system

Abstract

The system according to this embodiment aims to propose the optimal route for avoiding disaster risks. [Solution] The system according to the embodiment comprises a reception unit, a data collection unit, and an analysis unit. The reception unit receives destination input from the user. The data collection unit collects traffic information and weather simulation results based on the information received by the reception unit. The analysis unit analyzes the data collected by the data collection unit and proposes the optimal route to avoid disaster risks.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, route proposals are made based on traffic information and weather information, but there is room for improvement in proposing an optimal route for avoiding disaster risks.

[0005] The system according to the embodiment aims to propose an optimal route for avoiding disaster risks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data collection unit, and an analysis unit. The reception unit receives destination input from the user. The data collection unit collects traffic information and weather simulation results based on the information received by the reception unit. The analysis unit analyzes the data collected by the data collection unit and proposes the optimal route to avoid disaster risks. [Effects of the Invention]

[0007] The system according to this embodiment can propose the optimal route to avoid disaster risks. [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 a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The disaster risk avoidance route suggestion system according to an embodiment of the present invention is a system that suggests routes to avoid disaster risks not only based on traffic information but also based on real-time weather forecasts and weather simulation results using generating AI. The disaster risk avoidance route suggestion system receives a destination input from the user. Next, the system collects traffic information, real-time weather forecasts, and weather simulation results generated by generating AI. This data includes disaster risks such as storms, heavy rain, flooding, river overflows, storm surges, rockfalls, landslides, mudslides, debris flows, and other landslides. Furthermore, the system also utilizes route topography, past disaster information, disasters that have occurred in similar terrains, and real-time data such as precipitation, wind speed, and tides. The system analyzes this data and proposes the optimal route to avoid disaster risks. For example, a user inputs "I want to go from home to work." This information is input into the disaster risk avoidance route suggestion system. Next, the system collects traffic information, real-time weather forecasts, and weather simulation results generated by generating AI. This includes disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, mudslides, cliff collapses, debris flows, and landslides. For example, current precipitation, wind speed, and tidal data are also collected. The disaster risk avoidance route suggestion system analyzes this data and proposes the optimal route to avoid disaster risks. For example, it suggests routes that avoid areas where heavy rain is predicted or areas with a high risk of river overflow. In this way, users can reach their destination safely. Furthermore, the disaster risk avoidance route suggestion system also utilizes past disaster information and data on disasters that have occurred in similar terrain. This allows it to predict future disaster risks and propose safer routes. For example, it suggests routes that avoid areas where mudslides have occurred in the past or areas with a high risk of cliff collapse. Through this mechanism, users can avoid disaster risks and reach their destination safely. In particular, it can cope with the increasing severity of disasters due to climate change and sudden rainfall such as torrential downpours, thus reducing life-threatening risks.This allows the disaster risk avoidance route suggestion system to provide the optimal route that ensures user safety and avoids disaster risks.

[0029] The disaster risk avoidance route suggestion system according to this embodiment comprises a reception unit, a data collection unit, and an analysis unit. The reception unit receives destination input from the user. The user can input the destination by methods such as text input, voice input, or selection on a map. The reception unit accepts, for example, the user's input of "I want to go from home to work." The data collection unit collects traffic information and weather simulation results based on the information received by the reception unit. The data collection unit collects, for example, traffic information such as congestion information, accident information, and road construction information. The data collection unit also collects data using weather models and prediction algorithms as weather simulation results. Furthermore, the data collection unit collects data related to disaster risks such as storms, heavy rain, flooding, river flooding, storm surges, rockfalls, landslides, cliff collapses, debris flows, and mudslides. For example, the data collection unit collects meteorological data, geological data, and past disaster history. The data collection unit also collects real-time data such as precipitation, wind speed, and tides. For example, the data collection unit collects meteorological observation data and traffic sensor data. The analysis unit analyzes the data collected by the collection unit and proposes the optimal route to avoid disaster risks. For example, the analysis unit proposes the optimal route based on criteria such as required time, distance, and safety, using the collected data. The analysis unit analyzes past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, the analysis unit predicts future disaster risks using simulation models and risk assessment algorithms based on disaster history data and geological data. As a result, the disaster risk avoidance route proposal system according to this embodiment can collect traffic information and weather simulation results based on user input and propose the optimal route to avoid disaster risks.

[0030] The reception desk receives destination input from the user. Users can input their destination using methods such as text input, voice input, or selection on a map. Specifically, users input their destination using a smartphone or computer via an application or web interface. For text input, users use a keyboard to enter the address or name of the destination. For voice input, users complete the input by speaking the destination into a microphone. Voice recognition technology is used to convert the voice data into text data and identify the destination. For selection on a map, users display a map and input the destination by tapping or clicking. This allows the reception desk to receive input such as "I want to go from home to work." Furthermore, the reception desk analyzes the user's input and obtains the destination's coordinate information. For example, it identifies latitude and longitude from the address or name and converts it into a format usable within the system. The reception desk also has a function to check the user's input and correct or complete it as needed. For example, if the entered address is incomplete, it displays a list of candidates and prompts the user to select one. In the case of voice input, it also provides a feedback function that prompts the user to re-enter if misrecognition occurs. This allows the reception desk to accurately understand the user's intent and prepare to proceed to the next processing step.

