system

Abstract

The system according to the embodiment aims to provide information on passable roads quickly and accurately even during a disaster. [Solution] A system according to an embodiment includes an information collection unit, an analysis unit, and a presentation unit. The information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and the telecommunications carrier Smart Fleet. The analysis unit analyzes the information collected by the information collection unit and identifies passable roads. The presentation unit presents the passable roads identified by the analysis unit to a user.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately grasp information on whether roads are passable during a disaster.

[0005] The system according to the embodiment aims to provide information on passable roads quickly and accurately even during a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a presentation unit. The information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private websites, and the telecommunications carrier Smart Fleet. The analysis unit analyzes the information collected by the information collection unit and identifies passable roads. The presentation unit presents the passable roads identified by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide passable road information quickly and accurately even during a disaster. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An AI system according to an embodiment of the present invention combines various information, such as information from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), data from private websites, and telecommunications carriers' Smart Fleets, to suggest passable roads for smooth recovery efforts following the 2024 Noto Peninsula Earthquake. This system first collects road information from the MLIT, private websites, and telecommunications carriers' Smart Fleets. Next, AI analyzes the collected information and identifies passable roads. Finally, it presents the identified passable roads to the user. This system overcomes travel restrictions caused by road closures and congestion and facilitates recovery efforts. For example, road information is collected from the MLIT, private websites, telecommunications carriers' Smart Fleets, and other sources. This information includes detailed information on road closures, congestion, and passable roads. For example, road closure status is obtained from the MLIT database, congestion information is obtained from private websites, and passable road information is obtained from telecommunications carriers' Smart Fleets. Next, AI analyzes the collected information. Based on the collected information, the AI ​​identifies passable roads. For example, it analyzes road closures and traffic congestion information to calculate passable routes. It also identifies passable roads in real time based on information obtained from telecommunications carriers' smart fleets. Finally, it presents the identified passable roads to the user. The user can plan recovery activities based on the passable roads identified by the AI. For example, recovery workers can reach the site using passable roads and carry out recovery work quickly. This system overcomes travel restrictions caused by road closures and traffic congestion, allowing recovery activities to proceed smoothly. In this way, the AI ​​system overcomes travel restrictions caused by road closures and traffic congestion, allowing recovery activities to proceed smoothly.

[0029] The AI ​​system according to the embodiment includes an information collection unit, an analysis unit, and a presentation unit. The information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), a private website, or a telecommunications carrier's Smart Fleet. For example, the information collection unit can acquire road disruption status information from a database of the MLIT. The information collection unit can also acquire congestion information from a private website. The information collection unit can also acquire information on passable roads from a telecommunications carrier's Smart Fleet. The analysis unit analyzes the information collected by the information collection unit and identifies passable roads. For example, the analysis unit can calculate passable routes based on the collected road disruption status and congestion information. The analysis unit can also identify passable roads in real time based on information acquired from the telecommunications carrier's Smart Fleet. The presentation unit presents the passable roads identified by the analysis unit to a user. For example, the presentation unit can display the identified passable roads on a map. The presentation unit can also provide text guidance about the identified passable roads. As a result, the AI ​​system of the embodiment can collect and analyze road information and present passable roads to the user, allowing recovery activities to proceed smoothly.

[0030] The information collection unit can obtain road closure status from the Ministry of Land, Infrastructure, Transport and Tourism's database. The information collection unit obtains road closure status from the Ministry of Land, Infrastructure, Transport and Tourism's database, for example. The Ministry of Land, Infrastructure, Transport and Tourism's database includes road closure status such as complete road closures and partial road closures. For example, the information collection unit can access the Ministry of Land, Infrastructure, Transport and Tourism's database through a specific API to obtain the latest road closure information. The information collection unit can also periodically update the road closure status using the database's URL. This makes it possible to collect reliable information by obtaining road closure status from the Ministry of Land, Infrastructure, Transport and Tourism's database.

