System

Abstract

An object of a system according to an embodiment is to efficiently identify and manage a criminal or a person who has arranged for a name.SOLUTION: A system includes a collection unit, an analysis unit, an identification unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies the person based on the analysis result obtained by the analysis unit. The management unit manages the data specified by the specifying unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently identify and manage criminals and wanted criminals, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently identify and manage criminals and wanted criminals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies a person based on the analysis results obtained by the analysis unit. The management unit manages the data identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify and manage criminals and wanted criminals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses image and voice recognition technologies to centrally manage wanted criminals and criminals on a network and identify them from the identified data. The system collects data from surveillance cameras and audio collection devices, analyzes it using image and voice recognition technologies, and identifies individuals. The data on identified individuals is centrally managed via a network and provided to relevant organizations as needed. For example, the system collects data from surveillance cameras and audio collection devices installed in public places such as train stations, airports, and shopping malls. The system then analyzes the collected data using image and voice recognition technologies. For example, the system recognizes the face of a specific individual from surveillance camera footage and the voice of that individual from audio collection device data. The system then centrally manages the data on identified individuals via a network. This allows relevant organizations to share information in real time and respond quickly. For example, police and security companies can share information via a network to help identify and arrest wanted criminals and criminals. This allows the system to quickly and accurately identify wanted criminals and criminals, improving public safety. For example, criminals can be quickly identified and arrested in public places such as train stations and airports. Furthermore, by using voice recognition technology, it is possible to identify people from their voices even if their faces are hidden, which is expected to have a deterrent effect on crime.

[0029] A network management system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a management unit. The collection unit collects data from surveillance cameras and audio collection devices. For example, the collection unit collects video data from surveillance cameras installed in public places such as train stations, airports, and shopping malls. The collection unit can also collect audio data from audio collection devices. For example, the collection unit collects audio data from microphones installed to capture conversations and audio. The collection unit can also collect data from different devices such as smartphones and drones. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may analyze video data using image recognition technology to identify a person's face and features. The analysis unit may also analyze audio data using voice recognition technology to identify a person's voice and speaking style. For example, the analysis unit may execute a face recognition algorithm using deep learning to recognize a specific person's face. The analysis unit may execute a voice recognition algorithm using an acoustic model or a language model to recognize a specific person's voice. The identification unit identifies a person based on the analysis results obtained by the analysis unit. For example, the identification unit integrates facial recognition results and voice recognition results to identify a specific person. The identification unit can also predict the behavior of a specific person using behavioral pattern analysis. The management unit collectively manages the data identified by the identification unit via a network and provides it to relevant organizations. For example, the management unit stores data on identified people in a database and provides it to police and security companies. The management unit also has data backup and recovery functions to ensure data security. As a result, the network management system according to the embodiment can quickly and accurately identify wanted criminals and criminals. For example, the system can analyze data obtained from surveillance cameras and audio collection devices in real time and provide data on identified people to relevant organizations, enabling a rapid response.

[0030] The collection unit can collect data from surveillance cameras or audio collection devices. For example, the collection unit collects video data from surveillance cameras installed in public places such as train stations, airports, and shopping malls. The collection unit can also collect audio data from audio collection devices. For example, the collection unit collects audio data from microphones installed to collect conversations and audio. This allows for efficient collection of a wide range of data by collecting data from surveillance cameras and audio collection devices. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from the surveillance cameras or audio collection devices into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the video data using image recognition technology to identify a person's face and features. The analysis unit can analyze the video data using, for example, deep learning to identify a person's face and features. For example, the analysis unit can execute a facial recognition algorithm to recognize a specific person's face. The analysis unit can also analyze the video data using pattern recognition technology to identify a person's features. For example, the analysis unit can extract a specific pattern from the video data to identify a person's features. The analysis unit can also analyze the video data using image recognition technology to identify a person's movements and behavior. For example, the analysis unit can analyze a person's movements from the video data and identify specific behaviors. In this way, the image recognition technology can accurately identify a person's face and features from the video data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the video data to a generation AI and cause the generation AI to identify a person's face and features.

[0032] The analysis unit can analyze the voice data using voice recognition technology to identify a person's voice and speaking style. The analysis unit can, for example, analyze the voice data using an acoustic model to identify a person's voice and speaking style. For example, the analysis unit executes a voice recognition algorithm to recognize a specific person's voice. The analysis unit can also analyze the voice data using a language model to identify a person's speaking style. For example, the analysis unit can extract specific language patterns from the voice data to identify a person's speaking style. The analysis unit can also analyze the voice data using deep learning to identify a person's voice and speaking style. For example, the analysis unit can extract specific acoustic features from the voice data to identify a person's voice. In this way, the voice recognition technology can accurately identify a person's voice and speaking style from the voice data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the voice data to a generation AI and cause the generation AI to identify a person's voice and speaking style.

[0033] The management unit can centrally manage the identified data via a network and provide it to relevant organizations. For example, the management unit may store data on identified individuals in a database and provide it to police or security companies via a network. The management unit also has data backup and recovery functions to ensure data security. For example, the management unit may periodically back up data and provide a data recovery function. The management unit can also customize data access permissions for each user. For example, the management unit may grant access permissions to all data to administrators and grant access permissions to specific data to general users. This allows the identified data to be centrally managed via a network, enabling relevant organizations to quickly share information. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit may input the identified data into a generation AI and have the generation AI manage the data.