[0031] The data collection unit collects traffic information and weather simulation results based on information received by the reception unit. Specifically, it collects real-time traffic information such as congestion, accident information, and road construction information. This utilizes traffic sensors, cameras, and GPS data to identify road congestion and accident locations. It also integrates data from traffic information services to understand the latest traffic conditions. For weather simulation results, it collects data such as precipitation, wind speed, and temperature using weather models and prediction algorithms. This utilizes weather satellite data, ground observation data, and information from weather forecasting agencies. Furthermore, the data collection unit collects data on disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, landslides, mudslides, debris flows, and mudslides. Specifically, it collects geological data, past disaster history, and real-time data such as precipitation, wind speed, and tides. This utilizes data from geological survey agencies and disaster prevention agencies, as well as real-time data from sensors and observation equipment. For example, the data collection unit collects weather observation data and traffic sensor data, and manages this data centrally. The collected data is stored on a cloud server, making it accessible to the analysis unit. The data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0032] The analysis unit analyzes data collected by the data collection unit and proposes the optimal route to avoid disaster risks. Specifically, it proposes the optimal route based on criteria such as travel time, distance, and safety, using the collected data. The analysis unit uses AI to process data in real time and calculate the optimal route to avoid disaster risks. For example, it analyzes collected traffic information to identify routes that avoid congestion and accidents. Furthermore, it analyzes weather simulation results to propose routes that avoid weather risks such as heavy rain and strong winds. The analysis unit analyzes past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, it uses simulation models and risk assessment algorithms based on disaster history data and geological data to predict future disaster risks. This allows the analysis unit to propose the optimal route so that users can safely reach their destinations. In addition, the analysis unit can continuously revise the proposed routes based on data that is updated in real time, allowing it to respond to the latest conditions. For example, if traffic conditions or weather change rapidly, the analysis unit immediately incorporates new data and updates the proposed route. The analysis unit can also perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the analysis unit to provide highly accurate route suggestions based on the latest information at all times, ensuring user safety.

[0033] The data collection unit can collect data on disaster risks such as storms, heavy rain, flooding, river flooding, storm surges, rockfalls, landslides, mudslides, debris flows, and mudslides. For example, the data collection unit can collect meteorological data. For example, the data collection unit can assess the risk of storms and heavy rain based on meteorological observation data. The data collection unit can also collect geological data. For example, the data collection unit can assess the risk of landslides and mudslides based on geological data. The data collection unit can also collect historical disaster records. For example, the data collection unit can assess the risk of river flooding and storm surges based on historical disaster records. By collecting data on disaster risks, it is possible to propose more accurate risk avoidance routes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input meteorological data into a generating AI, which can analyze the meteorological data and assess disaster risks.

[0034] The data collection unit can collect real-time data such as precipitation, wind speed, and tides. For example, the data collection unit can collect real-time precipitation data. For example, the data collection unit can evaluate the current precipitation based on meteorological observation data. The data collection unit can also collect real-time wind speed data. For example, the data collection unit can evaluate the current wind speed based on wind speed sensor data. The data collection unit can also collect real-time tidal data. For example, the data collection unit can evaluate the current tidal conditions based on tidal observation data. By collecting real-time data, it becomes possible to propose routes based on the latest conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input real-time precipitation data into a generating AI, and the generating AI can analyze the precipitation data to evaluate disaster risk.

[0035] The analysis unit can analyze the collected data and propose the optimal route to avoid disaster risks. For example, the analysis unit can propose the optimal route based on criteria such as required time, distance, and safety, using the collected data. For example, the analysis unit can propose a route that avoids areas where heavy rainfall is predicted. It can also propose a route that avoids areas with a high risk of river flooding. Furthermore, it can propose a route that avoids areas with a high risk of landslides. In this way, by analyzing the collected data, the optimal route to avoid disaster risks can be proposed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and propose the optimal route.

[0036] The analysis unit can analyze past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, the analysis unit can predict future disaster risks based on past disaster information. For example, the analysis unit can propose routes that avoid areas where landslides have occurred in the past. It can also propose routes that avoid areas with a high risk of cliff collapses. It can also propose routes that avoid areas with a high risk of river flooding. In this way, by analyzing past disaster information, future disaster risks can be predicted and safer routes can be proposed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past disaster information into a generating AI, and the generating AI can analyze the data to predict future disaster risks.

[0037] The reception desk can analyze the user's past destination input history and suggest the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then analyze the data and suggest the optimal input method.

