system

Abstract

An object of the system according to the embodiment is to integrate various data sources and provide criminal investigation information in a wide range and quickly.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, and an acquisition unit. The collection unit collects data from various sensors. The analysis unit analyzes the data collected by the collection unit. The providing unit provides criminal investigation information on the basis of the analysis result obtained by the analysis unit. The acquisition section acquires real-time and recorded video information by a High Altitude Artificial Satellite (HAPS).SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to fully integrate diverse data sources in criminal investigations and provide information quickly and extensively, leaving room for improvement.

[0005] The system according to the embodiment aims to integrate various data sources and provide extensive and rapid criminal investigation information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an acquisition unit. The collection unit collects data from various sensors. The analysis unit analyzes the data collected by the collection unit. The provision unit provides criminal investigation information based on the analysis results obtained by the analysis unit. The acquisition unit acquires real-time and recorded video information from high altitude pseudo satellites (HAPS). [Effects of the Invention]

[0007] The system according to the embodiment can integrate various data sources and provide extensive and rapid criminal investigation information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information analysis system according to an embodiment of the present invention collects and analyzes data from various sensors to provide criminal investigation information. This system combines real-time and recorded video information from high-altitude pseudo satellites (HAPS) with information from street surveillance cameras, police vehicle information systems, and other sensors to provide criminal investigation information from a broader perspective, extending back in time from the time of an incident, accident, or crime. For example, the information analysis system acquires data from various sensors in real time and inputs it into the system. This data includes video from surveillance cameras, vehicle information from vehicle information systems, and environmental data from other sensors. The information analysis system then acquires real-time and recorded video information from HAPS. HAPS can acquire wide-area video from high altitudes, complementing information that cannot be captured by ground-based surveillance cameras or sensors. For example, this information can include information about wide-area travel routes and the status of large-scale events. This information is then input into a multimodal large-scale language model (LLM) for analysis. The LLM's integrated analysis of data in different formats, such as text, video, and audio, enables more accurate information analysis. For example, facial recognition from surveillance camera footage and comparing it with vehicle information from a vehicle information system can identify the perpetrator. Furthermore, based on the analysis results, information can be provided on a timeline going back from the time of the crime. This makes it possible to identify where the perpetrator came from, what they were doing, and where they fled to. For example, by combining surveillance camera footage with HAPS footage, it is possible to track the perpetrator's movement route. This enables the information analysis system to conduct extensive and swift investigations and collect evidence, increasing the chances of arresting the perpetrator. This can also serve as a deterrent to terrorism and other crimes. For example, it can identify terrorists' movement routes in advance and prevent them from occurring.

[0029] An information analysis system according to an embodiment includes a collection unit, an analysis unit, a providing unit, and an acquisition unit. The collection unit collects data from various sensors. The various sensors include, but are not limited to, surveillance cameras, vehicle information systems, temperature sensors, and pressure sensors. For example, the collection unit acquires surveillance camera footage in real time. The collection unit can also acquire vehicle license plate information from the vehicle information system. The collection unit can also acquire environmental data from temperature sensors and pressure sensors. For example, the collection unit acquires surveillance camera footage at high resolution and inputs it into the system in real time. Detailed information such as license plate information, vehicle model, and color is acquired from the vehicle information system. Temperature and air pressure data is acquired from the temperature and pressure sensors to monitor environmental changes. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit performs an integrated analysis of the collected data and performs facial recognition and vehicle identification. For example, the analysis unit performs facial recognition using a deep learning algorithm. The analysis unit can also identify vehicles using a license plate recognition algorithm. The analysis unit can also preprocess the collected data to improve the accuracy of the analysis. For example, the analysis unit preprocesses the collected video data and removes noise. It uses a deep learning algorithm to extract facial feature points and perform facial recognition. It uses a license plate recognition algorithm to identify the vehicle's license plate and obtain detailed vehicle information. The provision unit provides criminal investigation information based on the analysis results obtained by the analysis unit. For example, the provision unit provides information with a time axis going back from the time of the crime based on the analysis results. For example, the provision unit provides the perpetrator's travel route and behavioral history. The provision unit can also provide identifying information for the perpetrator based on the analysis results. The provision unit can also identify the location and time of the crime based on the analysis results. For example, the provision unit displays the perpetrator's travel route in chronological order based on the analysis results. Providing a detailed history of the perpetrator's behavior improves the efficiency of the investigation. It provides a facial photograph and license plate information as identifying information for the perpetrator. It identifies the location and time of the crime and clarifies the direction of the investigation.The acquisition unit acquires real-time and recorded video information from a high altitude pseudo satellite (HAPS). For example, the acquisition unit acquires wide-area video information from the HAPS to supplement information that cannot be captured by ground-based surveillance cameras or sensors. For example, the acquisition unit acquires wide-area video in real time using a HAPS camera. The acquisition unit can also acquire recorded video from the HAPS to check past conditions. The acquisition unit can also provide the HAPS video information to the analysis unit to improve the accuracy of the analysis. For example, the acquisition unit monitors a wide-area travel route in real time using a HAPS camera. The acquisition unit acquires recorded video from the HAPS to check past conditions. The acquisition unit provides the HAPS video information to the analysis unit to improve the accuracy of the analysis. This enables the information analysis system according to the embodiment to conduct wide-area and rapid investigations and evidence collection. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input data collected by the collection unit and perform analysis using an AI model that outputs facial recognition and vehicle identification. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide information using an AI model that inputs the analysis results obtained by the analyzing unit and outputs criminal investigation information. Some or all of the above-described processing in the acquiring unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquiring unit may input video data acquired by a camera of the HAPS to the generating AI and have the generating AI analyze the video data.

[0030] The collection unit can collect environmental data from surveillance camera footage, vehicle information from a vehicle information system, and other sensors. For example, the collection unit acquires surveillance camera footage in real time. Surveillance camera footage includes, but is not limited to, fixed cameras, PTZ cameras, etc. For example, the collection unit acquires high-resolution footage from a fixed camera and inputs it into the system in real time. It can also acquire footage from a PTZ camera to monitor a specific area. The collection unit can also acquire vehicle license plate information from the vehicle information system. Vehicle information from the vehicle information system includes, but is not limited to, license plate information, vehicle model, color, etc. For example, the collection unit acquires license plate information from the vehicle information system to identify a vehicle. It can also acquire vehicle model and color information to collect detailed vehicle information. The collection unit can also acquire environmental data from other sensors. Environmental data includes, but is not limited to, temperature, humidity, air pressure, etc. For example, the collection unit acquires temperature data from a temperature sensor to monitor environmental changes. It can also acquire humidity data from a humidity sensor to understand the environmental state. It is also possible to obtain atmospheric pressure data from a barometric pressure sensor and monitor weather changes. This allows for more detailed information to be obtained by collecting data from a variety of sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input video data from a surveillance camera into the generation AI and have the generation AI analyze the video data.

[0031] The analysis unit can perform an integrated analysis of the collected data to perform facial recognition and vehicle identification. The analysis unit, for example, preprocesses the collected data to improve the accuracy of the analysis. Examples of preprocessing include, but are not limited to, noise removal and data normalization. For example, the analysis unit preprocesses the collected video data to remove noise. Data normalization can also be performed to improve the accuracy of the analysis. The analysis unit can also perform facial recognition using a deep learning algorithm. Examples of facial recognition include, but are not limited to, extracting facial feature points and calculating facial similarity. For example, the analysis unit can use a deep learning algorithm to extract facial feature points and perform facial recognition. It can also calculate the facial similarity and identify a specific person. The analysis unit can also use a license plate recognition algorithm to identify a vehicle. Examples of license plate recognition include, but are not limited to, extracting license plates and character recognition. For example, the analysis unit can use a license plate recognition algorithm to extract license plates and perform character recognition. It can also identify a vehicle and obtain detailed vehicle information. This enables facial recognition and vehicle identification through integrated analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI. For example, the analysis unit may perform analysis using an AI model that receives data collected by the collection unit as input and outputs facial recognition and vehicle identification.