[0031] The information collection unit can acquire congestion information from private sites. The information collection unit acquires congestion information from, for example, private sites. Private sites include information such as traffic volume, average speed, and causes of congestion. For example, the information collection unit can acquire real-time congestion information from specific traffic information sites or apps. The information collection unit can also periodically update congestion information using the API of private sites. In this way, by acquiring congestion information from private sites, real-time traffic conditions can be grasped.

[0032] The information collection unit can acquire information about passable roads from the telecommunications carrier's smart fleet. The information collection unit acquires information about passable roads, for example, from the telecommunications carrier's smart fleet. The smart fleet includes information such as real-time traffic conditions and road opening status. For example, the information collection unit can acquire the latest passable road information using the API of the telecommunications carrier's smart fleet service. The information collection unit can also access the smart fleet database and periodically update the passable road information. In this way, by acquiring information about passable roads from the telecommunications carrier's smart fleet, the latest passable road information can be obtained.

[0033] The analysis unit can identify passable roads based on the collected information. The analysis unit, for example, identifies passable roads based on the collected information. The analysis unit analyzes road disruption status and congestion information to calculate passable routes. For example, the analysis unit uses an algorithm to identify passable roads based on road disruption status. The analysis unit can also use an algorithm to calculate an optimal route based on congestion information. Furthermore, the analysis unit can also identify passable roads based on real-time information obtained from the telecommunications carrier's Smart Fleet. In this way, by identifying passable roads based on the collected information, it is possible to provide an optimal route.

[0034] The presentation unit can present the identified passable roads to the user. For example, the presentation unit presents the identified passable roads to the user. The presentation unit can display the identified passable roads on a map. For example, the presentation unit highlights the passable roads on the map to visually present them to the user. The presentation unit can also provide text guidance on the identified passable roads. For example, the presentation unit displays a list of passable roads in text format and provides it to the user. In this way, presenting the identified passable roads to the user allows the user to quickly plan recovery activities.

[0035] The information collection unit can analyze past disaster data and select the optimal information collection method. For example, the information collection unit analyzes past disaster data and selects the optimal information collection method. The information collection unit can analyze past earthquake data and identify the most effective information collection means. The information collection unit can also analyze past typhoon data and determine the priority of information collection based on wind speed and rainfall. Furthermore, the information collection unit can analyze past flood data and select an information collection method according to rising water levels. In this way, the optimal information collection method can be selected by analyzing past disaster data.

[0036] The information collection unit can evaluate the reliability of the information to be collected when collecting information, and prioritize collecting highly reliable information. For example, the information collection unit can evaluate the reliability of the information to be collected when collecting information, and prioritize collecting highly reliable information. The information collection unit can prioritize collecting information from the Ministry of Land, Infrastructure, Transport and Tourism database as being highly reliable. The information collection unit can also cross-check information from private sites and collect only highly reliable information. Furthermore, the information collection unit can prioritize collecting real-time information from telecommunications carriers' smart fleets. In this way, by evaluating the reliability of the information to be collected, highly reliable information can be prioritized.

[0037] The information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the information collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. The information collection unit can prioritize collecting information about roads close to the user's current location. The information collection unit can also prioritize collecting information related to the user's planned travel destination. Furthermore, the information collection unit can also prioritize collecting highly relevant information based on the user's past travel history. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0038] The information collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the information collection unit can analyze the user's social media activities and collect related information when collecting information. The information collection unit can collect related road information based on location information shared by the user on social media. The information collection unit can also collect related information based on information about accounts the user follows on social media. Furthermore, the information collection unit can analyze content posted by the user on social media and collect related information. In this way, related information can be collected by analyzing the user's social media activities.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected information during analysis. The analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail of information with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected information.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply an analysis algorithm using a geographic information system (GIS) to road disruption information. The analysis unit can also apply an analysis algorithm using a traffic simulation to congestion information. Furthermore, the analysis unit can apply a real-time data analysis algorithm to passable road information. In this way, applying different analysis algorithms depending on the category of information enables more accurate analysis.

