System

Abstract

An object of the system according to the embodiment is to clarify the characteristics and occurrence conditions of a crime by aggregating and analyzing investigation related records and to utilize the investigation related records for crime prevention.SOLUTION: A system according to an embodiment includes an information security system, a record collection system, and a generation AI. An information security system encrypts investigation related records collected from a plurality of investigation agencies. The record collection system collects investigation related records from a plurality of investigation agencies. The production AI analyzes the collected forensic records.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] With conventional technology, investigation-related records were managed by each police station and were not consolidated, which meant that they were not being fully utilized.

[0005] The system according to the embodiment aims to compile and analyze investigation-related records to clarify the characteristics and conditions under which crimes occur, and to utilize the information for crime prevention. [Means for solving the problem]

[0006] A system according to an embodiment includes an information security system, a record collection system, and a generation AI. The information security system encrypts investigation-related records collected from multiple investigative agencies. The record collection system collects investigation-related records from multiple investigative agencies. The generation AI analyzes the collected investigation-related records. [Effects of the Invention]

[0007] The system according to the embodiment can compile and analyze investigation-related records to clarify the characteristics of crimes and the conditions under which they occur, and can be used for crime prevention. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The investigative materials utilization system according to an embodiment of the present invention is a system that efficiently collects investigative materials such as reports prepared for each case and analyzes them using a generation AI. As a result, the investigative materials utilization system can clarify the characteristics and occurrence conditions of each crime and utilize them in crime prevention measures.

[0029] An investigation-related material utilization system according to an embodiment includes an information security system, a record collection system, and a generation AI. The information security system encrypts investigation-related records collected from multiple investigation agencies. For example, the information security system encrypts data using the AES encryption algorithm. The information security system can also protect data transmission and reception using the TLS protocol. The information security system can also record audit logs and detect unauthorized access. The record collection system collects investigation-related records from multiple investigation agencies. For example, the record collection system collects data from each investigation agency's database using an API. The record collection system can also collect publicly available investigation-related information using web scraping technology. The record collection system can also collect manually entered data. The generation AI analyzes the collected investigation-related records. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the investigation-related records and extract characteristics of a crime. The generation AI can also use a multimodal generation AI to integrate and analyze text data and image data. The generation AI can also use natural language processing technology to understand the context of the investigation-related records and perform more accurate analysis. As a result, the investigation-related material utilization system according to the embodiment can clarify the characteristics and occurrence conditions of each crime and utilize them for crime prevention measures. For example, the system can identify time periods and locations where crimes are likely to occur and effectively allocate police patrols. The system can also analyze the characteristics of crimes and provide clues that can lead to the identification and arrest of perpetrators.

[0030] The information security system can link with the systems of each investigative agency in real time, automatically encrypting and transferring investigative records to an independent system each time they are created. For example, the information security system can link with the systems of each investigative agency in real time, automatically encrypting and transferring investigative records to an independent system each time they are created. For example, as soon as an investigator prepares a report, the data is encrypted and sent to the independent system. The information security system can also transfer data in real time using WebSocket communication. The information security system can also exchange data with the systems of each investigative agency using API integration. This strengthens the security of investigative records and enables efficient collection.

[0031] An information security system can introduce blockchain technology to prevent the falsification of investigation-related records. For example, an information security system can introduce blockchain technology to prevent the falsification of investigation-related records. For example, each record can be registered on the blockchain, making it impossible to falsify. An information security system can also use a private blockchain to strengthen the security of investigation-related records. An information security system can also use blockchain technology to ensure data integrity. This can prevent the falsification of investigation-related records and ensure their reliability.

[0032] Record collection systems can incorporate voice recognition technology to enable the collection of investigative records through voice input. Record collection systems can incorporate voice recognition technology to enable the collection of investigative records through voice input. For example, an investigator can input a report by voice, and the voice data is automatically converted to text. Record collection systems can also achieve highly accurate voice recognition using deep learning models. Record collection systems can also use voice codecs to compress and efficiently collect voice data. This makes it possible to collect investigative records through voice input, resulting in efficient data collection.