[0032] The providing unit can provide information covering a specific time range from the time of the crime based on the analysis results. The providing unit can provide, for example, information covering a time axis extending back from the time of the crime based on the analysis results. Specific time ranges include, but are not limited to, one hour or 24 hours before the crime. For example, the providing unit can provide information covering one hour before the crime based on the analysis results. Providing information covering the 24 hours before the crime can also identify the perpetrator's behavioral history. The providing unit can also provide the perpetrator's travel route and behavioral history based on the analysis results. For example, the providing unit can display the perpetrator's travel route in chronological order. Providing detailed information on the perpetrator's behavioral history improves the efficiency of investigations. By providing information extending back from the time of the crime, the perpetrator's behavioral history can be identified. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can provide information using an AI model that inputs the analysis results obtained by the analysis unit and outputs information extending back from the time of the crime.

[0033] The acquisition unit acquires video information from the HAPS and can complement information that cannot be captured by ground-based surveillance cameras and sensors. For example, the acquisition unit acquires wide-area video information from the HAPS and complements information that cannot be captured by ground-based surveillance cameras and sensors. Wide-area video information includes, but is not limited to, the coverage area and resolution of the HAPS. For example, the acquisition unit acquires wide-area video in real time using the HAPS camera. It can also acquire recorded video from the HAPS to check past conditions. The acquisition unit can also provide the HAPS video information to the analysis unit to improve the accuracy of the analysis. For example, the acquisition unit monitors a wide-area movement route in real time using the HAPS camera. It acquires recorded video from the HAPS and checks past conditions. It provides the HAPS video information to the analysis unit to improve the accuracy of the analysis. In this way, by acquiring wide-area video information from the HAPS, it can complement information that cannot be captured by ground-based surveillance cameras and sensors. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input video data acquired by the HAPS camera into the generation AI and have the generation AI analyze the video data.

[0034] The providing unit can provide the criminal's movement route and behavioral history. The providing unit provides the criminal's movement route and behavioral history, for example, based on the analysis results. The criminal's movement route includes, but is not limited to, GPS data, eyewitness information, etc. The providing unit, for example, displays the criminal's movement route in chronological order. The criminal's behavioral history includes, but is not limited to, past crime history, movement patterns, etc. The providing unit, for example, provides the criminal's behavioral history in detail, thereby improving the efficiency of the investigation. By providing the criminal's movement route and behavioral history, the efficiency of the investigation is improved. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the analysis results obtained by the analysis unit and outputs the criminal's movement route and behavioral history.

[0035] The analysis unit can perform an integrated analysis of data of different formats, such as text, video, and audio. The analysis unit performs an integrated analysis of data of different formats, such as text data, video data, and audio data. Data of different formats includes, but is not limited to, text data, video data, and audio data. The analysis unit, for example, analyzes text data using a natural language processing algorithm. Video data can also be analyzed using a deep learning algorithm. Audio data can also be analyzed using a speech recognition algorithm. For example, the analysis unit analyzes text data using a natural language processing algorithm to extract important information. Video data can be analyzed using a deep learning algorithm to perform face recognition and object recognition. Audio data can be analyzed using a speech recognition algorithm to convert the audio content into text. This enables more accurate information analysis by performing an integrated analysis of data of different formats. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may perform analysis using an AI model that receives text data, video data, and audio data as input and outputs an integrated analysis result.

[0036] The collection unit can evaluate the data accuracy of each sensor during collection and select the optimal data collection method. For example, the collection unit evaluates the data accuracy of each sensor during collection and selects the optimal data collection method. Data accuracy includes, but is not limited to, sensor accuracy and data reliability. For example, if the video from a surveillance camera is unclear, the collection unit prioritizes collecting data from other cameras and sensors. If the vehicle information from the vehicle information system is incomplete, the collection unit can also collect complementary data from other sensors. If the environmental data indicates an abnormal value, the collection unit can also collect additional data from other sensors. For example, if the video from a surveillance camera is unclear, the collection unit prioritizes collecting video data from other cameras. If the vehicle information from the vehicle information system is incomplete, the collection unit collects complementary data from other sensors. If the environmental data indicates an abnormal value, the collection unit collects additional data from other sensors. This allows the data accuracy of each sensor to be evaluated and the optimal data collection method to be selected. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can select the optimal data collection method using an AI model that evaluates the data accuracy of each sensor.

[0037] The collection unit can trigger data collection based on a specific event or anomaly detection during collection. For example, the collection unit triggers data collection based on a specific event or anomaly detection during collection. Examples of specific events include, but are not limited to, abnormal behavior and specific sounds. For example, the collection unit can immediately start data collection when a surveillance camera detects suspicious behavior. When a vehicle information system detects a specific vehicle, it can collect data related to the vehicle. When an environmental sensor detects an abnormal value, it can collect data about the surrounding area. For example, the collection unit can immediately start data collection when a surveillance camera detects suspicious behavior. When a vehicle information system detects a specific vehicle, it collects data about the vehicle. When an environmental sensor detects an abnormal value, it collects data about the surrounding area. By triggering data collection based on a specific event or anomaly detection, important data can be collected quickly. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can start data collection using an AI model that is triggered by a specific event or anomaly detection.

[0038] The collection unit can select the optimal collection means depending on the type of data at the time of collection. For example, the collection unit selects the optimal collection means depending on the type of data at the time of collection. Data types include, but are not limited to, video data, audio data, text data, etc. For example, the collection unit can collect video data using a high-resolution camera. For audio data, it can also collect using a high-sensitivity microphone. For text data, it can also collect digital data from paper media using OCR technology. For example, the collection unit can collect video data using a high-resolution camera. For audio data, it can collect using a high-sensitivity microphone. For text data, it can collect digital data from paper media using OCR technology. This enables efficient data collection by selecting the optimal collection means depending on the type of data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that selects the optimal collection means depending on the type of data.

[0039] The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking geographical location information into consideration when collecting data. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the collection unit prioritizes collecting surveillance camera footage around the crime scene. It can also prioritize collecting vehicle information from a vehicle information system around the crime scene. It can also prioritize collecting environmental data around the crime scene. For example, the collection unit prioritizes collecting surveillance camera footage around the crime scene. It prioritizes collecting vehicle information from a vehicle information system around the crime scene. It prioritizes collecting environmental data around the crime scene. In this way, highly relevant data can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that prioritizes collecting highly relevant data by taking geographical location information into consideration.

[0040] The collection unit may integrate data from social media and other online platforms during collection. For example, the collection unit may integrate data from social media and other online platforms during collection. Social media includes, but is not limited to, Twitter (registered trademark), Facebook (registered trademark), Instagram (registered trademark), etc. Online platforms include, but are not limited to, YouTube (registered trademark), blogs, news sites, etc. For example, the collection unit may collect posts on social media and extract information related to the incident. It may also collect videos on online platforms and analyze footage related to the incident. It may also collect location information on social media to understand activity around the incident scene. For example, the collection unit may collect posts on social media and extract information related to the incident. It may collect videos on online platforms and analyze footage related to the incident. It may collect location information on social media to understand activity around the incident scene. This allows for the collection of more diverse information by integrating data from social media and other online platforms. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may collect data using an AI model that integrates data from social media and other online platforms.

[0041] The collection unit can customize the collection method by referring to past collected data during collection. For example, the collection unit customizes the collection method by referring to past collected data during collection. Past collected data includes, but is not limited to, past incident data, past sensor data, etc. For example, the collection unit collects data by referring to collection methods that were effective in past incidents. The collection unit can also analyze past collected data to select optimal collection methods. The collection range and frequency can also be adjusted based on the past collected data. For example, the collection unit collects data by referring to collection methods that were effective in past incidents. The collection unit analyzes past collected data to select optimal collection methods. The collection range and frequency are adjusted based on the past collected data. In this way, the optimal collection method can be customized by referring to the past collected data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that customizes the collection method by referring to past collected data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data includes, but is not limited to, the relevance of the incident and the reliability of the data. For example, the analysis unit can perform a detailed analysis of data with high importance. For data with low importance, it can also perform a simplified analysis. For data with medium importance, it can also perform an analysis with a moderate level of detail. For example, the analysis unit can perform a detailed analysis of data with high importance. For data with low importance, it can perform a simplified analysis. For data with medium importance, it can perform an analysis with a moderate level of detail. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that adjusts the level of detail of the analysis based on the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, video data, audio data, and text data. For example, the analysis unit can apply a facial recognition algorithm to video data. A voice recognition algorithm can also be applied to audio data. A natural language processing algorithm can also be applied to text data. For example, the analysis unit can apply a facial recognition algorithm to video data to extract facial features. A voice recognition algorithm can be applied to audio data to convert the audio content into text. A natural language processing algorithm can be applied to text data to extract important information. By applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the data category.