[0041] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit determines the priority of analysis based on the time of submission of information during analysis. The analysis unit can prioritize analyzing the latest information. The analysis unit can also lower the priority of information that was submitted recently. Furthermore, the analysis unit can analyze information that was submitted recently with a moderate priority. In this way, by determining the priority of analysis based on the time of submission of information, the latest information can be prioritized for analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. The analysis unit can prioritize analysis of information with high relevance. The analysis unit can also postpone the order of analysis of information with low relevance. Furthermore, the analysis unit can analyze information with medium relevance in an appropriate order. In this way, adjusting the order of analysis based on the relevance of information enables efficient analysis.

[0043] The presentation unit can select the optimal presentation method by referring to the user's past operation history when presenting. For example, the presentation unit can select the optimal presentation method by referring to the user's past operation history when presenting. The presentation unit can preferentially provide a presentation method that the user has used favorably in the past. The presentation unit can also select the most effective presentation method from the user's past operation history. Furthermore, the presentation unit can analyze the user's past operation history and suggest the optimal presentation method. In this way, the optimal presentation method can be provided by referring to the user's past operation history.

[0044] The presentation unit can customize the presentation content based on the user's current situation at the time of presentation. For example, the presentation unit customizes the presentation content based on the user's current situation at the time of presentation. When the user is in an emergency, the presentation unit can prioritize the presentation of important information. Furthermore, when the user is relaxed, the presentation unit can also provide presentation content including detailed information. Furthermore, when the user is moving, the presentation unit can present information that is updated in real time. In this way, by customizing the presentation content based on the user's current situation, more appropriate information can be provided.

[0045] The presentation unit can select the optimal presentation method by taking into consideration the device information of the user at the time of presentation. For example, the presentation unit selects the optimal presentation method by taking into consideration the device information of the user at the time of presentation. If the user is using a smartphone, the presentation unit can provide a presentation method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can also provide a presentation method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a presentation method that is simple and highly visible. In this way, the optimal presentation method can be provided by taking into consideration the device information of the user.

[0046] The presentation unit can analyze the user's social media activity and present related information at the time of presentation. For example, the presentation unit can analyze the user's social media activity and present related information at the time of presentation. The presentation unit can present related road information based on location information shared by the user on social media. The presentation unit can also present related information based on information about accounts the user follows on social media. Furthermore, the presentation unit can analyze content posted by the user on social media and present related information. In this way, related information can be provided by analyzing the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The information collection unit can analyze the user's past movement history and identify optimal information collection points. For example, the information collection unit can identify roads or areas that the user has frequently traveled in the past and prioritize collecting information about those areas. The information collection unit can also identify areas that the user has avoided in the past and reduce the frequency of collecting information about those areas. Furthermore, the information collection unit can analyze the user's past movement patterns and predict and collect information that will be needed in the future. This makes it possible to collect information more efficiently by utilizing the user's past movement history.

[0049] The analysis unit can identify evacuation routes in the event of a disaster based on the collected information. For example, the analysis unit can analyze road closures and traffic congestion information to calculate the safest evacuation route. The analysis unit can also change evacuation routes in real time based on real-time information obtained from telecommunications carriers' Smart Fleet services. Furthermore, the analysis unit can analyze past disaster data and evaluate the effectiveness of evacuation routes. This enables quick and safe evacuation in the event of a disaster.

[0050] The information collection unit can use drones to collect real-time road conditions. For example, the information collection unit can fly a drone to capture images of road disruptions and send the footage to the analysis unit. The information collection unit can also use drones to monitor traffic congestion in real time and collect that information. Furthermore, the information collection unit can use drones to check the road reopening status after a disaster and collect that information. This makes it possible to use drones to collect information more accurately and quickly.

