System

Abstract

To efficiently and safely perform a search for mountain distress by a system according to an embodiment.SOLUTION: A system according to an embodiment comprises a collection unit, a calculation unit, and a control unit. The collection unit collects past distress data and topographic data. The calculation unit calculates a search route based on the data collected by the collection unit. The control unit makes a drone autonomously fly based on the route calculated by the calculation unit.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there were issues with mountain rescue searches, such as the risk of secondary damage and the time it took to train personnel with extensive search experience.

[0005] The system according to the embodiment aims to perform efficient and safe searches in mountain accidents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a calculation unit, and a control unit. The collection unit collects past distress data and topographical data. The calculation unit calculates a search route based on the data collected by the collection unit. The control unit autonomously flies the drone based on the route calculated by the calculation unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently and safely conduct searches in mountain accidents. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The mountain distress search system according to the embodiment of the present invention is a system that uses a drone equipped with AI to reduce the time required to prevent secondary damage during a search and to train personnel with extensive search experience. This system collects past distress data and terrain data, calculates a search route based on the data, and flies the drone autonomously. For example, a collection unit is provided that collects past distress data and terrain data. This collection unit collects data such as the age and sex of the victim, information on equipment and belongings, and seasonal and weather fluctuations, and performs filtering and complementation. For example, when collecting past distress data, the data is filtered based on the age and sex of the victim, and when collecting terrain data, the data is complemented based on seasonal and weather fluctuations. Next, a calculation unit is provided that calculates a search route based on the collected data. This calculation unit analyzes the collected data and calculates the optimal search route. For example, past distress patterns and terrain data are analyzed to identify places where there is a high possibility of a distressed person being present, and the route is calculated. Furthermore, a control unit is provided that autonomously flies the drone based on the calculated route. This control unit autonomously flies the drone based on the calculated route, analyzes the data collected in real time, and adjusts the flight. For example, the system analyzes real-time data collected by the drone, identifies the location of the person in distress, and adjusts the flight route based on that location. In addition, a response instruction unit is provided that instructs how to respond when a person in distress is found. This response instruction unit instructs the appropriate response method when a person in distress is found. For example, it estimates the emotions of the person in distress, determines the priority of the data to be collected based on those emotions, and instructs the appropriate response method. In this way, by using a drone equipped with AI, it is possible to reduce the time required for secondary damage during the search and training personnel with extensive search experience, thereby saving the lives of the person in distress and the rescuer. As a result, the mountain distress search system can reduce the time required for secondary damage during the search and training personnel with extensive search experience, thereby saving the lives of the person in distress and the rescuer.

[0029] A mountain distress search system according to an embodiment includes a collection unit, a calculation unit, and a control unit. The collection unit collects past distress data and terrain data. The past distress data includes, for example, the date and time of the distress, the location, and the cause, but is not limited to such examples. The terrain data includes, for example, the altitude, the type of terrain, and the change in the terrain, but is not limited to such examples. The collection unit filters the data based on, for example, the age and sex of the victim, and complements the data based on seasonal and weather fluctuations when collecting the terrain data. The calculation unit calculates a search route based on the collected data. The search route is calculated based on criteria such as, for example, the shortest distance, danger avoidance, and efficient coverage, but is not limited to such examples. The calculation unit, for example, analyzes past distress patterns and terrain data, identifies a place where the victim is likely to be, and calculates the route. The control unit autonomously flies the drone based on the calculated route. The control unit, for example, analyzes real-time data collected by the drone, identifies the location of the victim, and adjusts the flight route based on the location. As a result, the mountain distress search system of the embodiment improves search efficiency by calculating the optimal search route based on past distress data and topographical data, and flying the drone autonomously.

[0030] The collection unit collects past accident data and terrain data. The past accident data includes, for example, the date and time of the accident, the location, and the cause, but is not limited to such examples. Specifically, detailed information such as the age, sex, years of experience, type of equipment, and the presence or absence of an accompanying person of the victim is also collected. This allows for more precise analysis of accident patterns and trends. The terrain data includes, for example, the altitude, type of terrain, and changes in terrain, but is not limited to such examples. Furthermore, the terrain data also includes environmental data such as the type of vegetation, soil condition, precipitation, and snowfall. The collection unit filters data based on, for example, the age and sex of the victim, and complements the data based on seasonal and weather fluctuations when collecting the terrain data. Specifically, data such as the amount of snowfall and freezing conditions in winter, and the amount of rainfall and sunshine hours in summer are taken into consideration. This makes it easier for the collection unit to identify specific conditions and situations that increase the risk of accident. The collected data is stored in a cloud server and shared with other departments and systems. In addition, the collection unit will periodically update and verify the data to ensure its accuracy and reliability, which will enable the collection unit to provide the latest and most accurate data and improve the performance of the entire system.

[0031] The calculation unit calculates a search route based on the collected data. The search route is calculated based on criteria such as, for example, the shortest distance, risk avoidance, and efficient coverage, but is not limited to such examples. Specifically, the calculation unit analyzes past distress patterns and terrain data, identifies locations where the distressed person is likely to be, and calculates the route. The calculation unit uses an AI algorithm to analyze past distress data and terrain data, and identifies routes that are easy for the distressed person to move to and locations where the distressed person can easily evacuate. For example, the calculation unit learns the behavior patterns of the distressed person under specific terrain and environmental conditions from past data, and generates an optimal search route based on the learned behavior patterns. In addition, the calculation unit can incorporate weather data and terrain data that are updated in real time and dynamically adjust the search route. As a result, the calculation unit can always provide an optimal search route based on the latest information, thereby improving the efficiency of the search. In addition, the calculation unit simulates multiple search routes and selects the most effective route. As a result, the search team can optimally allocate resources and perform search activities quickly and effectively.

[0032] The control unit autonomously flies the drone based on the calculated route. For example, the control unit analyzes real-time data collected by the drone, identifies the location of the victim, and adjusts the flight route based on the location. Specifically, the control unit uses a camera and a sensor mounted on the drone to monitor changes in the terrain and environment in real time and detects traces and abnormal movements of the victim. For example, an infrared camera can be used to detect the body temperature of the victim even at night or in bad weather, and identify the location. The control unit dynamically adjusts the flight route of the drone based on these data to achieve an efficient search. Furthermore, the control unit can control multiple drones in cooperation to cover a wide range. This can expand the search range and improve the discovery rate of the victim. The control unit also has a function of monitoring the remaining battery level and communication status of the drone, and automatically returning the drone as necessary. This can maximize the operational efficiency of the drone and ensure the continuity of search activities. Through these functions, the control unit can autonomously and efficiently operate the drone and increase the success rate of mountain rescue searches.

[0033] The mountain distress search system includes an analysis unit that analyzes real-time data collected by the drone, and the control unit adjusts the flight of the drone based on the data analyzed by the analysis unit. The analysis unit analyzes the real-time data collected by the drone. The real-time data includes, for example, video data, location data, and environmental data, but is not limited to such examples. For example, the analysis unit analyzes the video data collected by the drone to identify the location of the victim. The analysis unit can also analyze the location data collected by the drone to identify the movement pattern of the victim. Furthermore, the analysis unit can analyze the environmental data collected by the drone to grasp the situation of the search area. The control unit adjusts the flight of the drone based on the data analyzed by the analysis unit. For example, the control unit adjusts the flight route of the drone based on the location of the victim identified by the analysis unit. The control unit can also adjust the flight altitude and speed of the drone based on the situation of the search area identified by the analysis unit. In this way, the accuracy of the search is improved by analyzing the real-time data and adjusting the flight of the drone. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input video data collected by a drone to the generation AI and cause the generation AI to execute an analysis to identify the location of the victim from the video data.

