system

Abstract

The system according to the embodiment aims to assist in locating disaster victims at disaster sites where humans cannot enter. [Solution] A system according to an embodiment includes a control unit, an analysis unit, an identification unit, and a provision unit. The control unit controls the movement of an insect-type robot deployed to a disaster site. The analysis unit analyzes data collected by the robot controlled by the control unit. The identification unit identifies the location or situation of a disaster victim based on the data analyzed by the analysis unit. The provision unit provides the information identified by the identification unit to a rescue team.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in search activities at disaster sites in that it was difficult to locate victims in places that humans could not enter.

[0005] The system according to the embodiment aims to assist in locating disaster victims at disaster sites where humans cannot enter. [Means for solving the problem]

[0006] The system according to the embodiment includes a control unit, an analysis unit, an identification unit, and a provision unit. The control unit controls the movement of an insect-type robot deployed to a disaster site. The analysis unit analyzes data collected by the robot controlled by the control unit. The identification unit identifies the location or situation of a disaster victim based on the data analyzed by the analysis unit. The provision unit provides the information identified by the identification unit to a rescue team. [Effects of the Invention]

[0007] The system according to the embodiment can assist in locating disaster victims in disaster areas where humans cannot enter. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster site search support system according to an embodiment of the present invention uses an insect-like robot equipped with a platform equipped with a generative AI to support search activities at disaster sites where humans cannot enter. In this system, an insect-like robot is deployed to a disaster site, where a generative AI controls the robot's movements and analyzes the situation in real time. The generative AI identifies the location and status of victims based on data collected by the robot. This allows rescue teams to quickly and efficiently locate victims and perform rescue operations. For example, an insect-like robot is deployed to a disaster site. Many disaster sites, such as those affected by earthquakes or fires, are difficult for humans to enter. By deploying an insect-like robot in such locations, the situation at the site can be grasped. Next, a generative AI controls the robot's movements. The generative AI analyzes data obtained from the robot's sensors and calculates the optimal route. For example, when moving through rubble, the generative AI calculates a route that avoids obstacles. The generative AI identifies the location and status of victims based on the data collected by the robot. For example, if the robot is equipped with a camera and microphone, the generative AI can analyze video and audio data to identify the location of victims. Furthermore, if a temperature sensor is installed, the generative AI can analyze the temperature data and detect the victim's body temperature. This mechanism allows rescue teams to quickly and efficiently locate victims and carry out rescue operations. For example, if the generative AI identifies the victim's location, it can provide that information to the rescue team in real time, allowing them to quickly rush to the victim's location. Furthermore, by analyzing the victim's condition, the generative AI can enable the rescue team to select an appropriate rescue method. In this way, using an insect-type robot equipped with generative AI can make search operations at disaster sites more efficient and save many lives. As a result, the disaster site search support system can use generative AI to efficiently support search operations at disaster sites.

[0029] A disaster site search support system according to an embodiment includes a control unit, an analysis unit, an identification unit, and a provision unit. The control unit controls the movement of an insect-like robot deployed to a disaster site. For example, the control unit analyzes data obtained from the robot's sensors and calculates an optimal route. For example, the control unit can calculate a route for the robot to move through rubble while avoiding obstacles. The control unit can also control the robot's movement in real time using a generative AI. For example, the control unit can adjust the robot's movement speed and direction using the generative AI. The analysis unit analyzes data collected by the robot controlled by the control unit. For example, the analysis unit can analyze video and audio data obtained from a camera and a microphone to identify the location of a disaster victim. For example, the analysis unit can use the generative AI to detect the appearance of a disaster victim from video data and the voice of the disaster victim from audio data. The analysis unit can also analyze temperature data obtained from a temperature sensor to detect the body temperature of the disaster victim. For example, the analysis unit can use the generative AI to detect the body temperature of a disaster victim from temperature data and identify the location of the disaster victim. The identification unit identifies the location or situation of the victim based on the data analyzed by the analysis unit. The identification unit can, for example, use a generation AI to identify the location of the victim and transmit the information to the provision unit. The provision unit provides the information identified by the identification unit to a rescue team. The provision unit can, for example, use a generation AI to provide the location and situation of the victim to a rescue team in real time. For example, the provision unit can display the location information of the victim on a map using the generation AI and provide it to the rescue team. As a result, the disaster site search support system according to the embodiment can efficiently support search activities at the disaster site using the generation AI.

[0030] The control unit can analyze data obtained from the robot's sensors and calculate an efficient route. The control unit, for example, analyzes data obtained from the robot's sensors and calculates an optimal route. For example, the control unit can calculate a route that avoids obstacles when moving through rubble. The control unit can also control the robot's movement in real time using a generative AI. For example, the control unit can adjust the robot's movement speed and direction using the generative AI. This allows the robot to move efficiently. Some or all of the above-mentioned processing in the control unit may be performed using, or without, the generative AI. For example, the control unit can input data obtained from the robot's sensors into the generative AI and cause the generative AI to calculate an efficient route.

[0031] The analysis unit can analyze video or audio data obtained from a camera or microphone to identify the location of the victim. The analysis unit can, for example, analyze video or audio data obtained from a camera or microphone to identify the location of the victim. For example, the analysis unit can use a generation AI to detect the victim's appearance from the video data and the victim's voice from the audio data. The analysis unit can also use a generation AI to integrate the video data and audio data to identify the location of the victim. For example, the analysis unit can combine the video data from the camera and the audio data from the microphone to more accurately identify the location of the victim. This allows the location of the victim to be accurately identified. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the video and audio data obtained from the camera or microphone into the generation AI and have the generation AI identify the location of the victim.