[0034] The collection unit can change the frequency of data collection based on specific time periods or events. For example, the collection unit can increase the frequency of data collection during rush hour at a station. The collection unit can also increase the frequency of data collection during a shopping mall sale. For example, the collection unit can increase the frequency of data collection during rush hour departures and arrivals at an airport. This enables efficient data collection by adjusting the frequency of data collection according to specific time periods or events. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can cause the generation AI to execute the frequency of data collection based on specific time periods or events.

[0035] The collection unit can collect data from different devices. For example, the collection unit collects video data in real time from a smartphone camera. The collection unit can also collect wide-area video data from a drone camera. For example, the collection unit collects audio data from a smartwatch microphone. This allows for wide-area data collection by collecting data from different devices. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from a smartphone or drone into a generation AI and have the generation AI collect data.

[0036] The collection unit may have a filtering function that removes environmental sounds and background noise when collecting data. For example, the collection unit may remove train running sounds to collect conversational audio at a station platform. The collection unit may also remove background music to collect human voices at a shopping mall. For example, the collection unit may remove announcement sounds to collect specific conversations at an airport. This enables more accurate data collection by removing environmental sounds and background noise. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input collected audio data to a generation AI and have the generation AI remove environmental sounds and background noise.

[0037] The collection unit can prioritize collecting data from a specific area by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data around a station. The collection unit can also prioritize collecting data around a specific store in a shopping mall. For example, the collection unit prioritizes collecting data around a security checkpoint at an airport. This allows data to be collected efficiently from a specific area by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect data from a specific area.

[0038] The collection unit can collect public data from social media and the internet. For example, the collection unit collects location information from social media posts. The collection unit can also collect public video data on the internet. For example, the collection unit collects audio data from live streaming on social media. This enables extensive data collection by collecting public data from social media and the internet. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from social media and the internet into the generation AI and have the generation AI collect the data.

[0039] The collection unit can customize the collection method by referring to past collected data. For example, the collection unit can increase the collection frequency during a specific time period based on past data. The collection unit can also optimize the collection method at a specific location based on past data. For example, the collection unit customizes the collection method at the time of a specific event based on past data. In this way, the collection method can be optimized by referring to past collected data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input past collected data into a generation AI and have the generation AI customize the collection method.

[0040] The analysis unit can select an analysis algorithm based on the resolution and quality of the image data. For example, the analysis unit applies a detailed analysis algorithm to high-resolution image data. The analysis unit can also apply a simplified analysis algorithm to low-resolution image data. For example, if the quality of the image data is poor, the analysis unit applies a noise removal algorithm before performing analysis. In this way, by selecting an analysis algorithm according to the resolution and quality of the image data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to a generation AI and have the generation AI select an analysis algorithm.

[0041] The analysis unit can apply different analysis methods depending on the language or dialect of the voice data. For example, the analysis unit applies a general voice recognition algorithm to voice data in standard Japanese. The analysis unit can also apply a voice recognition algorithm corresponding to a specific dialect to voice data in a dialect. For example, the analysis unit applies a language identification algorithm to multilingual voice data before analyzing it. This improves the accuracy of analysis by applying an analysis method depending on the language or dialect of the voice data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI and have the generation AI apply the analysis method.

[0042] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the analysis accuracy by learning specific patterns from past analysis results. The analysis unit can also apply an algorithm that reduces erroneous recognition based on past analysis results. For example, the analysis unit improves the analysis accuracy under specific conditions by referring to past analysis results. In this way, the analysis accuracy is improved by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0043] The analysis unit can integrate different data sources and perform analysis. For example, the analysis unit can integrate image data and audio data and perform analysis. The analysis unit can also integrate text data and image data and perform analysis. For example, the analysis unit can integrate audio data and text data and perform analysis. This enables more comprehensive analysis by integrating different data sources. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input different data sources into the generation AI and have the generation AI perform the integrated analysis.

[0044] The analysis unit can provide an interface that displays the analysis results in real time. For example, the analysis unit can provide a dashboard that displays the analysis results in real time. The analysis unit can also provide a mobile app that displays the analysis results in real time. For example, the analysis unit can provide a web interface that displays the analysis results in real time. This allows for quick response by displaying the analysis results in real time. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI and have the generation AI perform real-time display.

[0045] The analysis unit can share the analysis results in cooperation with other systems and devices. For example, the analysis unit can share the analysis results in cooperation with a police system. The analysis unit can also share the analysis results in cooperation with a security company's system. For example, the analysis unit can share the analysis results in cooperation with a smartphone or tablet. In this way, sharing the analysis results in cooperation with other systems and devices makes information sharing more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results into a generation AI and cause the generation AI to execute cooperation with other systems and devices.

[0046] The identification unit can adjust the identification accuracy based on a specific time period or location. For example, the identification unit adjusts the analysis algorithm for image data to improve identification accuracy at night. The identification unit can also adjust the analysis algorithm for voice data to improve identification accuracy during crowded times at train stations. For example, the identification unit analyzes both types of data in an integrated manner to improve identification accuracy at airport security checkpoints. This improves identification accuracy by adjusting the identification accuracy according to a specific time period or location. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can cause the generation AI to adjust the identification accuracy based on a specific time period or location.

