System

Abstract

An object of a system according to an embodiment is to quickly perform crime inquiry.SOLUTION: A system includes an image analysis part and an information transmission part. The image analysis unit analyzes the photograph. The information transmitter notifies the police officer of an analysis result of the photograph analyzed by the image analyzer through the messaging application.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 technology had the problem that it took a long time to make crime inquiries via police radio.

[0005] The system according to the embodiment aims to quickly conduct criminal inquiries. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit and an information transmission unit. The image analysis unit analyzes a photo. The information transmission unit notifies a police officer of the analysis result of the photo analyzed by the image analysis unit via a messaging app. [Effects of the Invention]

[0007] The system according to the embodiment can quickly conduct criminal inquiries. [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) The crime inquiry system according to an embodiment of the present invention is a system that uses generative AI and messaging apps to perform image judgment using photographs in order to shorten the time it takes to make a crime inquiry over police radio. As a result, the crime inquiry system significantly shortens the time it takes to make a crime inquiry over police radio, enabling a rapid response.

[0029] A crime inquiry system according to an embodiment includes an image analysis unit and an information transmission unit. The image analysis unit analyzes photographs. For example, a photograph taken by a police officer is input into the generation AI, which then analyzes the photograph and extracts information related to the crime. The image analysis unit can also analyze photographs of a crime scene to extract evidence and clues. For example, the generation AI analyzes photographs of a crime scene and identifies important evidence. The image analysis unit can also analyze facial photographs of suspects and compare them with a database to identify the suspect. For example, the generation AI analyzes facial photographs of suspects and compares them with an existing database to identify matching individuals. The information transmission unit notifies police officers of the analysis results of the photographs analyzed by the image analysis unit via a messaging app. For example, the generation AI sends the analysis results to police officers via a messaging app such as LINE. The information transmission unit can also notify police officers of the analysis results in real time. For example, the generation AI notifies police officers of the results via a messaging app immediately after completing the analysis. The information transmission unit can also notify multiple police officers of the analysis results simultaneously. For example, the generation AI can send important information to multiple police officers simultaneously. As a result, the crime inquiry system according to the embodiment significantly reduces the time required for a crime inquiry via police radio, enabling a quick response. For example, a police officer can input a photo taken at the scene into the generation AI and receive the results via a messaging app, completing a crime inquiry within a few seconds.

[0030] The image analysis unit analyzes the metadata of photos and can provide detailed information based on the location and time of the crime. For example, the image analysis unit uses generative AI to analyze the metadata of images and identify the location and time of the crime. For example, the area where the crime occurred can be identified based on the location information of the photo and compared with crime data for that area. The image analysis unit also analyzes the date and time of the photo to identify the time of the crime. For example, if the photo was taken at night, it can be compared with nighttime crime data to confirm the relevance. Furthermore, the image analysis unit combines the location information and the date and time of the photo to analyze crime occurrence patterns. For example, it can identify crimes that occur repeatedly in specific areas during specific times of the day and reflect this in police patrol plans. This makes it possible to provide detailed information based on the location and time of the crime.

[0031] The image analysis unit can continuously analyze multiple images to detect dynamic changes and abnormalities. For example, the image analysis unit analyzes images captured continuously by a generative AI to detect dynamic changes. For example, it analyzes the movements of people and objects in consecutive images to identify abnormal movements. The image analysis unit also compares multiple images to detect abnormalities. For example, it detects movements or changes that differ from normal situations and identifies the possibility of a crime. Furthermore, the image analysis unit integrates the analysis results of consecutive images to obtain a detailed understanding of the crime situation. For example, if a specific person is behaving suspiciously in consecutive images, it analyzes that behavior in detail. This enables more accurate crime investigations by detecting dynamic changes and abnormalities.

[0032] The image analysis unit can analyze not only photos of crime scenes, but also photos of traffic accidents or disaster scenes, enabling a rapid response. For example, the image analysis unit uses generative AI to analyze photos of traffic accident scenes to identify the circumstances of the accident. For example, it analyzes the location of the vehicle and the extent of damage to identify the cause of the accident. The image analysis unit also analyzes photos of disaster scenes to identify the extent of the damage. For example, it analyzes the extent of damage to buildings and the locations of victims to support rapid rescue efforts. Furthermore, the image analysis unit responds quickly based on the analysis results of traffic accidents and disaster scenes. For example, it notifies relevant parties of the analysis results via a messaging app to encourage a rapid response. This can also be applied to the analysis of traffic accidents and disaster scenes, enabling a rapid response.