[0032] The analysis unit can analyze temperature data obtained from a temperature sensor and detect the victim's body temperature. For example, the analysis unit can analyze temperature data obtained from a temperature sensor and detect the victim's body temperature. For example, the analysis unit can use a generation AI to detect the victim's body temperature from the temperature data and identify the victim's location. The analysis unit can also integrate the temperature data with other sensor data to more accurately identify the victim's location. For example, the analysis unit can combine the temperature data and video data to identify the victim's location. This makes it possible to detect the victim's body temperature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input temperature data obtained from a temperature sensor into the generation AI and cause the generation AI to detect the victim's body temperature.

[0033] The providing unit can provide the location or status of the victim to a rescue team in real time. The providing unit, for example, provides the location or status of the victim to a rescue team in real time. For example, the providing unit can use the generation AI to display the location information of the victim on a map and provide it to the rescue team. The providing unit can also use the generation AI to analyze the victim's status and provide the information to the rescue team. For example, the providing unit can use the generation AI to analyze the victim's biometric information, such as body temperature and heart rate, and provide the information to the rescue team. This allows the rescue team to respond quickly. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information on the location or status of the victim to the generation AI and have the generation AI provide the information.

[0034] The control unit can detect changes in the surrounding environment in real time while the robot is operating and dynamically change the operation route. For example, the control unit can detect changes in the surrounding environment in real time while the robot is operating and dynamically change the operation route. For example, if the control unit detects a rubble collapse, it can change the robot to a safe route. Furthermore, if the control unit detects a fire, it can change the robot to a route that avoids the source of the fire. Furthermore, if the control unit detects water intrusion, it can change the robot to a route that avoids the water. This makes it possible to change the operation route in response to environmental changes. Some or all of the above-mentioned processing in the control unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the control unit can input surrounding environmental data into the generation AI and cause the generation AI to change the operation route.

[0035] The control unit can apply different avoidance algorithms depending on the type of obstacle when the robot is operating. For example, the control unit can apply different avoidance algorithms depending on the type of obstacle when the robot is operating. For example, when the control unit detects rubble, it can apply an algorithm that causes the robot to climb over the rubble. Furthermore, when the control unit detects a fire, it can apply an algorithm that causes the robot to avoid the source of the fire. Furthermore, when the control unit detects water intrusion, it can apply an algorithm that causes the robot to avoid the water. This enables avoidance depending on the type of obstacle. Some or all of the above-mentioned processing in the control unit can be performed using, or without, the generation AI. For example, the control unit can input data of the obstacle into the generation AI and cause the generation AI to apply the avoidance algorithm.

[0036] The control unit can analyze the surrounding sound environment when the robot is operating and adjust the movement route based on the source of the sound. For example, the control unit can analyze the surrounding sound environment when the robot is operating and adjust the movement route based on the source of the sound. For example, when the control unit detects a human voice, it can adjust the route the robot takes in that direction. Furthermore, when the control unit detects a mechanical sound, it can adjust the route the robot takes to avoid the sound source. Furthermore, when the control unit detects an explosion sound, it can adjust the route the robot takes to head in a safe direction. This makes it possible to adjust the movement route according to the sound environment. Some or all of the above-mentioned processing in the control unit may be performed using, or without, a generation AI. For example, the control unit can input surrounding sound data into the generation AI and cause the generation AI to adjust the movement route.

[0037] The control unit can analyze the surrounding light environment when the robot is operating and adjust the movement route based on the intensity of the light. For example, when the robot is operating, the control unit can analyze the surrounding light environment and adjust the movement route based on the intensity of the light. For example, when the control unit detects darkness, it can adjust the route the robot takes while turning on the light. Furthermore, when the control unit detects strong light, it can adjust the route the robot takes to avoid the light source. Furthermore, when the control unit detects flashing light, it can adjust the route the robot takes to avoid the light source. This makes it possible to adjust the movement route according to the light environment. Some or all of the above-mentioned processing in the control unit can be performed using, or without, a generation AI. For example, the control unit can input surrounding light data into the generation AI and cause the generation AI to adjust the movement route.

[0038] During analysis, the analysis unit can integrate data obtained from multiple sensors to generate more accurate analysis results. For example, during analysis, the analysis unit can integrate data obtained from multiple sensors to generate more accurate analysis results. For example, the analysis unit can integrate data from a camera and a microphone to identify the location of a disaster victim. The analysis unit can also integrate data from a temperature sensor and a humidity sensor to analyze the environmental condition. The analysis unit can also integrate GPS data and acceleration sensor data to identify the exact location of a robot. This allows for more accurate analysis results to be obtained by integrating data from multiple sensors. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data obtained from multiple sensors into a generation AI and have the generation AI integrate and analyze the data.

[0039] The analysis unit can apply an algorithm for detecting anomalies by comparing with past data during analysis. For example, the analysis unit can detect an abnormality by comparing with past data during analysis. For example, the analysis unit can detect an abnormal temperature rise by comparing with past temperature data. The analysis unit can detect an abnormal sound by comparing with past audio data. The analysis unit can detect abnormal movement by comparing with past video data. This makes it possible to detect anomalies by comparing with past data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past data and current data into a generation AI and have the generation AI detect anomalies.