[0034] The mountain distress search system includes a response instruction unit that instructs how to respond when a victim is found. The response instruction unit instructs how to respond when a victim is found. The response methods include, for example, rescue procedures, first aid, and contact methods, but are not limited to such examples. The response instruction unit, for example, estimates the emotions of the victim and instructs an appropriate response method based on the emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the response instruction unit takes a picture of the facial expression of the victim with a camera and estimates the emotion using an emotion estimation algorithm. The response instruction unit can also record the voice of the victim and estimate the emotion using a voice analysis technology. Furthermore, the response instruction unit can collect the biological data (heart rate and electrodermal activity) of the victim with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, the efficiency of rescue operations is improved by instructing an appropriate response method based on the emotions of the victim. A part or all of the above-mentioned processing in the response instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the response instruction unit may input image data of a victim captured by a camera to the generation AI and cause the generation AI to estimate the emotion.

[0035] The collection unit can analyze the past behavioral patterns of the victim and select an appropriate data collection method. For example, the collection unit determines a priority area for data collection based on evacuation sites often used by past victim. The collection unit can also select a data collection method for checking the presence or absence of equipment based on equipment often carried by past victim. Furthermore, the collection unit can also select a data collection method specialized for a time period based on a time period in which past victim often gets lost. In this way, the optimal data collection method can be selected by analyzing the past behavioral patterns. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input behavioral data of past victim to the generation AI and cause the generation AI to execute an analysis of the behavioral patterns.

[0036] The collection unit can collect information on the equipment and belongings of the victim in real time and use it in the search. For example, the collection unit can collect signals from a GPS device carried by the victim in real time to identify location information. The collection unit can also collect the amount of food and water carried by the victim in real time to determine search priorities. Furthermore, the collection unit can collect the status of a communication device carried by the victim in real time to ensure a means of communication. This improves the accuracy of the search by collecting information on the equipment and belongings in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the belongings data of the victim to the generation AI and cause the generation AI to perform an analysis of the belongings.

[0037] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the victim. For example, when the victim is in a mountainous area, the collection unit preferentially collects topographical data and elevation data. In addition, when the victim is in a forested area, the collection unit can also preferentially collect vegetation data and animal habitat information. Furthermore, when the victim is near a river, the collection unit can also preferentially collect water level data and flow rate data. In this way, highly relevant data can be preferentially collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the location data of the victim to the generation AI and cause the generation AI to select highly relevant data.

[0038] The collection unit can analyze the social media activity of the victim and collect related data. The collection unit can, for example, identify a search area based on the location information last posted by the victim. The collection unit can also analyze photos and videos posted by the victim to identify the surrounding environment. Furthermore, the collection unit can analyze the message history of the victim with friends and family to identify the behavior pattern of the victim. In this way, related data can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input social media posting data to the generation AI and cause the generation AI to perform analysis of the related data.

[0039] The calculation unit can analyze past distress patterns and calculate an optimal search route. The calculation unit calculates an optimal search route, for example, based on an evacuation site frequently used by past distressed people. The calculation unit can also calculate a search route specialized for a time period based on a time period when past distressed people often get lost. Furthermore, the calculation unit can calculate a search route for checking the presence or absence of equipment based on equipment often carried by past distressed people. In this way, the optimal search route can be calculated by analyzing past distress patterns. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input past distress data to the generation AI and cause the generation AI to execute an analysis of the distress pattern.

[0040] The calculation unit can analyze the topographical data in detail and improve the accuracy of the search route. The calculation unit calculates an optimal search route, for example, taking into account the undulations and slopes of the topography. The calculation unit can also adjust the search route, taking into account the features of the topography (cliffs, valleys, forests, etc.). Furthermore, the calculation unit can identify dangerous points on the search route based on the topographical data and calculate a route to avoid them. This improves the accuracy of the search route by analyzing the topographical data in detail. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the topographical data to the generation AI and cause the generation AI to perform the analysis of the topography.

[0041] The calculation unit can calculate a search route taking into account attribute information such as the age and gender of the victim. For example, if the victim is an elderly person, the calculation unit calculates a route taking into account physical strength. Furthermore, if the victim is a child, the calculation unit can also calculate a route that prioritizes safety. Furthermore, if the victim is a woman, the calculation unit can also calculate a route that takes into account specific risks. In this way, a more appropriate search route can be calculated by taking into account attribute information such as age and gender. Part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input attribute information of the victim to the generation AI and cause the generation AI to execute calculation of the search route.

[0042] The calculation unit can adjust the search route based on the equipment and belongings of the victim. For example, if the victim has a GPS device, the calculation unit adjusts the route based on the signal from the device. In addition, if the victim has food or water, the calculation unit can adjust the route taking into account the amount of food or water. Furthermore, if the victim has a communication device, the calculation unit can adjust the route taking into account the condition of the communication device. This allows a more appropriate route to be selected by adjusting the search route based on the equipment and belongings. Part or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the equipment data of the victim to the generation AI and cause the generation AI to adjust the search route.

[0043] The control unit can analyze data collected during the flight of the drone in real time and adjust the flight route. The control unit can adjust the flight route based on, for example, real-time location information collected by the drone. The control unit can also adjust the flight route based on real-time weather information collected by the drone. Furthermore, the control unit can adjust the flight route based on real-time topographical data collected by the drone. This improves the accuracy of the search by analyzing data in real time and optimizing the flight route. A part or all of the above-mentioned processing in the control unit can be performed, for example, using AI or can be performed without using AI. For example, the control unit can input real-time data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0044] The control unit can adjust the flight speed and altitude of the drone to improve the accuracy of the search. For example, the control unit can adjust the flight speed of the drone to efficiently cover the search area. The control unit can also adjust the flight altitude of the drone to collect detailed topographical data. Furthermore, the control unit can adjust the flight speed and altitude of the drone to realize an optimal search route. This improves the efficiency of the search by adjusting the flight speed and altitude. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input flight data of the drone to the generation AI and cause the generation AI to adjust the flight speed and altitude.

[0045] The control unit can update the location information of the victim in real time during the flight of the drone and adjust the flight route. The control unit adjusts the flight route based on, for example, real-time location information collected by the drone. The control unit can also adjust the flight route based on real-time weather information collected by the drone. Furthermore, the control unit can adjust the flight route based on real-time topographical data collected by the drone. This improves the accuracy of the search by updating the location information in real time and adjusting the flight route. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input real-time location data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0046] The control unit can update the location information of the victim in real time during the flight of the drone and adjust the flight route. The control unit adjusts the flight route based on, for example, real-time location information collected by the drone. The control unit can also adjust the flight route based on real-time weather information collected by the drone. Furthermore, the control unit can adjust the flight route based on real-time topographical data collected by the drone. This improves the accuracy of the search by updating the location information in real time and adjusting the flight route. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input real-time location data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0047] The control unit can adjust the flight route taking into account weather information during the flight of the drone. The control unit adjusts the flight route based on, for example, real-time weather information collected by the drone. The control unit can also adjust the flight route based on real-time weather data collected by the drone. Furthermore, the control unit can also adjust the flight route based on real-time wind speed data collected by the drone. This improves the accuracy of the search by adjusting the flight route taking into account weather information. A part or all of the above-mentioned processing in the control unit may be performed, for example, using AI or may be performed without using AI. For example, the control unit can input real-time weather data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0048] The analysis unit can analyze the real-time data collected by the drone in detail and identify the location of the victim. The analysis unit can, for example, analyze the video data collected by the drone and identify the location of the victim. The analysis unit can also analyze the audio data collected by the drone and identify the location of the victim. Furthermore, the analysis unit can analyze the temperature data collected by the drone and identify the location of the victim. In this way, the location of the victim can be quickly identified by analyzing the real-time data in detail. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the real-time data collected by the drone to the generation AI and cause the generation AI to identify the location of the victim.