[0033] The record collection system can add an automatic classification function for investigation-related records and set an appropriate security level according to the type and importance of the record. The record collection system can, for example, add an automatic classification function for investigation-related records and set an appropriate security level according to the type and importance of the record. For example, more stringent security measures can be applied to records of higher importance. The record collection system can also automatically classify the type and importance of the record using a machine learning model. The record collection system can also classify records using a rule-based classification algorithm. This makes it possible to set an appropriate security level according to the type and importance of the investigation-related record.

[0034] The record collection system can automate the record collection process of each investigative agency so that each investigative record is immediately uploaded to an independent system whenever it is created. The record collection system, for example, can automate the record collection process of each investigative agency so that each investigative record is immediately uploaded to an independent system whenever it is created. For example, as soon as an investigator creates a report, the data is automatically uploaded to the system. The record collection system can also automate the data collection flow using process automation tools. The record collection system can also monitor the data collection process and issue an alert if an abnormality occurs. This makes it possible to automate the process of collecting investigative records and upload data efficiently.

[0035] The record collection system may unify the data formats of each investigative agency and introduce standardized protocols to ensure data consistency. For example, the record collection system may unify the data formats of each investigative agency and introduce standardized protocols to ensure data consistency. For example, each investigative agency may enter data in the same format. The record collection system may also use standardized data formats such as JSON or XML. The record collection system may also introduce API standards and unify data exchange protocols. This ensures data consistency and enables efficient data management.

[0036] Record collection systems can be cloud-based, enabling data collection over the internet. For example, record collection systems can be cloud-based, enabling data collection over the internet. For example, they can allow investigators to upload data directly from the field to the cloud. Record collection systems can also use cloud services such as AWS, Azure, or Google Cloud to securely store data. Record collection systems can also implement security measures such as VPNs and SSL / TLS to protect data transmission over the internet. This allows cloud-based systems to collect data over the internet.

[0037] Generative AI can analyze time series data from investigative records to reveal crime occurrence patterns over time. For example, generative AI can analyze time series data from investigative records to reveal crime occurrence patterns over time. For example, it can analyze the tendency for crimes to occur frequently during specific time periods. Generative AI can also analyze the frequency of crimes using time-stamped data. Generative AI can also analyze crime trends using time series analysis algorithms. This can reveal crime occurrence patterns over time, which can be useful for preventive measures.

[0038] Generative AI can achieve highly accurate analysis by incorporating natural language processing technology and gaining a deep understanding of the context of investigative records. Generative AI can achieve highly accurate analysis by incorporating natural language processing technology and gaining a deep understanding of the context of investigative records. For example, it can analyze the meaning of sentences and extract characteristics of crimes. Generative AI can also analyze the structure of sentences using morphological analysis. Generative AI can also use context analysis algorithms to understand the context of sentences. This allows for a deep understanding of the context of investigative records and more accurate analysis.

[0039] Generative AI can analyze investigative records from different regions and countries to identify global crime trends. Generative AI can, for example, analyze investigative records from different regions and countries to identify global crime trends. For example, it can analyze common crime patterns in specific regions. Generative AI can also analyze crime trends using international crime databases. Generative AI can also use trend analysis algorithms to identify global crime trends. This allows it to analyze investigative records from different regions and countries to identify global crime trends.

[0040] Generative AI can analyze image data from investigative records and extract characteristics of crimes from visual information. For example, generative AI can analyze image data from investigative records and extract characteristics of crimes from visual information. For example, it can identify the modus operandi from crime scene photos. Generative AI can also analyze image data using image analysis algorithms. Generative AI can also integrate visual information with text data to perform more accurate analysis. This makes it possible to extract characteristics of crimes from visual information and conduct more multifaceted analysis.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The investigative materials utilization system can also be equipped with a voice recognition unit, allowing investigators to input records by voice. For example, investigators can input voice data at the scene, and the voice data is automatically converted to text. The voice recognition unit can also achieve highly accurate voice recognition using a deep learning model. Furthermore, the voice recognition unit can compress voice data and collect it efficiently. This makes it possible to collect investigative records by voice input, resulting in efficient data collection.

[0043] The investigative materials utilization system further includes an automatic classification unit that can automatically classify collected investigative records. For example, a machine learning model can be used to automatically classify the type and importance of the records. The automatic classification unit can also classify the records using a rule-based classification algorithm. Furthermore, the automatic classification unit can set an appropriate security level according to the classified records. This allows an appropriate security level to be set according to the type and importance of the investigative records.