[0033] The image analysis unit links the analyzed information with police databases and can automatically search for related cases. For example, the image analysis unit compares the information analyzed by the generative AI with past crime data to identify related cases. For example, if a crime using the same modus operandi has occurred in the past, that information will be automatically searched for. The image analysis unit also links with unsolved case data and identifies clues to unsolved cases based on the analysis results. For example, if a person with the same characteristics as a suspect in an unsolved case is photographed, that information will be automatically searched for. Furthermore, the image analysis unit links with other police databases and automatically searches for related cases based on the analysis results. For example, it can identify relationships between multiple crimes that occurred in the same area. This improves the efficiency of investigations by automatically searching for related cases.

[0034] The information transmission unit can simultaneously notify not only police officers but also other related organizations of the information analyzed by the generation AI. For example, the information transmission unit simultaneously notifies not only police officers but also related organizations such as fire departments and emergency services via a messaging app with the information analyzed by the generation AI. For example, crime scene information can be shared with fire departments and emergency services to encourage a rapid response. The information transmission unit also builds a system using a messaging app to notify related organizations of the information analyzed by the generation AI in real time. For example, crime scene photos and analysis results can be shared immediately. Furthermore, the information transmission unit strengthens cooperation with related organizations based on the information analyzed by the generation AI. For example, crime scene information can be shared to enable fire departments and emergency services to respond quickly. This allows the information analyzed by the generation AI to be simultaneously notified to other related organizations, enabling rapid cooperation.

[0035] The information transmission unit can automatically generate an action plan based on the information analyzed by the generation AI and provide it to police officers. For example, the information transmission unit automatically generates a tracking route for a suspect based on the information analyzed by the generation AI and provides it to police officers via a messaging app. For example, it analyzes the suspect's escape route and presents the tracking route. The information transmission unit also automatically generates an evacuation route based on the information analyzed by the generation AI and provides it to police officers via a messaging app. For example, it presents a safe evacuation route from a crime scene. Furthermore, the information transmission unit automatically generates an action plan based on the information analyzed by the generation AI and provides it to police officers via a messaging app. For example, it presents response procedures at a crime scene. In this way, automatically generating an action plan based on the information analyzed by the generation AI and providing it to police officers enables a rapid response.

[0036] The information transmission unit also notifies citizens of the information analyzed by the generation AI, allowing local safety information to be shared in real time. For example, the information transmission unit notifies citizens of the information analyzed by the generation AI via a messaging app, sharing local safety information in real time. For example, it notifies citizens of crime occurrence information and urges them to be careful. The information transmission unit also builds a system that uses a messaging app to notify citizens of the information analyzed by the generation AI in real time. For example, it shares photos of crime scenes and analysis results with citizens. Furthermore, the information transmission unit provides safety information to citizens based on the information analyzed by the generation AI. For example, it notifies citizens of crime-prone areas and evacuation routes. This allows the information analyzed by the generation AI to be notified to citizens, allowing local safety information to be shared in real time.

[0037] The information transmission unit adds a voice input function to a messaging app, allowing police officers to give voice instructions, thereby enabling hands-free operation. For example, the information transmission unit adds a voice input function to a messaging app, allowing police officers to give voice instructions, thereby enabling hands-free operation. For example, a crime inquiry may be given by voice. The information transmission unit also uses the voice input function to build a system that allows police officers to operate the messaging app without using their hands. For example, a photo analysis may be given by voice. Furthermore, the information transmission unit enables police officers to give quick instructions based on the voice input function. For example, a suspect identification may be given by voice. This allows police officers to give voice instructions, thereby enabling hands-free operation.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The crime inquiry system can further be equipped with a voice recognition unit. The voice recognition unit allows police officers to input information by voice at the scene, thereby enabling quicker crime inquiries. For example, a police officer can input the characteristics of a suspect by voice, and the information is analyzed by the generation AI and compared with the database. The voice recognition unit can also allow police officers to report the situation at the scene by voice, which can then be automatically converted into text and recorded. Furthermore, the voice recognition unit allows police officers to give voice instructions at the scene, allowing information to be transmitted to other police officers and related agencies in real time. This makes it possible to further improve the efficiency of crime inquiries by utilizing voice input.