[0044] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to past analysis results during analysis. Past analysis results include, but are not limited to, past incident analysis results and past data analysis results. For example, the analysis unit improves the analysis algorithm based on the past analysis results. It can also refer to past analysis results and apply them to similar data. It can also improve the analysis accuracy by feeding back the past analysis results. For example, the analysis unit improves the analysis algorithm based on the past analysis results. It refers to past analysis results and applies them to similar data. It feeds back the past analysis results and improves the analysis accuracy. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that improves the accuracy of the analysis by referring to past analysis results.

[0045] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. The time of data collection includes, but is not limited to, the collection date and time, the collection period, etc. The analysis unit, for example, prioritizes analysis of the most recent data. It can also prioritize analysis of data immediately after the incident occurred. It can also prioritize analysis of data before the incident occurred. For example, the analysis unit prioritizes analysis of the most recent data. It prioritizes analysis of data immediately after the incident occurred. It prioritizes analysis of data before the incident occurred. In this way, efficient analysis is possible by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that determines the analysis priority based on the time of data collection.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of data during analysis. Data relevance includes, but is not limited to, incident relevance and data correlation. For example, the analysis unit prioritizes analysis of data directly related to the incident. Data indirectly related to the incident can be analyzed next. Data unrelated to the incident can also be analyzed last. For example, the analysis unit prioritizes analysis of data directly related to the incident. Data indirectly related to the incident can be analyzed next. Data unrelated to the incident can be analyzed last. In this way, by adjusting the order of analysis based on data relevance, important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that adjusts the order of analysis based on data relevance.

[0047] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The user's level of expertise includes, but is not limited to, beginner, intermediate, and expert. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with technical expertise. For a user without technical expertise, it can also provide analysis results that are explained in simple language. For a user with intermediate expertise, it can also provide analysis results that use a moderate amount of technical terms. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with technical expertise. For a user without technical expertise, it can provide analysis results that use a moderate amount of technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform the analysis using an AI model that adjusts the use of analytical terminology depending on the user's level of expertise.

[0048] The providing unit can adjust the level of detail of the analysis results when providing the information based on the importance of the analysis results. For example, the providing unit adjusts the level of detail of the analysis results when providing the information based on the importance of the analysis results. The importance of the analysis results includes, but is not limited to, the relevance of the incident and the reliability of the data. For example, the providing unit can provide detailed information for analysis results with high importance. For analysis results with low importance, simplified information can be provided. For analysis results with medium importance, information can be provided with an appropriate level of detail. For example, the providing unit can provide detailed information for analysis results with high importance. For analysis results with low importance, simplified information can be provided. For analysis results with medium importance, information can be provided with an appropriate level of detail. This enables efficient information provision by adjusting the level of detail of the analysis results based on the importance of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the level of detail of the information based on the importance of the analysis results.

[0049] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit applies different provision algorithms depending on the category of information when providing the information. Categories of information include, but are not limited to, video information, audio information, text information, etc. For example, the providing unit provides video information in a visually easy-to-understand format. Audio information can also be provided using an audio playback function. Text information can also be provided in an easy-to-read format. For example, the providing unit provides video information in a visually easy-to-understand format. Audio information can be provided using an audio playback function. Text information can be provided in an easy-to-read format. This enables more appropriate information to be provided by applying different provision algorithms depending on the category of information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that applies different provision algorithms depending on the category of information.

[0050] The providing unit can improve the accuracy of the information provided by referring to past provision results when providing the information. For example, the providing unit can improve the accuracy of the information provided by referring to past provision results when providing the information. Past provision results include, but are not limited to, past incident provision results, past data provision results, etc. For example, the providing unit improves the provision algorithm based on past provision results. It can also refer to past provision results and apply them to similar information. It can also improve the accuracy of the information provided by feeding back past provision results. For example, the providing unit improves the provision algorithm based on past provision results. It refers to past provision results and applies them to similar information. It feeds back past provision results and improves the accuracy of the information provided. In this way, the accuracy of the information provided is improved by referring to past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that improves the accuracy of the information provided by referring to past provision results.

[0051] The providing unit can determine the priority of provision based on the time when the information was collected at the time of provision. For example, the providing unit determines the priority of provision based on the time when the information was collected at the time of provision. The time when the information was collected includes, but is not limited to, the collection date and time, the collection period, etc. The providing unit, for example, prioritizes providing the latest information. It can also prioritize providing information immediately after the incident occurred. It can also prioritize providing information before the incident occurred. For example, the providing unit prioritizes providing the latest information. It prioritizes providing information immediately after the incident occurred. It prioritizes providing information before the incident occurred. This enables efficient information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that determines the priority of provision based on the time when the information was collected.

[0052] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit adjusts the order of provision based on the relevance of the information when providing the information. The relevance of the information includes, but is not limited to, the relevance of the incidents and the correlation of the data. For example, the providing unit can provide information directly related to the incident preferentially. It can also provide information indirectly related to the incident next. It can also provide information unrelated to the incident last. For example, the providing unit can provide information directly related to the incident preferentially. It can provide information indirectly related to the incident next. It can provide information unrelated to the incident last. In this way, by adjusting the order of provision based on the relevance of the information, important information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the order of provision based on the relevance of the information.

[0053] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, the providing unit adjusts the use of technical terminology provided during provision according to the user's level of expertise. The user's level of expertise includes, but is not limited to, beginner, intermediate, and expert. For example, the providing unit can provide information that uses a lot of technical terminology to a user with specialized knowledge. For a user without specialized knowledge, it can also provide information explained in simple language. For a user with intermediate expertise, it can also provide information that uses a moderate amount of technical terminology. For example, the providing unit can provide information that uses a lot of technical terminology to a user with specialized knowledge. For a user without specialized knowledge, it can provide information that uses a moderate amount of technical terminology to a user with intermediate expertise. By adjusting the use of technical terminology provided according to the user's level of expertise, it is possible to provide information that is easier to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide information using an AI model that adjusts the use of technical terminology provided according to the user's level of expertise.

[0054] The acquisition unit can evaluate the accuracy of HAPS data during acquisition and select the optimal acquisition method. For example, the acquisition unit evaluates the accuracy of HAPS data during acquisition and selects the optimal acquisition method. HAPS data accuracy includes, but is not limited to, resolution and reliability. For example, if the HAPS image is unclear, the acquisition unit prioritizes acquiring data from other cameras or sensors. If the HAPS data is incomplete, the acquisition unit can also acquire complementary data from other sensors. If the HAPS data indicates an abnormal value, the acquisition unit can also acquire additional data from other sensors. For example, if the HAPS image is unclear, the acquisition unit prioritizes acquiring image data from other cameras. If the HAPS data is incomplete, the acquisition unit acquires complementary data from other sensors. If the HAPS data indicates an abnormal value, the acquisition unit acquires additional data from other sensors. This allows the accuracy of HAPS data to be evaluated and the optimal acquisition method to be selected. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can select the optimal acquisition method using an AI model that evaluates the accuracy of HAPS data.

[0055] The acquisition unit can trigger the acquisition of video information based on a specific event or anomaly detection during acquisition. For example, the acquisition unit triggers the acquisition of video information based on a specific event or anomaly detection during acquisition. Specific events include, but are not limited to, abnormal behavior and specific audio. For example, the acquisition unit can immediately acquire video information when the HAPS detects suspicious behavior. When the HAPS detects a specific vehicle, it can also acquire video information related to the vehicle. When the HAPS detects an abnormal value, it can also acquire video information of the surrounding area. For example, the acquisition unit can immediately acquire video information when the HAPS detects suspicious behavior. When the HAPS detects a specific vehicle, it can acquire video information related to the vehicle. When the HAPS detects an abnormal value, it can acquire video information of the surrounding area. By triggering the acquisition of video information based on a specific event or anomaly detection, important video information can be quickly acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can start acquiring video information using an AI model triggered by a specific event or anomaly detection.

[0056] The acquisition unit can select the optimal acquisition means depending on the type of video at the time of acquisition. For example, the acquisition unit selects the optimal acquisition means depending on the type of video at the time of acquisition. Types of video include, but are not limited to, real-time video and recorded video. For example, in the case of real-time video, the acquisition unit uses a high-resolution camera to acquire the video. In the case of recorded video, past data can also be quickly acquired. In the case of video related to a specific event, data from multiple cameras can be integrated and acquired. For example, the acquisition unit uses a high-resolution camera to acquire the real-time video. In the case of recorded video, past data can be quickly acquired. In the case of video related to a specific event, data from multiple cameras can be integrated and acquired. This enables efficient acquisition of video information by selecting the optimal acquisition means depending on the type of video. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire video information using an AI model that selects the optimal acquisition means depending on the type of video.