[0040] The analysis unit can determine the analysis priority based on the data acquisition time during analysis. The analysis unit can, for example, determine the analysis priority based on the data acquisition time during analysis. For example, the analysis unit can prioritize analyzing the most recent data. The analysis unit can also prioritize analyzing data acquired during a specific time period. The analysis unit can also prioritize analyzing data in which an abnormality has been detected compared with past data. This makes it possible to determine the analysis priority based on the data acquisition time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data acquisition time into the generation AI and have the generation AI determine the analysis priority.

[0041] The analysis unit can improve the accuracy of the analysis based on the location where the data was acquired during analysis. For example, the analysis unit can improve the accuracy of the analysis based on the location where the data was acquired during analysis. For example, the analysis unit can prioritize analyzing data from locations where there are many victims. The analysis unit can also prioritize analyzing data from high-risk locations. The analysis unit can also prioritize analyzing data from locations where rescue operations are being carried out. This makes it possible to improve the accuracy of the analysis based on the location where the data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the location where the data was acquired into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The identification unit can track the movements and voice of the victim in real time during identification and dynamically update the location. For example, the identification unit can track the movements and voice of the victim in real time during identification and dynamically update the location. For example, the identification unit can detect the voice of the victim and update the location in real time. The identification unit can also track the movements of the victim with a camera and dynamically update the location. The identification unit can also detect the body temperature of the victim and update the location in real time. This makes it possible to update the location of the victim in real time. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the movement and voice data of the victim to the generation AI and cause the generation AI to dynamically update the location.

[0043] The identification unit can analyze the victim's biometric information and apply an algorithm to evaluate the victim's health condition during identification. For example, the identification unit can analyze the victim's biometric information and apply an algorithm to evaluate the victim's health condition during identification. For example, the identification unit can analyze the victim's body temperature and evaluate the victim's health condition. The identification unit can also analyze the victim's heart rate and evaluate the victim's health condition. The identification unit can also analyze the victim's respiratory rate and evaluate the victim's health condition. This makes it possible to evaluate the victim's health condition. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input the victim's biometric information into the generation AI and cause the generation AI to evaluate the victim's health condition.

[0044] The identification unit can analyze the surrounding environment of the victim at the time of identification and propose an evacuation route. The identification unit can, for example, analyze the surrounding environment of the victim at the time of identification and propose an evacuation route. For example, the identification unit can analyze the state of rubble and propose a safe evacuation route. The identification unit can also analyze the state of a fire and propose an evacuation route that avoids the source of the fire. The identification unit can also analyze the state of water intrusion and propose an evacuation route that avoids the water. This makes it possible to analyze the surrounding environment of the victim and propose a safe evacuation route. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input surrounding environment data of the victim into the generation AI and cause the generation AI to propose an evacuation route.

[0045] The identification unit can improve the accuracy of the location by referring to the victim's past location data during identification. For example, the identification unit can improve the accuracy of the location by referring to the victim's past location data during identification. For example, the identification unit can identify the victim's current location based on the victim's past location data. The identification unit can also analyze the victim's past movement patterns to identify the victim's current location. The identification unit can also improve the accuracy of the location by comparing the victim's past location data with the current data. This makes it possible to improve the accuracy of the location by referring to the victim's past location data. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input the victim's past location data into the generation AI and cause the generation AI to improve the accuracy of the location.

[0046] The providing unit can customize the content to be provided based on the importance of the information when providing the information. The providing unit customizes the content to be provided based on the importance of the information when providing the information, for example. For example, the providing unit can provide important information with priority. The providing unit can also postpone information with low urgency. The providing unit can also provide more detailed content to be provided depending on the importance of the information. This makes it possible to customize the content to be provided depending on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the importance of the information to the generation AI and cause the generation AI to customize the content to be provided.

[0047] The providing unit can select an appropriate provision method by taking into consideration the location information of the recipient of the information when providing the information. For example, the providing unit can select an appropriate provision method by taking into consideration the location information of the recipient of the information when providing the information. For example, if the recipient is nearby, the providing unit can provide the information by voice. Furthermore, if the recipient is far away, the providing unit can provide the information by text message. Furthermore, if the recipient is moving, the providing unit can provide the information while updating the location information in real time. This makes it possible to select the optimal provision method based on the location information of the recipient of the information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the recipient's location information into the generation AI and have the generation AI select the provision method.

[0048] The providing unit can optimize the provided content by referring to the past behavioral history of the information recipient when providing the information. For example, the providing unit can optimize the provided content by referring to the past behavioral history of the information recipient when providing the information. For example, the providing unit can provide optimal information based on information used by the recipient in the past. The providing unit can also analyze the recipient's past behavioral patterns and provide necessary information. The providing unit can also select the most effective method of providing information from the recipient's past behavioral history. This makes it possible to optimize the provided content based on the recipient's past behavioral history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the recipient's past behavioral history into the generation AI and cause the generation AI to optimize the provided content.

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

[0050] When the robot is operating, the control unit can analyze the surrounding electromagnetic wave environment and adjust the operation route based on the strength of the electromagnetic waves. For example, if the control unit detects strong electromagnetic waves, it can adjust the robot's route to avoid that area. Also, if there is equipment that is susceptible to the effects of electromagnetic waves, the control unit can adjust the route to avoid that equipment. Furthermore, the control unit can adjust the robot's operation speed to minimize the effects of electromagnetic waves. This makes it possible to adjust the operation route according to the electromagnetic wave environment.

[0051] During identification, the identification unit can analyze the victim's biometric information and apply an algorithm to evaluate the victim's stress level. For example, the identification unit can analyze the victim's heart rate and evaluate the victim's stress level. The identification unit can also analyze the victim's respiratory rate and evaluate the victim's stress level. Furthermore, the identification unit can analyze the victim's body temperature and evaluate the victim's stress level. This makes it possible to evaluate the victim's stress level.