[0047] The identification unit can improve the accuracy of identification by referring to the behavioral patterns and history of the specific person. The identification unit can improve the accuracy of identification by referring to, for example, the past behavioral patterns of the specific person. The identification unit can also improve the accuracy of identification by referring to the past movement history of the specific person. For example, the identification unit can improve the accuracy of identification by referring to the past voice data of the specific person. In this way, the accuracy of identification is improved by referring to the behavioral patterns and history of the specific person. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the behavioral patterns and history of the specific person into the generation AI and cause the generation AI to improve the accuracy of identification.

[0048] The identification unit can perform identification by integrating different data sources. For example, the identification unit can perform identification by integrating image data and voice data. The identification unit can also perform identification by integrating text data and image data. For example, the identification unit can perform identification by integrating voice data and text data. In this way, by integrating different data sources, the identification accuracy is improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input different data sources to a generation AI and cause the generation AI to perform integrated identification.

[0049] The identification unit can improve the accuracy of identification by taking geographical location information into consideration. For example, the identification unit performs analysis by taking location information into consideration to improve the accuracy of identification around a station. The identification unit can also perform analysis by taking location information into consideration to improve the accuracy of identification around a specific store in a shopping mall. For example, the identification unit performs analysis by taking location information into consideration to improve the accuracy of identification around a security checkpoint at an airport. As a result, the accuracy of identification is improved by taking geographical location information into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical location information into the generation AI and cause the generation AI to improve the accuracy of identification.

[0050] The identification unit can perform identification by analyzing the social media activity of the specific person. For example, the identification unit can perform identification based on the locations where the specific person has checked in on social media. The identification unit can also perform identification by analyzing the content of the specific person's social media posts. For example, the identification unit can perform identification by referring to the activities of the specific person's friends on social media. In this way, analyzing social media activity improves the accuracy of identification. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input social media activity into a generation AI and have the generation AI perform the identification.

[0051] The identification unit can customize the identification method by reflecting the specific person's past feedback. For example, the identification unit adjusts the identification method based on the specific person's past feedback. The identification unit can also improve the identification accuracy by reflecting the specific person's past feedback. For example, the identification unit customizes the identification method by referring to the specific person's past feedback. In this way, the identification method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past feedback into the generation AI and cause the generation AI to customize the identification method.

[0052] The management unit can adjust the data update frequency based on specific time periods or events. For example, the management unit can increase the data update frequency during rush hour at a station. The management unit can also increase the data update frequency during a shopping mall sale. For example, the management unit can increase the data update frequency during rush hour departures and arrivals at an airport. This enables efficient data management by adjusting the data update frequency according to specific time periods or events. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can have the generation AI adjust the data update frequency based on specific time periods or events.

[0053] The management unit can manage data in cooperation with different devices and systems. The management unit can manage data in cooperation with, for example, a smartphone. The management unit can also manage data in cooperation with a drone. For example, the management unit can manage data in cooperation with a smartwatch. This allows for cooperation with different devices and systems, thereby making data management more efficient. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can cause a generation AI to execute cooperation with different devices and systems.

[0054] The management unit can have a data backup and recovery function. For example, the management unit performs periodic backup of data. The management unit can also provide a data recovery function. For example, the management unit automates data backup and recovery. As a result, having the data backup and recovery function improves data security. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can have the generation AI perform data backup and recovery.

[0055] The management unit can manage data taking geographical location information into consideration. For example, the management unit prioritizes managing data around train stations. The management unit can also prioritize managing data around specific stores in a shopping mall. For example, the management unit prioritizes managing data around security checkpoints at airports. This allows data for specific areas to be managed efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input geographical location information into a generation AI and have the generation AI perform data management.

[0056] The management unit can adjust and manage the security level of data. For example, the management unit sets a high security level for important data. The management unit can also set a medium security level for general data. For example, the management unit sets a low security level for public data. By adjusting the security level of data, the safety of the data is improved. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can have the generation AI adjust the security level of the data.

[0057] The management unit can customize data access permissions for each user. For example, the management unit grants access permissions to all data to an administrator. The management unit can also grant access permissions to specific data to general users. For example, the management unit grants access permissions to limited data to guest users. This allows data access permissions to be customized for each user, thereby making data management more efficient. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can have a generation AI customize the data access permissions.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can change the frequency of data collection based on specific time periods or events. For example, the frequency of data collection can be increased during rush hour at a station. The frequency of data collection can also be increased during a shopping mall sale. For example, the frequency of data collection can be increased during rush hour departures and arrivals at an airport. This enables efficient data collection by adjusting the frequency of data collection according to specific time periods or events. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can cause the generation AI to execute the frequency of data collection based on specific time periods or events.

[0060] The collection unit can collect data from different devices. For example, it can collect video data in real time from a smartphone camera. It can also collect wide-area video data from a drone camera. For example, it can collect audio data from a smartwatch microphone. By collecting data from different devices, it becomes possible to collect wide-area data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from a smartphone or a drone into a generation AI and have the generation AI collect the data.