[0057] The acquisition unit can prioritize acquiring highly relevant video information by taking geographical location information into consideration during acquisition. For example, the acquisition unit prioritizes acquiring highly relevant video information by taking geographical location information into consideration during acquisition. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the acquisition unit prioritizes acquiring HAPS video around the crime scene. It can also prioritize acquiring surveillance camera video around the crime scene. It can also prioritize acquiring video information from a vehicle information system around the crime scene. For example, the acquisition unit prioritizes acquiring HAPS video around the crime scene. It prioritizes acquiring surveillance camera video around the crime scene. It prioritizes acquiring video information from a vehicle information system around the crime scene. In this way, highly relevant video information can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire video information using an AI model that prioritizes acquiring highly relevant video information by taking geographical location information into consideration.

[0058] The acquisition unit may integrate video information from social media and other online platforms during acquisition. For example, the acquisition unit may integrate video information from social media and other online platforms during acquisition. Social media includes, but is not limited to, Twitter, Facebook, Instagram, etc. Online platforms include, but are not limited to, YouTube, blogs, news sites, etc. For example, the acquisition unit may acquire videos posted on social media and analyze videos related to the incident. It may also acquire live videos on online platforms to collect information related to the incident. It may also acquire related video information based on location information on social media. For example, the acquisition unit may acquire videos posted on social media and analyze videos related to the incident. It may acquire live videos on online platforms to collect information related to the incident. It may acquire related video information based on location information on social media. This allows for the integration of video information from social media and other online platforms to collect more diverse information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit may acquire video information using an AI model that integrates video information from social media and other online platforms.

[0059] The acquisition unit can customize the acquisition method by referring to previously acquired data at the time of acquisition. For example, the acquisition unit customizes the acquisition method by referring to previously acquired data at the time of acquisition. Previously acquired data includes, but is not limited to, past incident data, past sensor data, etc. For example, the acquisition unit acquires video information by referring to acquisition methods that were effective in past incidents. It can also analyze previously acquired data to select an optimal acquisition method. It can also adjust the acquisition range and frequency based on the previously acquired data. For example, the acquisition unit acquires video information by referring to acquisition methods that were effective in past incidents. It analyzes previously acquired data to select an optimal acquisition method. It adjusts the acquisition range and frequency based on the previously acquired data. In this way, the optimal acquisition method can be customized by referring to the previously acquired data. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire video information using an AI model that customizes the acquisition method by referring to previously acquired data.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can also evaluate the accuracy of data from each sensor during collection and select the optimal data collection method. For example, if the footage from a surveillance camera is unclear, data from other cameras and sensors can be collected preferentially. If the vehicle information from the vehicle information system is incomplete, data from other sensors can be collected as a supplement. If the environmental data indicates an abnormal value, additional data from other sensors can be collected. In this way, the accuracy of data from each sensor can be evaluated to select the optimal data collection method. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can select the optimal data collection method using an AI model that evaluates the accuracy of data from each sensor.

[0062] During analysis, the analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data of high importance. A simplified analysis can be performed on data of low importance. An analysis can be performed with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that adjusts the level of detail of the analysis based on the importance of the data.

[0063] The providing unit can also apply different providing algorithms depending on the category of information when providing the information. For example, video information can be provided in a visually easy-to-understand format. Audio information can be provided using an audio playback function. Text information can be provided in an easy-to-read format. This makes it possible to provide more appropriate information by applying different providing algorithms depending on the category of information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide information using an AI model that applies different providing algorithms depending on the category of information.

[0064] The acquisition unit can also trigger the acquisition of video information based on a specific event or anomaly detection during acquisition. For example, if HAPS detects suspicious activity, it can immediately acquire video information. If HAPS detects a specific vehicle, it can also acquire video information related to that vehicle. If HAPS detects an abnormal value, it can also acquire video information of the surrounding area. This allows important video information to be acquired quickly by triggering the acquisition of video information based on a specific event or anomaly detection. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can start acquiring video information using an AI model that is triggered by a specific event or anomaly detection.

[0065] The collection unit may also integrate data from social media and other online platforms during collection. For example, it may collect posts on social media and extract information related to the incident. It may also collect videos on online platforms and analyze footage related to the incident. It may also collect location information on social media to understand activity around the incident scene. This allows for the collection of more diverse information by integrating data from social media and other online platforms. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may collect data using an AI model that integrates data from social media and other online platforms.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data from various sensors. These sensors include surveillance cameras, vehicle information systems, temperature sensors, and pressure sensors. The collection unit acquires video from the surveillance cameras in real time and license plate information from the vehicle information systems. It also acquires environmental data from the temperature and pressure sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit comprehensively analyzes the collected data and performs facial recognition and vehicle identification. It uses a deep learning algorithm for facial recognition and a license plate recognition algorithm for vehicle identification. It also preprocesses the collected data and removes noise. Step 3: The provision unit provides criminal investigation information based on the analysis results obtained by the analysis unit. Based on the analysis results, the provision unit provides information on a timeline going back from the time of the crime, the perpetrator's movement route, behavioral history, information identifying the perpetrator, and the location and time of the crime. Step 4: The acquisition unit acquires real-time and recorded video information from the High Altitude Pseudo Satellite (HAPS). The acquisition unit uses the HAPS camera to acquire wide-area video in real time and acquires recorded video to check past conditions. The acquisition unit provides the HAPS video information to the analysis unit to improve the accuracy of the analysis.

[0068] (Example 2) An information analysis system according to an embodiment of the present invention collects and analyzes data from various sensors to provide criminal investigation information. This system combines real-time and recorded video information from high-altitude pseudo satellites (HAPS) with information from street surveillance cameras, police vehicle information systems, and other sensors to provide criminal investigation information from a broader perspective, extending back in time from the time of an incident, accident, or crime. For example, the information analysis system acquires data from various sensors in real time and inputs it into the system. This data includes video from surveillance cameras, vehicle information from vehicle information systems, and environmental data from other sensors. The information analysis system then acquires real-time and recorded video information from HAPS. HAPS can acquire wide-area video from high altitudes, complementing information that cannot be captured by ground-based surveillance cameras or sensors. For example, this information can include information about wide-area travel routes and the status of large-scale events. This information is then input into a multimodal large-scale language model (LLM) for analysis. The LLM's integrated analysis of data in different formats, such as text, video, and audio, enables more accurate information analysis. For example, facial recognition from surveillance camera footage and comparing it with vehicle information from a vehicle information system can identify the perpetrator. Furthermore, based on the analysis results, information can be provided on a timeline going back from the time of the crime. This makes it possible to identify where the perpetrator came from, what they were doing, and where they fled to. For example, by combining surveillance camera footage with HAPS footage, it is possible to track the perpetrator's movement route. This enables the information analysis system to conduct extensive and swift investigations and collect evidence, increasing the chances of arresting the perpetrator. This can also serve as a deterrent to terrorism and other crimes. For example, it can identify terrorists' movement routes in advance and prevent them from occurring.