[0044] Investigative materials utilization systems can also be cloud-based, allowing data collection over the internet. For example, investigators can upload data directly from the field to the cloud. Data can also be stored securely using cloud services such as AWS, Azure, or Google Cloud. Security measures such as VPN and SSL / TLS can also be implemented to protect data transmission over the internet. This allows cloud-based systems to collect data over the internet.

[0045] The investigative materials utilization system can also analyze investigative records from different regions and countries to identify global crime trends. For example, it can analyze common crime patterns in specific regions. It can also analyze crime trends using international crime databases. Furthermore, it can use trend analysis algorithms to identify global crime trends. This makes it possible to analyze investigative records from different regions and countries to identify global crime trends.

[0046] The investigative materials utilization system can also analyze image data from investigative records and extract characteristics of crimes from visual information. For example, it can identify the modus operandi from crime scene photos. It can also analyze image data using image analysis algorithms. It can also integrate visual information with text data for more accurate analysis. This makes it possible to extract characteristics of crimes from visual information and conduct more multifaceted analysis.

[0047] The investigative materials utilization system can also unify the data formats of each investigative agency and introduce standardized protocols to ensure data consistency. For example, each investigative agency can input data in the same format. Standardized data formats such as JSON and XML can also be used. Furthermore, API standards can be introduced to unify data exchange protocols. This ensures data consistency and enables efficient data management.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The record collection system collects investigation-related records from multiple investigative agencies. For example, the record collection system uses an API to collect data from the databases of each investigative agency. The record collection system can also use web scraping technology to collect publicly available investigation-related information. It can also collect manually entered data. Step 2: The information security system encrypts the investigation-related records collected from multiple investigation agencies. For example, the information security system encrypts the data using the AES encryption algorithm. It can also protect data transmission and reception using the TLS protocol. It also has the ability to record audit logs and detect unauthorized access. Step 3: The generative AI analyzes the collected investigative records. For example, the generative AI uses text generation AI (e.g., LLM) to analyze the investigative records and extract characteristics of the crime. It can also use multimodal generative AI to integrate and analyze text data and image data. Furthermore, it can use natural language processing technology to understand the context of the investigative records and perform more accurate analysis.

[0050] (Example 2) The investigative materials utilization system according to an embodiment of the present invention is a system that efficiently collects investigative materials such as reports prepared for each case and analyzes them using a generation AI. As a result, the investigative materials utilization system can clarify the characteristics and occurrence conditions of each crime and utilize them in crime prevention measures.

[0051] An investigation-related material utilization system according to an embodiment includes an information security system, a record collection system, and a generation AI. The information security system encrypts investigation-related records collected from multiple investigation agencies. For example, the information security system encrypts data using the AES encryption algorithm. The information security system can also protect data transmission and reception using the TLS protocol. The information security system can also record audit logs and detect unauthorized access. The record collection system collects investigation-related records from multiple investigation agencies. For example, the record collection system collects data from each investigation agency's database using an API. The record collection system can also collect publicly available investigation-related information using web scraping technology. The record collection system can also collect manually entered data. The generation AI analyzes the collected investigation-related records. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the investigation-related records and extract characteristics of a crime. The generation AI can also use a multimodal generation AI to integrate and analyze text data and image data. The generation AI can also use natural language processing technology to understand the context of the investigation-related records and perform more accurate analysis. As a result, the investigation-related material utilization system according to the embodiment can clarify the characteristics and occurrence conditions of each crime and utilize them for crime prevention measures. For example, the system can identify time periods and locations where crimes are likely to occur and effectively allocate police patrols. The system can also analyze the characteristics of crimes and provide clues that can lead to the identification and arrest of perpetrators.

[0052] The information security system can link with the systems of each investigative agency in real time, automatically encrypting and transferring investigative records to an independent system each time they are created. For example, the information security system can link with the systems of each investigative agency in real time, automatically encrypting and transferring investigative records to an independent system each time they are created. For example, as soon as an investigator prepares a report, the data is encrypted and sent to the independent system. The information security system can also transfer data in real time using WebSocket communication. The information security system can also exchange data with the systems of each investigative agency using API integration. This strengthens the security of investigative records and enables efficient collection.