[0061] The collection unit may have a filtering function that removes environmental sounds and background noise when collecting data. For example, on a station platform, the sound of trains running can be removed to collect conversational audio. It is also possible to remove background music to collect human voices at a shopping mall. For example, at an airport, announcement sounds can be removed to collect specific conversations. This enables more accurate data collection by removing environmental sounds and background noise. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input collected audio data into a generation AI and have the generation AI remove environmental sounds and background noise.

[0062] The analysis unit can select an analysis algorithm based on the resolution and quality of the image data. For example, a detailed analysis algorithm can be applied to high-resolution image data. A simplified analysis algorithm can also be applied to low-resolution image data. For example, if the quality of the image data is poor, a noise removal algorithm can be applied before analysis. By selecting an analysis algorithm according to the resolution and quality of the image data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input image data to a generation AI and have the generation AI select an analysis algorithm.

[0063] The analysis unit can apply different analysis methods depending on the language and dialect of the voice data. For example, a general voice recognition algorithm can be applied to voice data in standard Japanese. Also, a voice recognition algorithm tailored to a specific dialect can be applied to voice data in a dialect. For example, a language identification algorithm is applied to multilingual voice data before analysis. This improves the accuracy of analysis by applying an analysis method depending on the language and dialect of the voice data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI and have the generation AI apply the analysis method.

[0064] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, it can improve the analysis accuracy by learning specific patterns from past analysis results. It can also apply an algorithm that reduces misrecognition based on past analysis results. For example, it can improve the analysis accuracy under specific conditions by referring to past analysis results. In this way, the analysis accuracy is improved by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data from surveillance cameras and audio collection devices. For example, the collection unit collects video data from surveillance cameras installed in public places such as stations, airports, and shopping malls. The collection unit can also collect audio data from audio collection devices. Furthermore, the collection unit can collect data from different devices such as smartphones and drones. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may use image recognition technology to analyze video data and identify a person's face and features. The analysis unit may also use voice recognition technology to analyze audio data and identify a person's voice and speaking style. For example, the analysis unit may use deep learning to run a facial recognition algorithm and recognize a specific person's face. The analysis unit may also use an acoustic model or language model to run a voice recognition algorithm and recognize a specific person's voice. Step 3: The identification unit identifies a person based on the analysis results obtained by the analysis unit. For example, the identification unit may integrate the facial recognition results and the voice recognition results to identify a specific person. The identification unit may also predict the behavior of a specific person using behavioral pattern analysis. Step 4: The management department centrally manages the data identified by the identification department via a network and provides it to relevant organizations. For example, the management department stores the data of identified individuals in a database and provides it to the police or security companies. The management department also has data backup and recovery functions to ensure the safety of the data.

[0067] (Example 2) A system according to an embodiment of the present invention uses image and voice recognition technologies to centrally manage wanted criminals and criminals on a network and identify them from the identified data. The system collects data from surveillance cameras and audio collection devices, analyzes it using image and voice recognition technologies, and identifies individuals. The data on identified individuals is centrally managed via a network and provided to relevant organizations as needed. For example, the system collects data from surveillance cameras and audio collection devices installed in public places such as train stations, airports, and shopping malls. The system then analyzes the collected data using image and voice recognition technologies. For example, the system recognizes the face of a specific individual from surveillance camera footage and the voice of that individual from audio collection device data. The system then centrally manages the data on identified individuals via a network. This allows relevant organizations to share information in real time and respond quickly. For example, police and security companies can share information via a network to help identify and arrest wanted criminals and criminals. This allows the system to quickly and accurately identify wanted criminals and criminals, improving public safety. For example, criminals can be quickly identified and arrested in public places such as train stations and airports. Furthermore, by using voice recognition technology, it is possible to identify people from their voices even if their faces are hidden, which is expected to have a deterrent effect on crime.

[0068] A network management system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a management unit. The collection unit collects data from surveillance cameras and audio collection devices. For example, the collection unit collects video data from surveillance cameras installed in public places such as train stations, airports, and shopping malls. The collection unit can also collect audio data from audio collection devices. For example, the collection unit collects audio data from microphones installed to capture conversations and audio. The collection unit can also collect data from different devices such as smartphones and drones. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may analyze video data using image recognition technology to identify a person's face and features. The analysis unit may also analyze audio data using voice recognition technology to identify a person's voice and speaking style. For example, the analysis unit may execute a face recognition algorithm using deep learning to recognize a specific person's face. The analysis unit may execute a voice recognition algorithm using an acoustic model or a language model to recognize a specific person's voice. The identification unit identifies a person based on the analysis results obtained by the analysis unit. For example, the identification unit integrates facial recognition results and voice recognition results to identify a specific person. The identification unit can also predict the behavior of a specific person using behavioral pattern analysis. The management unit collectively manages the data identified by the identification unit via a network and provides it to relevant organizations. For example, the management unit stores data on identified people in a database and provides it to police and security companies. The management unit also has data backup and recovery functions to ensure data security. As a result, the network management system according to the embodiment can quickly and accurately identify wanted criminals and criminals. For example, the system can analyze data obtained from surveillance cameras and audio collection devices in real time and provide data on identified people to relevant organizations, enabling a rapid response.