[0038] The reception unit can suggest input options based on the user's current situation and areas of interest when the user enters a destination. For example, the reception unit can suggest nearby popular spots as options based on the user's current location. The reception unit can also suggest relevant destinations based on the user's areas of interest (e.g., restaurants, tourist attractions). Furthermore, the reception unit can suggest the most suitable destination according to the user's current situation (e.g., weather and time of day). By suggesting input options based on the user's current situation and areas of interest, it is possible to suggest a more appropriate destination. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current location information into a generating AI, which can then analyze the location information and suggest relevant destinations.

[0039] The reception desk can prioritize and present highly relevant destinations when the user enters a destination, taking into account the user's geographical location. For example, the reception desk can prioritize locations close to the user's current location. It can also prioritize destinations near places the user has visited in the past. Furthermore, the reception desk can prioritize easily accessible destinations, taking into account the user's means of transportation from their current location. In this way, by considering the user's geographical location, highly relevant destinations can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI, which can then analyze the location information and suggest highly relevant destinations.

[0040] The reception desk can analyze the user's social media activity when a destination is entered and suggest relevant destinations. For example, the reception desk can suggest relevant destinations based on places the user has checked in to on social media. It can also analyze the content of posts from accounts the user follows on social media and suggest relevant destinations. Furthermore, it can suggest relevant destinations based on posts the user has "liked" on social media. In this way, relevant destinations can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze the data and suggest relevant destinations.

[0041] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the optimal method based on data collection methods previously used by the user. The data collection unit can also propose an efficient collection method based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and optimize collection frequency and timing. This allows for the selection of the optimal collection method by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI, which can then analyze the data and propose the optimal collection method.

[0042] The data collection unit can filter data based on the user's current situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting relevant data based on the user's current location. It can also collect relevant data based on the user's areas of interest (e.g., weather, traffic information). Furthermore, the data collection unit can collect optimal data according to the user's current situation (e.g., weather, time of day). This allows for the collection of more relevant data by filtering data based on the user's current situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current situation data into a generating AI, which can then analyze the data and collect relevant data.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from locations close to the user's current location. It can also prioritize the collection of data near locations the user has visited in the past. Furthermore, the data collection unit can prioritize the collection of data from easily accessible locations by considering the user's means of transportation from their current location. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the location information and collect highly relevant data.

[0044] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on locations where the user has checked in on social media. The data collection unit can also analyze the content of posts from accounts that the user follows on social media and collect relevant data. Furthermore, the data collection unit can collect relevant data based on posts that the user has "liked" on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0045] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also propose an efficient analysis method from past analysis data. Furthermore, the analysis unit can analyze past analysis data to improve the accuracy of the analysis algorithm. Thus, the accuracy of the analysis algorithm can be improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI, which can then analyze the data and propose the optimal analysis algorithm.

[0046] The analysis unit can customize the analysis method based on the user's current situation and areas of interest during analysis. For example, the analysis unit can select a relevant analysis method based on the user's current location information. The analysis unit can also suggest a relevant analysis method based on the user's areas of interest (e.g., weather, traffic information). Furthermore, the analysis unit can select the optimal analysis method according to the user's current situation (e.g., weather, time of day). By customizing the analysis method based on the user's current situation and areas of interest, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current situation data into a generating AI, which can then analyze the data and suggest a relevant analysis method.

[0047] The analysis unit can select the optimal analysis method by considering the user's geographical location information during analysis. For example, the analysis unit can select the optimal analysis method based on data of locations close to the user's current location. It can also select the optimal analysis method based on data of locations near places the user has visited in the past. Furthermore, the analysis unit can select the optimal analysis method based on data of easily accessible locations, taking into account the user's means of transportation from their current location. In this way, the optimal analysis method can be selected by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then analyze the location information and propose the optimal analysis method.

[0048] The analysis unit can analyze a user's social media activity during analysis and propose relevant analysis methods. For example, the analysis unit can propose the optimal analysis method based on data of locations where the user has checked in on social media. The analysis unit can also analyze the content of posts from accounts that the user follows on social media and propose relevant analysis methods. Furthermore, the analysis unit can propose the optimal analysis method based on posts that the user has "liked" on social media. In this way, relevant analysis methods can be proposed by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI, which can then analyze the data and propose relevant analysis methods.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the user's past travel history and select the optimal data collection method. For example, it can prioritize collecting data on routes the user has frequently traveled in the past. It can also collect data on routes the user has avoided in the past to aid in risk assessment. Furthermore, it can predict travel patterns during specific time periods based on the user's past travel history and optimize the timing of data collection. This allows for the suggestion of more accurate risk-avoidance routes by analyzing past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past travel history data into a generating AI, which can then analyze the data and suggest the optimal collection method.

[0051] The reception desk can analyze the user's past destination input history and suggest the optimal input method. For example, it can automatically display destinations that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest destinations to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then analyze the data and suggest the optimal input method.

[0052] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, it can select the optimal analysis algorithm based on past analysis data. It can also propose an efficient analysis method from past analysis data. Furthermore, it can improve the accuracy of the analysis algorithm by analyzing past analysis data. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can analyze the data and propose the optimal analysis algorithm.