[0069] An information analysis system according to an embodiment includes a collection unit, an analysis unit, a providing unit, and an acquisition unit. The collection unit collects data from various sensors. The various sensors include, but are not limited to, surveillance cameras, vehicle information systems, temperature sensors, and pressure sensors. For example, the collection unit acquires surveillance camera footage in real time. The collection unit can also acquire vehicle license plate information from the vehicle information system. The collection unit can also acquire environmental data from temperature sensors and pressure sensors. For example, the collection unit acquires surveillance camera footage at high resolution and inputs it into the system in real time. Detailed information such as license plate information, vehicle model, and color is acquired from the vehicle information system. Temperature and air pressure data is acquired from the temperature and pressure sensors to monitor environmental changes. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit performs an integrated analysis of the collected data and performs facial recognition and vehicle identification. For example, the analysis unit performs facial recognition using a deep learning algorithm. The analysis unit can also identify vehicles using a license plate recognition algorithm. The analysis unit can also preprocess the collected data to improve the accuracy of the analysis. For example, the analysis unit preprocesses the collected video data and removes noise. It uses a deep learning algorithm to extract facial feature points and perform facial recognition. It uses a license plate recognition algorithm to identify the vehicle's license plate and obtain detailed vehicle information. The provision unit provides criminal investigation information based on the analysis results obtained by the analysis unit. For example, the provision unit provides information with a time axis going back from the time of the crime based on the analysis results. For example, the provision unit provides the perpetrator's travel route and behavioral history. The provision unit can also provide identifying information for the perpetrator based on the analysis results. The provision unit can also identify the location and time of the crime based on the analysis results. For example, the provision unit displays the perpetrator's travel route in chronological order based on the analysis results. Providing a detailed history of the perpetrator's behavior improves the efficiency of the investigation. It provides a facial photograph and license plate information as identifying information for the perpetrator. It identifies the location and time of the crime and clarifies the direction of the investigation.The acquisition unit acquires real-time and recorded video information from a high altitude pseudo satellite (HAPS). For example, the acquisition unit acquires wide-area video information from the HAPS to supplement information that cannot be captured by ground-based surveillance cameras or sensors. For example, the acquisition unit acquires wide-area video in real time using a HAPS camera. The acquisition unit can also acquire recorded video from the HAPS to check past conditions. The acquisition unit can also provide the HAPS video information to the analysis unit to improve the accuracy of the analysis. For example, the acquisition unit monitors a wide-area travel route in real time using a HAPS camera. The acquisition unit acquires recorded video from the HAPS to check past conditions. The acquisition unit provides the HAPS video information to the analysis unit to improve the accuracy of the analysis. This enables the information analysis system according to the embodiment to conduct wide-area and rapid investigations and evidence collection. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input data collected by the collection unit and perform analysis using an AI model that outputs facial recognition and vehicle identification. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide information using an AI model that inputs the analysis results obtained by the analyzing unit and outputs criminal investigation information. Some or all of the above-described processing in the acquiring unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquiring unit may input video data acquired by a camera of the HAPS to the generating AI and have the generating AI analyze the video data.

[0070] The collection unit can collect environmental data from surveillance camera footage, vehicle information from a vehicle information system, and other sensors. For example, the collection unit acquires surveillance camera footage in real time. Surveillance camera footage includes, but is not limited to, fixed cameras, PTZ cameras, etc. For example, the collection unit acquires high-resolution footage from a fixed camera and inputs it into the system in real time. It can also acquire footage from a PTZ camera to monitor a specific area. The collection unit can also acquire vehicle license plate information from the vehicle information system. Vehicle information from the vehicle information system includes, but is not limited to, license plate information, vehicle model, color, etc. For example, the collection unit acquires license plate information from the vehicle information system to identify a vehicle. It can also acquire vehicle model and color information to collect detailed vehicle information. The collection unit can also acquire environmental data from other sensors. Environmental data includes, but is not limited to, temperature, humidity, air pressure, etc. For example, the collection unit acquires temperature data from a temperature sensor to monitor environmental changes. It can also acquire humidity data from a humidity sensor to understand the environmental state. It is also possible to obtain atmospheric pressure data from a barometric pressure sensor and monitor weather changes. This allows for more detailed information to be obtained by collecting data from a variety of sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input video data from a surveillance camera into the generation AI and have the generation AI analyze the video data.

[0071] The analysis unit can perform an integrated analysis of the collected data to perform facial recognition and vehicle identification. The analysis unit, for example, preprocesses the collected data to improve the accuracy of the analysis. Examples of preprocessing include, but are not limited to, noise removal and data normalization. For example, the analysis unit preprocesses the collected video data to remove noise. Data normalization can also be performed to improve the accuracy of the analysis. The analysis unit can also perform facial recognition using a deep learning algorithm. Examples of facial recognition include, but are not limited to, extracting facial feature points and calculating facial similarity. For example, the analysis unit can use a deep learning algorithm to extract facial feature points and perform facial recognition. It can also calculate the facial similarity and identify a specific person. The analysis unit can also use a license plate recognition algorithm to identify a vehicle. Examples of license plate recognition include, but are not limited to, extracting license plates and character recognition. For example, the analysis unit can use a license plate recognition algorithm to extract license plates and perform character recognition. It can also identify a vehicle and obtain detailed vehicle information. This enables facial recognition and vehicle identification through integrated analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI. For example, the analysis unit may perform analysis using an AI model that receives data collected by the collection unit as input and outputs facial recognition and vehicle identification.

[0072] The providing unit can provide information covering a specific time range from the time of the crime based on the analysis results. The providing unit can provide, for example, information covering a time axis extending back from the time of the crime based on the analysis results. Specific time ranges include, but are not limited to, one hour or 24 hours before the crime. For example, the providing unit can provide information covering one hour before the crime based on the analysis results. Providing information covering the 24 hours before the crime can also identify the perpetrator's behavioral history. The providing unit can also provide the perpetrator's travel route and behavioral history based on the analysis results. For example, the providing unit can display the perpetrator's travel route in chronological order. Providing detailed information on the perpetrator's behavioral history improves the efficiency of investigations. By providing information extending back from the time of the crime, the perpetrator's behavioral history can be identified. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can provide information using an AI model that inputs the analysis results obtained by the analysis unit and outputs information extending back from the time of the crime.

[0073] The acquisition unit acquires video information from the HAPS and can complement information that cannot be captured by ground-based surveillance cameras and sensors. For example, the acquisition unit acquires wide-area video information from the HAPS and complements information that cannot be captured by ground-based surveillance cameras and sensors. Wide-area video information includes, but is not limited to, the coverage area and resolution of the HAPS. For example, the acquisition unit acquires wide-area video in real time using the HAPS camera. It can also acquire recorded video from the HAPS to check past conditions. The acquisition unit can also provide the HAPS video information to the analysis unit to improve the accuracy of the analysis. For example, the acquisition unit monitors a wide-area movement route in real time using the HAPS camera. It acquires recorded video from the HAPS and checks past conditions. It provides the HAPS video information to the analysis unit to improve the accuracy of the analysis. In this way, by acquiring wide-area video information from the HAPS, it can complement information that cannot be captured by ground-based surveillance cameras and sensors. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input video data acquired by the HAPS camera into the generation AI and have the generation AI analyze the video data.

[0074] The providing unit can provide the criminal's movement route and behavioral history. The providing unit provides the criminal's movement route and behavioral history, for example, based on the analysis results. The criminal's movement route includes, but is not limited to, GPS data, eyewitness information, etc. The providing unit, for example, displays the criminal's movement route in chronological order. The criminal's behavioral history includes, but is not limited to, past crime history, movement patterns, etc. The providing unit, for example, provides the criminal's behavioral history in detail, thereby improving the efficiency of the investigation. By providing the criminal's movement route and behavioral history, the efficiency of the investigation is improved. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the analysis results obtained by the analysis unit and outputs the criminal's movement route and behavioral history.

[0075] The analysis unit can perform an integrated analysis of data of different formats, such as text, video, and audio. The analysis unit performs an integrated analysis of data of different formats, such as text data, video data, and audio data. Data of different formats includes, but is not limited to, text data, video data, and audio data. The analysis unit, for example, analyzes text data using a natural language processing algorithm. Video data can also be analyzed using a deep learning algorithm. Audio data can also be analyzed using a speech recognition algorithm. For example, the analysis unit analyzes text data using a natural language processing algorithm to extract important information. Video data can be analyzed using a deep learning algorithm to perform face recognition and object recognition. Audio data can be analyzed using a speech recognition algorithm to convert the audio content into text. This enables more accurate information analysis by performing an integrated analysis of data of different formats. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may perform analysis using an AI model that receives text data, video data, and audio data as input and outputs an integrated analysis result.

[0076] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tension, the collection unit can increase the frequency of data collection and collect more detailed information. If the user is relaxed, the collection unit can reduce the frequency of data collection and collect the minimum necessary information. If the user is hurrying, the collection unit can collect data in real time and analyze it quickly. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. It can also record the user's voice and estimate the emotions using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The collection unit can evaluate the data accuracy of each sensor during collection and select the optimal data collection method. For example, the collection unit evaluates the data accuracy of each sensor during collection and selects the optimal data collection method. Data accuracy includes, but is not limited to, sensor accuracy and data reliability. For example, if the video from a surveillance camera is unclear, the collection unit prioritizes collecting data from other cameras and sensors. If the vehicle information from the vehicle information system is incomplete, the collection unit can also collect complementary data from other sensors. If the environmental data indicates an abnormal value, the collection unit can also collect additional data from other sensors. For example, if the video from a surveillance camera is unclear, the collection unit prioritizes collecting video data from other cameras. If the vehicle information from the vehicle information system is incomplete, the collection unit collects complementary data from other sensors. If the environmental data indicates an abnormal value, the collection unit collects additional data from other sensors. This allows the data accuracy of each sensor to be evaluated and the optimal data collection method to be selected. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can select the optimal data collection method using an AI model that evaluates the data accuracy of each sensor.