[0069] The collection unit can collect data from surveillance cameras or audio collection devices. For example, the collection unit collects video data from surveillance cameras installed in public places such as train stations, airports, and shopping malls. The collection unit can also collect audio data from audio collection devices. For example, the collection unit collects audio data from microphones installed to collect conversations and audio. This allows for efficient collection of a wide range of data by collecting data from surveillance cameras and audio collection devices. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from the surveillance cameras or audio collection devices into the generation AI and have the generation AI collect the data.

[0070] The analysis unit can analyze the video data using image recognition technology to identify a person's face and features. The analysis unit can analyze the video data using, for example, deep learning to identify a person's face and features. For example, the analysis unit can execute a facial recognition algorithm to recognize a specific person's face. The analysis unit can also analyze the video data using pattern recognition technology to identify a person's features. For example, the analysis unit can extract a specific pattern from the video data to identify a person's features. The analysis unit can also analyze the video data using image recognition technology to identify a person's movements and behavior. For example, the analysis unit can analyze a person's movements from the video data and identify specific behaviors. In this way, the image recognition technology can accurately identify a person's face and features from the video data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the video data to a generation AI and cause the generation AI to identify a person's face and features.

[0071] The analysis unit can analyze the voice data using voice recognition technology to identify a person's voice and speaking style. The analysis unit can, for example, analyze the voice data using an acoustic model to identify a person's voice and speaking style. For example, the analysis unit executes a voice recognition algorithm to recognize a specific person's voice. The analysis unit can also analyze the voice data using a language model to identify a person's speaking style. For example, the analysis unit can extract specific language patterns from the voice data to identify a person's speaking style. The analysis unit can also analyze the voice data using deep learning to identify a person's voice and speaking style. For example, the analysis unit can extract specific acoustic features from the voice data to identify a person's voice. In this way, the voice recognition technology can accurately identify a person's voice and speaking style from the voice data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the voice data to a generation AI and cause the generation AI to identify a person's voice and speaking style.

[0072] The management unit can centrally manage the identified data via a network and provide it to relevant organizations. For example, the management unit may store data on identified individuals in a database and provide it to police or security companies via a network. The management unit also has data backup and recovery functions to ensure data security. For example, the management unit may periodically back up data and provide a data recovery function. The management unit can also customize data access permissions for each user. For example, the management unit may grant access permissions to all data to administrators and grant access permissions to specific data to general users. This allows the identified data to be centrally managed via a network, enabling relevant organizations to quickly share information. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit may input the identified data into a generation AI and have the generation AI manage the data.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can increase the frequency of data collection when a user is nervous in a location where a surveillance camera is installed. The collection unit can also decrease the frequency of data collection when a user is relaxed in a location where an audio collection device is installed. For example, the collection unit can adjust the timing of data collection in real time when a user is excited in a public location. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input data obtained from a surveillance camera or an audio collection device into the generation AI and have the generation AI adjust the timing of data collection.

[0074] The collection unit can change the frequency of data collection based on specific time periods or events. For example, the collection unit can increase the frequency of data collection during rush hour at a station. The collection unit can also increase the frequency of data collection during a shopping mall sale. For example, the collection unit can increase the frequency of data collection during rush hour departures and arrivals at an airport. This enables efficient data collection by adjusting the frequency of data collection according to specific time periods or events. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can cause the generation AI to execute the frequency of data collection based on specific time periods or events.

[0075] The collection unit can collect data from different devices. For example, the collection unit collects video data in real time from a smartphone camera. The collection unit can also collect wide-area video data from a drone camera. For example, the collection unit collects audio data from a smartwatch microphone. This allows for wide-area data collection by collecting data from different devices. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from a smartphone or drone into a generation AI and have the generation AI collect data.

[0076] The collection unit may have a filtering function that removes environmental sounds and background noise when collecting data. For example, the collection unit may remove train running sounds to collect conversational audio at a station platform. The collection unit may also remove background music to collect human voices at a shopping mall. For example, the collection unit may remove announcement sounds to collect specific conversations at an airport. This enables more accurate data collection by removing environmental sounds and background noise. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input collected audio data to a generation AI and have the generation AI remove environmental sounds and background noise.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize collecting facial recognition data. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting voice recognition data. For example, if the user is excited, the collection unit can simultaneously collect both types of data. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the collected data into the generation AI and have the generation AI determine the priority of the data.

[0078] The collection unit can prioritize collecting data from a specific area by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data around a station. The collection unit can also prioritize collecting data around a specific store in a shopping mall. For example, the collection unit prioritizes collecting data around a security checkpoint at an airport. This allows data to be collected efficiently from a specific area by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect data from a specific area.

[0079] The collection unit can collect public data from social media and the internet. For example, the collection unit collects location information from social media posts. The collection unit can also collect public video data on the internet. For example, the collection unit collects audio data from live streaming on social media. This enables extensive data collection by collecting public data from social media and the internet. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from social media and the internet into the generation AI and have the generation AI collect the data.

[0080] The collection unit can customize the collection method by referring to past collected data. For example, the collection unit can increase the collection frequency during a specific time period based on past data. The collection unit can also optimize the collection method at a specific location based on past data. For example, the collection unit customizes the collection method at the time of a specific event based on past data. In this way, the collection method can be optimized by referring to past collected data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input past collected data into a generation AI and have the generation AI customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows for adjusting the presentation method of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the analysis results to the generation AI and have the generation AI adjust the presentation method of the analysis.