[0052] The control unit can analyze the surrounding humidity environment while the robot is operating and adjust the operation route based on changes in humidity. For example, if the control unit detects high humidity, it can adjust the robot's route to avoid that area. Also, if there is equipment that is sensitive to humidity, the control unit can adjust the route to avoid that equipment. Furthermore, the control unit can adjust the robot's operation speed to minimize the effects of humidity. This makes it possible to adjust the operation route according to the humidity environment.

[0053] During identification, the identification unit can analyze the sound environment around the victim and identify the victim's location based on the source of the sound. For example, the identification unit can detect a human voice and identify the location. The identification unit can also detect a mechanical sound and identify a location to avoid the source of the sound. Furthermore, the identification unit can detect an explosion sound and identify a location to move to in a safe direction. This makes it possible to identify the victim's location based on the sound environment.

[0054] The control unit can analyze the surrounding atmospheric pressure environment while the robot is operating and adjust the movement route based on changes in atmospheric pressure. For example, if the control unit detects low atmospheric pressure, it can adjust the robot's route to avoid that area. Also, if there is equipment that is easily affected by atmospheric pressure, the control unit can adjust the route to avoid that equipment. Furthermore, the control unit can adjust the movement speed of the robot to minimize the effect of atmospheric pressure. This makes it possible to adjust the movement route according to the atmospheric pressure environment.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The control unit controls the movement of the insect-like robot deployed at the disaster site. The control unit analyzes data obtained from the robot's sensors and calculates the optimal route. For example, when moving through rubble, it can calculate a route that avoids obstacles. It can also use generative AI to adjust the robot's movement speed and direction in real time. Step 2: The analysis unit analyzes the data collected by the robot controlled by the control unit. The analysis unit analyzes the video and audio data obtained from the camera and microphone to identify the location of the victim. Using generative AI, it can detect the victim's appearance from the video data and the victim's voice from the audio data. It can also analyze temperature data obtained from the temperature sensor to detect the victim's body temperature. Step 3: The identification unit identifies the location or situation of the victim based on the data analyzed by the analysis unit. The generation AI can be used to identify the location of the victim and send that information to the provision unit. Step 4: The provision unit provides the information identified by the identification unit to the rescue team. Using the generation AI, the location and status of the victim can be provided to the rescue team in real time. For example, the location information of the victim can be displayed on a map and provided to the rescue team.

[0057] (Example 2) A disaster site search support system according to an embodiment of the present invention uses an insect-like robot equipped with a platform equipped with a generative AI to support search activities at disaster sites where humans cannot enter. In this system, an insect-like robot is deployed to a disaster site, where a generative AI controls the robot's movements and analyzes the situation in real time. The generative AI identifies the location and status of victims based on data collected by the robot. This allows rescue teams to quickly and efficiently locate victims and perform rescue operations. For example, an insect-like robot is deployed to a disaster site. Many disaster sites, such as those affected by earthquakes or fires, are difficult for humans to enter. By deploying an insect-like robot in such locations, the situation at the site can be grasped. Next, a generative AI controls the robot's movements. The generative AI analyzes data obtained from the robot's sensors and calculates the optimal route. For example, when moving through rubble, the generative AI calculates a route that avoids obstacles. The generative AI identifies the location and status of victims based on the data collected by the robot. For example, if the robot is equipped with a camera and microphone, the generative AI can analyze video and audio data to identify the location of victims. Furthermore, if a temperature sensor is installed, the generative AI can analyze the temperature data and detect the victim's body temperature. This mechanism allows rescue teams to quickly and efficiently locate victims and carry out rescue operations. For example, if the generative AI identifies the victim's location, it can provide that information to the rescue team in real time, allowing them to quickly rush to the victim's location. Furthermore, by analyzing the victim's condition, the generative AI can enable the rescue team to select an appropriate rescue method. In this way, using an insect-type robot equipped with generative AI can make search operations at disaster sites more efficient and save many lives. As a result, the disaster site search support system can use generative AI to efficiently support search operations at disaster sites.

[0058] A disaster site search support system according to an embodiment includes a control unit, an analysis unit, an identification unit, and a provision unit. The control unit controls the movement of an insect-like robot deployed to a disaster site. For example, the control unit analyzes data obtained from the robot's sensors and calculates an optimal route. For example, the control unit can calculate a route for the robot to move through rubble while avoiding obstacles. The control unit can also control the robot's movement in real time using a generative AI. For example, the control unit can adjust the robot's movement speed and direction using the generative AI. The analysis unit analyzes data collected by the robot controlled by the control unit. For example, the analysis unit can analyze video and audio data obtained from a camera and a microphone to identify the location of a disaster victim. For example, the analysis unit can use the generative AI to detect the appearance of a disaster victim from video data and the voice of the disaster victim from audio data. The analysis unit can also analyze temperature data obtained from a temperature sensor to detect the body temperature of the disaster victim. For example, the analysis unit can use the generative AI to detect the body temperature of a disaster victim from temperature data and identify the location of the disaster victim. The identification unit identifies the location or situation of the victim based on the data analyzed by the analysis unit. The identification unit can, for example, use a generation AI to identify the location of the victim and transmit the information to the provision unit. The provision unit provides the information identified by the identification unit to a rescue team. The provision unit can, for example, use a generation AI to provide the location and situation of the victim to a rescue team in real time. For example, the provision unit can display the location information of the victim on a map using the generation AI and provide it to the rescue team. As a result, the disaster site search support system according to the embodiment can efficiently support search activities at the disaster site using the generation AI.