[0053] The reception unit can suggest input options based on the user's current situation and areas of interest when the user enters a destination. For example, it can suggest nearby popular spots as options based on the user's current location. It can also suggest relevant destinations based on the user's areas of interest (e.g., restaurants, tourist attractions). Furthermore, it can suggest the most suitable destination according to the user's current situation (e.g., weather and time of day). By suggesting input options based on the user's current situation and areas of interest, it can suggest a more appropriate destination. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current location information into a generating AI, which can then analyze the location information and suggest relevant destinations.

[0054] The data collection unit can filter data based on the user's current situation and areas of interest during data collection. For example, it can prioritize collecting relevant data based on the user's current location. It can also collect relevant data based on the user's areas of interest (e.g., weather, traffic information). Furthermore, it can collect optimal data according to the user's current situation (e.g., weather and time of day). By filtering data based on the user's current situation and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current situation data into a generating AI, which can then analyze the data and collect relevant data.

[0055] The analysis unit can customize the analysis method based on the user's current situation and areas of interest during analysis. For example, it can select a relevant analysis method based on the user's current location information. It can also suggest a relevant analysis method based on the user's areas of interest (e.g., weather, traffic information). Furthermore, it can select the optimal analysis method according to the user's current situation (e.g., weather, time of day). By customizing the analysis method based on the user's current situation and areas of interest, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current situation data into a generating AI, which can then analyze the data and suggest a relevant analysis method.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives destination input from the user. Users can enter their destination using methods such as text input, voice input, or selection on a map. For example, the reception desk accepts the user's input, "I want to go from home to work." Step 2: The data collection unit collects traffic information and weather simulation results based on the information received by the reception unit. For example, the data collection unit collects traffic information such as congestion information, accident information, and road construction information. The data collection unit also collects data using weather models and prediction algorithms as weather simulation results. Furthermore, the data collection unit collects data related to disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, mudslides, cliff collapses, debris flows, and landslides. For example, the data collection unit collects meteorological data, geological data, and past disaster history. The data collection unit also collects real-time data such as precipitation, wind speed, and tides. For example, the data collection unit collects meteorological observation data and traffic sensor data. Step 3: The analysis unit analyzes the data collected by the collection unit and proposes the optimal route to avoid disaster risks. For example, the analysis unit proposes the optimal route based on criteria such as required time, distance, and safety, using the collected data. The analysis unit analyzes past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, the analysis unit predicts future disaster risks using simulation models and risk assessment algorithms based on disaster history data and geological data.

[0058] (Example of form 2) The disaster risk avoidance route suggestion system according to an embodiment of the present invention is a system that suggests routes to avoid disaster risks not only based on traffic information but also based on real-time weather forecasts and weather simulation results using generating AI. The disaster risk avoidance route suggestion system receives a destination input from the user. Next, the system collects traffic information, real-time weather forecasts, and weather simulation results generated by generating AI. This data includes disaster risks such as storms, heavy rain, flooding, river overflows, storm surges, rockfalls, landslides, mudslides, debris flows, and other landslides. Furthermore, the system also utilizes route topography, past disaster information, disasters that have occurred in similar terrains, and real-time data such as precipitation, wind speed, and tides. The system analyzes this data and proposes the optimal route to avoid disaster risks. For example, a user inputs "I want to go from home to work." This information is input into the disaster risk avoidance route suggestion system. Next, the system collects traffic information, real-time weather forecasts, and weather simulation results generated by generating AI. This includes disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, mudslides, cliff collapses, debris flows, and landslides. For example, current precipitation, wind speed, and tidal data are also collected. The disaster risk avoidance route suggestion system analyzes this data and proposes the optimal route to avoid disaster risks. For example, it suggests routes that avoid areas where heavy rain is predicted or areas with a high risk of river overflow. In this way, users can reach their destination safely. Furthermore, the disaster risk avoidance route suggestion system also utilizes past disaster information and data on disasters that have occurred in similar terrain. This allows it to predict future disaster risks and propose safer routes. For example, it suggests routes that avoid areas where mudslides have occurred in the past or areas with a high risk of cliff collapse. Through this mechanism, users can avoid disaster risks and reach their destination safely. In particular, it can cope with the increasing severity of disasters due to climate change and sudden rainfall such as torrential downpours, thus reducing life-threatening risks.This allows the disaster risk avoidance route suggestion system to provide the optimal route that ensures user safety and avoids disaster risks.