[0051] The information gathering unit can collect road conditions over a wide area using satellite data. For example, the information gathering unit can analyze satellite images to identify road disruptions. The information gathering unit can also use satellite data to grasp traffic congestion. Furthermore, the information gathering unit can also use satellite data to confirm road reopening status after a disaster occurs. This makes it possible to accurately grasp road conditions over a wide area using satellite data.

[0052] The information collection unit can collect real-time road conditions using a sensor network. For example, the information collection unit can collect road closure and congestion information from sensors installed on roads. The information collection unit can also monitor road reopening status in real time using the sensor network. Furthermore, the information collection unit can also use the sensor network to quickly grasp road conditions after a disaster occurs. This makes it possible to use the sensor network to collect information more accurately and quickly.

[0053] The information collecting unit can collect information from users using crowdsourcing. For example, the information collecting unit can collect road closure status and traffic congestion information provided by users. The information collecting unit can also collect real-time road reopening status provided by users. Furthermore, the information collecting unit can also collect road conditions after a disaster occurs provided by users. This makes it possible to collect more information quickly by utilizing crowdsourcing.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and telecommunications carriers' smart fleets. For example, the information collection unit can obtain road disruption status from the Ministry of Land, Infrastructure, Transport and Tourism's database. The information collection unit can also obtain congestion information from private sites. Furthermore, the information collection unit can obtain information on passable roads from telecommunications carriers' smart fleets. Step 2: The analysis unit analyzes the information collected by the information collection unit and identifies passable roads. For example, the analysis unit can calculate passable routes based on the collected information on road closures and traffic congestion. The analysis unit can also identify passable roads in real time based on information obtained from the telecommunications carrier's Smart Fleet. Step 3: The presentation unit presents the passable roads identified by the analysis unit to the user. For example, the presentation unit can display the identified passable roads on a map. The presentation unit can also provide text guidance about the identified passable roads.

[0056] (Example 2) An AI system according to an embodiment of the present invention combines various information, such as information from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), data from private websites, and telecommunications carriers' Smart Fleets, to suggest passable roads for smooth recovery efforts following the 2024 Noto Peninsula Earthquake. This system first collects road information from the MLIT, private websites, and telecommunications carriers' Smart Fleets. Next, AI analyzes the collected information and identifies passable roads. Finally, it presents the identified passable roads to the user. This system overcomes travel restrictions caused by road closures and congestion and facilitates recovery efforts. For example, road information is collected from the MLIT, private websites, telecommunications carriers' Smart Fleets, and other sources. This information includes detailed information on road closures, congestion, and passable roads. For example, road closure status is obtained from the MLIT database, congestion information is obtained from private websites, and passable road information is obtained from telecommunications carriers' Smart Fleets. Next, AI analyzes the collected information. Based on the collected information, the AI ​​identifies passable roads. For example, it analyzes road closures and traffic congestion information to calculate passable routes. It also identifies passable roads in real time based on information obtained from telecommunications carriers' smart fleets. Finally, it presents the identified passable roads to the user. The user can plan recovery activities based on the passable roads identified by the AI. For example, recovery workers can reach the site using passable roads and carry out recovery work quickly. This system overcomes travel restrictions caused by road closures and traffic congestion, allowing recovery activities to proceed smoothly. In this way, the AI ​​system overcomes travel restrictions caused by road closures and traffic congestion, allowing recovery activities to proceed smoothly.

[0057] The AI ​​system according to the embodiment includes an information collection unit, an analysis unit, and a presentation unit. The information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), a private website, or a telecommunications carrier's Smart Fleet. For example, the information collection unit can acquire road disruption status information from a database of the MLIT. The information collection unit can also acquire congestion information from a private website. The information collection unit can also acquire information on passable roads from a telecommunications carrier's Smart Fleet. The analysis unit analyzes the information collected by the information collection unit and identifies passable roads. For example, the analysis unit can calculate passable routes based on the collected road disruption status and congestion information. The analysis unit can also identify passable roads in real time based on information acquired from the telecommunications carrier's Smart Fleet. The presentation unit presents the passable roads identified by the analysis unit to a user. For example, the presentation unit can display the identified passable roads on a map. The presentation unit can also provide text guidance about the identified passable roads. As a result, the AI ​​system of the embodiment can collect and analyze road information and present passable roads to the user, allowing recovery activities to proceed smoothly.