[0040] The crime inquiry system can further include a predictive analysis unit. The predictive analysis unit can predict future crime risks based on past crime data. For example, it can analyze crime trends in specific areas or time periods and reflect the results in police patrol plans. The predictive analysis unit can also analyze crime patterns and identify locations and time periods where crimes are likely to occur. Furthermore, the predictive analysis unit can monitor crime risks in real time and issue warnings to police officers. In this way, predictive analysis can be used to contribute to crime prevention.

[0041] The crime investigation system can further include a drone control unit. The drone control unit can monitor crime scenes and their surrounding areas using drones and acquire video footage in real time. For example, a drone can capture images of a crime scene from above, and the generated video can be analyzed by AI to extract important information. The drone control unit can also remotely control the drone to track suspects and collect evidence. Furthermore, the drone control unit can provide drone video footage to police officers in real time, enabling them to quickly grasp the situation at the scene. This allows drones to be used to improve the accuracy and efficiency of crime investigations.

[0042] The crime inquiry system can further be equipped with a voice recognition unit. The voice recognition unit allows police officers to input information by voice at the scene, thereby enabling quicker crime inquiries. For example, a police officer can input the characteristics of a suspect by voice, and the information is analyzed by the generation AI and compared with the database. The voice recognition unit can also allow police officers to report the situation at the scene by voice, which can then be automatically converted into text and recorded. Furthermore, the voice recognition unit allows police officers to give voice instructions at the scene, allowing information to be transmitted to other police officers and related agencies in real time. This makes it possible to further improve the efficiency of crime inquiries by utilizing voice input.

[0043] The crime inquiry system can further include a predictive analysis unit. The predictive analysis unit can predict future crime risks based on past crime data. For example, it can analyze crime trends in specific areas or time periods and reflect the results in police patrol plans. The predictive analysis unit can also analyze crime patterns and identify locations and time periods where crimes are likely to occur. Furthermore, the predictive analysis unit can monitor crime risks in real time and issue warnings to police officers. In this way, predictive analysis can be used to contribute to crime prevention.

[0044] The crime investigation system can further include a drone control unit. The drone control unit can monitor crime scenes and their surrounding areas using drones and acquire video footage in real time. For example, a drone can capture images of a crime scene from above, and the generated video can be analyzed by AI to extract important information. The drone control unit can also remotely control the drone to track suspects and collect evidence. Furthermore, the drone control unit can provide drone video footage to police officers in real time, enabling them to quickly grasp the situation at the scene. This allows drones to be used to improve the accuracy and efficiency of crime investigations.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The image analysis unit analyzes the photo. For example, a photo taken by a police officer is input into the generation AI, which then analyzes the photo and extracts information related to the crime. The image analysis unit can also analyze photos of crime scenes to extract evidence and clues. Furthermore, the image analysis unit can analyze photos of suspects' faces and compare them with a database to identify the suspect. Step 2: The information transmission unit notifies the police officer of the analysis results of the photo analyzed by the image analysis unit via a messaging app. For example, the generation AI sends the analysis results to the police officer via a messaging app such as LINE. The information transmission unit can also notify the police officer of the analysis results in real time. Furthermore, the information transmission unit can also notify multiple police officers of the analysis results simultaneously.

[0047] (Example 2) The crime inquiry system according to an embodiment of the present invention is a system that uses generative AI and messaging apps to perform image judgment using photographs in order to shorten the time it takes to make a crime inquiry over police radio. As a result, the crime inquiry system significantly shortens the time it takes to make a crime inquiry over police radio, enabling a rapid response.