[0049] The analysis unit can analyze the past behavioral patterns of the victim and improve the accuracy of the search. For example, the analysis unit can analyze the past behavioral patterns of the victim and predict the location of the victim. The analysis unit can also analyze the past movement history of the victim and identify the search area. Furthermore, the analysis unit can analyze the past use patterns of the equipment of the victim and improve the accuracy of the search. In this way, the accuracy of the search is improved by analyzing the past behavioral patterns. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input past distress data to the generation AI and cause the generation AI to execute the analysis of the behavioral patterns.

[0050] The analysis unit can geographically analyze the data collected by the drone and identify the search area. The analysis unit can, for example, analyze the topographical data collected by the drone and identify the search area. The analysis unit can also analyze the vegetation data collected by the drone and identify the search area. Furthermore, the analysis unit can analyze the water area data collected by the drone and identify the search area. This makes it possible to quickly identify the search area by analyzing the data geographically. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the geographical data collected by the drone to the generation AI and cause the generation AI to identify the search area.

[0051] The analysis unit can improve the accuracy of the search by performing an analysis based on the equipment and belongings of the victim. For example, the analysis unit can analyze the signal of a GPS device carried by the victim to identify location information. The analysis unit can also analyze the state of a communication device carried by the victim to ensure a means of contact. Furthermore, the analysis unit can analyze the amount of food and water carried by the victim to determine the priority of the search. In this way, the analysis can be performed based on the equipment and belongings to improve the accuracy of the search. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the belongings data of the victim to the generation AI and cause the generation AI to execute the analysis.

[0052] The response instruction unit can analyze the condition of the victim in real time and instruct the optimal response method. The response instruction unit can, for example, analyze the heart rate and respiratory rate of the victim in real time and instruct the appropriate response method. The response instruction unit can also analyze the location information of the victim in real time and instruct the optimal rescue route. Furthermore, the response instruction unit can analyze the body temperature and hydration status of the victim in real time and instruct the appropriate response method. In this way, the efficiency of rescue activities is improved by analyzing the condition in real time and instructing the optimal response method. Part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the response instruction unit can input real-time data of the victim to the generation AI and cause the generation AI to execute instructions on the response method.

[0053] The response instruction unit can instruct a response method in consideration of the victim's past medical history. The response instruction unit, for example, instructs an appropriate medical response in consideration of the victim's past medical history. The response instruction unit can also instruct an appropriate response method in consideration of the victim's past allergy information. Furthermore, the response instruction unit can also instruct an appropriate response method in consideration of the victim's past injury history. This makes it possible to provide a more appropriate medical response by taking the past medical history into consideration. A part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the response instruction unit can input the victim's medical history data into the generation AI and cause the generation AI to execute an instruction on a response method.

[0054] The response instruction unit can instruct the optimal response method by taking into account the geographical location information of the victim. For example, when the victim is in a mountainous area, the response instruction unit instructs a response method that takes into account the terrain. Furthermore, when the victim is in a forested area, the response instruction unit can also instruct a response method that takes into account the vegetation. Furthermore, when the victim is near a river, the response instruction unit can also instruct a response method that takes into account the water level. In this way, a more appropriate response method can be instructed by taking into account the geographical location information. A part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the response instruction unit can input the location data of the victim to the generation AI and cause the generation AI to execute an instruction on the response method.

[0055] The response instruction unit can instruct the response method based on the equipment and belongings of the victim. For example, if the victim has a GPS device, the response instruction unit instructs the response method based on the signal. In addition, if the victim has food or water, the response instruction unit can also instruct the response method taking into account the amount of food or water. Furthermore, if the victim has a communication device, the response instruction unit can also instruct the response method taking into account the state of the communication device. This allows for more appropriate response by instructing the response method based on the equipment and belongings. Part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the response instruction unit can input the belongings data of the victim to the generation AI and cause the generation AI to execute the response method instruction.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The mountain distress search system may further include a communication unit that shares the location information of the victim. The communication unit transmits the location information of the victim to the rescue team in real time, improving the efficiency of the search. For example, if the victim is moving, the location information can be updated in real time and notified to the rescue team. The communication unit can also share the location information of the victim with other drones and search systems to cooperate in the search. Furthermore, the communication unit can notify the location information of the victim to family and friends to reassure them. This allows the location information of the victim to be shared in real time, improving the accuracy and efficiency of the search.

[0058] The mountain rescue search system may further include an environmental monitoring unit that monitors the environment around the victim. The environmental monitoring unit collects environmental data such as temperature, humidity, and wind speed in real time and transmits it to the analysis unit. For example, if the temperature is dropping rapidly, the victim is at higher risk of hypothermia, so an instruction can be issued indicating that prompt rescue is necessary. Also, if the wind speed is strong, the drone's flight route can be adjusted to safely conduct the search. Furthermore, if the humidity is high, data can be provided indicating that the victim is at lower risk of dehydration. This allows the environment around the victim to be monitored in real time and appropriate measures to be taken, thereby improving the accuracy and efficiency of rescue operations.

[0059] The mountain rescue search system may further include a behavior analysis unit that analyzes the behavior history of the victim. The behavior analysis unit analyzes the past behavior patterns of the victim to improve the accuracy of the search. For example, it is possible to analyze what route the victim has taken in the past and optimize the current search route. It is also possible to analyze what equipment the victim has used in the past and estimate the current equipment status. It is also possible to analyze what environment the victim was in when he or she was lost in the past and optimize the method of responding to the current environment. In this way, it is possible to improve the accuracy and efficiency of rescue operations by analyzing the behavior history of the victim and taking appropriate measures.

[0060] The mountain rescue search system can further include an equipment analysis unit that analyzes the equipment and belongings of the lost person. The equipment analysis unit analyzes the equipment and belongings of the lost person to improve the accuracy of the search. For example, if the lost person has a GPS device, the signal can be analyzed to identify the location information. Also, if the lost person has a communication device, the condition of the device can be analyzed to ensure a means of contact. Furthermore, if the lost person has food or water, the amount of food or water can be analyzed to determine the search priority. In this way, the accuracy of the search can be improved by analyzing the equipment and belongings.

[0061] The mountain rescue search system can further include a location prediction unit that predicts location information of the lost person. The location prediction unit analyzes the past behavioral patterns and current location information of the lost person to predict the location of the lost person. For example, it can analyze what route the lost person has taken in the past and predict his / her current location. It can also analyze the direction in which the lost person is currently moving and predict his / her future location. It can also analyze the environment in which the lost person is in and predict his / her location based on that environment. This makes it possible to predict the location of the lost person and improve the accuracy of the search.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects past accident data and terrain data. Past accident data includes the date, time, location, and cause of the accident, and terrain data includes altitude, terrain type, and terrain changes. The collection unit filters the data based on the age and gender of the victim, and complements the data based on seasonal and weather variations when collecting terrain data. Step 2: The calculation unit calculates a search route based on the collected data. The search route is calculated based on criteria such as the shortest distance, avoiding danger, and efficient coverage area. The calculation unit analyzes past distress patterns and terrain data to identify locations where distressed people are likely to be and calculates the route. Step 3: The control unit autonomously flies the drone based on the calculated route. The control unit analyzes the real-time data collected by the drone, identifies the location of the victim, and adjusts the flight route based on the location.