[0082] The analysis unit can select an analysis algorithm based on the resolution and quality of the image data. For example, the analysis unit applies a detailed analysis algorithm to high-resolution image data. The analysis unit can also apply a simplified analysis algorithm to low-resolution image data. For example, if the quality of the image data is poor, the analysis unit applies a noise removal algorithm before performing analysis. In this way, by selecting an analysis algorithm according to the resolution and quality of the image data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to a generation AI and have the generation AI select an analysis algorithm.

[0083] The analysis unit can apply different analysis methods depending on the language or dialect of the voice data. For example, the analysis unit applies a general voice recognition algorithm to voice data in standard Japanese. The analysis unit can also apply a voice recognition algorithm corresponding to a specific dialect to voice data in a dialect. For example, the analysis unit applies a language identification algorithm to multilingual voice data before analyzing it. This improves the accuracy of analysis by applying an analysis method depending on the language or dialect of the voice data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI and have the generation AI apply the analysis method.

[0084] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the analysis accuracy by learning specific patterns from past analysis results. The analysis unit can also apply an algorithm that reduces erroneous recognition based on past analysis results. For example, the analysis unit improves the analysis accuracy under specific conditions by referring to past analysis results. In this way, the analysis accuracy is improved by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0085] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. For example, if the user is excited, the analysis unit can prioritize displaying visually stimulating analysis results. Thus, by determining the analysis priority according to the user's emotions, important analysis results can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the analysis results to the generation AI and have the generation AI determine the analysis priority.

[0086] The analysis unit can integrate different data sources and perform analysis. For example, the analysis unit can integrate image data and audio data and perform analysis. The analysis unit can also integrate text data and image data and perform analysis. For example, the analysis unit can integrate audio data and text data and perform analysis. This enables more comprehensive analysis by integrating different data sources. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input different data sources into the generation AI and have the generation AI perform the integrated analysis.

[0087] The analysis unit can provide an interface that displays the analysis results in real time. For example, the analysis unit can provide a dashboard that displays the analysis results in real time. The analysis unit can also provide a mobile app that displays the analysis results in real time. For example, the analysis unit can provide a web interface that displays the analysis results in real time. This allows for quick response by displaying the analysis results in real time. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI and have the generation AI perform real-time display.

[0088] The analysis unit can share the analysis results in cooperation with other systems and devices. For example, the analysis unit can share the analysis results in cooperation with a police system. The analysis unit can also share the analysis results in cooperation with a security company's system. For example, the analysis unit can share the analysis results in cooperation with a smartphone or tablet. In this way, sharing the analysis results in cooperation with other systems and devices makes information sharing more efficient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results into a generation AI and cause the generation AI to execute cooperation with other systems and devices.

[0089] The identification unit can estimate the user's emotion and adjust the identification method based on the estimated user's emotion. For example, if the user is nervous, the identification unit provides a simple, highly visible identification method. The identification unit can also provide a detailed identification method if the user is relaxed. For example, if the user is excited, the identification unit provides a visually stimulating identification method. This allows for more appropriate identification by adjusting the identification method according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the identification method into the generation AI and have the generation AI perform the adjustment.

[0090] The identification unit can adjust the identification accuracy based on a specific time period or location. For example, the identification unit adjusts the analysis algorithm for image data to improve identification accuracy at night. The identification unit can also adjust the analysis algorithm for voice data to improve identification accuracy during crowded times at train stations. For example, the identification unit analyzes both types of data in an integrated manner to improve identification accuracy at airport security checkpoints. This improves identification accuracy by adjusting the identification accuracy according to a specific time period or location. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can cause the generation AI to adjust the identification accuracy based on a specific time period or location.

[0091] The identification unit can improve the accuracy of identification by referring to the behavioral patterns and history of the specific person. The identification unit can improve the accuracy of identification by referring to, for example, the past behavioral patterns of the specific person. The identification unit can also improve the accuracy of identification by referring to the past movement history of the specific person. For example, the identification unit can improve the accuracy of identification by referring to the past voice data of the specific person. In this way, the accuracy of identification is improved by referring to the behavioral patterns and history of the specific person. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the behavioral patterns and history of the specific person into the generation AI and cause the generation AI to improve the accuracy of identification.

[0092] The identification unit can perform identification by integrating different data sources. For example, the identification unit can perform identification by integrating image data and voice data. The identification unit can also perform identification by integrating text data and image data. For example, the identification unit can perform identification by integrating voice data and text data. In this way, by integrating different data sources, the identification accuracy is improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input different data sources to a generation AI and cause the generation AI to perform integrated identification.

[0093] The identification unit can estimate the user's emotions and determine a specific priority based on the estimated user's emotions. For example, if the user is nervous, the identification unit can prioritize displaying important identified results. Furthermore, if the user is relaxed, the identification unit can also prioritize displaying detailed identified results. For example, if the user is excited, the identification unit can prioritize displaying visually stimulating identified results. Thus, by determining the specific priority according to the user's emotions, important identified results can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the identified results to the generation AI and have the generation AI determine the priority.