[0048] A crime inquiry system according to an embodiment includes an image analysis unit and an information transmission unit. The image analysis unit analyzes photographs. For example, a photograph taken by a police officer is input into the generation AI, which then analyzes the photograph and extracts information related to the crime. The image analysis unit can also analyze photographs of a crime scene to extract evidence and clues. For example, the generation AI analyzes photographs of a crime scene and identifies important evidence. The image analysis unit can also analyze facial photographs of suspects and compare them with a database to identify the suspect. For example, the generation AI analyzes facial photographs of suspects and compares them with an existing database to identify matching individuals. The information transmission unit notifies police officers of the analysis results of the photographs analyzed by the image analysis unit via a messaging app. For example, the generation AI sends the analysis results to police officers via a messaging app such as LINE. The information transmission unit can also notify police officers of the analysis results in real time. For example, the generation AI notifies police officers of the results via a messaging app immediately after completing the analysis. The information transmission unit can also notify multiple police officers of the analysis results simultaneously. For example, the generation AI can send important information to multiple police officers simultaneously. As a result, the crime inquiry system according to the embodiment significantly reduces the time required for a crime inquiry via police radio, enabling a quick response. For example, a police officer can input a photo taken at the scene into the generation AI and receive the results via a messaging app, completing a crime inquiry within a few seconds.

[0049] The image analysis unit analyzes the metadata of photos and can provide detailed information based on the location and time of the crime. For example, the image analysis unit uses generative AI to analyze the metadata of images and identify the location and time of the crime. For example, the area where the crime occurred can be identified based on the location information of the photo and compared with crime data for that area. The image analysis unit also analyzes the date and time of the photo to identify the time of the crime. For example, if the photo was taken at night, it can be compared with nighttime crime data to confirm the relevance. Furthermore, the image analysis unit combines the location information and the date and time of the photo to analyze crime occurrence patterns. For example, it can identify crimes that occur repeatedly in specific areas during specific times of the day and reflect this in police patrol plans. This makes it possible to provide detailed information based on the location and time of the crime.

[0050] The image analysis unit can continuously analyze multiple images to detect dynamic changes and abnormalities. For example, the image analysis unit analyzes images captured continuously by a generative AI to detect dynamic changes. For example, it analyzes the movements of people and objects in consecutive images to identify abnormal movements. The image analysis unit also compares multiple images to detect abnormalities. For example, it detects movements or changes that differ from normal situations and identifies the possibility of a crime. Furthermore, the image analysis unit integrates the analysis results of consecutive images to obtain a detailed understanding of the crime situation. For example, if a specific person is behaving suspiciously in consecutive images, it analyzes that behavior in detail. This enables more accurate crime investigations by detecting dynamic changes and abnormalities.

[0051] The image analysis unit uses the emotion estimation function to analyze the emotions of people in crime scene photos and identify those who are feeling nervous or scared. For example, the image analysis unit uses a generative AI to analyze the facial expressions of people in photos and estimate their emotions. For example, it can identify people who look nervous or scared and suggest that they may be suspects or victims. The image analysis unit also uses the emotion estimation function to analyze the emotions of multiple people in photos and identify people with abnormal emotions. For example, it can identify people who look different from others and suggest that they may be involved in a crime. Furthermore, the image analysis unit performs a detailed analysis of the crime scene situation based on the emotion estimation results. For example, if a person looking nervous or scared is in a specific location, it can suggest that that location may be the site of a crime. This helps identify people who look nervous or scared, thereby helping to identify suspects and victims.

[0052] The image analysis unit can analyze not only photos of crime scenes, but also photos of traffic accidents or disaster scenes, enabling a rapid response. For example, the image analysis unit uses generative AI to analyze photos of traffic accident scenes to identify the circumstances of the accident. For example, it analyzes the location of the vehicle and the extent of damage to identify the cause of the accident. The image analysis unit also analyzes photos of disaster scenes to identify the extent of the damage. For example, it analyzes the extent of damage to buildings and the locations of victims to support rapid rescue efforts. Furthermore, the image analysis unit responds quickly based on the analysis results of traffic accidents and disaster scenes. For example, it notifies relevant parties of the analysis results via a messaging app to encourage a rapid response. This can also be applied to the analysis of traffic accidents and disaster scenes, enabling a rapid response.

[0053] The image analysis unit links the analyzed information with police databases and can automatically search for related cases. For example, the image analysis unit compares the information analyzed by the generative AI with past crime data to identify related cases. For example, if a crime using the same modus operandi has occurred in the past, that information will be automatically searched for. The image analysis unit also links with unsolved case data and identifies clues to unsolved cases based on the analysis results. For example, if a person with the same characteristics as a suspect in an unsolved case is photographed, that information will be automatically searched for. Furthermore, the image analysis unit links with other police databases and automatically searches for related cases based on the analysis results. For example, it can identify relationships between multiple crimes that occurred in the same area. This improves the efficiency of investigations by automatically searching for related cases.

