provide suggestions that include transfer information. For example, it can suggest the optimal route based on bus and train transfer information. This allows for providing a level of detail in suggestions that corresponds to the importance of the mode of transportation.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk accepts destination input. Users can enter their destination in text format, and voice input is also accepted. In the case of voice input, speech recognition technology is used to convert it to text. Furthermore, destination suggestions can be presented based on past destination history. Step 2: The collection unit collects information such as current location, schedule, weather, and traffic conditions based on the destination information received by the reception unit. For example, it obtains the current location using GPS and schedule information from calendar apps and schedule management systems. Furthermore, it obtains the latest weather information from online weather databases and real-time traffic conditions from traffic information services. Step 3: The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. For example, it calculates the optimal route based on the collected information using a generation AI, and integrates and analyzes multiple pieces of information using a multimodal generation AI. Step 4: The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user. For example, it displays route guidance on the user's smartphone and provides route guidance via voice using speech synthesis technology. Furthermore, it proposes a route that takes into account travel time and arrival time based on the user's schedule.

[0070] (Example of form 2) The optimal route guidance system according to an embodiment of the present invention is a system that uses a generating AI to analyze multifaceted information and propose the optimal travel route. The optimal route guidance system collects and analyzes information such as destination, current location, schedule, weather, and traffic conditions to propose the optimal route for all modes of transportation, including walking, cycling, public transport, and driving. First, the user inputs their destination. Next, the generating AI collects and analyzes information such as the current location, schedule, weather, and traffic conditions. Based on this information, the generating AI calculates the optimal travel route and proposes it to the user. For example, if walking is the optimal mode of transportation, the generating AI proposes a pedestrian-only route, and if traffic congestion is expected, it recommends using public transport. Furthermore, the generating AI proposes a route that takes into account travel time and arrival time based on the user's schedule. For example, if the user has a meeting to attend, the generating AI calculates the optimal route to arrive on time for the meeting. It can also consider weather information and prioritize routes with roofs in rainy weather. This system allows users to select the optimal route for all modes of transportation, thereby improving the efficiency of travel. For example, if cycling is the optimal mode of transport, the generating AI will suggest a dedicated cycling route, thus avoiding traffic congestion. Similarly, if using public transport, the generating AI will suggest the most efficient transfer route, reducing travel time. In this way, the optimal route guidance system uses generating AI to analyze multifaceted information and propose the most suitable travel route to the user, improving the efficiency and convenience of travel. Thus, the optimal route guidance system can streamline and improve the convenience of user travel.

[0071] The optimal route guidance system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, and a suggestion unit. The reception unit receives destination input. For example, the user can input the destination in text format. The reception unit can also accept voice input. For example, the user can input the destination by voice and convert it to text using voice recognition technology. Furthermore, the reception unit can also suggest destination candidates based on past destination history. For example, it can automatically display places the user has visited in the past as candidates. The data collection unit collects information such as current location, schedule, weather, and traffic conditions based on the destination information received by the reception unit. For example, the data collection unit obtains the current location using GPS. Furthermore, the data collection unit can obtain schedule information from calendar applications or schedule management systems. Furthermore, the data collection unit can obtain weather information based on meteorological data and weather forecasts. For example, the data collection unit obtains the latest weather information from weather databases on the internet. Regarding traffic conditions, the data collection unit can obtain traffic congestion information and the operating status of public transportation. For example, the data collection unit obtains real-time traffic conditions from traffic information provision services. The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. The analysis unit calculates the optimal route based on the collected information, for example, using a generation AI. The generation AI uses a text generation AI (e.g., LLM) to calculate the travel route. The analysis unit can also use a multimodal generation AI to integrate and analyze multiple pieces of information. For example, the analysis unit integrates information such as current location, schedule, weather, and traffic conditions to calculate the optimal route. The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user. The suggestion unit displays route guidance on the user's smartphone, for example. The suggestion unit can also provide route guidance by voice. For example, the suggestion unit uses speech synthesis technology to provide route guidance to the user by voice. Furthermore, the suggestion unit can propose a route that takes into account travel time and arrival time based on the user's schedule. For example, the suggestion unit calculates and proposes the optimal route to arrive on time for the start of a meeting.As a result, the optimal route guidance system according to the embodiment can consistently perform everything from inputting the destination to proposing the optimal travel route.

[0072] The reception desk accepts destination input. For example, users can input their destination in text format. The reception desk can also accept voice input. For example, a user can input their destination by voice, and speech recognition technology can convert it to text. Furthermore, the reception desk can suggest destinations based on past destination history. For example, it can automatically display places the user has visited in the past as suggestions. The reception desk provides various means for accepting destination input through the user interface. For text input, users can input their destination using a keyboard or touchscreen. For voice input, voice is collected through a microphone, and a speech recognition engine converts the voice to text. The speech recognition engine uses natural language processing technology to accurately understand the user's speech and convert it into appropriate text. Furthermore, the reception desk has a function that analyzes the user's past behavior history and automatically suggests destinations based on frequently visited places and previously entered destinations. This allows users to select destinations quickly and easily. For example, it can automatically display the addresses of restaurants or offices the user has visited in the past as suggestions, allowing for one-click selection. This significantly improves user convenience.