[0059] The disaster risk avoidance route suggestion system according to this embodiment comprises a reception unit, a data collection unit, and an analysis unit. The reception unit receives destination input from the user. The user can input the destination by methods such as text input, voice input, or selection on a map. The reception unit accepts, for example, the user's input of "I want to go from home to work." The data collection unit collects traffic information and weather simulation results based on the information received by the reception unit. The data collection unit collects, for example, traffic information such as congestion information, accident information, and road construction information. The data collection unit also collects data using weather models and prediction algorithms as weather simulation results. Furthermore, the data collection unit collects data related to disaster risks such as storms, heavy rain, flooding, river flooding, storm surges, rockfalls, landslides, cliff collapses, debris flows, and mudslides. For example, the data collection unit collects meteorological data, geological data, and past disaster history. The data collection unit also collects real-time data such as precipitation, wind speed, and tides. For example, the data collection unit collects meteorological observation data and traffic sensor data. The analysis unit analyzes the data collected by the collection unit and proposes the optimal route to avoid disaster risks. For example, the analysis unit proposes the optimal route based on criteria such as required time, distance, and safety, using the collected data. The analysis unit analyzes past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, the analysis unit predicts future disaster risks using simulation models and risk assessment algorithms based on disaster history data and geological data. As a result, the disaster risk avoidance route proposal system according to this embodiment can collect traffic information and weather simulation results based on user input and propose the optimal route to avoid disaster risks.

[0060] The reception desk receives destination input from the user. Users can input their destination using methods such as text input, voice input, or selection on a map. Specifically, users input their destination using a smartphone or computer via an application or web interface. For text input, users use a keyboard to enter the address or name of the destination. For voice input, users complete the input by speaking the destination into a microphone. Voice recognition technology is used to convert the voice data into text data and identify the destination. For selection on a map, users display a map and input the destination by tapping or clicking. This allows the reception desk to receive input such as "I want to go from home to work." Furthermore, the reception desk analyzes the user's input and obtains the destination's coordinate information. For example, it identifies latitude and longitude from the address or name and converts it into a format usable within the system. The reception desk also has a function to check the user's input and correct or complete it as needed. For example, if the entered address is incomplete, it displays a list of candidates and prompts the user to select one. In the case of voice input, it also provides a feedback function that prompts the user to re-enter if misrecognition occurs. This allows the reception desk to accurately understand the user's intent and prepare to proceed to the next processing step.

[0061] The data collection unit collects traffic information and weather simulation results based on information received by the reception unit. Specifically, it collects real-time traffic information such as congestion, accident information, and road construction information. This utilizes traffic sensors, cameras, and GPS data to identify road congestion and accident locations. It also integrates data from traffic information services to understand the latest traffic conditions. For weather simulation results, it collects data such as precipitation, wind speed, and temperature using weather models and prediction algorithms. This utilizes weather satellite data, ground observation data, and information from weather forecasting agencies. Furthermore, the data collection unit collects data on disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, landslides, mudslides, debris flows, and mudslides. Specifically, it collects geological data, past disaster history, and real-time data such as precipitation, wind speed, and tides. This utilizes data from geological survey agencies and disaster prevention agencies, as well as real-time data from sensors and observation equipment. For example, the data collection unit collects weather observation data and traffic sensor data, and manages this data centrally. The collected data is stored on a cloud server, making it accessible to the analysis unit. The data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0062] The analysis unit analyzes data collected by the data collection unit and proposes the optimal route to avoid disaster risks. Specifically, it proposes the optimal route based on criteria such as travel time, distance, and safety, using the collected data. The analysis unit uses AI to process data in real time and calculate the optimal route to avoid disaster risks. For example, it analyzes collected traffic information to identify routes that avoid congestion and accidents. Furthermore, it analyzes weather simulation results to propose routes that avoid weather risks such as heavy rain and strong winds. The analysis unit analyzes past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, it uses simulation models and risk assessment algorithms based on disaster history data and geological data to predict future disaster risks. This allows the analysis unit to propose the optimal route so that users can safely reach their destinations. In addition, the analysis unit can continuously revise the proposed routes based on data that is updated in real time, allowing it to respond to the latest conditions. For example, if traffic conditions or weather change rapidly, the analysis unit immediately incorporates new data and updates the proposed route. The analysis unit can also perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the analysis unit to provide highly accurate route suggestions based on the latest information at all times, ensuring user safety.

[0063] The data collection unit can collect data on disaster risks such as storms, heavy rain, flooding, river flooding, storm surges, rockfalls, landslides, mudslides, debris flows, and mudslides. For example, the data collection unit can collect meteorological data. For example, the data collection unit can assess the risk of storms and heavy rain based on meteorological observation data. The data collection unit can also collect geological data. For example, the data collection unit can assess the risk of landslides and mudslides based on geological data. The data collection unit can also collect historical disaster records. For example, the data collection unit can assess the risk of river flooding and storm surges based on historical disaster records. By collecting data on disaster risks, it is possible to propose more accurate risk avoidance routes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input meteorological data into a generating AI, which can analyze the meteorological data and assess disaster risks.