[0064] (Example 2) The mountain distress search system according to the embodiment of the present invention is a system that uses a drone equipped with AI to reduce the time required to prevent secondary damage during a search and to train personnel with extensive search experience. This system collects past distress data and terrain data, calculates a search route based on the data, and flies the drone autonomously. For example, a collection unit is provided that collects past distress data and terrain data. This collection unit collects data such as the age and sex of the victim, information on equipment and belongings, and seasonal and weather fluctuations, and performs filtering and complementation. For example, when collecting past distress data, the data is filtered based on the age and sex of the victim, and when collecting terrain data, the data is complemented based on seasonal and weather fluctuations. Next, a calculation unit is provided that calculates a search route based on the collected data. This calculation unit analyzes the collected data and calculates the optimal search route. For example, past distress patterns and terrain data are analyzed to identify places where there is a high possibility of a distressed person being present, and the route is calculated. Furthermore, a control unit is provided that autonomously flies the drone based on the calculated route. This control unit autonomously flies the drone based on the calculated route, analyzes the data collected in real time, and adjusts the flight. For example, the system analyzes real-time data collected by the drone, identifies the location of the person in distress, and adjusts the flight route based on that location. In addition, a response instruction unit is provided that instructs how to respond when a person in distress is found. This response instruction unit instructs the appropriate response method when a person in distress is found. For example, it estimates the emotions of the person in distress, determines the priority of the data to be collected based on those emotions, and instructs the appropriate response method. In this way, by using a drone equipped with AI, it is possible to reduce the time required for secondary damage during the search and training personnel with extensive search experience, thereby saving the lives of the person in distress and the rescuer. As a result, the mountain distress search system can reduce the time required for secondary damage during the search and training personnel with extensive search experience, thereby saving the lives of the person in distress and the rescuer.

[0065] A mountain distress search system according to an embodiment includes a collection unit, a calculation unit, and a control unit. The collection unit collects past distress data and terrain data. The past distress data includes, for example, the date and time of the distress, the location, and the cause, but is not limited to such examples. The terrain data includes, for example, the altitude, the type of terrain, and the change in the terrain, but is not limited to such examples. The collection unit filters the data based on, for example, the age and sex of the victim, and complements the data based on seasonal and weather fluctuations when collecting the terrain data. The calculation unit calculates a search route based on the collected data. The search route is calculated based on criteria such as, for example, the shortest distance, danger avoidance, and efficient coverage, but is not limited to such examples. The calculation unit, for example, analyzes past distress patterns and terrain data, identifies a place where the victim is likely to be, and calculates the route. The control unit autonomously flies the drone based on the calculated route. The control unit, for example, analyzes real-time data collected by the drone, identifies the location of the victim, and adjusts the flight route based on the location. As a result, the mountain distress search system of the embodiment improves search efficiency by calculating the optimal search route based on past distress data and topographical data, and flying the drone autonomously.

[0066] The collection unit collects past accident data and terrain data. The past accident data includes, for example, the date and time of the accident, the location, and the cause, but is not limited to such examples. Specifically, detailed information such as the age, sex, years of experience, type of equipment, and the presence or absence of an accompanying person of the victim is also collected. This allows for more precise analysis of accident patterns and trends. The terrain data includes, for example, the altitude, type of terrain, and changes in terrain, but is not limited to such examples. Furthermore, the terrain data also includes environmental data such as the type of vegetation, soil condition, precipitation, and snowfall. The collection unit filters data based on, for example, the age and sex of the victim, and complements the data based on seasonal and weather fluctuations when collecting the terrain data. Specifically, data such as the amount of snowfall and freezing conditions in winter, and the amount of rainfall and sunshine hours in summer are taken into consideration. This makes it easier for the collection unit to identify specific conditions and situations that increase the risk of accident. The collected data is stored in a cloud server and shared with other departments and systems. In addition, the collection unit will periodically update and verify the data to ensure its accuracy and reliability, which will enable the collection unit to provide the latest and most accurate data and improve the performance of the entire system.

[0067] The calculation unit calculates a search route based on the collected data. The search route is calculated based on criteria such as, for example, the shortest distance, risk avoidance, and efficient coverage, but is not limited to such examples. Specifically, the calculation unit analyzes past distress patterns and terrain data, identifies locations where the distressed person is likely to be, and calculates the route. The calculation unit uses an AI algorithm to analyze past distress data and terrain data, and identifies routes that are easy for the distressed person to move to and locations where the distressed person can easily evacuate. For example, the calculation unit learns the behavior patterns of the distressed person under specific terrain and environmental conditions from past data, and generates an optimal search route based on the learned behavior patterns. In addition, the calculation unit can incorporate weather data and terrain data that are updated in real time and dynamically adjust the search route. As a result, the calculation unit can always provide an optimal search route based on the latest information, thereby improving the efficiency of the search. In addition, the calculation unit simulates multiple search routes and selects the most effective route. As a result, the search team can optimally allocate resources and perform search activities quickly and effectively.

[0068] The control unit autonomously flies the drone based on the calculated route. For example, the control unit analyzes real-time data collected by the drone, identifies the location of the victim, and adjusts the flight route based on the location. Specifically, the control unit uses a camera and a sensor mounted on the drone to monitor changes in the terrain and environment in real time and detects traces and abnormal movements of the victim. For example, an infrared camera can be used to detect the body temperature of the victim even at night or in bad weather, and identify the location. The control unit dynamically adjusts the flight route of the drone based on these data to achieve an efficient search. Furthermore, the control unit can control multiple drones in cooperation to cover a wide range. This can expand the search range and improve the discovery rate of the victim. The control unit also has a function of monitoring the remaining battery level and communication status of the drone, and automatically returning the drone as necessary. This can maximize the operational efficiency of the drone and ensure the continuity of search activities. Through these functions, the control unit can autonomously and efficiently operate the drone and increase the success rate of mountain rescue searches.

[0069] The mountain distress search system includes an analysis unit that analyzes real-time data collected by the drone, and the control unit adjusts the flight of the drone based on the data analyzed by the analysis unit. The analysis unit analyzes the real-time data collected by the drone. The real-time data includes, for example, video data, location data, and environmental data, but is not limited to such examples. For example, the analysis unit analyzes the video data collected by the drone to identify the location of the victim. The analysis unit can also analyze the location data collected by the drone to identify the movement pattern of the victim. Furthermore, the analysis unit can analyze the environmental data collected by the drone to grasp the situation of the search area. The control unit adjusts the flight of the drone based on the data analyzed by the analysis unit. For example, the control unit adjusts the flight route of the drone based on the location of the victim identified by the analysis unit. The control unit can also adjust the flight altitude and speed of the drone based on the situation of the search area identified by the analysis unit. In this way, the accuracy of the search is improved by analyzing the real-time data and adjusting the flight of the drone. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input video data collected by a drone to the generation AI and cause the generation AI to execute an analysis to identify the location of the victim from the video data.

[0070] The mountain distress search system includes a response instruction unit that instructs how to respond when a victim is found. The response instruction unit instructs how to respond when a victim is found. The response methods include, for example, rescue procedures, first aid, and contact methods, but are not limited to such examples. The response instruction unit, for example, estimates the emotions of the victim and instructs an appropriate response method based on the emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the response instruction unit takes a picture of the facial expression of the victim with a camera and estimates the emotion using an emotion estimation algorithm. The response instruction unit can also record the voice of the victim and estimate the emotion using a voice analysis technology. Furthermore, the response instruction unit can collect the biological data (heart rate and electrodermal activity) of the victim with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, the efficiency of rescue operations is improved by instructing an appropriate response method based on the emotions of the victim. A part or all of the above-mentioned processing in the response instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the response instruction unit may input image data of a victim captured by a camera to the generation AI and cause the generation AI to estimate the emotion.