[0073] The data collection unit collects information such as current location, schedule, weather, and traffic conditions based on destination information received by the reception unit. For example, the data collection unit obtains the current location using GPS. It can also obtain schedule information from calendar apps and schedule management systems. Furthermore, the data collection unit can obtain weather information based on meteorological data and weather forecasts. For example, the data collection unit obtains the latest weather information from online weather databases. Regarding traffic conditions, the data collection unit can obtain traffic congestion information and public transport operating status. For example, the data collection unit obtains real-time traffic conditions from traffic information services. The data collection unit has an interface for centrally managing this information and providing it to the analysis unit. Obtaining the current location using GPS is done using location information services from smartphones and in-car navigation systems. This allows for real-time tracking of the user's precise current location. Obtaining schedule information from calendar apps and schedule management systems is done with the user's permission, and detailed information such as the start and end times and location of the schedule is collected. Weather data is obtained from online weather databases and weather forecasting services, taking into account weather conditions that may affect the travel route. The collection of traffic conditions involves obtaining real-time traffic congestion information and public transport operation status from traffic information services, which are used as crucial data for calculating the optimal travel route. This allows the collection unit to efficiently collect diverse information related to the user's travel and provide it to the analysis unit.

[0074] The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. For example, the analysis unit uses a generative AI to calculate the optimal route based on the collected information. The generative AI uses a text generation AI (e.g., LLM) to calculate the travel route. The analysis unit can also use a multimodal generative AI to integrate and analyze multiple pieces of information. For example, the analysis unit integrates information such as current location, schedule, weather, and traffic conditions to calculate the optimal route. The generative AI learns from a vast dataset and uses advanced algorithms to generate the optimal route. Specifically, the text generation AI calculates the optimal route from the user's current location to their destination and generates a detailed description of that route. The multimodal generative AI can integrate and analyze multiple data sources, including not only text data but also image data and sensor data. This allows the analysis unit to provide more accurate and reliable route guidance. For example, the analysis unit integrates GPS data of the current location, calendar information of the schedule, weather data, and traffic congestion information to calculate the optimal travel route. Furthermore, the analysis unit can analyze past travel history and user travel patterns to propose routes optimized for individual users. This allows the analysis unit to provide customized route guidance tailored to the user's needs, improving the efficiency and comfort of travel.

[0075] The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user. The suggestion unit, for example, displays route guidance on the user's smartphone. The suggestion unit can also provide route guidance via voice. For example, it uses speech synthesis technology to provide route guidance to the user verbally. Furthermore, the suggestion unit can propose routes that take into account travel and arrival times based on the user's schedule. For example, it calculates and proposes the optimal route to ensure the user arrives on time for a meeting. The suggestion unit has various means of providing route guidance visually and audibly through the user interface. The route is displayed on a map on the smartphone screen, allowing the user to check their current location and progress toward their destination in real time. Voice guidance uses speech synthesis technology to inform the user of the next turn and direction of travel. This allows the user to rely on voice guidance even in situations where visual confirmation is difficult. Furthermore, the suggestion unit optimizes travel and arrival times by considering the user's schedule information. For example, it proposes the optimal departure time and travel route to ensure the user arrives on time for a meeting. This allows users to travel with ample time and reduce stress. The suggestion department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This enables the suggestion department to provide users with the optimal travel route, improving the efficiency and comfort of their journey.

[0076] The climate unit takes climate information into consideration. For example, it obtains the latest weather information from a weather database. The climate unit uses generative AI to analyze weather data and identify climate conditions that affect travel routes. For example, the climate unit prioritizes suggesting routes with roofs during rainy weather. It can also suggest comfortable travel routes based on information such as temperature and wind speed. For example, on hot days, the climate unit suggests routes with plenty of shade. This allows for the suggestion of optimal travel routes that take climate information into account.

[0077] The Ministry of Transportation takes traffic conditions into consideration. For example, it obtains real-time traffic information from traffic information services. The Ministry of Transportation uses generative AI to analyze traffic congestion information and the operating status of public transportation and calculates the optimal travel route. For example, the Ministry of Transportation recommends using public transportation when traffic congestion is expected. The Ministry of Transportation can also suggest detour routes, taking into account traffic accidents and construction information. For example, if a traffic accident occurs, the Ministry of Transportation will suggest a route that avoids its impact. This allows for the suggestion of the optimal travel route that takes traffic conditions into account.

[0078] The scheduling function considers travel and arrival times based on the user's schedule. For example, it retrieves schedule information from calendar apps or scheduling management systems. Using a generative AI, it analyzes the schedule information and calculates the optimal route considering travel and arrival times. For example, it calculates and proposes the best route to ensure the user arrives on time for a meeting. The scheduling function can also select a mode of transportation based on the user's schedule. For example, it might recommend using a taxi if the user needs to travel quickly. This allows the system to propose the optimal travel route based on the user's schedule.

[0079] The suggestion function proposes the optimal route based on the mode of transportation, such as walking, cycling, public transport, or driving. For example, if walking is the optimal mode of transport, the suggestion function will propose a pedestrian-only route. The suggestion function uses generative AI to calculate and propose a pedestrian-only route. The suggestion function can also propose a bicycle-only route if cycling is the optimal mode of transport. For example, the suggestion function will prioritize suggesting bicycle paths. Furthermore, if using public transport, the suggestion function can propose the optimal transfer route. For example, the suggestion function will calculate and propose the optimal route based on bus and train transfer information. This allows the system to propose the optimal route according to the mode of transport.

[0080] The reception desk estimates the user's emotions and adjusts the destination input method based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. The reception desk uses generative AI to estimate the user's emotions. For example, the generative AI uses facial recognition technology to estimate the user's emotions. The reception desk can also provide detailed input options and suggest customizable input methods if the user is relaxed. For example, if the user is relaxed, the reception desk can present multiple input methods for the user to choose from. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick destination input. For example, the reception desk uses speech recognition technology to allow the user to input their destination by voice. This allows for destination input methods tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk analyzes the user's past destination history and suggests the optimal input method. For example, the reception desk automatically displays destinations that the user has frequently entered in the past as suggestions. The reception desk uses generative AI to analyze past destination history. For example, the generative AI analyzes the user's destination history data and identifies places that the user frequently visits. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest destinations that the user will use at specific times of day based on their past destination history. For example, the reception desk can predict places the user visits on specific days of the week and display them as destination suggestions. This allows the system to provide the optimal input method based on past destination history.