[0058] The information collection unit can obtain road closure status from the Ministry of Land, Infrastructure, Transport and Tourism's database. The information collection unit obtains road closure status from the Ministry of Land, Infrastructure, Transport and Tourism's database, for example. The Ministry of Land, Infrastructure, Transport and Tourism's database includes road closure status such as complete road closures and partial road closures. For example, the information collection unit can access the Ministry of Land, Infrastructure, Transport and Tourism's database through a specific API to obtain the latest road closure information. The information collection unit can also periodically update the road closure status using the database's URL. This makes it possible to collect reliable information by obtaining road closure status from the Ministry of Land, Infrastructure, Transport and Tourism's database.

[0059] The information collection unit can acquire congestion information from private sites. The information collection unit acquires congestion information from, for example, private sites. Private sites include information such as traffic volume, average speed, and causes of congestion. For example, the information collection unit can acquire real-time congestion information from specific traffic information sites or apps. The information collection unit can also periodically update congestion information using the API of private sites. In this way, by acquiring congestion information from private sites, real-time traffic conditions can be grasped.

[0060] The information collection unit can acquire information about passable roads from the telecommunications carrier's smart fleet. The information collection unit acquires information about passable roads, for example, from the telecommunications carrier's smart fleet. The smart fleet includes information such as real-time traffic conditions and road opening status. For example, the information collection unit can acquire the latest passable road information using the API of the telecommunications carrier's smart fleet service. The information collection unit can also access the smart fleet database and periodically update the passable road information. In this way, by acquiring information about passable roads from the telecommunications carrier's smart fleet, the latest passable road information can be obtained.

[0061] The analysis unit can identify passable roads based on the collected information. The analysis unit, for example, identifies passable roads based on the collected information. The analysis unit analyzes road disruption status and congestion information to calculate passable routes. For example, the analysis unit uses an algorithm to identify passable roads based on road disruption status. The analysis unit can also use an algorithm to calculate an optimal route based on congestion information. Furthermore, the analysis unit can also identify passable roads based on real-time information obtained from the telecommunications carrier's Smart Fleet. In this way, by identifying passable roads based on the collected information, it is possible to provide an optimal route.

[0062] The presentation unit can present the identified passable roads to the user. For example, the presentation unit presents the identified passable roads to the user. The presentation unit can display the identified passable roads on a map. For example, the presentation unit highlights the passable roads on the map to visually present them to the user. The presentation unit can also provide text guidance on the identified passable roads. For example, the presentation unit displays a list of passable roads in text format and provides it to the user. In this way, presenting the identified passable roads to the user allows the user to quickly plan recovery activities.

[0063] The information collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the information collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. When the user is feeling stressed, the information collection unit can reduce the frequency of information collection and collect only important information. When the user is relaxed, the information collection unit can increase the frequency of information collection and collect detailed information. Furthermore, when the user is in an emergency, the information collection unit can collect information in real time and immediately analyze it. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The information collection unit can analyze past disaster data and select the optimal information collection method. For example, the information collection unit analyzes past disaster data and selects the optimal information collection method. The information collection unit can analyze past earthquake data and identify the most effective information collection means. The information collection unit can also analyze past typhoon data and determine the priority of information collection based on wind speed and rainfall. Furthermore, the information collection unit can analyze past flood data and select an information collection method according to rising water levels. In this way, the optimal information collection method can be selected by analyzing past disaster data.