[0094] The identification unit can improve the accuracy of identification by taking geographical location information into consideration. For example, the identification unit performs analysis by taking location information into consideration to improve the accuracy of identification around a station. The identification unit can also perform analysis by taking location information into consideration to improve the accuracy of identification around a specific store in a shopping mall. For example, the identification unit performs analysis by taking location information into consideration to improve the accuracy of identification around a security checkpoint at an airport. As a result, the accuracy of identification is improved by taking geographical location information into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical location information into the generation AI and cause the generation AI to improve the accuracy of identification.

[0095] The identification unit can perform identification by analyzing the social media activity of the specific person. For example, the identification unit can perform identification based on the locations where the specific person has checked in on social media. The identification unit can also perform identification by analyzing the content of the specific person's social media posts. For example, the identification unit can perform identification by referring to the activities of the specific person's friends on social media. In this way, analyzing social media activity improves the accuracy of identification. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input social media activity into a generation AI and have the generation AI perform the identification.

[0096] The identification unit can customize the identification method by reflecting the specific person's past feedback. For example, the identification unit adjusts the identification method based on the specific person's past feedback. The identification unit can also improve the identification accuracy by reflecting the specific person's past feedback. For example, the identification unit customizes the identification method by referring to the specific person's past feedback. In this way, the identification method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past feedback into the generation AI and cause the generation AI to customize the identification method.

[0097] The management unit can estimate the user's emotions and adjust the data management method based on the estimated user emotions. For example, if the user is nervous, the management unit provides a simple, highly visible data management method. Furthermore, if the user is relaxed, the management unit can provide a detailed data management method. For example, if the user is excited, the management unit provides a visually stimulating data management method. This allows for more appropriate data management by adjusting the data management method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the data management method into the generation AI and have the generation AI execute the adjustment.

[0098] The management unit can adjust the data update frequency based on specific time periods or events. For example, the management unit can increase the data update frequency during rush hour at a station. The management unit can also increase the data update frequency during a shopping mall sale. For example, the management unit can increase the data update frequency during rush hour departures and arrivals at an airport. This enables efficient data management by adjusting the data update frequency according to specific time periods or events. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can have the generation AI adjust the data update frequency based on specific time periods or events.

[0099] The management unit can manage data in cooperation with different devices and systems. The management unit can manage data in cooperation with, for example, a smartphone. The management unit can also manage data in cooperation with a drone. For example, the management unit can manage data in cooperation with a smartwatch. This allows for cooperation with different devices and systems, thereby making data management more efficient. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can cause a generation AI to execute cooperation with different devices and systems.

[0100] The management unit can have a data backup and recovery function. For example, the management unit performs periodic backup of data. The management unit can also provide a data recovery function. For example, the management unit automates data backup and recovery. As a result, having the data backup and recovery function improves data security. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can have the generation AI perform data backup and recovery.

[0101] The management unit can estimate the user's emotions and determine the priority of data management based on the estimated user emotions. For example, if the user is nervous, the management unit prioritizes managing important data. Furthermore, if the user is relaxed, the management unit can prioritize managing detailed data. For example, if the user is excited, the management unit prioritizes managing visually stimulating data. Thus, by determining the priority of data management according to the user's emotions, important data can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the data management priority to the generation AI and have the generation AI execute the decision.

[0102] The management unit can manage data taking geographical location information into consideration. For example, the management unit prioritizes managing data around train stations. The management unit can also prioritize managing data around specific stores in a shopping mall. For example, the management unit prioritizes managing data around security checkpoints at airports. This allows data for specific areas to be managed efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input geographical location information into a generation AI and have the generation AI perform data management.

[0103] The management unit can adjust and manage the security level of data. For example, the management unit sets a high security level for important data. The management unit can also set a medium security level for general data. For example, the management unit sets a low security level for public data. By adjusting the security level of data, the safety of the data is improved. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can have the generation AI adjust the security level of the data.

[0104] The management unit can customize data access permissions for each user. For example, the management unit grants access permissions to all data to an administrator. The management unit can also grant access permissions to specific data to general users. For example, the management unit grants access permissions to limited data to guest users. This allows data access permissions to be customized for each user, thereby making data management more efficient. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can have a generation AI customize the data access permissions. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using image recognition and voice recognition technology. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a person based on the analysis results. For example, the management unit is realized by the identification processing unit 290 of the data processing device 12 and collectively manages the identified data via a network and provides it to relevant organizations. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using image recognition and voice recognition technology. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a person based on the analysis results. For example, the management unit is realized by the identification processing unit 290 of the data processing device 12 and manages the identified data collectively via a network and provides it to relevant organizations. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and management unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using image recognition and voice recognition technology. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a person based on the analysis results. For example, the management unit is realized by the identification processing unit 290 of the data processing device 12 and collectively manages the identified data via a network and provides it to relevant organizations. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data using image recognition and voice recognition technology. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a person based on the analysis results. For example, the management unit is realized by the identification processing unit 290 of the data processing device 12 and collectively manages the identified data via a network and provides it to relevant organizations.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if a user is nervous in a location where a surveillance camera is installed, the frequency of data collection can be increased. Also, if a user is relaxed in a location where an audio collection device is installed, the frequency of data collection can be reduced. For example, if a user is excited in a public location, the timing of data collection can be adjusted in real time. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input data obtained from a surveillance camera or an audio collection device into the generation AI and have the generation AI adjust the timing of data collection.