[0059] The control unit can analyze data obtained from the robot's sensors and calculate an efficient route. The control unit, for example, analyzes data obtained from the robot's sensors and calculates an optimal route. For example, the control unit can calculate a route that avoids obstacles when moving through rubble. The control unit can also control the robot's movement in real time using a generative AI. For example, the control unit can adjust the robot's movement speed and direction using the generative AI. This allows the robot to move efficiently. Some or all of the above-mentioned processing in the control unit may be performed using, or without, the generative AI. For example, the control unit can input data obtained from the robot's sensors into the generative AI and cause the generative AI to calculate an efficient route.

[0060] The analysis unit can analyze video or audio data obtained from a camera or microphone to identify the location of the victim. The analysis unit can, for example, analyze video or audio data obtained from a camera or microphone to identify the location of the victim. For example, the analysis unit can use a generation AI to detect the victim's appearance from the video data and the victim's voice from the audio data. The analysis unit can also use a generation AI to integrate the video data and audio data to identify the location of the victim. For example, the analysis unit can combine the video data from the camera and the audio data from the microphone to more accurately identify the location of the victim. This allows the location of the victim to be accurately identified. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the video and audio data obtained from the camera or microphone into the generation AI and have the generation AI identify the location of the victim.

[0061] The analysis unit can analyze temperature data obtained from a temperature sensor and detect the victim's body temperature. For example, the analysis unit can analyze temperature data obtained from a temperature sensor and detect the victim's body temperature. For example, the analysis unit can use a generation AI to detect the victim's body temperature from the temperature data and identify the victim's location. The analysis unit can also integrate the temperature data with other sensor data to more accurately identify the victim's location. For example, the analysis unit can combine the temperature data and video data to identify the victim's location. This makes it possible to detect the victim's body temperature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input temperature data obtained from a temperature sensor into the generation AI and cause the generation AI to detect the victim's body temperature.

[0062] The providing unit can provide the location or status of the victim to a rescue team in real time. The providing unit, for example, provides the location or status of the victim to a rescue team in real time. For example, the providing unit can use the generation AI to display the location information of the victim on a map and provide it to the rescue team. The providing unit can also use the generation AI to analyze the victim's status and provide the information to the rescue team. For example, the providing unit can use the generation AI to analyze the victim's biometric information, such as body temperature and heart rate, and provide the information to the rescue team. This allows the rescue team to respond quickly. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information on the location or status of the victim to the generation AI and have the generation AI provide the information.

[0063] The control unit can estimate the user's emotions and adjust the robot's movement speed based on the estimated user's emotions. The control unit, for example, estimates the user's emotions and adjusts the robot's movement speed based on the estimated user's emotions. For example, if the user is nervous, the control unit can slow down the robot's movement speed to ensure safety. Furthermore, if the user is relaxed, the control unit can return the robot's movement speed to normal. Furthermore, if the user is in a hurry, the control unit can increase the robot's movement speed to perform a quick search. This enables the movement speed to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the control unit can input the user's emotion data into the generation AI and have the generation AI adjust the movement speed.

[0064] The control unit can detect changes in the surrounding environment in real time while the robot is operating and dynamically change the operation route. For example, the control unit can detect changes in the surrounding environment in real time while the robot is operating and dynamically change the operation route. For example, if the control unit detects a rubble collapse, it can change the robot to a safe route. Furthermore, if the control unit detects a fire, it can change the robot to a route that avoids the source of the fire. Furthermore, if the control unit detects water intrusion, it can change the robot to a route that avoids the water. This makes it possible to change the operation route in response to environmental changes. Some or all of the above-mentioned processing in the control unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the control unit can input surrounding environmental data into the generation AI and cause the generation AI to change the operation route.

[0065] The control unit can apply different avoidance algorithms depending on the type of obstacle when the robot is operating. For example, the control unit can apply different avoidance algorithms depending on the type of obstacle when the robot is operating. For example, when the control unit detects rubble, it can apply an algorithm that causes the robot to climb over the rubble. Furthermore, when the control unit detects a fire, it can apply an algorithm that causes the robot to avoid the source of the fire. Furthermore, when the control unit detects water intrusion, it can apply an algorithm that causes the robot to avoid the water. This enables avoidance depending on the type of obstacle. Some or all of the above-mentioned processing in the control unit can be performed using, or without, the generation AI. For example, the control unit can input data of the obstacle into the generation AI and cause the generation AI to apply the avoidance algorithm.

[0066] The control unit can estimate the user's emotions and switch the robot's operation mode based on the estimated user's emotions. The control unit, for example, estimates the user's emotions and switches the robot's operation mode based on the estimated user's emotions. For example, the control unit can switch the robot to a cautious operation mode when the user is nervous. The control unit can return the robot to a normal operation mode when the user is relaxed. The control unit can switch the robot to a rapid operation mode when the user is in a hurry. This enables switching of the operation mode according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the control unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the control unit can input the user's emotion data into the generation AI and cause the generation AI to switch the operation mode.

[0067] The control unit can analyze the surrounding sound environment when the robot is operating and adjust the movement route based on the source of the sound. For example, the control unit can analyze the surrounding sound environment when the robot is operating and adjust the movement route based on the source of the sound. For example, when the control unit detects a human voice, it can adjust the route the robot takes in that direction. Furthermore, when the control unit detects a mechanical sound, it can adjust the route the robot takes to avoid the sound source. Furthermore, when the control unit detects an explosion sound, it can adjust the route the robot takes to head in a safe direction. This makes it possible to adjust the movement route according to the sound environment. Some or all of the above-mentioned processing in the control unit may be performed using, or without, a generation AI. For example, the control unit can input surrounding sound data into the generation AI and cause the generation AI to adjust the movement route.