[0065] The information collection unit can evaluate the reliability of the information to be collected when collecting information, and prioritize collecting highly reliable information. For example, the information collection unit can evaluate the reliability of the information to be collected when collecting information, and prioritize collecting highly reliable information. The information collection unit can prioritize collecting information from the Ministry of Land, Infrastructure, Transport and Tourism database as being highly reliable. The information collection unit can also cross-check information from private sites and collect only highly reliable information. Furthermore, the information collection unit can prioritize collecting real-time information from telecommunications carriers' smart fleets. In this way, by evaluating the reliability of the information to be collected, highly reliable information can be prioritized.

[0066] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. When the user is feeling anxious, the information collection unit can prioritize collecting the most important information. When the user is calm, the information collection unit can also collect detailed information. Furthermore, when the user is in an emergency, the information collection unit can prioritize collecting important information in real time. This enables more appropriate information collection by determining the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the information collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. The information collection unit can prioritize collecting information about roads close to the user's current location. The information collection unit can also prioritize collecting information related to the user's planned travel destination. Furthermore, the information collection unit can also prioritize collecting highly relevant information based on the user's past travel history. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0068] The information collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the information collection unit can analyze the user's social media activities and collect related information when collecting information. The information collection unit can collect related road information based on location information shared by the user on social media. The information collection unit can also collect related information based on information about accounts the user follows on social media. Furthermore, the information collection unit can analyze content posted by the user on social media and collect related information. In this way, related information can be collected by analyzing the user's social media activities.

[0069] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can provide simple and easy-to-understand analysis results when the user is feeling anxious. The analysis unit can also provide detailed analysis results when the user is calm. Furthermore, the analysis unit can quickly provide analysis results when the user is in an emergency. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected information during analysis. The analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail of information with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected information.

[0071] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply an analysis algorithm using a geographic information system (GIS) to road disruption information. The analysis unit can also apply an analysis algorithm using a traffic simulation to congestion information. Furthermore, the analysis unit can apply a real-time data analysis algorithm to passable road information. In this way, applying different analysis algorithms depending on the category of information enables more accurate analysis.

[0072] 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, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. If the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. If the user is calm, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is in an emergency, the analysis unit can also provide a display method that can be quickly understood. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit determines the priority of analysis based on the time of submission of information during analysis. The analysis unit can prioritize analyzing the latest information. The analysis unit can also lower the priority of information that was submitted recently. Furthermore, the analysis unit can analyze information that was submitted recently with a moderate priority. In this way, by determining the priority of analysis based on the time of submission of information, the latest information can be prioritized for analysis.

[0074] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. The analysis unit can prioritize analysis of information with high relevance. The analysis unit can also postpone the order of analysis of information with low relevance. Furthermore, the analysis unit can analyze information with medium relevance in an appropriate order. In this way, adjusting the order of analysis based on the relevance of information enables efficient analysis.

[0075] The presentation unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, the presentation unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. The presentation unit can provide a simple, highly visible presentation method when the user is feeling anxious. The presentation unit can also provide a presentation method including detailed information when the user is calm. Furthermore, the presentation unit can provide a presentation method that can be quickly understood when the user is in an emergency. This enables more appropriate information presentation by adjusting the presentation method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The presentation unit can select the optimal presentation method by referring to the user's past operation history when presenting. For example, the presentation unit can select the optimal presentation method by referring to the user's past operation history when presenting. The presentation unit can preferentially provide a presentation method that the user has used favorably in the past. The presentation unit can also select the most effective presentation method from the user's past operation history. Furthermore, the presentation unit can analyze the user's past operation history and suggest the optimal presentation method. In this way, the optimal presentation method can be provided by referring to the user's past operation history.

[0077] The presentation unit can customize the presentation content based on the user's current situation at the time of presentation. For example, the presentation unit customizes the presentation content based on the user's current situation at the time of presentation. When the user is in an emergency, the presentation unit can prioritize the presentation of important information. Furthermore, when the user is relaxed, the presentation unit can also provide presentation content including detailed information. Furthermore, when the user is moving, the presentation unit can present information that is updated in real time. In this way, by customizing the presentation content based on the user's current situation, more appropriate information can be provided.