[0082] The reception desk, when a user enters a destination, presents input suggestions based on the user's current activities and areas of interest. For example, the reception desk may suggest destinations related to an event the user is currently attending. The reception desk uses generative AI to analyze the user's current activities. For example, the generative AI estimates the user's activities based on smartphone sensor information. The reception desk can also prioritize displaying relevant destinations based on the user's areas of interest. For example, the reception desk may analyze the user's social media posts and suggest places of interest. Furthermore, the reception desk can suggest relevant destinations based on the user's current activities (e.g., shopping). For example, if the user is in a shopping mall, the reception desk may suggest nearby restaurants or cafes. This allows the system to provide destination input suggestions based on the user's current activities and areas of interest.

[0083] The reception desk estimates the user's emotions and determines the priority of destination input based on the estimated emotions. For example, if the user is tired, the reception desk will prioritize displaying the nearest destination. The reception desk uses generative AI to estimate the user's emotions. For example, the generative AI uses voice analysis technology to estimate the user's emotions. The reception desk can also prioritize displaying destinations that interest the user if the user is excited. For example, if the reception desk is excited, it will prioritize suggesting tourist attractions or event venues. Furthermore, if the user is relaxed, the reception desk can also provide detailed destination options. For example, if the reception desk is relaxed, it will present multiple destination options for the user to choose from. This allows for destination input prioritization according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk prioritizes and displays highly relevant destinations when the user enters a destination, taking into account the user's geographical location. For example, the reception desk prioritizes displaying destinations close to the user's current location. The reception desk uses generative AI to analyze the user's geographical location. For example, the generative AI identifies the user's current location based on GPS data. The reception desk can also suggest destinations with convenient transportation based on the user's current location. For example, if the reception desk is near a train station, it will prioritize displaying destinations around the station. Furthermore, the reception desk can also prioritize displaying popular tourist destinations based on the user's current location. For example, if the reception desk is in a tourist destination, it will suggest nearby tourist spots. This allows the system to provide destination input candidates based on the user's geographical location.

[0085] The reception desk analyzes the user's social media activity when they enter their destination and suggests relevant destinations. For example, the reception desk suggests relevant destinations based on places the user has checked in to on social media. The reception desk uses generative AI to analyze the user's social media activity. For example, the generative AI analyzes the content of social media posts to identify the user's interests. The reception desk can also analyze the content of the user's social media posts and suggest destinations that they might be interested in. For example, if the reception desk posts a lot about food, it will suggest restaurants and cafes. Furthermore, the reception desk can suggest relevant destinations based on places the user's friends have visited. For example, it will suggest tourist spots that the user's friends have visited. This allows the system to provide destination input suggestions based on the user's social media activity.

[0086] The data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect information slowly. The data collection unit estimates the user's emotions using generative AI. For example, the generative AI may use facial recognition technology to estimate the user's emotions. The data collection unit can also collect information quickly if the user is in a hurry. For example, if the user is in a hurry, the data collection unit will prioritize collecting necessary information. Furthermore, if the user is stressed, the data collection unit can collect only the minimum necessary information. For example, if the user is stressed, the data collection unit will collect only important information. This allows for information collection timing that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The data collection unit analyzes the user's past information gathering history and selects the optimal collection method. For example, the unit prioritizes collecting information that the user has frequently collected in the past. The data collection unit uses generative AI to analyze past information gathering history. For example, the generative AI analyzes the user's information gathering history data and identifies information that is frequently collected. The data collection unit can also prioritize the use of information gathering methods (APIs, websites, etc.) that the user has used in the past. For example, if the data collection unit has frequently used a particular API in the past, it will prioritize the use of that API. Furthermore, the data collection unit can predict and collect information that the user will collect at specific times of day based on their past information gathering history. For example, the data collection unit predicts and prioritizes the collection of information that the user will collect on specific days of the week. This allows the system to provide the optimal data collection method based on past information gathering history.

[0088] The data collection unit filters information based on the user's current lifestyle and areas of interest. For example, it prioritizes collecting relevant information based on the user's current lifestyle (e.g., at work). The data collection unit analyzes the user's lifestyle using generative AI. For example, the generative AI estimates the user's lifestyle based on data from smart home devices. The data collection unit can also filter relevant information based on the user's areas of interest. For example, it analyzes the user's social media posts and prioritizes collecting information of interest. Furthermore, the data collection unit can collect relevant information based on the user's current activities (e.g., traveling). For example, if the user is traveling, it prioritizes collecting tourist information about the travel destination. This allows for information collection based on the user's current lifestyle and areas of interest.

[0089] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is tired, the data collection unit prioritizes collecting the most important information. The data collection unit estimates the user's emotions using generative AI. For example, the generative AI estimates the user's emotions using voice analysis technology. The data collection unit can also prioritize collecting information that interests the user if the user is excited. For example, if the user is excited, the data collection unit prioritizes collecting entertainment information. Furthermore, if the user is relaxed, the data collection unit can also collect detailed information. For example, if the user is relaxed, the data collection unit collects detailed information from multiple sources. This allows for prioritizing information collection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The data collection unit prioritizes collecting highly relevant information, taking into account the user's geographical location. For example, it prioritizes collecting information close to the user's current location. The data collection unit analyzes the user's geographical location using generative AI. For example, the generative AI identifies the user's current location based on GPS data. The data collection unit can also collect information on convenient transportation based on the user's current location. For example, if the user is near a train station, the data collection unit prioritizes collecting transportation information around the station. Furthermore, the data collection unit can prioritize collecting information on popular tourist destinations based on the user's current location. For example, if the user is in a tourist destination, the data collection unit collects information on nearby tourist spots. This enables the collection of information based on the user's geographical location.