[0053] An information security system can introduce blockchain technology to prevent the falsification of investigation-related records. For example, an information security system can introduce blockchain technology to prevent the falsification of investigation-related records. For example, each record can be registered on the blockchain, making it impossible to falsify. An information security system can also use a private blockchain to strengthen the security of investigation-related records. An information security system can also use blockchain technology to ensure data integrity. This can prevent the falsification of investigation-related records and ensure their reliability.

[0054] An information security system can use an emotion estimation function to monitor the stress level of a system user and issue a warning before a security risk increases. For example, an information security system can use an emotion estimation function to monitor the stress level of a system user and issue a warning before a security risk increases. For example, the system can analyze the user's facial expression or voice and issue a warning if the stress level increases. The information security system can also measure the heart rate and galvanic skin response to evaluate the stress level. The information security system can also temporarily restrict system access privileges if the stress level increases. This makes it possible to monitor the stress level of a system user and prevent security risks before they occur.

[0055] Record collection systems can incorporate voice recognition technology to enable the collection of investigative records through voice input. Record collection systems can incorporate voice recognition technology to enable the collection of investigative records through voice input. For example, an investigator can input a report by voice, and the voice data is automatically converted to text. Record collection systems can also achieve highly accurate voice recognition using deep learning models. Record collection systems can also use voice codecs to compress and efficiently collect voice data. This makes it possible to collect investigative records through voice input, resulting in efficient data collection.

[0056] The record collection system can add an automatic classification function for investigation-related records and set an appropriate security level according to the type and importance of the record. The record collection system can, for example, add an automatic classification function for investigation-related records and set an appropriate security level according to the type and importance of the record. For example, more stringent security measures can be applied to records of higher importance. The record collection system can also automatically classify the type and importance of the record using a machine learning model. The record collection system can also classify records using a rule-based classification algorithm. This makes it possible to set an appropriate security level according to the type and importance of the investigation-related record.

[0057] The record collection system can use the emotion estimation function to automatically identify particularly sensitive information based on the content of investigation-related records and apply additional security measures. The record collection system can, for example, use the emotion estimation function to automatically identify particularly sensitive information based on the content of investigation-related records and apply additional security measures. For example, highly sensitive information can be identified through emotion analysis and strengthened encryption. The record collection system can also strengthen access control for highly sensitive information. The record collection system can also automatically classify highly sensitive information and set an appropriate security level. This makes it possible to automatically identify highly sensitive information and apply additional security measures.

[0058] The record collection system can automate the record collection process of each investigative agency so that each investigative record is immediately uploaded to an independent system whenever it is created. The record collection system, for example, can automate the record collection process of each investigative agency so that each investigative record is immediately uploaded to an independent system whenever it is created. For example, as soon as an investigator creates a report, the data is automatically uploaded to the system. The record collection system can also automate the data collection flow using process automation tools. The record collection system can also monitor the data collection process and issue an alert if an abnormality occurs. This makes it possible to automate the process of collecting investigative records and upload data efficiently.

[0059] The record collection system may unify the data formats of each investigative agency and introduce standardized protocols to ensure data consistency. For example, the record collection system may unify the data formats of each investigative agency and introduce standardized protocols to ensure data consistency. For example, each investigative agency may enter data in the same format. The record collection system may also use standardized data formats such as JSON or XML. The record collection system may also introduce API standards and unify data exchange protocols. This ensures data consistency and enables efficient data management.

[0060] The recording collection system can use an emotion estimation function to analyze the emotional state of an investigator during recording collection and pause the collection process if stress or fatigue is increasing. The recording collection system, for example, can use the emotion estimation function to analyze the emotional state of an investigator during recording collection and pause the collection process if stress or fatigue is increasing. For example, the recording collection system can analyze the investigator's facial expressions and voice and pause collection if the stress level is increasing. The recording collection system can also measure heart rate and galvanic skin response to evaluate stress levels. The recording collection system can also temporarily pause the collection process and prompt the investigator to take a break if the stress level is increasing. This makes it possible to monitor the investigator's stress and fatigue and pause the collection process at the appropriate time.