[0078] The collection unit can trigger data collection based on a specific event or anomaly detection during collection. For example, the collection unit triggers data collection based on a specific event or anomaly detection during collection. Examples of specific events include, but are not limited to, abnormal behavior and specific sounds. For example, the collection unit can immediately start data collection when a surveillance camera detects suspicious behavior. When a vehicle information system detects a specific vehicle, it can collect data related to the vehicle. When an environmental sensor detects an abnormal value, it can collect data about the surrounding area. For example, the collection unit can immediately start data collection when a surveillance camera detects suspicious behavior. When a vehicle information system detects a specific vehicle, it collects data about the vehicle. When an environmental sensor detects an abnormal value, it collects data about the surrounding area. By triggering data collection based on a specific event or anomaly detection, important data can be collected quickly. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can start data collection using an AI model that is triggered by a specific event or anomaly detection.

[0079] The collection unit can select the optimal collection means depending on the type of data at the time of collection. For example, the collection unit selects the optimal collection means depending on the type of data at the time of collection. Data types include, but are not limited to, video data, audio data, text data, etc. For example, the collection unit can collect video data using a high-resolution camera. For audio data, it can also collect using a high-sensitivity microphone. For text data, it can also collect digital data from paper media using OCR technology. For example, the collection unit can collect video data using a high-resolution camera. For audio data, it can collect using a high-sensitivity microphone. For text data, it can collect digital data from paper media using OCR technology. This enables efficient data collection by selecting the optimal collection means depending on the type of data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that selects the optimal collection means depending on the type of data.

[0080] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tensioned, the collection unit can prioritize collecting data of high importance. If the user is relaxed, the collection unit can also collect data of low importance. If the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thus, by determining the priority of data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative 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 collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0081] The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking geographical location information into consideration when collecting data. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the collection unit prioritizes collecting surveillance camera footage around the crime scene. It can also prioritize collecting vehicle information from a vehicle information system around the crime scene. It can also prioritize collecting environmental data around the crime scene. For example, the collection unit prioritizes collecting surveillance camera footage around the crime scene. It prioritizes collecting vehicle information from a vehicle information system around the crime scene. It prioritizes collecting environmental data around the crime scene. In this way, highly relevant data can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that prioritizes collecting highly relevant data by taking geographical location information into consideration.

[0082] The collection unit may integrate data from social media and other online platforms during collection. For example, the collection unit may integrate data from social media and other online platforms during collection. Social media includes, but is not limited to, Twitter, Facebook, Instagram, etc. Online platforms include, but are not limited to, YouTube, blogs, news sites, etc. For example, the collection unit may collect posts on social media and extract information related to the incident. It may also collect videos on online platforms and analyze footage related to the incident. It may also collect location information on social media to understand activity around the incident scene. For example, the collection unit may collect posts on social media and extract information related to the incident. It may collect videos on online platforms and analyze footage related to the incident. It may collect location information on social media to understand activity around the incident scene. This allows for the collection of more diverse information by integrating data from social media and other online platforms. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may collect data using an AI model that integrates data from social media and other online platforms.

[0083] The collection unit can customize the collection method by referring to past collected data during collection. For example, the collection unit customizes the collection method by referring to past collected data during collection. Past collected data includes, but is not limited to, past incident data, past sensor data, etc. For example, the collection unit collects data by referring to collection methods that were effective in past incidents. The collection unit can also analyze past collected data to select optimal collection methods. The collection range and frequency can also be adjusted based on the past collected data. For example, the collection unit collects data by referring to collection methods that were effective in past incidents. The collection unit analyzes past collected data to select optimal collection methods. The collection range and frequency are adjusted based on the past collected data. In this way, the optimal collection method can be customized by referring to the past collected data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that customizes the collection method by referring to past collected data.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tension, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is hurrying, the analysis unit can provide a summary analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data includes, but is not limited to, the relevance of the incident and the reliability of the data. For example, the analysis unit can perform a detailed analysis of data with high importance. For data with low importance, it can also perform a simplified analysis. For data with medium importance, it can also perform an analysis with a moderate level of detail. For example, the analysis unit can perform a detailed analysis of data with high importance. For data with low importance, it can perform a simplified analysis. For data with medium importance, it can perform an analysis with a moderate level of detail. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that adjusts the level of detail of the analysis based on the importance of the data.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, video data, audio data, and text data. For example, the analysis unit can apply a facial recognition algorithm to video data. A voice recognition algorithm can also be applied to audio data. A natural language processing algorithm can also be applied to text data. For example, the analysis unit can apply a facial recognition algorithm to video data to extract facial features. A voice recognition algorithm can be applied to audio data to convert the audio content into text. A natural language processing algorithm can be applied to text data to extract important information. By applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the data category.

[0087] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to past analysis results during analysis. Past analysis results include, but are not limited to, past incident analysis results and past data analysis results. For example, the analysis unit improves the analysis algorithm based on the past analysis results. It can also refer to past analysis results and apply them to similar data. It can also improve the analysis accuracy by feeding back the past analysis results. For example, the analysis unit improves the analysis algorithm based on the past analysis results. It refers to past analysis results and applies them to similar data. It feeds back the past analysis results and improves the analysis accuracy. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that improves the accuracy of the analysis by referring to past analysis results.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the analysis unit to adjust the length of the analysis based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0089] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. The time of data collection includes, but is not limited to, the collection date and time, the collection period, etc. The analysis unit, for example, prioritizes analysis of the most recent data. It can also prioritize analysis of data immediately after the incident occurred. It can also prioritize analysis of data before the incident occurred. For example, the analysis unit prioritizes analysis of the most recent data. It prioritizes analysis of data immediately after the incident occurred. It prioritizes analysis of data before the incident occurred. In this way, efficient analysis is possible by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that determines the analysis priority based on the time of data collection.

[0090] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of data during analysis. Data relevance includes, but is not limited to, incident relevance and data correlation. For example, the analysis unit prioritizes analysis of data directly related to the incident. Data indirectly related to the incident can be analyzed next. Data unrelated to the incident can also be analyzed last. For example, the analysis unit prioritizes analysis of data directly related to the incident. Data indirectly related to the incident can be analyzed next. Data unrelated to the incident can be analyzed last. In this way, by adjusting the order of analysis based on data relevance, important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that adjusts the order of analysis based on data relevance.

[0091] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The user's level of expertise includes, but is not limited to, beginner, intermediate, and expert. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with technical expertise. For a user without technical expertise, it can also provide analysis results that are explained in simple language. For a user with intermediate expertise, it can also provide analysis results that use a moderate amount of technical terms. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with technical expertise. For a user without technical expertise, it can provide analysis results that use a moderate amount of technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform the analysis using an AI model that adjusts the use of analytical terminology depending on the user's level of expertise.

[0092] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tensioned, the providing unit can provide simple, highly visible information. If the user is relaxed, detailed information can be provided. If the user is hurrying, information that focuses on the main points can be provided. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the presentation method of the information to be provided to be adjusted according to the user's emotions, enabling more appropriate information to be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] The providing unit can adjust the level of detail of the analysis results when providing the information based on the importance of the analysis results. For example, the providing unit adjusts the level of detail of the analysis results when providing the information based on the importance of the analysis results. The importance of the analysis results includes, but is not limited to, the relevance of the incident and the reliability of the data. For example, the providing unit can provide detailed information for analysis results with high importance. For analysis results with low importance, simplified information can be provided. For analysis results with medium importance, information can be provided with an appropriate level of detail. For example, the providing unit can provide detailed information for analysis results with high importance. For analysis results with low importance, simplified information can be provided. For analysis results with medium importance, information can be provided with an appropriate level of detail. This enables efficient information provision by adjusting the level of detail of the analysis results based on the importance of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the level of detail of the information based on the importance of the analysis results.