[0071] The collection unit can estimate the emotion of the victim and determine the priority of data to be collected based on the estimated emotion of the victim. For example, when the victim is in a panic state, the collection unit prioritizes collection of biometric data such as heart rate and respiratory rate. In addition, when the victim is calm, the collection unit can also prioritize collection of location information and surrounding environmental data. Furthermore, when the victim is fatigued, the collection unit can also prioritize collection of body temperature and hydration status. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, more appropriate data can be collected by determining the priority of data based on the emotion of the victim. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data of the victim to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The collection unit can analyze the past behavioral patterns of the victim and select an appropriate data collection method. For example, the collection unit determines a priority area for data collection based on evacuation sites often used by past victim. The collection unit can also select a data collection method for checking the presence or absence of equipment based on equipment often carried by past victim. Furthermore, the collection unit can also select a data collection method specialized for a time period based on a time period in which past victim often gets lost. In this way, the optimal data collection method can be selected by analyzing the past behavioral patterns. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input behavioral data of past victim to the generation AI and cause the generation AI to execute an analysis of the behavioral patterns.

[0073] The collection unit can collect information on the equipment and belongings of the victim in real time and use it in the search. For example, the collection unit can collect signals from a GPS device carried by the victim in real time to identify location information. The collection unit can also collect the amount of food and water carried by the victim in real time to determine search priorities. Furthermore, the collection unit can collect the status of a communication device carried by the victim in real time to ensure a means of communication. This improves the accuracy of the search by collecting information on the equipment and belongings in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the belongings data of the victim to the generation AI and cause the generation AI to perform an analysis of the belongings.

[0074] The collection unit can estimate the emotion of the victim and select the type of data to be collected based on the estimated emotion of the victim. For example, when the victim feels scared, the collection unit prioritizes collecting surrounding audio and video data. In addition, when the victim feels safe, the collection unit can also prioritize collecting location information and movement history. Furthermore, when the victim is confused, the collection unit can also prioritize collecting surrounding environmental data and weather information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, more appropriate data can be collected by selecting the type of data based on the emotion of the victim. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data of the victim to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the victim. For example, when the victim is in a mountainous area, the collection unit preferentially collects topographical data and elevation data. In addition, when the victim is in a forested area, the collection unit can also preferentially collect vegetation data and animal habitat information. Furthermore, when the victim is near a river, the collection unit can also preferentially collect water level data and flow rate data. In this way, highly relevant data can be preferentially collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the location data of the victim to the generation AI and cause the generation AI to select highly relevant data.

[0076] The collection unit can analyze the social media activity of the victim and collect related data. The collection unit can, for example, identify a search area based on the location information last posted by the victim. The collection unit can also analyze photos and videos posted by the victim to identify the surrounding environment. Furthermore, the collection unit can analyze the message history of the victim with friends and family to identify the behavior pattern of the victim. In this way, related data can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input social media posting data to the generation AI and cause the generation AI to perform analysis of the related data.

[0077] The calculation unit can estimate the emotion of the victim and adjust the calculation method of the search route based on the estimated emotion of the victim. For example, when the victim is in a panic state, the calculation unit calculates the shortest route with priority. In addition, when the victim is calm, the calculation unit can also calculate the optimal route taking into account detailed topographical data. Furthermore, when the victim is tired, the calculation unit can calculate a route including a rest point. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate route can be calculated by adjusting the calculation method of the search route based on the emotion of the victim. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input emotion data of the victim to the generation AI and cause the generation AI to execute the calculation of the search route.

[0078] The calculation unit can analyze past distress patterns and calculate an optimal search route. The calculation unit calculates an optimal search route, for example, based on an evacuation site frequently used by past distressed people. The calculation unit can also calculate a search route specialized for a time period based on a time period when past distressed people often get lost. Furthermore, the calculation unit can calculate a search route for checking the presence or absence of equipment based on equipment often carried by past distressed people. In this way, the optimal search route can be calculated by analyzing past distress patterns. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input past distress data to the generation AI and cause the generation AI to execute an analysis of the distress pattern.

[0079] The calculation unit can analyze the topographical data in detail and improve the accuracy of the search route. The calculation unit calculates an optimal search route, for example, taking into account the undulations and slopes of the topography. The calculation unit can also adjust the search route, taking into account the features of the topography (cliffs, valleys, forests, etc.). Furthermore, the calculation unit can identify dangerous points on the search route based on the topographical data and calculate a route to avoid them. This improves the accuracy of the search route by analyzing the topographical data in detail. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the topographical data to the generation AI and cause the generation AI to perform the analysis of the topography.

[0080] The calculation unit can estimate the emotion of the victim and determine the priority of the search route based on the estimated emotion of the victim. For example, when the victim is in a panic state, the calculation unit searches the shortest route with priority. In addition, when the victim is calm, the calculation unit can also determine the search route by considering detailed topographical data. Furthermore, when the victim is tired, the calculation unit can also search with priority on a route including a rest point. The estimation of emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the priority of the search route is determined based on the emotion of the victim, so that a more appropriate route can be selected. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input emotion data of the victim to the generation AI and cause the generation AI to execute the determination of the priority of the search route.

[0081] The calculation unit can calculate a search route taking into account attribute information such as the age and gender of the victim. For example, if the victim is an elderly person, the calculation unit calculates a route taking into account physical strength. Furthermore, if the victim is a child, the calculation unit can also calculate a route that prioritizes safety. Furthermore, if the victim is a woman, the calculation unit can also calculate a route that takes into account specific risks. In this way, a more appropriate search route can be calculated by taking into account attribute information such as age and gender. Part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input attribute information of the victim to the generation AI and cause the generation AI to execute calculation of the search route.

[0082] The calculation unit can adjust the search route based on the equipment and belongings of the victim. For example, if the victim has a GPS device, the calculation unit adjusts the route based on the signal from the device. In addition, if the victim has food or water, the calculation unit can adjust the route taking into account the amount of food or water. Furthermore, if the victim has a communication device, the calculation unit can adjust the route taking into account the condition of the communication device. This allows a more appropriate route to be selected by adjusting the search route based on the equipment and belongings. Part or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the equipment data of the victim to the generation AI and cause the generation AI to adjust the search route.

[0083] The control unit can estimate the emotion of the victim and adjust the flight route of the drone based on the estimated emotion of the victim. For example, when the victim is in a panic state, the control unit flies the drone by giving priority to the shortest route. In addition, when the victim is calm, the control unit can also adjust the flight route by taking into account detailed topographical data. Furthermore, when the victim is tired, the control unit can also fly by giving priority to a route including a rest point. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the flight route based on the emotion of the victim, a more appropriate search is possible. A part or all of the above-mentioned processing in the control unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the control unit can input emotion data of the victim to the generation AI and cause the generation AI to adjust the flight route.

[0084] The control unit can analyze data collected during the flight of the drone in real time and adjust the flight route. The control unit can adjust the flight route based on, for example, real-time location information collected by the drone. The control unit can also adjust the flight route based on real-time weather information collected by the drone. Furthermore, the control unit can adjust the flight route based on real-time topographical data collected by the drone. This improves the accuracy of the search by analyzing data in real time and optimizing the flight route. A part or all of the above-mentioned processing in the control unit can be performed, for example, using AI or can be performed without using AI. For example, the control unit can input real-time data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0085] The control unit can adjust the flight speed and altitude of the drone to improve the accuracy of the search. For example, the control unit can adjust the flight speed of the drone to efficiently cover the search area. The control unit can also adjust the flight altitude of the drone to collect detailed topographical data. Furthermore, the control unit can adjust the flight speed and altitude of the drone to realize an optimal search route. This improves the efficiency of the search by adjusting the flight speed and altitude. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input flight data of the drone to the generation AI and cause the generation AI to adjust the flight speed and altitude.