[0068] The control unit can analyze the surrounding light environment when the robot is operating and adjust the movement route based on the intensity of the light. For example, when the robot is operating, the control unit can analyze the surrounding light environment and adjust the movement route based on the intensity of the light. For example, when the control unit detects darkness, it can adjust the route the robot takes while turning on the light. Furthermore, when the control unit detects strong light, it can adjust the route the robot takes to avoid the light source. Furthermore, when the control unit detects flashing light, it can adjust the route the robot takes to avoid the light source. This makes it possible to adjust the movement route according to the light environment. Some or all of the above-mentioned processing in the control unit can be performed using, or without, a generation AI. For example, the control unit can input surrounding light data into the generation AI and cause the generation AI to adjust the movement route.

[0069] The analysis unit can estimate the user's emotions and determine the priority of data analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the priority of data analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize analyzing important data. Furthermore, if the user is relaxed, the analysis unit can maintain the normal data analysis order. Furthermore, if the user is in a hurry, the analysis unit can prioritize data that requires quick analysis. This enables the priority of data analysis to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data analysis.

[0070] During analysis, the analysis unit can integrate data obtained from multiple sensors to generate more accurate analysis results. For example, during analysis, the analysis unit can integrate data obtained from multiple sensors to generate more accurate analysis results. For example, the analysis unit can integrate data from a camera and a microphone to identify the location of a disaster victim. The analysis unit can also integrate data from a temperature sensor and a humidity sensor to analyze the environmental condition. The analysis unit can also integrate GPS data and acceleration sensor data to identify the exact location of a robot. This allows for more accurate analysis results to be obtained by integrating data from multiple sensors. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data obtained from multiple sensors into a generation AI and have the generation AI integrate and analyze the data.

[0071] The analysis unit can apply an algorithm for detecting anomalies by comparing with past data during analysis. For example, the analysis unit can detect an abnormality by comparing with past data during analysis. For example, the analysis unit can detect an abnormal temperature rise by comparing with past temperature data. The analysis unit can detect an abnormal sound by comparing with past audio data. The analysis unit can detect abnormal movement by comparing with past video data. This makes it possible to detect anomalies by comparing with past data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past data and current data into a generation AI and have the generation AI detect anomalies.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0073] The analysis unit can determine the analysis priority based on the data acquisition time during analysis. The analysis unit can, for example, determine the analysis priority based on the data acquisition time during analysis. For example, the analysis unit can prioritize analyzing the most recent data. The analysis unit can also prioritize analyzing data acquired during a specific time period. The analysis unit can also prioritize analyzing data in which an abnormality has been detected compared with past data. This makes it possible to determine the analysis priority based on the data acquisition time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data acquisition time into the generation AI and have the generation AI determine the analysis priority.

[0074] The analysis unit can improve the accuracy of the analysis based on the location where the data was acquired during analysis. For example, the analysis unit can improve the accuracy of the analysis based on the location where the data was acquired during analysis. For example, the analysis unit can prioritize analyzing data from locations where there are many victims. The analysis unit can also prioritize analyzing data from high-risk locations. The analysis unit can also prioritize analyzing data from locations where rescue operations are being carried out. This makes it possible to improve the accuracy of the analysis based on the location where the data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the location where the data was acquired into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0075] The identification unit can estimate the user's emotions and adjust the accuracy of the identification based on the estimated user's emotions. For example, the identification unit can estimate the user's emotions and adjust the accuracy of the identification based on the estimated user's emotions. For example, if the user is nervous, the identification unit can analyze detailed data to increase the accuracy of the identification. If the user is relaxed, the identification unit can perform identification with normal accuracy. If the user is in a hurry, the identification unit can adjust the accuracy to perform identification quickly. This enables the accuracy of the identification to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's emotion data into the generation AI and have the generation AI adjust the accuracy of the identification.

[0076] The identification unit can track the movements and voice of the victim in real time during identification and dynamically update the location. For example, the identification unit can track the movements and voice of the victim in real time during identification and dynamically update the location. For example, the identification unit can detect the voice of the victim and update the location in real time. The identification unit can also track the movements of the victim with a camera and dynamically update the location. The identification unit can also detect the body temperature of the victim and update the location in real time. This makes it possible to update the location of the victim in real time. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the movement and voice data of the victim to the generation AI and cause the generation AI to dynamically update the location.

[0077] The identification unit can analyze the victim's biometric information and apply an algorithm to evaluate the victim's health condition during identification. For example, the identification unit can analyze the victim's biometric information and apply an algorithm to evaluate the victim's health condition during identification. For example, the identification unit can analyze the victim's body temperature and evaluate the victim's health condition. The identification unit can also analyze the victim's heart rate and evaluate the victim's health condition. The identification unit can also analyze the victim's respiratory rate and evaluate the victim's health condition. This makes it possible to evaluate the victim's health condition. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input the victim's biometric information into the generation AI and cause the generation AI to evaluate the victim's health condition.

[0078] The identification unit can estimate the user's emotion and adjust the display method of the identified result based on the estimated user emotion. For example, the identification unit can estimate the user's emotion and adjust the display method of the identified result based on the estimated user emotion. For example, if the user is nervous, the identification unit can provide a simple, highly visible display method. If the user is relaxed, the identification unit can provide a display method including detailed information. If the user is in a hurry, the identification unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the identified result according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the identified result.