[0064] The data collection unit can collect real-time data such as precipitation, wind speed, and tides. For example, the data collection unit can collect real-time precipitation data. For example, the data collection unit can evaluate the current precipitation based on meteorological observation data. The data collection unit can also collect real-time wind speed data. For example, the data collection unit can evaluate the current wind speed based on wind speed sensor data. The data collection unit can also collect real-time tidal data. For example, the data collection unit can evaluate the current tidal conditions based on tidal observation data. By collecting real-time data, it becomes possible to propose routes based on the latest conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input real-time precipitation data into a generating AI, and the generating AI can analyze the precipitation data to evaluate disaster risk.

[0065] The analysis unit can analyze the collected data and propose the optimal route to avoid disaster risks. For example, the analysis unit can propose the optimal route based on criteria such as required time, distance, and safety, using the collected data. For example, the analysis unit can propose a route that avoids areas where heavy rainfall is predicted. It can also propose a route that avoids areas with a high risk of river flooding. Furthermore, it can propose a route that avoids areas with a high risk of landslides. In this way, by analyzing the collected data, the optimal route to avoid disaster risks can be proposed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and propose the optimal route.

[0066] The analysis unit can analyze past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, the analysis unit can predict future disaster risks based on past disaster information. For example, the analysis unit can propose routes that avoid areas where landslides have occurred in the past. It can also propose routes that avoid areas with a high risk of cliff collapses. It can also propose routes that avoid areas with a high risk of river flooding. In this way, by analyzing past disaster information, future disaster risks can be predicted and safer routes can be proposed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past disaster information into a generating AI, and the generating AI can analyze the data to predict future disaster risks.

[0067] The reception desk can estimate the user's emotions and adjust the destination input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick destination input. This provides a more comfortable user experience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can analyze the facial expression data to estimate the user's emotions.

[0068] The reception desk can analyze the user's past destination input history and suggest the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then analyze the data and suggest the optimal input method.

[0069] The reception unit can suggest input options based on the user's current situation and areas of interest when the user enters a destination. For example, the reception unit can suggest nearby popular spots as options based on the user's current location. The reception unit can also suggest relevant destinations based on the user's areas of interest (e.g., restaurants, tourist attractions). Furthermore, the reception unit can suggest the most suitable destination according to the user's current situation (e.g., weather and time of day). By suggesting input options based on the user's current situation and areas of interest, it is possible to suggest a more appropriate destination. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current location information into a generating AI, which can then analyze the location information and suggest relevant destinations.

[0070] The reception desk can estimate the user's emotions and determine the priority of the entered destinations based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize suggesting places where the user can relax. If the user is in a hurry, the reception desk can also prioritize suggesting destinations that can be reached in the shortest amount of time. If the user is having fun, the reception desk can also prioritize suggesting entertainment facilities. In this way, by prioritizing destinations according to the user's emotions, a more appropriate route can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can analyze the facial expression data to estimate the user's emotions.

[0071] The reception desk can prioritize and present highly relevant destinations when the user enters a destination, taking into account the user's geographical location. For example, the reception desk can prioritize locations close to the user's current location. It can also prioritize destinations near places the user has visited in the past. Furthermore, the reception desk can prioritize easily accessible destinations, taking into account the user's means of transportation from their current location. In this way, by considering the user's geographical location, highly relevant destinations can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI, which can then analyze the location information and suggest highly relevant destinations.

[0072] The reception desk can analyze the user's social media activity when a destination is entered and suggest relevant destinations. For example, the reception desk can suggest relevant destinations based on places the user has checked in to on social media. It can also analyze the content of posts from accounts the user follows on social media and suggest relevant destinations. Furthermore, it can suggest relevant destinations based on posts the user has "liked" on social media. In this way, relevant destinations can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze the data and suggest relevant destinations.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data frequently when the user is relaxed. Conversely, it can reduce the frequency of data collection when the user is stressed. Furthermore, if the user is in a hurry, the data collection unit can collect only the minimum necessary data. This allows for more appropriate data collection by adjusting the timing of data collection according 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI, which can then analyze the facial expression data to estimate the user's emotions.

[0074] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the optimal method based on data collection methods previously used by the user. The data collection unit can also propose an efficient collection method based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and optimize collection frequency and timing. This allows for the selection of the optimal collection method by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI, which can then analyze the data and propose the optimal collection method.

[0075] The data collection unit can filter data based on the user's current situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting relevant data based on the user's current location. It can also collect relevant data based on the user's areas of interest (e.g., weather, traffic information). Furthermore, the data collection unit can collect optimal data according to the user's current situation (e.g., weather, time of day). This allows for the collection of more relevant data by filtering data based on the user's current situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current situation data into a generating AI, which can then analyze the data and collect relevant data.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that promotes relaxation. If the user is in a hurry, the data collection unit can also prioritize collecting data that can be collected quickly. Furthermore, if the user is enjoying themselves, the data collection unit can prioritize collecting entertainment-related data. This allows for more appropriate data collection by prioritizing data according 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI, which can then analyze the facial expression data to estimate the user's emotions.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from locations close to the user's current location. It can also prioritize the collection of data near locations the user has visited in the past. Furthermore, the data collection unit can prioritize the collection of data from easily accessible locations by considering the user's means of transportation from their current location. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the location information and collect highly relevant data.