[0054] The image analysis unit uses the emotion estimation function to collect citizens' emotional responses to photos taken by police officers, enabling a real-time understanding of the local security situation. The image analysis unit, for example, uses the emotion estimation function to analyze the emotional responses to photos taken by citizens. For example, it analyzes the facial expressions of people in the photos to understand the local security situation. The image analysis unit also builds a system that understands the local security situation in real time based on citizens' emotional response data. For example, it identifies areas with low emotion scores and reflects this in police patrol plans. Furthermore, the image analysis unit visualizes the local security situation based on the emotion estimation results. For example, it displays the emotion scores on a map to identify areas where security is deteriorating. This allows a real-time understanding of the local security situation by collecting citizens' emotional responses.

[0055] The information transmission unit can simultaneously notify not only police officers but also other related organizations of the information analyzed by the generation AI. For example, the information transmission unit simultaneously notifies not only police officers but also related organizations such as fire departments and emergency services via a messaging app with the information analyzed by the generation AI. For example, crime scene information can be shared with fire departments and emergency services to encourage a rapid response. The information transmission unit also builds a system using a messaging app to notify related organizations of the information analyzed by the generation AI in real time. For example, crime scene photos and analysis results can be shared immediately. Furthermore, the information transmission unit strengthens cooperation with related organizations based on the information analyzed by the generation AI. For example, crime scene information can be shared to enable fire departments and emergency services to respond quickly. This allows the information analyzed by the generation AI to be simultaneously notified to other related organizations, enabling rapid cooperation.

[0056] The information transmission unit can automatically generate an action plan based on the information analyzed by the generation AI and provide it to police officers. For example, the information transmission unit automatically generates a tracking route for a suspect based on the information analyzed by the generation AI and provides it to police officers via a messaging app. For example, it analyzes the suspect's escape route and presents the tracking route. The information transmission unit also automatically generates an evacuation route based on the information analyzed by the generation AI and provides it to police officers via a messaging app. For example, it presents a safe evacuation route from a crime scene. Furthermore, the information transmission unit automatically generates an action plan based on the information analyzed by the generation AI and provides it to police officers via a messaging app. For example, it presents response procedures at a crime scene. In this way, automatically generating an action plan based on the information analyzed by the generation AI and providing it to police officers enables a rapid response.

[0057] The information transmission unit can use the emotion estimation function to analyze the emotional response of a police officer to information sent through a messaging app and provide support to reduce stress and fatigue. For example, the information transmission unit uses the emotion estimation function to analyze the emotional response of a police officer to information sent through a messaging app. For example, it detects signs of stress or fatigue and provides appropriate support. The information transmission unit also builds a system that provides support to reduce stress and fatigue based on the emotional response data of the police officer. For example, it suggests relaxation methods and breaks. Furthermore, the information transmission unit provides feedback to reduce stress and fatigue to the police officer based on the emotion estimation results. For example, it sends an encouraging message if the emotion score is low. This makes it possible to provide support to reduce stress and fatigue to the police officer.

[0058] The information transmission unit also notifies citizens of the information analyzed by the generation AI, allowing local safety information to be shared in real time. For example, the information transmission unit notifies citizens of the information analyzed by the generation AI via a messaging app, sharing local safety information in real time. For example, it notifies citizens of crime occurrence information and urges them to be careful. The information transmission unit also builds a system that uses a messaging app to notify citizens of the information analyzed by the generation AI in real time. For example, it shares photos of crime scenes and analysis results with citizens. Furthermore, the information transmission unit provides safety information to citizens based on the information analyzed by the generation AI. For example, it notifies citizens of crime-prone areas and evacuation routes. This allows the information analyzed by the generation AI to be notified to citizens, allowing local safety information to be shared in real time.

[0059] The information transmission unit adds a voice input function to a messaging app, allowing police officers to give voice instructions, thereby enabling hands-free operation. For example, the information transmission unit adds a voice input function to a messaging app, allowing police officers to give voice instructions, thereby enabling hands-free operation. For example, a crime inquiry may be given by voice. The information transmission unit also uses the voice input function to build a system that allows police officers to operate the messaging app without using their hands. For example, a photo analysis may be given by voice. Furthermore, the information transmission unit enables police officers to give quick instructions based on the voice input function. For example, a suspect identification may be given by voice. This allows police officers to give voice instructions, thereby enabling hands-free operation.