[0086] The control unit can estimate the emotion of the victim and change the flight pattern of the drone based on the estimated emotion of the victim. For example, when the victim is in a panic state, the control unit flies the drone by giving priority to the shortest route. In addition, when the victim is calm, the control unit can also change the flight pattern by taking into account detailed topographical data. Furthermore, when the victim is tired, the control unit can also fly by giving priority to a route including a rest point. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This enables a more appropriate search by changing the flight pattern based on the emotion of the victim. A part or all of the above-mentioned processing in the control unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the control unit can input emotion data of the victim to the generation AI and cause the generation AI to execute the change of the flight pattern.

[0087] The control unit can update the location information of the victim in real time during the flight of the drone and adjust the flight route. The control unit adjusts the flight route based on, for example, real-time location information collected by the drone. The control unit can also adjust the flight route based on real-time weather information collected by the drone. Furthermore, the control unit can adjust the flight route based on real-time topographical data collected by the drone. This improves the accuracy of the search by updating the location information in real time and adjusting the flight route. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input real-time location data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0088] The control unit can update the location information of the victim in real time during the flight of the drone and adjust the flight route. The control unit adjusts the flight route based on, for example, real-time location information collected by the drone. The control unit can also adjust the flight route based on real-time weather information collected by the drone. Furthermore, the control unit can adjust the flight route based on real-time topographical data collected by the drone. This improves the accuracy of the search by updating the location information in real time and adjusting the flight route. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input real-time location data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0089] The control unit can adjust the flight route taking into account weather information during the flight of the drone. The control unit adjusts the flight route based on, for example, real-time weather information collected by the drone. The control unit can also adjust the flight route based on real-time weather data collected by the drone. Furthermore, the control unit can also adjust the flight route based on real-time wind speed data collected by the drone. This improves the accuracy of the search by adjusting the flight route taking into account weather information. A part or all of the above-mentioned processing in the control unit may be performed, for example, using AI or may be performed without using AI. For example, the control unit can input real-time weather data collected by the drone to the generation AI and cause the generation AI to adjust the flight route.

[0090] The analysis unit can estimate the emotion of the victim and adjust the analysis method based on the estimated emotion of the victim. For example, when the victim is in a panic state, the analysis unit selects a method for quickly analyzing the location information. Also, when the victim is calm, the analysis unit can perform detailed data analysis to improve accuracy. Furthermore, when the victim is tired, the analysis unit can select an analysis method that takes into account the victim's physical strength and health condition. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the analysis method based on the emotion of the victim, more appropriate analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input emotion data of the victim to the generation AI and cause the generation AI to adjust the analysis method.

[0091] The analysis unit can analyze the real-time data collected by the drone in detail and identify the location of the victim. The analysis unit can, for example, analyze the video data collected by the drone and identify the location of the victim. The analysis unit can also analyze the audio data collected by the drone and identify the location of the victim. Furthermore, the analysis unit can analyze the temperature data collected by the drone and identify the location of the victim. In this way, the location of the victim can be quickly identified by analyzing the real-time data in detail. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the real-time data collected by the drone to the generation AI and cause the generation AI to identify the location of the victim.

[0092] The analysis unit can analyze the past behavioral patterns of the victim and improve the accuracy of the search. For example, the analysis unit can analyze the past behavioral patterns of the victim and predict the location of the victim. The analysis unit can also analyze the past movement history of the victim and identify the search area. Furthermore, the analysis unit can analyze the past use patterns of the equipment of the victim and improve the accuracy of the search. In this way, the accuracy of the search is improved by analyzing the past behavioral patterns. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input past distress data to the generation AI and cause the generation AI to execute the analysis of the behavioral patterns.

[0093] The analysis unit can estimate the emotion of the victim and adjust the display method of the analysis result based on the estimated emotion of the victim. For example, when the victim is in a panic state, the analysis unit provides a simple and highly visible display method. Furthermore, when the victim is calm, the analysis unit can also provide a display method including detailed information. Furthermore, when the victim is tired, the analysis unit can also provide a simple and highly visible display method. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the display method of the analysis result based on the emotion of the victim, more appropriate information can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input emotion data of the victim to the generation AI and cause the generation AI to adjust the display method.

[0094] The analysis unit can geographically analyze the data collected by the drone and identify the search area. The analysis unit can, for example, analyze the topographical data collected by the drone and identify the search area. The analysis unit can also analyze the vegetation data collected by the drone and identify the search area. Furthermore, the analysis unit can analyze the water area data collected by the drone and identify the search area. This makes it possible to quickly identify the search area by analyzing the data geographically. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the geographical data collected by the drone to the generation AI and cause the generation AI to identify the search area.

[0095] The analysis unit can improve the accuracy of the search by performing an analysis based on the equipment and belongings of the victim. For example, the analysis unit can analyze the signal of a GPS device carried by the victim to identify location information. The analysis unit can also analyze the state of a communication device carried by the victim to ensure a means of contact. Furthermore, the analysis unit can analyze the amount of food and water carried by the victim to determine the priority of the search. In this way, the analysis can be performed based on the equipment and belongings to improve the accuracy of the search. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the belongings data of the victim to the generation AI and cause the generation AI to execute the analysis.

[0096] The response instruction unit can estimate the emotion of the victim and instruct the victim on a response method based on the estimated emotion of the victim. For example, if the victim is in a panic state, the response instruction unit transmits a voice message to calm the victim. Also, if the victim is calm, the response instruction unit can provide detailed instructions. Furthermore, if the victim is tired, the response instruction unit can provide instructions to encourage the victim to take a rest. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This enables a more appropriate response by instructing a response method based on the emotion of the victim. A part or all of the above-mentioned processing in the response instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response instruction unit can input emotion data of the victim to the generation AI and cause the generation AI to execute an instruction on a response method.

[0097] The response instruction unit can analyze the condition of the victim in real time and instruct the optimal response method. The response instruction unit can, for example, analyze the heart rate and respiratory rate of the victim in real time and instruct the appropriate response method. The response instruction unit can also analyze the location information of the victim in real time and instruct the optimal rescue route. Furthermore, the response instruction unit can analyze the body temperature and hydration status of the victim in real time and instruct the appropriate response method. In this way, the efficiency of rescue activities is improved by analyzing the condition in real time and instructing the optimal response method. Part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the response instruction unit can input real-time data of the victim to the generation AI and cause the generation AI to execute instructions on the response method.

[0098] The response instruction unit can instruct a response method in consideration of the victim's past medical history. The response instruction unit, for example, instructs an appropriate medical response in consideration of the victim's past medical history. The response instruction unit can also instruct an appropriate response method in consideration of the victim's past allergy information. Furthermore, the response instruction unit can also instruct an appropriate response method in consideration of the victim's past injury history. This makes it possible to provide a more appropriate medical response by taking the past medical history into consideration. A part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the response instruction unit can input the victim's medical history data into the generation AI and cause the generation AI to execute an instruction on a response method.

[0099] The response instruction unit can estimate the emotion of the victim and determine the priority of the response based on the estimated emotion of the victim. For example, when the victim is in a panic state, the response instruction unit responds with the highest priority. In addition, when the victim is calm, the response instruction unit can also prioritize the response of other victims. Furthermore, when the victim is tired, the response instruction unit can also prioritize the instruction to encourage the victim to take a rest. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by determining the priority of the response based on the emotion of the victim, a more appropriate response is possible. A part or all of the above-mentioned processing in the response instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response instruction unit can input emotion data of the victim to the generation AI and cause the generation AI to execute the determination of the priority of the response.