[0078] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on locations where the user has checked in on social media. The data collection unit can also analyze the content of posts from accounts that the user follows on social media and collect relevant data. Furthermore, the data collection unit can collect relevant data based on posts that the user has "liked" on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, and the generative AI can analyze the facial expression data to estimate the user's emotions.

[0080] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also propose an efficient analysis method from past analysis data. Furthermore, the analysis unit can analyze past analysis data to improve the accuracy of the analysis algorithm. Thus, the accuracy of the analysis algorithm can be improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI, which can then analyze the data and propose the optimal analysis algorithm.

[0081] The analysis unit can customize the analysis method based on the user's current situation and areas of interest during analysis. For example, the analysis unit can select a relevant analysis method based on the user's current location information. The analysis unit can also suggest a relevant analysis method based on the user's areas of interest (e.g., weather, traffic information). Furthermore, the analysis unit can select the optimal analysis method according to the user's current situation (e.g., weather, time of day). By customizing the analysis method based on the user's current situation and areas of interest, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current situation data into a generating AI, which can then analyze the data and suggest a relevant analysis method.

[0082] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying analysis results that promote relaxation. If the user is in a hurry, the analysis unit can also prioritize displaying analysis results that can be displayed quickly. Furthermore, if the user is enjoying themselves, the analysis unit can prioritize displaying entertainment-related analysis results. This allows for the provision of more appropriate information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI, which can then analyze the facial expression data to estimate the user's emotions.

[0083] The analysis unit can select the optimal analysis method by considering the user's geographical location information during analysis. For example, the analysis unit can select the optimal analysis method based on data of locations close to the user's current location. It can also select the optimal analysis method based on data of locations near places the user has visited in the past. Furthermore, the analysis unit can select the optimal analysis method based on data of easily accessible locations, taking into account the user's means of transportation from their current location. In this way, the optimal analysis method can be selected by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then analyze the location information and propose the optimal analysis method.

[0084] The analysis unit can analyze a user's social media activity during analysis and propose relevant analysis methods. For example, the analysis unit can propose the optimal analysis method based on data of locations where the user has checked in on social media. The analysis unit can also analyze the content of posts from accounts that the user follows on social media and propose relevant analysis methods. Furthermore, the analysis unit can propose the optimal analysis method based on posts that the user has "liked" on social media. In this way, relevant analysis methods can be proposed by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI, which can then analyze the data and propose relevant analysis methods.

[0085] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying analysis results that promote relaxation. If the user is in a hurry, the analysis unit can also prioritize displaying analysis results that can be displayed quickly. Furthermore, if the user is enjoying themselves, the analysis unit can prioritize displaying entertainment-related analysis results. This allows for the provision of more appropriate information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI, which can then analyze the facial expression data to estimate the user's emotions.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can monitor the user's health status and adjust the destination input method based on that status. For example, if the user is feeling fatigued, it can provide a simple interface and minimize the input steps. If the user is healthy, it can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick destination input. This allows for a more comfortable user experience by adjusting the input method according to the user's health status. Health status monitoring is performed, for example, using sensor data from wearable devices or smartphones. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's heart rate data into a generating AI, which can analyze the heart rate data to estimate the user's health status.

[0088] The data collection unit can analyze the user's past travel history and select the optimal data collection method. For example, it can prioritize collecting data on routes the user has frequently traveled in the past. It can also collect data on routes the user has avoided in the past to aid in risk assessment. Furthermore, it can predict travel patterns during specific time periods based on the user's past travel history and optimize the timing of data collection. This allows for the suggestion of more accurate risk-avoidance routes by analyzing past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past travel history data into a generating AI, which can then analyze the data and suggest the optimal collection method.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI, and the generative AI can analyze the facial expression data to estimate the user's emotions.

[0090] The reception desk can analyze the user's past destination input history and suggest the optimal input method. For example, it can automatically display destinations that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest destinations to be used during specific time periods based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then analyze the data and suggest the optimal input method.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, data collection can be performed more frequently. Conversely, if the user is stressed, the frequency of data collection can be reduced. Furthermore, if the user is in a hurry, only the minimum necessary data can be collected. This allows for more appropriate data collection by adjusting the timing of data collection according 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI, which can then analyze the facial expression data to estimate the user's emotions.

[0092] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, it can select the optimal analysis algorithm based on past analysis data. It can also propose an efficient analysis method from past analysis data. Furthermore, it can improve the accuracy of the analysis algorithm by analyzing past analysis data. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can analyze the data and propose the optimal analysis algorithm.