[0094] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit applies different provision algorithms depending on the category of information when providing the information. Categories of information include, but are not limited to, video information, audio information, text information, etc. For example, the providing unit provides video information in a visually easy-to-understand format. Audio information can also be provided using an audio playback function. Text information can also be provided in an easy-to-read format. For example, the providing unit provides video information in a visually easy-to-understand format. Audio information can be provided using an audio playback function. Text information can be provided in an easy-to-read format. This enables more appropriate information to be provided by applying different provision algorithms depending on the category of information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that applies different provision algorithms depending on the category of information.

[0095] The providing unit can improve the accuracy of the information provided by referring to past provision results when providing the information. For example, the providing unit can improve the accuracy of the information provided by referring to past provision results when providing the information. Past provision results include, but are not limited to, past incident provision results, past data provision results, etc. For example, the providing unit improves the provision algorithm based on past provision results. It can also refer to past provision results and apply them to similar information. It can also improve the accuracy of the information provided by feeding back past provision results. For example, the providing unit improves the provision algorithm based on past provision results. It refers to past provision results and applies them to similar information. It feeds back past provision results and improves the accuracy of the information provided. In this way, the accuracy of the information provided is improved by referring to past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that improves the accuracy of the information provided by referring to past provision results.

[0096] The providing unit can estimate the user's emotions and adjust the length of information to be provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the length of information to be provided based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is in a hurry, the providing unit can provide short, concise information. If the user is relaxed, the providing unit can provide detailed information. If the user is excited, the providing unit can provide information with visually stimulating effects. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the length of information to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0097] The providing unit can determine the priority of provision based on the time when the information was collected at the time of provision. For example, the providing unit determines the priority of provision based on the time when the information was collected at the time of provision. The time when the information was collected includes, but is not limited to, the collection date and time, the collection period, etc. The providing unit, for example, prioritizes providing the latest information. It can also prioritize providing information immediately after the incident occurred. It can also prioritize providing information before the incident occurred. For example, the providing unit prioritizes providing the latest information. It prioritizes providing information immediately after the incident occurred. It prioritizes providing information before the incident occurred. This enables efficient information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that determines the priority of provision based on the time when the information was collected.

[0098] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit adjusts the order of provision based on the relevance of the information when providing the information. The relevance of the information includes, but is not limited to, the relevance of the incidents and the correlation of the data. For example, the providing unit can provide information directly related to the incident preferentially. It can also provide information indirectly related to the incident next. It can also provide information unrelated to the incident last. For example, the providing unit can provide information directly related to the incident preferentially. It can provide information indirectly related to the incident next. It can provide information unrelated to the incident last. In this way, by adjusting the order of provision based on the relevance of the information, important information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that adjusts the order of provision based on the relevance of the information.

[0099] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, the providing unit adjusts the use of technical terminology provided during provision according to the user's level of expertise. The user's level of expertise includes, but is not limited to, beginner, intermediate, and expert. For example, the providing unit can provide information that uses a lot of technical terminology to a user with specialized knowledge. For a user without specialized knowledge, it can also provide information explained in simple language. For a user with intermediate expertise, it can also provide information that uses a moderate amount of technical terminology. For example, the providing unit can provide information that uses a lot of technical terminology to a user with specialized knowledge. For a user without specialized knowledge, it can provide information that uses a moderate amount of technical terminology to a user with intermediate expertise. By adjusting the use of technical terminology provided according to the user's level of expertise, it is possible to provide information that is easier to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide information using an AI model that adjusts the use of technical terminology provided according to the user's level of expertise.

[0100] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring video information based on the estimated user emotions. For example, the acquisition unit estimates the user's emotions and adjusts the timing of acquiring video information based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, when the user is tensioned, the acquisition unit acquires video information frequently. When the user is relaxed, the acquisition unit can acquire the minimum amount of video information necessary. When the user is hurrying, the acquisition unit can acquire video information in real time. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate acquisition of video information by adjusting the timing of acquiring video information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0101] The acquisition unit can evaluate the accuracy of HAPS data during acquisition and select the optimal acquisition method. For example, the acquisition unit evaluates the accuracy of HAPS data during acquisition and selects the optimal acquisition method. HAPS data accuracy includes, but is not limited to, resolution and reliability. For example, if the HAPS image is unclear, the acquisition unit prioritizes acquiring data from other cameras or sensors. If the HAPS data is incomplete, the acquisition unit can also acquire complementary data from other sensors. If the HAPS data indicates an abnormal value, the acquisition unit can also acquire additional data from other sensors. For example, if the HAPS image is unclear, the acquisition unit prioritizes acquiring image data from other cameras. If the HAPS data is incomplete, the acquisition unit acquires complementary data from other sensors. If the HAPS data indicates an abnormal value, the acquisition unit acquires additional data from other sensors. This allows the accuracy of HAPS data to be evaluated and the optimal acquisition method to be selected. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can select the optimal acquisition method using an AI model that evaluates the accuracy of HAPS data.

[0102] The acquisition unit can trigger the acquisition of video information based on a specific event or anomaly detection during acquisition. For example, the acquisition unit triggers the acquisition of video information based on a specific event or anomaly detection during acquisition. Specific events include, but are not limited to, abnormal behavior and specific audio. For example, the acquisition unit can immediately acquire video information when the HAPS detects suspicious behavior. When the HAPS detects a specific vehicle, it can also acquire video information related to the vehicle. When the HAPS detects an abnormal value, it can also acquire video information of the surrounding area. For example, the acquisition unit can immediately acquire video information when the HAPS detects suspicious behavior. When the HAPS detects a specific vehicle, it can acquire video information related to the vehicle. When the HAPS detects an abnormal value, it can acquire video information of the surrounding area. By triggering the acquisition of video information based on a specific event or anomaly detection, important video information can be quickly acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can start acquiring video information using an AI model triggered by a specific event or anomaly detection.

[0103] The acquisition unit can select the optimal acquisition means depending on the type of video at the time of acquisition. For example, the acquisition unit selects the optimal acquisition means depending on the type of video at the time of acquisition. Types of video include, but are not limited to, real-time video and recorded video. For example, in the case of real-time video, the acquisition unit uses a high-resolution camera to acquire the video. In the case of recorded video, past data can also be quickly acquired. In the case of video related to a specific event, data from multiple cameras can be integrated and acquired. For example, the acquisition unit uses a high-resolution camera to acquire the real-time video. In the case of recorded video, past data can be quickly acquired. In the case of video related to a specific event, data from multiple cameras can be integrated and acquired. This enables efficient acquisition of video information by selecting the optimal acquisition means depending on the type of video. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire video information using an AI model that selects the optimal acquisition means depending on the type of video.

[0104] The acquisition unit can estimate the user's emotions and determine the priority of the video information to be acquired based on the estimated user emotions. For example, the acquisition unit can estimate the user's emotions and determine the priority of the video information to be acquired based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, when the user is tensioned, the acquisition unit prioritizes acquiring video information of high importance. When the user is relaxed, the acquisition unit can also acquire video information of low importance. When the user is in a hurry, the acquisition unit can prioritize acquiring video information that can be acquired quickly. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. It can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thus, by determining the priority of the video information to be acquired based on the user's emotions, important video information can be acquired preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative 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-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0105] The acquisition unit can prioritize acquiring highly relevant video information by taking geographical location information into consideration during acquisition. For example, the acquisition unit prioritizes acquiring highly relevant video information by taking geographical location information into consideration during acquisition. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the acquisition unit prioritizes acquiring HAPS video around the crime scene. It can also prioritize acquiring surveillance camera video around the crime scene. It can also prioritize acquiring video information from a vehicle information system around the crime scene. For example, the acquisition unit prioritizes acquiring HAPS video around the crime scene. It prioritizes acquiring surveillance camera video around the crime scene. It prioritizes acquiring video information from a vehicle information system around the crime scene. In this way, highly relevant video information can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire video information using an AI model that prioritizes acquiring highly relevant video information by taking geographical location information into consideration.

[0106] The acquisition unit may integrate video information from social media and other online platforms during acquisition. For example, the acquisition unit may integrate video information from social media and other online platforms during acquisition. Social media includes, but is not limited to, Twitter, Facebook, Instagram, etc. Online platforms include, but are not limited to, YouTube, blogs, news sites, etc. For example, the acquisition unit may acquire videos posted on social media and analyze videos related to the incident. It may also acquire live videos on online platforms to collect information related to the incident. It may also acquire related video information based on location information on social media. For example, the acquisition unit may acquire videos posted on social media and analyze videos related to the incident. It may acquire live videos on online platforms to collect information related to the incident. It may acquire related video information based on location information on social media. This allows for the integration of video information from social media and other online platforms to collect more diverse information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit may acquire video information using an AI model that integrates video information from social media and other online platforms.