[0107] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. For example, if the user is excited, a visually stimulating analysis result can be provided. By adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the analysis results to the generation AI and have the generation AI adjust the way the analysis is presented.

[0108] The identification unit can estimate the user's emotions and adjust the identification method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible identification method can be provided. Also, if the user is relaxed, a detailed identification method can be provided. For example, if the user is excited, a visually stimulating identification method can be provided. This allows for more appropriate identification by adjusting the identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the identification unit can input the identification method into the generation AI and have the generation AI perform the adjustment.

[0109] The management unit can estimate the user's emotions and adjust the data management method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible data management method can be provided. Also, if the user is relaxed, a detailed data management method can be provided. For example, if the user is excited, a visually stimulating data management method can be provided. This enables more appropriate data management by adjusting the data management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the management unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the management unit can input the data management method into the generation AI and have the generation AI perform the adjustment.

[0110] The collection unit can change the frequency of data collection based on specific time periods or events. For example, the frequency of data collection can be increased during rush hour at a station. The frequency of data collection can also be increased during a shopping mall sale. For example, the frequency of data collection can be increased during rush hour departures and arrivals at an airport. This enables efficient data collection by adjusting the frequency of data collection according to specific time periods or events. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can cause the generation AI to execute the frequency of data collection based on specific time periods or events.

[0111] The collection unit can collect data from different devices. For example, it can collect video data in real time from a smartphone camera. It can also collect wide-area video data from a drone camera. For example, it can collect audio data from a smartwatch microphone. By collecting data from different devices, it becomes possible to collect wide-area data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from a smartphone or a drone into a generation AI and have the generation AI collect the data.

[0112] The collection unit may have a filtering function that removes environmental sounds and background noise when collecting data. For example, on a station platform, the sound of trains running can be removed to collect conversational audio. It is also possible to remove background music to collect human voices at a shopping mall. For example, at an airport, announcement sounds can be removed to collect specific conversations. This enables more accurate data collection by removing environmental sounds and background noise. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input collected audio data into a generation AI and have the generation AI remove environmental sounds and background noise.

[0113] The analysis unit can select an analysis algorithm based on the resolution and quality of the image data. For example, a detailed analysis algorithm can be applied to high-resolution image data. A simplified analysis algorithm can also be applied to low-resolution image data. For example, if the quality of the image data is poor, a noise removal algorithm can be applied before analysis. By selecting an analysis algorithm according to the resolution and quality of the image data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input image data to a generation AI and have the generation AI select an analysis algorithm.

[0114] The analysis unit can apply different analysis methods depending on the language and dialect of the voice data. For example, a general voice recognition algorithm can be applied to voice data in standard Japanese. Also, a voice recognition algorithm tailored to a specific dialect can be applied to voice data in a dialect. For example, a language identification algorithm is applied to multilingual voice data before analysis. This improves the accuracy of analysis by applying an analysis method depending on the language and dialect of the voice data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI and have the generation AI apply the analysis method.

[0115] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, it can improve the analysis accuracy by learning specific patterns from past analysis results. It can also apply an algorithm that reduces misrecognition based on past analysis results. For example, it can improve the analysis accuracy under specific conditions by referring to past analysis results. In this way, the analysis accuracy is improved by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects data from surveillance cameras and audio collection devices. For example, the collection unit collects video data from surveillance cameras installed in public places such as stations, airports, and shopping malls. The collection unit can also collect audio data from audio collection devices. Furthermore, the collection unit can collect data from different devices such as smartphones and drones. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may use image recognition technology to analyze video data and identify a person's face and features. The analysis unit may also use voice recognition technology to analyze audio data and identify a person's voice and speaking style. For example, the analysis unit may use deep learning to run a facial recognition algorithm and recognize a specific person's face. The analysis unit may also use an acoustic model or language model to run a voice recognition algorithm and recognize a specific person's voice. Step 3: The identification unit identifies a person based on the analysis results obtained by the analysis unit. For example, the identification unit may integrate the facial recognition results and the voice recognition results to identify a specific person. The identification unit may also predict the behavior of a specific person using behavioral pattern analysis. Step 4: The management department centrally manages the data identified by the identification department via a network and provides it to relevant organizations. For example, the management department stores the data of identified individuals in a database and provides it to the police or security companies. The management department also has data backup and recovery functions to ensure the safety of the data.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 AI 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; an identification unit that identifies a person based on the analysis result obtained by the analysis unit; a management unit that manages the data identified by the identification unit. A system characterized by:

2. The collecting unit Collect data from surveillance cameras or audio collection devices 2. The system of claim 1.

3. The analysis unit Analyze video data using image recognition technology to identify people's faces and features 2. The system of claim 1.

4. The analysis unit Analyzes audio data using voice recognition technology to identify a person's voice and speaking style 2. The system of claim 1.

5. The management unit Identified data will be centrally managed through a network and provided to relevant organizations.

2. The system of claim 1.

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

7. The collecting unit Vary the frequency of data collection based on specific times or events 2. The system of claim 1.

8. The collecting unit Collect data from different devices 2. The system of claim 1.

9. The collecting unit Has a filtering function to remove environmental and background noise when collecting data 2. The system of claim 1.

Images