[0093] The reception unit can suggest input options based on the user's current situation and areas of interest when the user enters a destination. For example, it can suggest nearby popular spots as options based on the user's current location. It can also suggest relevant destinations based on the user's areas of interest (e.g., restaurants, tourist attractions). Furthermore, it can suggest the most suitable destination according to the user's current situation (e.g., weather and time of day). By suggesting input options based on the user's current situation and areas of interest, it can suggest a more appropriate destination. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current location information into a generating AI, which can then analyze the location information and suggest relevant destinations.

[0094] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, it can prioritize displaying analysis results that promote relaxation. If the user is in a hurry, it can prioritize displaying analysis results that can be displayed quickly. Furthermore, if the user is enjoying themselves, it can prioritize displaying entertainment-related analysis results. This allows for the provision of more appropriate information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, which can analyze the facial expression data to estimate the user's emotions.

[0095] The data collection unit can filter data based on the user's current situation and areas of interest during data collection. For example, it can prioritize collecting relevant data based on the user's current location. It can also collect relevant data based on the user's areas of interest (e.g., weather, traffic information). Furthermore, it can collect optimal data according to the user's current situation (e.g., weather and time of day). By filtering data based on the user's current situation and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current situation data into a generating AI, which can then analyze the data and collect relevant data.

[0096] The analysis unit can customize the analysis method based on the user's current situation and areas of interest during analysis. For example, it can select a relevant analysis method based on the user's current location information. It can also suggest a relevant analysis method based on the user's areas of interest (e.g., weather, traffic information). Furthermore, it can select the optimal analysis method according to the user's current situation (e.g., weather, time of day). By customizing the analysis method based on the user's current situation and areas of interest, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current situation data into a generating AI, which can then analyze the data and suggest a relevant analysis method.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives destination input from the user. Users can enter their destination using methods such as text input, voice input, or selection on a map. For example, the reception desk accepts the user's input, "I want to go from home to work." Step 2: The data collection unit collects traffic information and weather simulation results based on the information received by the reception unit. For example, the data collection unit collects traffic information such as congestion information, accident information, and road construction information. The data collection unit also collects data using weather models and prediction algorithms as weather simulation results. Furthermore, the data collection unit collects data related to disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, mudslides, cliff collapses, debris flows, and landslides. For example, the data collection unit collects meteorological data, geological data, and past disaster history. The data collection unit also collects real-time data such as precipitation, wind speed, and tides. For example, the data collection unit collects meteorological observation data and traffic sensor data. Step 3: The analysis unit analyzes the data collected by the collection unit and proposes the optimal route to avoid disaster risks. For example, the analysis unit proposes the optimal route based on criteria such as required time, distance, and safety, using the collected data. The analysis unit analyzes past disaster information and data from disasters that have occurred in similar terrains to predict future disaster risks. For example, the analysis unit predicts future disaster risks using simulation models and risk assessment algorithms based on disaster history data and geological data.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the reception unit, collection unit, and analysis unit, is implemented in 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 receives destination input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects traffic information and weather simulation results. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal route to avoid disaster risks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, collection unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives destination input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects traffic information and weather simulation results. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal route to avoid disaster risks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, collection unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives destination input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects traffic information and weather simulation results. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal route to avoid disaster risks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[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 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.

[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 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).

[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] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, collection unit, and analysis 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 control unit 46A of the robot 414 and receives destination input from the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects traffic information and weather simulation results. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the optimal route to avoid disaster risks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A reception area that receives destination input from the user, A collection unit collects traffic information and weather simulation results based on the information received by the aforementioned reception unit, The system includes an analysis unit that analyzes the data collected by the aforementioned collection unit and proposes the optimal route to avoid disaster risks. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, mudslides, cliff collapses, debris flows, and landslides. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It collects real-time data such as precipitation, wind speed, and tides. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is analyzed to propose the optimal route to avoid disaster risks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, By analyzing past disaster information and data from disasters that have occurred in similar terrain, we can predict future disaster risks. 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 input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering a destination, the system suggests input options based on the user's current situation 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 the entered destinations 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 a destination is entered, the system prioritizes and displays highly relevant destinations, taking into account 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 a 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 We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze past data collection 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 collecting data, filtering is performed based on the user's current situation 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 data 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 During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the optimal analysis method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, we analyze users' social media activity and propose relevant analytical methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 area that receives destination input from the user, A collection unit collects traffic information and weather simulation results based on the information received by the aforementioned reception unit, The system includes an analysis unit that analyzes the data collected by the aforementioned collection unit and proposes the optimal route to avoid disaster risks. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on disaster risks such as storms, heavy rain, flooding, river overflow, storm surges, rockfalls, mudslides, cliff collapses, debris flows, and landslides. The system according to feature 1.

3. The aforementioned collection unit is It collects real-time data such as precipitation, wind speed, and tides. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is analyzed to propose the optimal route to avoid disaster risks. The system according to feature 1.

5. The aforementioned analysis unit, By analyzing past disaster information and data from disasters that have occurred in similar terrain, we can predict future disaster risks. 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 input 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 situation and areas of interest. The system according to feature 1.

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