[0107] The acquisition unit can customize the acquisition method by referring to previously acquired data at the time of acquisition. For example, the acquisition unit customizes the acquisition method by referring to previously acquired data at the time of acquisition. Previously acquired data includes, but is not limited to, past incident data, past sensor data, etc. For example, the acquisition unit acquires video information by referring to acquisition methods that were effective in past incidents. It can also analyze previously acquired data to select an optimal acquisition method. It can also adjust the acquisition range and frequency based on the previously acquired data. For example, the acquisition unit acquires video information by referring to acquisition methods that were effective in past incidents. It analyzes previously acquired data to select an optimal acquisition method. It adjusts the acquisition range and frequency based on the previously acquired data. In this way, the optimal acquisition method can be customized by referring to the previously acquired data. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire video information using an AI model that customizes the acquisition method by referring to previously acquired data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and acquisition 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 collection unit can collect data from various sensors using the camera 42 and microphone 38B of the smart device 14. The analysis unit can analyze the collected data by, for example, the identification processing unit 290 of the data processing device 12 and perform facial recognition and vehicle identification. The provision unit can provide criminal investigation information based on the analysis results by, for example, the control unit 46A of the smart device 14. The acquisition unit can acquire HAPS video information by, for example, the identification processing unit 290 of the data processing device 12 and provide the information to the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and acquisition 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 collection unit can collect data from various sensors using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit can analyze the collected data by, for example, the specific processing unit 290 of the data processing device 12 and perform facial recognition and vehicle identification. The provision unit can provide criminal investigation information based on the analysis results by, for example, the control unit 46A of the smart glasses 214. The acquisition unit can acquire HAPS video information by, for example, the specific processing unit 290 of the data processing device 12 and provide the information to the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and acquisition unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data from various sensors using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit can analyze the collected data by, for example, the identification processing unit 290 of the data processing device 12 and perform facial recognition and vehicle identification. The provision unit can provide criminal investigation information based on the analysis results by, for example, the control unit 46A of the headset-type terminal 314. The acquisition unit can acquire HAPS video information by, for example, the identification processing unit 290 of the data processing device 12 and provide the information to the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and acquisition unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data from various sensors using the camera 42 and microphone 238 of the robot 414. The analysis unit can analyze the collected data by, for example, the identification processing unit 290 of the data processing device 12 and perform facial recognition and vehicle identification. The provision unit can provide criminal investigation information based on the analysis results by, for example, the control unit 46A of the robot 414. The acquisition unit can acquire video information of the HAPS by, for example, the identification processing unit 290 of the data processing device 12 and provide the information to the analysis unit.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The analysis unit can also estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is nervous, it can prioritize analysis of important data and provide results quickly. If the user is relaxed, it can perform a detailed analysis and provide comprehensive information. If the user is in a hurry, it can provide concise analysis results that focus on the main points. This allows for adjusting the analysis priority according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved 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-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0110] The providing unit can also estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the information can be provided in a visually simple and easy-to-understand format. If the user is relaxed, the information can be provided in a rich format including detailed information. If the user is in a hurry, the information can be provided in a concise format that focuses on the main points. This allows for more appropriate information to be provided by adjusting the format of the information according to the user's emotions. Emotion estimation is achieved 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 providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0111] The collection unit can also estimate the user's emotions and adjust the scope of data collection based on the estimated user emotions. For example, if the user is nervous, a wide range of data can be collected and detailed information can be provided. If the user is relaxed, the minimum necessary data can be collected and information can be provided efficiently. If the user is in a hurry, data can be collected in real time and analyzed quickly. This enables more appropriate data collection by adjusting the scope of data collection according to the user's emotions. 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 collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0112] The analysis unit can also estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is nervous, a detailed analysis can be performed to provide comprehensive information. If the user is relaxed, a concise analysis can be performed to provide information that focuses on the main points. If the user is in a hurry, a quick analysis can be performed to provide results in a short time. This allows for adjusting the level of analysis detail according to the user's emotions to provide more appropriate analysis results. 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0113] The providing unit can also estimate the user's emotions and prioritize the information to be provided based on the estimated user emotions. For example, if the user is nervous, it can prioritize information of high importance. If the user is relaxed, it can provide information in a rich format including detailed information. If the user is in a hurry, it can provide information in a concise format that focuses on the main points. This allows for more appropriate information provision by adjusting the priority of information according to the user's emotions. Emotion estimation is achieved 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 providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0114] The collection unit can also evaluate the accuracy of data from each sensor during collection and select the optimal data collection method. For example, if the footage from a surveillance camera is unclear, data from other cameras and sensors can be collected preferentially. If the vehicle information from the vehicle information system is incomplete, data from other sensors can be collected as a supplement. If the environmental data indicates an abnormal value, additional data from other sensors can be collected. In this way, the accuracy of data from each sensor can be evaluated to select the optimal data collection method. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can select the optimal data collection method using an AI model that evaluates the accuracy of data from each sensor.

[0115] During analysis, the analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data of high importance. A simplified analysis can be performed on data of low importance. An analysis can be performed with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that adjusts the level of detail of the analysis based on the importance of the data.

[0116] The providing unit can also apply different providing algorithms depending on the category of information when providing the information. For example, video information can be provided in a visually easy-to-understand format. Audio information can be provided using an audio playback function. Text information can be provided in an easy-to-read format. This makes it possible to provide more appropriate information by applying different providing algorithms depending on the category of information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide information using an AI model that applies different providing algorithms depending on the category of information.

[0117] The acquisition unit can also trigger the acquisition of video information based on a specific event or anomaly detection during acquisition. For example, if HAPS detects suspicious activity, it can immediately acquire video information. If HAPS detects a specific vehicle, it can also acquire video information related to that vehicle. If HAPS detects an abnormal value, it can also acquire video information of the surrounding area. This allows important video information to be acquired quickly by triggering the acquisition of video information based on a specific event or anomaly detection. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can start acquiring video information using an AI model that is triggered by a specific event or anomaly detection.

[0118] The collection unit may also integrate data from social media and other online platforms during collection. For example, it may collect posts on social media and extract information related to the incident. It may also collect videos on online platforms and analyze footage related to the incident. It may also collect location information on social media to understand activity around the incident scene. This allows for the collection of more diverse information by integrating data from social media and other online platforms. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may collect data using an AI model that integrates data from social media and other online platforms.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects data from various sensors. These sensors include surveillance cameras, vehicle information systems, temperature sensors, and pressure sensors. The collection unit acquires video from the surveillance cameras in real time and license plate information from the vehicle information systems. It also acquires environmental data from the temperature and pressure sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit comprehensively analyzes the collected data and performs facial recognition and vehicle identification. It uses a deep learning algorithm for facial recognition and a license plate recognition algorithm for vehicle identification. It also preprocesses the collected data and removes noise. Step 3: The provision unit provides criminal investigation information based on the analysis results obtained by the analysis unit. Based on the analysis results, the provision unit provides information on a timeline going back from the time of the crime, the perpetrator's movement route, behavioral history, information identifying the perpetrator, and the location and time of the crime. Step 4: The acquisition unit acquires real-time and recorded video information from the High Altitude Pseudo Satellite (HAPS). The acquisition unit uses the HAPS camera to acquire wide-area video in real time and acquires recorded video to check past conditions. The acquisition unit provides the HAPS video information to the analysis unit to improve the accuracy of the analysis.

[0121] 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.

[0122] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data from various sensors; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides criminal investigation information based on the analysis results obtained by the analyzing unit; an acquisition unit that acquires real-time and recorded video information from a high altitude pseudo satellite (HAPS); A system characterized by:

2. The collecting unit Collecting environmental data from surveillance camera footage, vehicle information systems, and other sensors 2. The system of claim 1.

3. The analysis unit Comprehensive analysis of collected data for facial recognition and vehicle identification 2. The system of claim 1.

4. The providing unit Based on the analysis results, provide information with a specific time range from the time of the crime 2. The system of claim 1.

5. The acquisition unit Obtaining visual information from HAPS will complement information that cannot be captured by ground-based surveillance cameras and sensors.

2. The system of claim 1.

6. The providing unit Providing the criminal's travel route and behavioral history 2. The system of claim 1.

7. The analysis unit Integrated analysis of different types of data, including text, video, and audio 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images