[0060] The information transmission unit can use the emotion estimation function to collect citizens' emotional reactions to information sent through a messaging app and reflect the collected information in local security measures. For example, the information transmission unit uses the emotion estimation function to collect citizens' emotional reactions to information sent through a messaging app. For example, it analyzes citizens' emotions regarding crime information and reflects the results in local security measures. The information transmission unit also builds a system to strengthen local security measures based on citizens' emotional reaction data. For example, it may conduct intensive patrols in areas with low emotion scores. Furthermore, the information transmission unit improves local security measures based on the emotion estimation results. For example, it may propose crime prevention measures based on citizens' emotional reactions. In this way, citizens' emotional reactions can be collected and reflected in local security measures.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The crime inquiry system can further be equipped with a voice recognition unit. The voice recognition unit allows police officers to input information by voice at the scene, thereby enabling quicker crime inquiries. For example, a police officer can input the characteristics of a suspect by voice, and the information is analyzed by the generation AI and compared with the database. The voice recognition unit can also allow police officers to report the situation at the scene by voice, which can then be automatically converted into text and recorded. Furthermore, the voice recognition unit allows police officers to give voice instructions at the scene, allowing information to be transmitted to other police officers and related agencies in real time. This makes it possible to further improve the efficiency of crime inquiries by utilizing voice input.

[0063] The crime inquiry system can further include a predictive analysis unit. The predictive analysis unit can predict future crime risks based on past crime data. For example, it can analyze crime trends in specific areas or time periods and reflect the results in police patrol plans. The predictive analysis unit can also analyze crime patterns and identify locations and time periods where crimes are likely to occur. Furthermore, the predictive analysis unit can monitor crime risks in real time and issue warnings to police officers. In this way, predictive analysis can be used to contribute to crime prevention.

[0064] The crime investigation system can further include a drone control unit. The drone control unit can monitor crime scenes and their surrounding areas using drones and acquire video footage in real time. For example, a drone can capture images of a crime scene from above, and the generated video can be analyzed by AI to extract important information. The drone control unit can also remotely control the drone to track suspects and collect evidence. Furthermore, the drone control unit can provide drone video footage to police officers in real time, enabling them to quickly grasp the situation at the scene. This allows drones to be used to improve the accuracy and efficiency of crime investigations.

[0065] The crime inquiry system can also use emotion estimation to analyze the emotions of people in crime scene photos and identify those who appear to be feeling nervous or fearful. For example, the generative AI analyzes the facial expressions of people in photos to estimate their emotions. For example, it can identify people who appear to be nervous or fearful and suggest that they may be suspects or victims. The emotion estimation function can also be used to analyze the emotions of multiple people in a photo and identify those with abnormal emotions. For example, it can identify people whose emotions are different from those of other people and suggest that they may be involved in a crime. Furthermore, the emotion estimation results can be used to perform a detailed analysis of the crime scene. For example, if a person who appears to be feeling nervous or fearful is in a specific location, it can suggest that that location may be the site of a crime. This can help identify people who appear to be feeling nervous or fearful, thereby assisting in the identification of suspects and victims.

[0066] The crime inquiry system can also use the emotion estimation function to collect citizens' emotional reactions to photos taken by police officers, allowing for a real-time understanding of the local security situation. For example, the emotion estimation function can be used to analyze the emotional reactions to photos taken by citizens. For example, the facial expressions of people in the photos can be analyzed to understand the local security situation. It is also possible to build a system that understands the local security situation in real time based on citizen emotional reaction data. For example, areas with low emotion scores can be identified and reflected in police patrol plans. Furthermore, the emotion estimation results can be used to visualize the local security situation. For example, emotion scores can be displayed on a map to identify areas where security is deteriorating. This makes it possible to collect citizens' emotional reactions and understand the local security situation in real time.

[0067] The crime inquiry system can further use the emotion estimation function to analyze the emotional reactions of police officers to information sent through messaging apps and provide support to reduce stress and fatigue. For example, the emotion estimation function can be used to analyze the emotional reactions of police officers to information sent through messaging apps. For example, signs of stress or fatigue can be detected and appropriate support can be provided. A system can also be built that provides support to reduce stress and fatigue based on the emotional response data of police officers. For example, it can suggest relaxation methods or breaks. Furthermore, it can also provide feedback to reduce stress and fatigue to police officers based on the emotion estimation results. For example, it can send an encouraging message if the emotion score is low. This makes it possible to provide support to reduce stress and fatigue to police officers.