[0091] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the unit collects information related to places the user has checked in to on social media. The data collection unit uses generative AI to analyze the user's social media activity. For example, the generative AI analyzes the content of social media posts to identify the user's interests. The data collection unit can also analyze the content of the user's social media posts and collect information that they might be interested in. For example, if the user posts a lot about food, the data collection unit will collect information about restaurants and cafes. Furthermore, the data collection unit can also collect information related to places the user's friends have visited. For example, the data collection unit will collect information about tourist destinations visited by the user's friends. This allows for data collection based on the user's social media activity.

[0092] The analysis unit estimates the user's emotions and adjusts the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. The analysis unit estimates the user's emotions using generative AI. For example, the generative AI estimates the user's emotions using facial recognition technology. The analysis unit can also perform a rapid analysis if the user is in a hurry. For example, if the user is in a hurry, the analysis unit prioritizes analyzing only the necessary information. Furthermore, if the user is stressed, the analysis unit can perform only the minimum necessary analysis. For example, if the user is stressed, the analysis unit analyzes only the important information. This allows for the provision of analysis criteria tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The analysis unit improves the accuracy of the analysis by considering the interrelationships of information during the analysis. For example, the analysis unit performs analysis considering the interrelationships between traffic information and climate information. The analysis unit uses a generative AI to analyze the interrelationships of information. For example, the generative AI performs a correlation analysis between traffic information and climate information. The analysis unit can also perform analysis considering the interrelationships between planned information and current location information. For example, the analysis unit performs a network analysis of planned information and current location information. Furthermore, the analysis unit can also perform analysis considering the interrelationships between destination information and past travel history. For example, the analysis unit performs a correlation analysis between destination information and past travel history. This enables the provision of analysis that takes into account the interrelationships of information.

[0094] The analysis unit considers the attribute information of the information provider during the analysis. For example, the analysis unit considers the reliability of the traffic information provider during the analysis. The analysis unit uses a generation AI to analyze the attribute information of the information provider. For example, the generation AI evaluates the reliability of the traffic information provider. The analysis unit can also consider the reliability of the climate information provider during the analysis. For example, the analysis unit evaluates the reliability of the climate information provider. Furthermore, the analysis unit can also consider the reliability of the schedule information provider during the analysis. For example, the analysis unit evaluates the reliability of the schedule information provider. This allows for analysis that takes into account the attribute information of the information provider.

[0095] The analysis unit estimates the user's emotions and adjusts the display order of the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will prioritize displaying the most important analysis results. The analysis unit estimates the user's emotions using generative AI. For example, the generative AI estimates the user's emotions using voice analysis technology. The analysis unit can also display detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit will present multiple analysis results. Furthermore, if the user is stressed, the analysis unit can display concise analysis results. For example, if the user is stressed, the analysis unit will display only the most important analysis results. This provides an order for displaying analysis results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The analysis unit considers the geographical distribution of information during analysis. For example, the analysis unit considers the geographical distribution of traffic information. The analysis unit uses a generation AI to analyze the geographical distribution of information. For example, the generation AI identifies the geographical distribution of traffic information based on map data. The analysis unit can also consider the geographical distribution of climate information. For example, the analysis unit analyzes climate information by region. Furthermore, the analysis unit can also consider the geographical distribution of scheduled information. For example, the analysis unit analyzes scheduled information by region. This allows for analysis that takes the geographical distribution of information into account.

[0097] The analysis unit improves the accuracy of its analysis by referring to relevant literature during the analysis process. For example, when analyzing traffic information, the analysis unit refers to relevant research papers. The analysis unit uses a generative AI to analyze the relevant literature. For example, the generative AI retrieves relevant research papers from academic paper databases. The analysis unit can also refer to relevant meteorological data when analyzing climate information. For example, the analysis unit refers to data from the Japan Meteorological Agency. Furthermore, the analysis unit can also refer to relevant calendar data when analyzing schedule information. For example, the analysis unit refers to data from a calendar application. In this way, the accuracy of the analysis can be improved by referring to relevant literature.

[0098] The suggestion function estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function provides detailed suggestions. The suggestion function uses generative AI to estimate the user's emotions. For example, the generative AI uses facial recognition technology to estimate the user's emotions. The suggestion function can also provide concise suggestions if the user is in a hurry. For example, if the user is in a hurry, the suggestion function provides suggestions that get straight to the point. Furthermore, if the user is stressed, the suggestion function can provide visually easy-to-understand suggestions. For example, if the user is stressed, the suggestion function uses graphs and charts. This allows for the presentation of suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The suggestion function adjusts the level of detail in its suggestions based on the importance of the mode of transportation. For example, if the user is walking, the suggestion function provides detailed route guidance. The suggestion function uses generative AI to analyze the importance of the mode of transportation. For example, the generative AI evaluates the importance of the mode of transportation. In addition, if the user is cycling, the suggestion function can also provide suggestions that include traffic safety information. For example, the suggestion function can suggest dedicated bicycle paths and safe routes. Furthermore, if the user is using public transport, the suggestion function can also provide suggestions that include transfer information. For example, the suggestion function can suggest the optimal route based on bus and train transfer information. This allows for a level of detail in suggestions that corresponds to the importance of the mode of transportation.