[0078] The presentation unit can estimate the user's emotions and determine the priority of information to be presented based on the estimated user emotions. For example, the presentation unit can estimate the user's emotions and determine the priority of information to be presented based on the estimated user emotions. When the user is feeling anxious, the presentation unit can prioritize presenting the most important information. When the user is calm, the presentation unit can also prioritize presenting detailed information. Furthermore, when the user is in an emergency, the presentation unit can prioritize presenting information that can be quickly understood. This enables more appropriate information presentation by determining the priority of information to be presented according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The presentation unit can select the optimal presentation method by taking into consideration the device information of the user at the time of presentation. For example, the presentation unit selects the optimal presentation method by taking into consideration the device information of the user at the time of presentation. If the user is using a smartphone, the presentation unit can provide a presentation method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can also provide a presentation method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a presentation method that is simple and highly visible. In this way, the optimal presentation method can be provided by taking into consideration the device information of the user.

[0080] The presentation unit can analyze the user's social media activity and present related information at the time of presentation. For example, the presentation unit can analyze the user's social media activity and present related information at the time of presentation. The presentation unit can present related road information based on location information shared by the user on social media. The presentation unit can also present related information based on information about accounts the user follows on social media. Furthermore, the presentation unit can analyze content posted by the user on social media and present related information. In this way, related information can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the information collection unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and the telecommunications carrier Smart Fleet using the communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies passable roads based on the collected information. The presentation unit presents the identified passable roads to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the information collection unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and the telecommunications carrier's Smart Fleet using the communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies passable roads based on the collected information. The presentation unit presents the identified passable roads to the user, for example, using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the information collection unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and the telecommunications carrier Smart Fleet using the communication I / F 44 of the headset terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies passable roads based on the collected information. The presentation unit presents the identified passable roads to the user using, for example, the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the information collection unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and the telecommunications carrier Smart Fleet using the communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies passable roads based on the collected information. The presentation unit presents the identified passable roads to the user using, for example, the speaker 240 of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The information collection unit can analyze the user's past movement history and identify optimal information collection points. For example, the information collection unit can identify roads or areas that the user has frequently traveled in the past and prioritize collecting information about those areas. The information collection unit can also identify areas that the user has avoided in the past and reduce the frequency of collecting information about those areas. Furthermore, the information collection unit can analyze the user's past movement patterns and predict and collect information that will be needed in the future. This makes it possible to collect information more efficiently by utilizing the user's past movement history.

[0083] The analysis unit can identify evacuation routes in the event of a disaster based on the collected information. For example, the analysis unit can analyze road closures and traffic congestion information to calculate the safest evacuation route. The analysis unit can also change evacuation routes in real time based on real-time information obtained from telecommunications carriers' Smart Fleet services. Furthermore, the analysis unit can analyze past disaster data and evaluate the effectiveness of evacuation routes. This enables quick and safe evacuation in the event of a disaster.

[0084] The presentation unit can estimate the user's emotions and adjust the level of detail of the information to be presented based on the estimated user's emotions. For example, if the user is feeling anxious, the presentation unit can provide simple, highly visible information. If the user is calm, the presentation unit can also provide detailed information. Furthermore, if the user is in an emergency, the presentation unit can also provide information that can be quickly understood. This makes it possible to present more appropriate information by adjusting the level of detail of information according to the user's emotions.

[0085] The information collection unit can use drones to collect real-time road conditions. For example, the information collection unit can fly a drone to capture images of road disruptions and send the footage to the analysis unit. The information collection unit can also use drones to monitor traffic congestion in real time and collect that information. Furthermore, the information collection unit can use drones to check the road reopening status after a disaster and collect that information. This makes it possible to use drones to collect information more accurately and quickly.