[0100] The response instruction unit can instruct the optimal response method by taking into account the geographical location information of the victim. For example, when the victim is in a mountainous area, the response instruction unit instructs a response method that takes into account the terrain. Furthermore, when the victim is in a forested area, the response instruction unit can also instruct a response method that takes into account the vegetation. Furthermore, when the victim is near a river, the response instruction unit can also instruct a response method that takes into account the water level. In this way, a more appropriate response method can be instructed by taking into account the geographical location information. A part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the response instruction unit can input the location data of the victim to the generation AI and cause the generation AI to execute an instruction on the response method.

[0101] The response instruction unit can instruct the response method based on the equipment and belongings of the victim. For example, if the victim has a GPS device, the response instruction unit instructs the response method based on the signal. In addition, if the victim has food or water, the response instruction unit can also instruct the response method taking into account the amount of food or water. Furthermore, if the victim has a communication device, the response instruction unit can also instruct the response method taking into account the state of the communication device. This allows for more appropriate response by instructing the response method based on the equipment and belongings. Part or all of the above-mentioned processing in the response instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the response instruction unit can input the belongings data of the victim to the generation AI and cause the generation AI to execute the response method instruction.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The mountain rescue search system may further include a health monitoring unit that monitors the health of the victim. The health monitoring unit collects biometric data such as the heart rate, blood pressure, and body temperature of the victim in real time and transmits it to the analysis unit. For example, if the heart rate is abnormally high, the victim may be in a panic, so the response instruction unit can issue instructions to calm the victim. If the body temperature is low, an instruction can be issued indicating that there is a risk of hypothermia and that prompt rescue is necessary. Furthermore, if the blood pressure is abnormally low, the victim may be dehydrated, so an instruction can be issued to encourage hydration. This allows the health of the victim to be monitored in real time and appropriate responses to be taken, thereby improving the accuracy and efficiency of rescue operations.

[0104] The mountain distress search system may further include a communication unit that shares the location information of the victim. The communication unit transmits the location information of the victim to the rescue team in real time, improving the efficiency of the search. For example, if the victim is moving, the location information can be updated in real time and notified to the rescue team. The communication unit can also share the location information of the victim with other drones and search systems to cooperate in the search. Furthermore, the communication unit can notify the location information of the victim to family and friends to reassure them. This allows the location information of the victim to be shared in real time, improving the accuracy and efficiency of the search.

[0105] The mountain distress search system may further include a psychological evaluation unit that evaluates the psychological state of the victim. The psychological evaluation unit analyzes the voice and facial expression of the victim to evaluate the psychological state. For example, the stress level of the victim can be evaluated from the tone of voice and manner of speech of the victim and transmitted to the response instruction unit. In addition, the facial expression of the victim can be photographed by a camera and the psychological state can be evaluated using an emotion estimation algorithm. Furthermore, the behavioral pattern of the victim can be analyzed to evaluate the psychological state. In this way, the psychological state of the victim can be evaluated and appropriate responses can be taken, thereby improving the accuracy and efficiency of rescue operations.

[0106] The mountain rescue search system may further include an environmental monitoring unit that monitors the environment around the victim. The environmental monitoring unit collects environmental data such as temperature, humidity, and wind speed in real time and transmits it to the analysis unit. For example, if the temperature is dropping rapidly, the victim is at higher risk of hypothermia, so an instruction can be issued indicating that prompt rescue is necessary. Also, if the wind speed is strong, the drone's flight route can be adjusted to safely conduct the search. Furthermore, if the humidity is high, data can be provided indicating that the victim is at lower risk of dehydration. This allows the environment around the victim to be monitored in real time and appropriate measures to be taken, thereby improving the accuracy and efficiency of rescue operations.

[0107] The mountain rescue search system may further include a behavior analysis unit that analyzes the behavior history of the victim. The behavior analysis unit analyzes the past behavior patterns of the victim to improve the accuracy of the search. For example, it is possible to analyze what route the victim has taken in the past and optimize the current search route. It is also possible to analyze what equipment the victim has used in the past and estimate the current equipment status. It is also possible to analyze what environment the victim was in when he or she was lost in the past and optimize the method of responding to the current environment. In this way, it is possible to improve the accuracy and efficiency of rescue operations by analyzing the behavior history of the victim and taking appropriate measures.

[0108] The mountain rescue search system may further include an emotion route adjustment unit that estimates the emotion of the victim and adjusts the search route based on the estimated emotion. The emotion route adjustment unit analyzes the emotion of the victim and calculates the optimal search route. For example, if the victim is in a panicked state, the shortest route can be calculated with priority. Also, if the victim is calm, the optimal route can be calculated taking into account detailed topographical data. Furthermore, if the victim is tired, a route including a rest point can be calculated. In this way, a more appropriate route can be calculated by adjusting the search route based on the emotion of the victim.

[0109] The mountain rescue search system can further include an equipment analysis unit that analyzes the equipment and belongings of the lost person. The equipment analysis unit analyzes the equipment and belongings of the lost person to improve the accuracy of the search. For example, if the lost person has a GPS device, the signal can be analyzed to identify the location information. Also, if the lost person has a communication device, the condition of the device can be analyzed to ensure a means of contact. Furthermore, if the lost person has food or water, the amount of food or water can be analyzed to determine the search priority. In this way, the accuracy of the search can be improved by analyzing the equipment and belongings.

[0110] The mountain distress search system may further include an emotion response instruction unit that estimates the emotion of the victim and instructs a response method based on the estimated emotion. The emotion response instruction unit analyzes the emotion of the victim and instructs the optimal response method. For example, if the victim is in a panicked state, a voice message to calm the victim can be sent. Also, if the victim is calm, detailed instructions can be provided. Furthermore, if the victim is tired, an instruction to take a break can be provided. In this way, a more appropriate response can be made by instructing a response method based on the emotion of the victim.

[0111] The mountain rescue search system can further include a location prediction unit that predicts location information of the lost person. The location prediction unit analyzes the past behavioral patterns and current location information of the lost person to predict the location of the lost person. For example, it can analyze what route the lost person has taken in the past and predict his / her current location. It can also analyze the direction in which the lost person is currently moving and predict his / her future location. It can also analyze the environment in which the lost person is in and predict his / her location based on that environment. This makes it possible to predict the location of the lost person and improve the accuracy of the search.

[0112] The mountain distress search system may further include an emotion priority determination unit that estimates the emotion of the victim and determines the search priority based on the estimated emotion. The emotion priority determination unit analyzes the emotion of the victim and determines the search priority. For example, if the victim is in a panicked state, the search can be given top priority. Also, if the victim is calm, the search for other victims can be prioritized. Furthermore, if the victim is tired, an instruction to take a rest can be prioritized. In this way, by determining the search priority based on the emotion of the victim, a more appropriate search can be performed.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The collection unit collects past accident data and terrain data. Past accident data includes the date, time, location, and cause of the accident, and terrain data includes altitude, terrain type, and terrain changes. The collection unit filters the data based on the age and gender of the victim, and complements the data based on seasonal and weather variations when collecting terrain data. Step 2: The calculation unit calculates a search route based on the collected data. The search route is calculated based on criteria such as the shortest distance, avoiding danger, and efficient coverage area. The calculation unit analyzes past distress patterns and terrain data to identify locations where distressed people are likely to be and calculates the route. Step 3: The control unit autonomously flies the drone based on the calculated route. The control unit analyzes the real-time data collected by the drone, identifies the location of the victim, and adjusts the flight route based on the location.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0116] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] For example, the collection unit is realized by either the smart device 14 or the data processing device 12. For example, the camera 42 or the microphone 38B of the smart device 14 is used to collect information on the victim, and the control unit 46A filters and complements the data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data and calculates the optimal search route. The control unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and autonomously flies the drone based on the calculated route. The analysis unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and analyzes real-time data collected by the drone. The response instruction unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and instructs the response method when the victim is found. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] Fig. 4 shows an example of 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.