[0079] The identification unit can analyze the surrounding environment of the victim at the time of identification and propose an evacuation route. The identification unit can, for example, analyze the surrounding environment of the victim at the time of identification and propose an evacuation route. For example, the identification unit can analyze the state of rubble and propose a safe evacuation route. The identification unit can also analyze the state of a fire and propose an evacuation route that avoids the source of the fire. The identification unit can also analyze the state of water intrusion and propose an evacuation route that avoids the water. This makes it possible to analyze the surrounding environment of the victim and propose a safe evacuation route. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input surrounding environment data of the victim into the generation AI and cause the generation AI to propose an evacuation route.

[0080] The identification unit can improve the accuracy of the location by referring to the victim's past location data during identification. For example, the identification unit can improve the accuracy of the location by referring to the victim's past location data during identification. For example, the identification unit can identify the victim's current location based on the victim's past location data. The identification unit can also analyze the victim's past movement patterns to identify the victim's current location. The identification unit can also improve the accuracy of the location by comparing the victim's past location data with the current data. This makes it possible to improve the accuracy of the location by referring to the victim's past location data. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input the victim's past location data into the generation AI and cause the generation AI to improve the accuracy of the location.

[0081] The providing unit can estimate the user's emotion and adjust the timing of information provision based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the timing of information provision based on the estimated user's emotion. For example, if the user is nervous, the providing unit can quickly provide important information. Furthermore, if the user is relaxed, the providing unit can provide information at a normal timing. Furthermore, if the user is in a hurry, the providing unit can immediately provide necessary information. This enables the timing of information provision to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information provision.

[0082] The providing unit can customize the content to be provided based on the importance of the information when providing the information. The providing unit customizes the content to be provided based on the importance of the information when providing the information, for example. For example, the providing unit can provide important information with priority. The providing unit can also postpone information with low urgency. The providing unit can also provide more detailed content to be provided depending on the importance of the information. This makes it possible to customize the content to be provided depending on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the importance of the information to the generation AI and cause the generation AI to customize the content to be provided.

[0083] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide information in a simple, highly visible format. If the user is relaxed, the providing unit can provide information in a format including detailed information. If the user is in a hurry, the providing unit can provide information in a format that focuses on the main points. This makes it possible to adjust the format of information provision according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the format of information provision.

[0084] The providing unit can select an appropriate provision method by taking into consideration the location information of the recipient of the information when providing the information. For example, the providing unit can select an appropriate provision method by taking into consideration the location information of the recipient of the information when providing the information. For example, if the recipient is nearby, the providing unit can provide the information by voice. Furthermore, if the recipient is far away, the providing unit can provide the information by text message. Furthermore, if the recipient is moving, the providing unit can provide the information while updating the location information in real time. This makes it possible to select the optimal provision method based on the location information of the recipient of the information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the recipient's location information into the generation AI and have the generation AI select the provision method.

[0085] The providing unit can optimize the provided content by referring to the past behavioral history of the information recipient when providing the information. For example, the providing unit can optimize the provided content by referring to the past behavioral history of the information recipient when providing the information. For example, the providing unit can provide optimal information based on information used by the recipient in the past. The providing unit can also analyze the recipient's past behavioral patterns and provide necessary information. The providing unit can also select the most effective method of providing information from the recipient's past behavioral history. This makes it possible to optimize the provided content based on the recipient's past behavioral history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the recipient's past behavioral history into the generation AI and cause the generation AI to optimize the provided content. === Hard Collateral 1-1 === Each of the multiple elements including the control unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the control unit is realized by the control unit 46A of the smart device 14 and controls the movement of the insect-type robot. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes data collected by the robot. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the location and situation of the disaster victim. The provision unit is realized by the control unit 46A of the smart device 14 and provides the identified information to a rescue team. === Hard Collateral 1-2 === Each of the multiple elements including the control unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the control unit is realized by the control unit 46A of the smart glasses 214 and controls the movement of the insect-type robot. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes data collected by the robot. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the location and situation of the victim. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the identified information to a rescue team. === Hard Collateral 1-3 === Each of the multiple elements including the control unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the control unit is realized by the control unit 46A of the headset terminal 314 and controls the movement of the insect-type robot. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes data collected by the robot. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the location and situation of the disaster victim. The provision unit is realized by the control unit 46A of the headset terminal 314 and provides the identified information to a rescue team. === Hard Collateral 1-4 === Each of the multiple elements including the control unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the control unit is realized by the control unit 46A of the robot 414 and controls the movement of the insect-type robot. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes data collected by the robot. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the location and situation of the disaster victim. The provision unit is realized by the control unit 46A of the robot 414 and provides the identified information to a rescue team.

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

[0087] When the robot is operating, the control unit can analyze the surrounding electromagnetic wave environment and adjust the operation route based on the strength of the electromagnetic waves. For example, if the control unit detects strong electromagnetic waves, it can adjust the robot's route to avoid that area. Also, if there is equipment that is susceptible to the effects of electromagnetic waves, the control unit can adjust the route to avoid that equipment. Furthermore, the control unit can adjust the robot's operation speed to minimize the effects of electromagnetic waves. This makes it possible to adjust the operation route according to the electromagnetic wave environment.

[0088] The analysis unit can estimate the user's emotions and filter data based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can perform filtering to display only important data. If the user is relaxed, the analysis unit can perform filtering to include detailed data. Furthermore, if the user is in a hurry, the analysis unit can perform filtering to quickly display necessary data. This makes it possible to filter data according to the user's emotions.