[0068] The crime inquiry system can further use the emotion estimation function to collect citizens' emotional reactions to information sent through messaging apps and reflect this in local public safety measures. For example, the emotion estimation function can be used to collect citizens' emotional reactions to information sent through messaging apps. For example, citizens' emotions regarding crime information can be analyzed and reflected in public safety measures. It is also possible to build a system that strengthens local public safety measures based on citizens' emotional reaction data. For example, it can conduct intensive patrols in areas with low emotional scores. Furthermore, local public safety measures can be improved based on the emotion estimation results. For example, crime prevention measures can be proposed based on citizens' emotional reactions. In this way, citizens' emotional reactions can be collected and reflected in local public safety measures.

[0069] The crime inquiry system can further be equipped with a voice recognition unit. The voice recognition unit allows police officers to input information by voice at the scene, thereby enabling quicker crime inquiries. For example, a police officer can input the characteristics of a suspect by voice, and the information is analyzed by the generation AI and compared with the database. The voice recognition unit can also allow police officers to report the situation at the scene by voice, which can then be automatically converted into text and recorded. Furthermore, the voice recognition unit allows police officers to give voice instructions at the scene, allowing information to be transmitted to other police officers and related agencies in real time. This makes it possible to further improve the efficiency of crime inquiries by utilizing voice input.

[0070] The crime inquiry system can further include a predictive analysis unit. The predictive analysis unit can predict future crime risks based on past crime data. For example, it can analyze crime trends in specific areas or time periods and reflect the results in police patrol plans. The predictive analysis unit can also analyze crime patterns and identify locations and time periods where crimes are likely to occur. Furthermore, the predictive analysis unit can monitor crime risks in real time and issue warnings to police officers. In this way, predictive analysis can be used to contribute to crime prevention.

[0071] The crime investigation system can further include a drone control unit. The drone control unit can monitor crime scenes and their surrounding areas using drones and acquire video footage in real time. For example, a drone can capture images of a crime scene from above, and the generated video can be analyzed by AI to extract important information. The drone control unit can also remotely control the drone to track suspects and collect evidence. Furthermore, the drone control unit can provide drone video footage to police officers in real time, enabling them to quickly grasp the situation at the scene. This allows drones to be used to improve the accuracy and efficiency of crime investigations.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The image analysis unit analyzes the photo. For example, a photo taken by a police officer is input into the generation AI, which then analyzes the photo and extracts information related to the crime. The image analysis unit can also analyze photos of crime scenes to extract evidence and clues. Furthermore, the image analysis unit can analyze photos of suspects' faces and compare them with a database to identify the suspect. Step 2: The information transmission unit notifies the police officer of the analysis results of the photo analyzed by the image analysis unit via a messaging app. For example, the generation AI sends the analysis results to the police officer via a messaging app such as LINE. The information transmission unit can also notify the police officer of the analysis results in real time. Furthermore, the information transmission unit can also notify multiple police officers of the analysis results simultaneously.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes photos; and an information transmission unit that notifies a police officer of the analysis result of the photo analyzed by the image analysis unit through a messaging app. A system characterized by:

2. The image analysis unit Analyzes the metadata of the photo to provide detailed information based on the location and time of the crime 2. The system of claim 1.

3. The image analysis unit Analyze multiple images consecutively to detect dynamic changes and anomalies 2. The system of claim 1.

4. The image analysis unit Analyzing the emotions of people in the crime scene photographs to identify people with the emotions of tension or fear 2. The system of claim 1.

5. The image analysis unit Analyze photos of crime scenes, as well as traffic accidents or disaster scenes, to enable rapid response 2. The system of claim 1.

6. The image analysis unit The analyzed information is linked to the police database, and related cases are automatically queried.

2. The system of claim 1.

7. The image analysis unit Collecting citizens' emotional responses to the photos taken by the police officers and understanding the local security situation in real time 2. The system of claim 1.

8. The information transmission unit The generated AI not only notifies the police officer but also other related agencies of the information analyzed.

2. The system of claim 1.

Images