[0127] 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.

[0128] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0132] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the collection unit is realized by either the smart glasses 214 or the data processing device 12. For example, the camera 42 or the microphone 238 of the smart glasses 214 is used to collect information on the victim, and the control unit 46A filters and complements the data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data and calculates the optimal search route. The control unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and autonomously flies the drone based on the calculated route. The analysis unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and analyzes real-time data collected by the drone. The response instruction unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and instructs the response method when the victim is found. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] 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 performed in a secure state.

[0142] 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.

[0143] 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.

[0144] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0146] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 voice indicating a user input for 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.

[0148] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the collection unit is realized by either the headset terminal 314 or the data processing device 12. For example, the camera 42 or the microphone 238 of the headset terminal 314 is used to collect information on the victim, and the control unit 46A filters and complements the data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data and calculates the optimal search route. The control unit is realized by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and autonomously flies the drone based on the calculated route. The analysis unit is realized by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and analyzes real-time data collected by the drone. The response instruction unit is realized by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and instructs the response method when the victim is found. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0154] 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.

[0155] 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 the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] 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 performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0159] 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.

[0160] 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.

[0161] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0163] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] 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 a voice indicating a user input for 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.

[0165] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0167] For example, the collection unit is realized by either the robot 414 or the data processing device 12. For example, the camera 42 or the microphone 238 of the robot 414 is used to collect information on the victim, and the control unit 46A filters and complements the data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data and calculates the optimal search route. The control unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and autonomously flies the drone based on the calculated route. The analysis unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and analyzes real-time data collected by the drone. The response instruction unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and instructs the response method when the victim is found. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0175] 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.

[0176] 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 Universal Serial Bus (USB) 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.

[0177] In addition, 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 upon request from the data processing device 12.

[0178] 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.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0181] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) A collection unit that collects past disaster data and topographical data; A calculation unit that calculates a search route based on the data collected by the collection unit; A control unit that autonomously flies the drone based on the route calculated by the calculation unit. A system characterized by: (Appendix 2) It is equipped with an analysis unit that analyzes real-time data collected by drones. The control unit is The flight of the drone is adjusted based on the data analyzed by the analysis unit. 2. The system of claim 1, (Appendix 3) Equipped with a response instruction unit that instructs how to respond when a person in distress is found 2. The system of claim 1, (Appendix 4) The collecting unit includes: Estimate the emotions of the victim and prioritize the data to be collected based on the estimated emotions of the victim. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze the victim's past behavioral patterns and select the appropriate data collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collecting information on the equipment and belongings of those lost in real time to aid in the search 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate the emotions of the victim and select the type of data to collect based on the estimated emotions of the victim. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Prioritize collection of relevant data by considering the geographic location of the victim 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the social media activity of victims and collect relevant data 2. The system of claim 1, (Appendix 10) The calculation unit is Estimate the emotions of the person lost in a disaster and adjust the calculation method for the search route based on the estimated emotions of the person lost in a disaster 2. The system of claim 1, (Appendix 11) The calculation unit is Analyzing past distress patterns and calculating optimal search routes 2. The system of claim 1, (Appendix 12) The calculation unit is Detailed analysis of terrain data to improve the accuracy of search routes 2. The system of claim 1, (Appendix 13) The calculation unit is Estimating the emotions of the victim and prioritizing search routes based on the estimated emotions of the victim 2. The system of claim 1, (Appendix 14) The calculation unit is Calculate search routes taking into account the age and gender attributes of the victims 2. The system of claim 1, (Appendix 15) The calculation unit is Adjust search routes based on the victim's equipment and belongings 2. The system of claim 1, (Appendix 16) The control unit is Estimate the emotions of the person in distress and adjust the drone's flight route based on the estimated emotions of the person in distress. 2. The system of claim 1, (Appendix 17) The control unit is Data collected during drone flights is analyzed in real time to adjust flight routes. 2. The system of claim 1, (Appendix 18) The control unit is Adjusting drone flight speed and altitude to improve search accuracy 2. The system of claim 1, (Appendix 19) The control unit is Estimate the emotions of the victim and change the drone's flight pattern based on the estimated emotions of the victim. 2. The system of claim 1, (Appendix 20) The control unit is The drone updates the victim's location in real time while flying and adjusts the flight route. 2. The system of claim 1, (Appendix 21) The control unit is The drone updates the victim's location in real time while flying and adjusts the flight route. 2. The system of claim 1, (Appendix 22) The control unit is Adjusting flight routes while taking into account weather information during drone flights 2. The system of claim 1, (Appendix 23) The analysis unit is Estimate the emotions of the victim and adjust the analysis method based on the estimated emotions of the victim 2. The system of claim 1, (Appendix 24) The analysis unit is Real-time data collected by drones is analyzed in detail to pinpoint the location of victims. 2. The system of claim 1, (Appendix 25) The analysis unit is Analyzing the past behavioral patterns of missing people to improve search accuracy 2. The system of claim 1, (Appendix 26) The analysis unit is Estimate the emotions of the victim and adjust the display method of the analysis results based on the estimated emotions of the victim 2. The system of claim 1, (Appendix 27) The analysis unit is Data collected by drones will be geographically analyzed to identify search areas. 2. The system of claim 1, (Appendix 28) The analysis unit is Improve the accuracy of searches by analyzing the equipment and belongings of those lost. 2. The system of claim 1, (Appendix 29) The response instruction unit is Estimate the emotions of the person stranded and provide instructions on how to respond based on the estimated emotions of the person stranded 2. The system of claim 1, (Appendix 30) The response instruction unit is Analyze the condition of the person in distress in real time and provide instructions on the best course of action 2. The system of claim 1, (Appendix 31) The response instruction unit is Instruct how to respond, taking into account the victim's past medical history 2. The system of claim 1, (Appendix 32) The response instruction unit is Estimate the emotions of the victim and determine response priorities based on the estimated emotions of the victim 2. The system of claim 1, (Appendix 33) The response instruction unit is Taking into account the geographic location of the victim, it provides the best response method 2. The system of claim 1, (Appendix 34) The response instruction unit is Give instructions on how to respond based on the victim's equipment and belongings 2. The system of claim 1, [Explanation of symbols]

[0187] 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. A collection unit that collects past disaster data and topographical data; A calculation unit that calculates a search route based on the data collected by the collection unit; A control unit that autonomously flies the drone based on the route calculated by the calculation unit. A system characterized by:

2. It is equipped with an analysis unit that analyzes real-time data collected by drones. The control unit is The flight of the drone is adjusted based on the data analyzed by the analysis unit.

2. The system of claim 1 .

3. Equipped with a response instruction unit that instructs how to respond when a person in distress is found 2. The system of claim 1 .

4. The collecting unit includes: Estimate the emotions of the victim and prioritize the data to be collected based on the estimated emotions of the victim.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the victim's past behavioral patterns and select the appropriate data collection method 2. The system of claim 1 .

6. The collecting unit includes: Collecting information on the equipment and belongings of those lost in real time to aid in the search 2. The system of claim 1 .

7. The collecting unit includes: Estimate the emotions of the victim and select the type of data to collect based on the estimated emotions of the victim.

2. The system of claim 1 .

8. The collecting unit includes: Prioritize collection of relevant data by considering the geographic location of the victim 2. The system of claim 1 .

9. The collecting unit includes: Analyze the social media activity of victims and collect relevant data 2. The system of claim 1 .

10. The calculation unit is Estimate the emotions of the person lost in a disaster and adjust the calculation method for the search route based on the estimated emotions of the person lost in a disaster 2. The system of claim 1 .

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