[0100] The suggestion function applies different suggestion algorithms depending on the mode of transport during the suggestion process. For example, for walking, the suggestion function applies an algorithm that suggests pedestrian-only routes. The suggestion function uses generative AI to analyze the mode of transport categories. For example, the generative AI classifies the modes of transport. The suggestion function can also apply an algorithm that suggests bicycle-only routes for cycling. For example, the suggestion function applies an algorithm that suggests bicycle-only paths and safe routes. Furthermore, for public transport, the suggestion function can apply an algorithm that suggests routes including transfer information. For example, the suggestion function applies an algorithm that suggests the optimal route based on bus and train transfer information. This allows the system to provide the optimal suggestion algorithm according to the mode of transport category.

[0101] The suggestion function estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. The suggestion function uses generative AI to estimate the user's emotions. For example, the generative AI may use speech analysis technology to estimate the user's emotions. The suggestion function can also provide detailed suggestions if the user is relaxed. For example, if the user is relaxed, the suggestion function will present multiple suggestion options. Furthermore, if the user is stressed, the suggestion function can provide visually easy-to-understand suggestions. For example, if the user is stressed, the suggestion function will provide suggestions using graphs and charts. This allows for suggestion lengths tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The suggestion function prioritizes suggestions based on the timing of travel use. For example, it might prioritize suggesting the most recent mode of transport. The suggestion function uses generative AI to analyze the timing of travel use. For example, the generative AI identifies the timing of travel use based on the user's schedule data. The suggestion function can also suggest the most suitable mode of transport based on the user's schedule. For example, it suggests the optimal mode of transport to match the user's schedule. Furthermore, the suggestion function can suggest the optimal mode of transport based on the user's past travel history. For example, it might prioritize suggesting modes of transport that the user has frequently used in the past. This allows for prioritizing suggestions based on the timing of travel use.

[0103] The suggestion unit adjusts the order of suggestions based on the relevance of the modes of transportation. For example, the suggestion unit prioritizes suggesting modes of transportation that the user has used in the past. The suggestion unit uses generative AI to analyze the relevance of modes of transportation. For example, the generative AI identifies the relevance of modes of transportation based on the user's travel history data. The suggestion unit can also prioritize suggesting the mode of transportation that is most suitable for the user's current situation. For example, the suggestion unit suggests the optimal mode of transportation based on the user's current location and situation. Furthermore, the suggestion unit can suggest the most efficient mode of transportation based on the user's schedule. For example, the suggestion unit suggests the most efficient mode of transportation according to the user's schedule. This allows for an order of suggestions that is tailored to the relevance of the modes of transportation.

[0104] The climate unit estimates the user's emotions and adjusts how climate information is displayed based on the estimated emotions. For example, if the user is stressed, the climate unit provides simple and easily visible climate information. The climate unit estimates the user's emotions using generative AI. For example, the generative AI estimates the user's emotions using facial recognition technology. The climate unit can also provide detailed climate information if the user is relaxed. For example, if the user is relaxed, the climate unit presents multiple pieces of climate information. Furthermore, if the user is in a hurry, the climate unit can provide concise climate information. For example, if the user is in a hurry, the climate unit displays only the most important climate information. This allows for a climate information display method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The Climate Department predicts the current climate by referencing past climate data when collecting climate information. For example, the Climate Department predicts the current climate based on past climate data. The Climate Department analyzes past climate data using generative AI. For example, generative AI identifies past climate patterns based on data from the Japan Meteorological Agency. The Climate Department can also predict the current climate by analyzing past climate patterns. For example, the Climate Department predicts the current climate by comparing past and current climate data. Furthermore, the Climate Department can make more accurate climate predictions by integrating past and current climate data. For example, the Climate Department builds a climate prediction model by integrating past and current climate data. This allows it to predict the current climate by referring to past climate data.

[0106] The climate unit estimates the user's emotions and adjusts the importance of climate information based on the estimated emotions. For example, if the user is stressed, the climate unit will prioritize displaying the most important climate information. The climate unit estimates the user's emotions using generative AI. For example, the generative AI may estimate the user's emotions using speech analysis technology. The climate unit can also display detailed climate information if the user is relaxed. For example, if the user is relaxed, the climate unit will present multiple pieces of climate information. Furthermore, if the user is in a hurry, the climate unit can display concise climate information. For example, if the user is in a hurry, the climate unit will display only the most important climate information. This allows for the provision of climate information importance tailored to 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.

[0107] The climate unit collects climate information while considering its geographical distribution. For example, it collects the most relevant climate information based on the user's current location. The climate unit analyzes geographical distribution using generative AI. For example, the generative AI identifies the geographical distribution of climate information based on map data. The climate unit can also collect climate information based on the user's destination. For example, it prioritizes collecting climate information for the user's destination. Furthermore, the climate unit can collect climate information based on the user's travel route. For example, it collects climate information along the user's travel route. This enables the collection of climate information that takes geographical distribution into consideration.

[0108] The traffic department estimates the user's emotions and adjusts how traffic information is displayed based on the estimated emotions. For example, if the user is stressed, the traffic department provides simple and highly visible traffic information. The traffic department uses generative AI to estimate the user's emotions. For example, the generative AI uses facial recognition technology to estimate the user's emotions. The traffic department can also provide detailed traffic information if the user is relaxed. For example, if the user is relaxed, the traffic department presents multiple traffic pieces. Furthermore, if the user is in a hurry, the traffic department can provide concise traffic information. For example, if the user is in a hurry, the traffic department displays only the most important traffic information. This allows the traffic information to be displayed in a way that suits the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The Ministry of Transportation predicts current traffic conditions by referencing past traffic data when collecting traffic information. For example, the Ministry of Transportation predicts current traffic conditions based on past traffic data. The Ministry of Transportation analyzes past traffic data using generative AI. For example, generative AI identifies past traffic patterns based on traffic volume survey data. The Ministry of Transportation can also predict current traffic conditions by analyzing past traffic patterns. For example, the Ministry of Transportation predicts current traffic conditions by comparing past and current traffic data. Furthermore, the Ministry of Transportation can make more accurate traffic predictions by integrating past and current traffic data. For example, the Ministry of Transportation builds a traffic prediction model by integrating past and current traffic data. This allows it to predict current traffic conditions by referring to past traffic data.