[0089] During identification, the identification unit can analyze the victim's biometric information and apply an algorithm to evaluate the victim's stress level. For example, the identification unit can analyze the victim's heart rate and evaluate the victim's stress level. The identification unit can also analyze the victim's respiratory rate and evaluate the victim's stress level. Furthermore, the identification unit can analyze the victim's body temperature and evaluate the victim's stress level. This makes it possible to evaluate the victim's stress level.

[0090] The providing unit can estimate the user's emotions and adjust the frequency of information provision based on the estimated user's emotions. For example, if the user is nervous, the providing unit can reduce the frequency of information provision to reduce stress. Also, if the user is relaxed, the providing unit can provide information at a normal frequency. Furthermore, if the user is in a hurry, the providing unit can increase the frequency to quickly provide necessary information. This makes it possible to adjust the frequency of information provision according to the user's emotions.

[0091] The control unit can analyze the surrounding humidity environment while the robot is operating and adjust the operation route based on changes in humidity. For example, if the control unit detects high humidity, it can adjust the robot's route to avoid that area. Also, if there is equipment that is sensitive to humidity, the control unit can adjust the route to avoid that equipment. Furthermore, the control unit can adjust the robot's operation speed to minimize the effects of humidity. This makes it possible to adjust the operation route according to the humidity environment.

[0092] The analysis unit can estimate the user's emotions and adjust the data visualization method based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible graphs and charts. If the user is relaxed, the analysis unit can provide complex graphs and charts containing detailed data. Furthermore, if the user is in a hurry, the analysis unit can provide a concise data display that focuses on the main points. This makes it possible to adjust the data visualization method according to the user's emotions.

[0093] During identification, the identification unit can analyze the sound environment around the victim and identify the victim's location based on the source of the sound. For example, the identification unit can detect a human voice and identify the location. The identification unit can also detect a mechanical sound and identify a location to avoid the source of the sound. Furthermore, the identification unit can detect an explosion sound and identify a location to move to in a safe direction. This makes it possible to identify the victim's location based on the sound environment.

[0094] The providing unit can estimate the user's emotions and adjust the content of the information provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide only important information. If the user is relaxed, the providing unit can provide content including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide concise information that focuses on the main points. This makes it possible to adjust the content of the information provided according to the user's emotions.

[0095] The control unit can analyze the surrounding atmospheric pressure environment while the robot is operating and adjust the movement route based on changes in atmospheric pressure. For example, if the control unit detects low atmospheric pressure, it can adjust the robot's route to avoid that area. Also, if there is equipment that is easily affected by atmospheric pressure, the control unit can adjust the route to avoid that equipment. Furthermore, the control unit can adjust the movement speed of the robot to minimize the effect of atmospheric pressure. This makes it possible to adjust the movement route according to the atmospheric pressure environment.

[0096] The analysis unit can estimate the user's emotions and adjust the data storage method based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize saving only important data. If the user is relaxed, the analysis unit can provide a storage method that includes detailed data. Furthermore, if the user is in a hurry, the analysis unit can provide a method for quickly saving necessary data. This makes it possible to adjust the data storage method according to the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The control unit controls the movement of the insect-like robot deployed at the disaster site. The control unit analyzes data obtained from the robot's sensors and calculates the optimal route. For example, when moving through rubble, it can calculate a route that avoids obstacles. It can also use generative AI to adjust the robot's movement speed and direction in real time. Step 2: The analysis unit analyzes the data collected by the robot controlled by the control unit. The analysis unit analyzes the video and audio data obtained from the camera and microphone to identify the location of the victim. Using generative AI, it can detect the victim's appearance from the video data and the victim's voice from the audio data. It can also analyze temperature data obtained from the temperature sensor to detect the victim's body temperature. Step 3: The identification unit identifies the location or situation of the victim based on the data analyzed by the analysis unit. The generation AI can be used to identify the location of the victim and send that information to the provision unit. Step 4: The provision unit provides the information identified by the identification unit to the rescue team. Using the generation AI, the location and status of the victim can be provided to the rescue team in real time. For example, the location information of the victim can be displayed on a map and provided to the rescue team.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[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 audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0126] 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.

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

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[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 audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] 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.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0170] [Explanation of symbols]

[0171] 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 control unit that controls the movement of the insect-type robot deployed at the disaster site, an analysis unit that analyzes data collected by the robot controlled by the control unit; an identification unit that identifies the location or situation of the disaster victim based on the data analyzed by the analysis unit; a providing unit that provides the information identified by the identifying unit to a rescue team. A system characterized by:

2. The control unit Analyzing data obtained from the robot's sensors and calculating efficient routes 2. The system of claim 1.

3. The analysis unit Analyze video or audio data obtained from cameras or microphones to identify the location of victims 2. The system of claim 1.

4. The analysis unit Analyze the temperature data obtained from the temperature sensor to detect the victim's body temperature 2. The system of claim 1.

5. The providing unit Providing real-time information on the location or status of victims to rescue teams 2. The system of claim 1.

6. The control unit Estimate the user's emotions and adjust the robot's movement speed based on the estimated user emotions.

2. The system of claim 1.

7. The control unit While the robot is operating, it detects changes in the surrounding environment in real time and dynamically changes its movement route.

2. The system of claim 1.

8. The control unit When the robot moves, different avoidance algorithms are applied depending on the type of obstacle.

2. The system of claim 1.

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