[0086] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing the most important information. Also, if the user is calm, the analysis unit can prioritize analyzing detailed information. Furthermore, if the user is in an emergency, the analysis unit can prioritize analyzing information that can be quickly understood. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis is possible.

[0087] The information gathering unit can collect road conditions over a wide area using satellite data. For example, the information gathering unit can analyze satellite images to identify road disruptions. The information gathering unit can also use satellite data to grasp traffic congestion. Furthermore, the information gathering unit can also use satellite data to confirm road reopening status after a disaster occurs. This makes it possible to accurately grasp road conditions over a wide area using satellite data.

[0088] The presentation unit can estimate the user's emotions and adjust the format of the information to be presented based on the estimated user's emotions. For example, if the user is feeling anxious, the presentation unit can provide information in a visually easy-to-understand graphical format. If the user is calm, the presentation unit can provide information in a detailed text format. Furthermore, if the user is in an emergency, the presentation unit can provide information in a concise and quickly understandable format. This allows for more appropriate information presentation by adjusting the format of information according to the user's emotions.

[0089] The information collection unit can collect real-time road conditions using a sensor network. For example, the information collection unit can collect road closure and congestion information from sensors installed on roads. The information collection unit can also monitor road reopening status in real time using the sensor network. Furthermore, the information collection unit can also use the sensor network to quickly grasp road conditions after a disaster occurs. This makes it possible to use the sensor network to collect information more accurately and quickly.

[0090] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible notification method. If the user is calm, the analysis unit can also provide a notification method that includes detailed information. Furthermore, if the user is in an emergency, the analysis unit can also provide a notification method that can be quickly understood. This makes it possible to provide more appropriate information by adjusting the notification method of the analysis results according to the user's emotions.

[0091] The information collecting unit can collect information from users using crowdsourcing. For example, the information collecting unit can collect road closure status and traffic congestion information provided by users. The information collecting unit can also collect real-time road reopening status provided by users. Furthermore, the information collecting unit can also collect road conditions after a disaster occurs provided by users. This makes it possible to collect more information quickly by utilizing crowdsourcing.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The information collection unit collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and telecommunications carriers' smart fleets. For example, the information collection unit can obtain road disruption status from the Ministry of Land, Infrastructure, Transport and Tourism's database. The information collection unit can also obtain congestion information from private sites. Furthermore, the information collection unit can obtain information on passable roads from telecommunications carriers' smart fleets. Step 2: The analysis unit analyzes the information collected by the information collection unit and identifies passable roads. For example, the analysis unit can calculate passable routes based on the collected information on road closures and traffic congestion. The analysis unit can also identify passable roads in real time based on information obtained from the telecommunications carrier's Smart Fleet. Step 3: The presentation unit presents the passable roads identified by the analysis unit to the user. For example, the presentation unit can display the identified passable roads on a map. The presentation unit can also provide text guidance about the identified passable roads.

[0094] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information collection department that collects road information from the Ministry of Land, Infrastructure, Transport and Tourism, private sites, and telecommunications carriers' smart fleets; an analysis unit that analyzes the information collected by the information collection unit and identifies passable roads; a presentation unit that presents the passable roads identified by the analysis unit to a user. A system characterized by:

2. The information collecting unit Obtaining road closure status from the Ministry of Land, Infrastructure, Transport and Tourism database The system of claim 1 .

3. The information collecting unit Obtaining traffic congestion information from private sites The system of claim 1 .

4. The information collecting unit Obtaining passable road information from telecommunications carriers' smart fleets The system of claim 1 .

5. The analysis unit Identifying passable roads based on collected information The system of claim 1 .

6. The presentation unit Present the identified passable roads to the user The system of claim 1 .

7. The information collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions The system of claim 1 .

8. The information collecting unit Analyzing past disaster data and selecting the most appropriate information gathering method The system of claim 1 .

Images