[0110] The traffic department estimates the user's emotions and adjusts the importance of traffic information based on the estimated emotions. For example, if the user is stressed, the traffic department prioritizes displaying the most important traffic information. The traffic department uses generative AI to estimate the user's emotions. For example, the generative AI uses voice analysis technology to estimate the user's emotions. The traffic department can also display detailed traffic information if the user is relaxed. For example, if the user is relaxed, the traffic department will present multiple traffic information. Furthermore, if the user is in a hurry, the traffic department can display concise traffic information. For example, if the user is in a hurry, the traffic department will display only the most important traffic information. This allows the importance of traffic information to be tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The Transportation Department collects traffic information while considering its geographical distribution. For example, it collects the most relevant traffic information based on the user's current location. The Transportation Department uses generative AI to analyze geographical distribution. For example, the generative AI identifies the geographical distribution of traffic information based on map data. The Transportation Department can also collect traffic information based on the user's destination. For example, it prioritizes collecting traffic information for the user's destination. Furthermore, the Transportation Department can collect traffic information based on the user's travel route. For example, it collects traffic information along the user's travel route. This allows for the collection of traffic information that takes geographical distribution into consideration.

[0112] The scheduling function estimates the user's emotions and adjusts how scheduling information is displayed based on the estimated emotions. For example, if the user is stressed, the scheduling function provides simple and easily readable scheduling information. The scheduling function estimates the user's emotions using generative AI. For example, the generative AI estimates the user's emotions using facial recognition technology. The scheduling function can also provide detailed scheduling information if the user is relaxed. For example, if the user is relaxed, the scheduling function presents multiple scheduling items. Furthermore, if the user is in a hurry, the scheduling function can provide concise scheduling information. For example, if the user is in a hurry, the scheduling function displays only the most important scheduling items. This allows for scheduling information to be displayed in a way that suits 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.

[0113] The scheduling unit predicts current schedules by referencing past schedule data when collecting schedule information. For example, the scheduling unit predicts current schedules based on past schedule data. The scheduling unit analyzes past schedule data using generative AI. For example, the generative AI identifies past schedule patterns based on data from a calendar application. The scheduling unit can also predict current schedules by analyzing past schedule patterns. For example, the scheduling unit predicts current schedules by comparing past and current schedule data. Furthermore, the scheduling unit can integrate past and current schedule data to make more accurate schedule predictions. For example, the scheduling unit integrates past and current schedule data to build a schedule prediction model. This allows it to predict current schedules by referencing past schedule data.

[0114] The scheduling function estimates the user's emotions and adjusts the importance of scheduling information based on those emotions. For example, if the user is stressed, the scheduling function prioritizes displaying the most important scheduling information. The scheduling function estimates the user's emotions using generative AI. For example, the generative AI estimates the user's emotions using speech analysis technology. The scheduling function can also display detailed scheduling information if the user is relaxed. For example, if the user is relaxed, the scheduling function presents multiple scheduling items. Furthermore, if the user is in a hurry, the scheduling function can display concise scheduling information. For example, if the user is in a hurry, the scheduling function displays only the most important scheduling information. This allows for scheduling information importance levels tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The planning unit collects planning information while considering its geographical distribution. For example, it collects the most relevant planning information based on the user's current location. The planning unit analyzes the geographical distribution using generative AI. For example, the generative AI identifies the geographical distribution of planning information based on map data. The planning unit can also collect planning information based on the user's destination. For example, it prioritizes collecting planning information for the user's destination. Furthermore, the planning unit can also collect planning information based on the user's travel route. For example, it collects planning information along the user's travel route. This allows for the collection of planning information that takes geographical distribution into consideration.

[0116] The scheduling function, when collecting scheduling information, refers to the user's calendar information to make suggestions based on the schedule. For example, the scheduling function refers to the schedules registered in the user's calendar and automatically sets the departure and destination points. The scheduling function analyzes the calendar information using generative AI. For example, the generative AI identifies scheduling information based on data from the calendar application. The scheduling function can also suggest locations related to specific events as candidate locations based on the user's calendar information. For example, the scheduling function suggests locations related to events registered in the user's calendar. Furthermore, the scheduling function can also suggest the optimal route based on the user's calendar information. For example, the scheduling function suggests the optimal travel route based on the user's calendar information. This allows the system to provide scheduling information suggestions based on the user's calendar information.

[0117] The scheduling unit analyzes the user's social media activity when collecting scheduling information and gathers relevant scheduling information. For example, the scheduling unit collects scheduling information related to events that the user has checked into on social media. The scheduling unit uses generative AI to analyze the user's social media activity. For example, the generative AI analyzes the content of social media posts to identify the user's interests. The scheduling unit can also analyze the content of the user's social media posts and collect scheduling information that is likely to interest them. For example, if the scheduling unit posts a lot about events, it will collect scheduling information related to those events. Furthermore, the scheduling unit can also collect scheduling information related to events that the user's friends are attending. For example, the scheduling unit collects scheduling information related to events that the user's friends are attending. This allows the system to provide scheduling information collection based on the user's social media activity.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception desk can also estimate the user's emotions and adjust the destination input method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. Generative AI can be used to estimate the user's emotions. For example, facial recognition technology can be used to estimate the user's emotions. Also, if the user is relaxed, it can provide detailed input options and suggest customizable input methods. For example, it can present multiple input methods and allow the user to choose. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick destination input. For example, voice recognition technology can be used to allow the user to input their destination by voice. This allows for destination input methods that are tailored to the user's emotions.

[0120] The data collection unit can also estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is relaxed, information collection can be performed slowly. Generative AI can be used to estimate the user's emotions. For example, facial recognition technology can be used to estimate the user's emotions. Furthermore, if the user is in a hurry, information can be collected quickly. For example, necessary information can be collected preferentially. In addition, if the user is stressed, only the minimum necessary information can be collected. For example, only important information can be collected. This allows for information collection timing that is tailored to the user's emotions.

[0121] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis. Generative AI can be used to estimate the user's emotions. For example, facial recognition technology can be used to estimate the user's emotions. If the user is in a hurry, it can perform a rapid analysis. For example, it can prioritize analyzing only the necessary information. Furthermore, if the user is stressed, it can perform a minimal analysis. For example, it can analyze only the important information. This allows for the provision of analysis criteria tailored to the user's emotions.

[0122] The suggestion function can also estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. Generative AI can be used to estimate the user's emotions. For example, facial recognition technology can be used to estimate the user's emotions. Also, if the user is in a hurry, it can provide concise suggestions. For example, it can provide suggestions that get straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand suggestions. For example, it can provide suggestions using graphs and charts. In this way, it is possible to provide suggestions that are presented in a way that suits the user's emotions.

[0123] The climate section can also estimate the user's emotions and adjust how climate information is displayed based on those emotions. For example, if the user is stressed, it can provide simple and easily understandable climate information. Generative AI can be used to estimate the user's emotions, for example, by using facial recognition technology. If the user is relaxed, it can also provide detailed climate information, for example, by presenting multiple pieces of climate information. Furthermore, if the user is in a hurry, it can provide concise climate information, for example, by displaying only the most important climate information. This allows for climate information to be displayed in a way that suits the user's emotions.

[0124] The reception desk can also analyze a user's past destination history and suggest the most suitable input method. For example, it can automatically display destinations that the user has frequently entered in the past as suggestions. Generative AI can be used to analyze past destination history. For example, it can analyze the user's destination history data to identify places that the user frequently visits. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, if the user has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, it can predict and suggest destinations that the user will use at specific times of day based on their past destination history. For example, it can predict places the user visits on specific days of the week and display them as destination suggestions. This allows the system to provide the most suitable input method based on past destination history.

[0125] The reception desk can also suggest destinations based on the user's current activities and areas of interest when the user enters their destination. For example, it can suggest destinations related to events the user is currently attending. Generative AI can be used to analyze the user's current activities. For example, it can estimate the user's activities based on smartphone sensor information. It can also prioritize displaying destinations relevant to the user's areas of interest. For example, it can analyze social media posts and suggest places of interest. Furthermore, it can suggest relevant destinations based on the user's current activities (e.g., shopping). For example, if the user is in a shopping mall, it can suggest nearby restaurants and cafes. This allows for the provision of destination input suggestions based on the user's current activities and areas of interest.

[0126] The data collection unit can analyze a user's past information collection history and select the optimal collection method. For example, it can prioritize collecting information that the user has frequently collected in the past. Generative AI can be used to analyze past information collection history. For example, it can analyze the user's information collection history data to identify information that is frequently collected. It can also prioritize the use of information collection methods (APIs, websites, etc.) that the user has used in the past. For example, if a user has frequently used a particular API in the past, that API will be used preferentially. Furthermore, it can predict and collect information that a user will collect at specific times of the week based on their past information collection history. For example, it can predict and prioritize the collection of information that a user will collect on a specific day of the week. This allows for the provision of the optimal collection method based on past information collection history.

[0127] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between information during the analysis process. For example, it can perform analysis while considering the interrelationships between traffic information and climate information. It can also use a generation AI to analyze the interrelationships between information. For example, it can perform correlation analysis between traffic information and climate information. Furthermore, it can perform analysis while considering the interrelationships between planned information and current location information. For example, it can perform network analysis of planned information and current location information. In addition, it can perform analysis while considering the interrelationships between destination information and past travel history. For example, it can perform correlation analysis between destination information and past travel history. This allows for analysis that takes into account the interrelationships between information.

[0128] The suggestion function can adjust the level of detail in suggestions based on the importance of the mode of transportation. For example, for walking, it can provide detailed route guidance. Generative AI can be used to analyze the importance of the mode of transportation. For example, it can evaluate the importance of the mode of transportation. In the case of cycling, it can also provide suggestions that include traffic safety information. For example, it can suggest dedicated bicycle paths and safe routes. Furthermore, for public transport, it can provide suggestions that include transfer information. For example, it can suggest the optimal route based on bus and train transfer information. This allows for providing a level of detail in suggestions that corresponds to the importance of the mode of transportation.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk accepts destination input. Users can enter their destination in text format, and voice input is also accepted. In the case of voice input, speech recognition technology is used to convert it to text. Furthermore, destination suggestions can be presented based on past destination history. Step 2: The collection unit collects information such as current location, schedule, weather, and traffic conditions based on the destination information received by the reception unit. For example, it obtains the current location using GPS and schedule information from calendar apps and schedule management systems. Furthermore, it obtains the latest weather information from online weather databases and real-time traffic conditions from traffic information services. Step 3: The analysis unit analyzes the information collected by the collection unit and calculates the optimal travel route. For example, it calculates the optimal route based on the collected information using a generation AI, and integrates and analyzes multiple pieces of information using a multimodal generation AI. Step 4: The suggestion unit proposes the optimal travel route calculated by the analysis unit to the user. For example, it displays route guidance on the user's smartphone and provides route guidance via voice using speech synthesis technology. Furthermore, it proposes a route that takes into account travel time and arrival time based on the user's schedule.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, climate unit, transportation unit, and scheduling unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts destination input. The collection unit collects current location and climate information using the camera 42 and GPS of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit proposes the optimal route to the user using the display 40A and speaker 40B of the smart device 14. The climate unit analyzes weather data using the specific processing unit 290 of the data processing unit 12 and identifies climate conditions that affect the travel route. The transportation unit analyzes traffic information using the specific processing unit 290 of the data processing unit 12 and calculates the optimal travel route. The scheduling unit analyzes the scheduling information using the specific processing unit 290 of the data processing unit 12 and proposes a route that takes travel time and arrival time into consideration. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, climate unit, transportation unit, and scheduling unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts voice input of the destination. The collection unit collects current location and climate information using the camera 42 and GPS of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit proposes the optimal route to the user using the display and speaker 240 of the smart glasses 214. The climate unit analyzes weather data using the identification processing unit 290 of the data processing unit 12 and identifies climate conditions that affect the travel route. The transportation unit analyzes traffic information using the identification processing unit 290 of the data processing unit 12 and calculates the optimal travel route. The scheduling unit analyzes the scheduling information using the identification processing unit 290 of the data processing unit 12 and proposes a route that takes travel time and arrival time into consideration. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, climate unit, transportation unit, and scheduling unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts voice input of the destination. The collection unit collects current location and climate information using the camera 42 and GPS of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit proposes the optimal route to the user using the display 343 and speaker 240 of the headset terminal 314. The climate unit analyzes weather data using the specific processing unit 290 of the data processing unit 12 and identifies climate conditions that affect the travel route. The transportation unit analyzes traffic information using the specific processing unit 290 of the data processing unit 12 and calculates the optimal travel route. The scheduling unit analyzes the scheduling information using the specific processing unit 290 of the data processing unit 12 and proposes a route that takes travel time and arrival time into consideration. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, climate unit, transportation unit, and scheduling unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts voice input of the destination. The collection unit collects current location and climate information using the camera 42 and GPS of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit proposes the optimal route to the user using the display and speaker 240 of the robot 414. The climate unit analyzes weather data using the specific processing unit 290 of the data processing unit 12 and identifies climate conditions that affect the travel route. The transportation unit analyzes traffic information using the specific processing unit 290 of the data processing unit 12 and calculates the optimal travel route. The scheduling unit analyzes the scheduling information using the specific processing unit 290 of the data processing unit 12 and proposes a route that takes travel time and arrival time into consideration. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A reception desk that accepts destination inputs, A collection unit collects information such as current location, schedule, weather, and traffic conditions based on destination information received by the aforementioned reception unit. An analysis unit analyzes the information collected by the aforementioned collection unit and calculates the optimal travel route, The system includes a proposal unit that proposes the optimal travel route calculated by the analysis unit to the user. A system characterized by the following features. (Note 2) Equipped with a climate section that takes climate information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a traffic department to take traffic conditions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a scheduling section that considers travel time and arrival time based on the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We suggest the optimal route based on your mode of transportation, such as walking, cycling, public transport, or driving. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the destination input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past destination history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering a destination, the system suggests input options based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of destination input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter a destination, the system prioritizes displaying highly relevant destinations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a user enters their destination, the system analyzes their social media activity and suggests relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, consider the interrelationships between pieces of information to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the attribute information of the information provider will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the mode of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making proposals, prioritize them based on when the mode of transport will be used. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the means of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned climate section is, It estimates the user's emotions and adjusts how climate information is displayed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned climate section is, When collecting climate information, historical climate data is used to predict the current climate. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned climate section is, It estimates the user's sentiment and adjusts the importance of climate information based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned climate section is, When collecting climate information, consider the geographical distribution. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned Transportation Department, The system estimates the user's emotions and adjusts how traffic information is displayed based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned Transportation Department, When collecting traffic information, we refer to past traffic data to predict current traffic conditions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned Transportation Department, The system estimates the user's emotions and adjusts the importance of traffic information based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned Transportation Department, When collecting traffic information, consider the geographical distribution of the information. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned planned section is, The system estimates the user's emotions and adjusts how schedule information is displayed based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned planned section is, When collecting schedule information, we predict current schedules by referring to past schedule data. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned planned section is, It estimates the user's emotions and adjusts the importance of scheduled information based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned planned section is, When collecting schedule information, consider the geographical distribution of the information. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned planned section is, When collecting schedule information, the system references the user's calendar information to make suggestions based on their schedule. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned planned section is, When collecting schedule information, we analyze users' social media activity and collect relevant schedule information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts destination inputs, A collection unit collects information such as current location, schedule, weather, and traffic conditions based on destination information received by the aforementioned reception unit. An analysis unit analyzes the information collected by the aforementioned collection unit and calculates the optimal travel route, The system includes a proposal unit that proposes the optimal travel route calculated by the analysis unit to the user. A system characterized by the following features.

2. Equipped with a climate section that takes climate information into consideration. The system according to feature 1.

3. Equipped with a traffic department to take traffic conditions into consideration. The system according to feature 1.

4. It includes a scheduling section that considers travel time and arrival time based on the user's schedule. The system according to feature 1.

5. The aforementioned proposal section is, We suggest the optimal route based on your mode of transportation, such as walking, cycling, public transport, or driving. The system according to feature 1.

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

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

8. The aforementioned reception unit is When entering a destination, the system suggests input options based on the user's current activities and areas of interest. The system according to feature 1.

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