[0061] Record collection systems can be cloud-based, enabling data collection over the internet. For example, record collection systems can be cloud-based, enabling data collection over the internet. For example, they can allow investigators to upload data directly from the field to the cloud. Record collection systems can also use cloud services such as AWS, Azure, or Google Cloud to securely store data. Record collection systems can also implement security measures such as VPNs and SSL / TLS to protect data transmission over the internet. This allows cloud-based systems to collect data over the internet.

[0062] The record collection system can automatically highlight particularly important information based on the content of records collected using an emotion estimation function, thereby encouraging a rapid response. The record collection system can, for example, automatically highlight particularly important information based on the content of records collected using an emotion estimation function, thereby encouraging a rapid response. For example, emotion analysis can be used to identify information with a high degree of urgency, and the system can automatically issue a notification. The record collection system can also display important information in a color-coded manner, allowing investigators to respond quickly. The record collection system can also implement an alert system that immediately notifies investigators when important information is detected. This allows important information to be automatically highlighted, thereby encouraging a rapid response.

[0063] Generative AI can analyze time series data from investigative records to reveal crime occurrence patterns over time. For example, generative AI can analyze time series data from investigative records to reveal crime occurrence patterns over time. For example, it can analyze the tendency for crimes to occur frequently during specific time periods. Generative AI can also analyze the frequency of crimes using time-stamped data. Generative AI can also analyze crime trends using time series analysis algorithms. This can reveal crime occurrence patterns over time, which can be useful for preventive measures.

[0064] Generative AI can achieve highly accurate analysis by incorporating natural language processing technology and gaining a deep understanding of the context of investigative records. Generative AI can achieve highly accurate analysis by incorporating natural language processing technology and gaining a deep understanding of the context of investigative records. For example, it can analyze the meaning of sentences and extract characteristics of crimes. Generative AI can also analyze the structure of sentences using morphological analysis. Generative AI can also use context analysis algorithms to understand the context of sentences. This allows for a deep understanding of the context of investigative records and more accurate analysis.

[0065] Generative AI can use its emotion estimation function to analyze the emotions of victims and witnesses contained in investigative records to clarify the motives and background of crimes. For example, generative AI can use its emotion estimation function to analyze the emotions of victims and witnesses contained in investigative records to clarify the motives and background of crimes. For example, it can analyze the victim's emotional state and identify the motive for the crime. Generative AI can also analyze witness testimony and detect changes in emotion. Generative AI can also use an emotion estimation algorithm to evaluate the intensity and type of emotion. This makes it possible to analyze the emotions of victims and witnesses and clarify the motives and background of crimes.

[0066] Generative AI can analyze investigative records from different regions and countries to identify global crime trends. Generative AI can, for example, analyze investigative records from different regions and countries to identify global crime trends. For example, it can analyze common crime patterns in specific regions. Generative AI can also analyze crime trends using international crime databases. Generative AI can also use trend analysis algorithms to identify global crime trends. This allows it to analyze investigative records from different regions and countries to identify global crime trends.

[0067] Generative AI can analyze image data from investigative records and extract characteristics of crimes from visual information. For example, generative AI can analyze image data from investigative records and extract characteristics of crimes from visual information. For example, it can identify the modus operandi from crime scene photos. Generative AI can also analyze image data using image analysis algorithms. Generative AI can also integrate visual information with text data to perform more accurate analysis. This makes it possible to extract characteristics of crimes from visual information and conduct more multifaceted analysis.

[0068] The generative AI can use its emotion estimation function to collect investigators' emotional responses to the results of its analysis, thereby improving the reliability of the analysis results. For example, the generative AI can use its emotion estimation function to collect investigators' emotional responses to the results of its analysis, thereby improving the reliability of the analysis results. For example, it can analyze the investigators' emotional responses and evaluate the accuracy of the analysis results. The generative AI can also analyze the investigators' facial expressions and voices to detect changes in emotion. The generative AI can also incorporate the emotional responses into a feedback loop to improve the analysis algorithm. This makes it possible to collect investigators' emotional responses and improve the reliability of the analysis results.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The investigative materials utilization system can also be equipped with a voice recognition unit, allowing investigators to input records by voice. For example, investigators can input voice data at the scene, and the voice data is automatically converted to text. The voice recognition unit can also achieve highly accurate voice recognition using a deep learning model. Furthermore, the voice recognition unit can compress voice data and collect it efficiently. This makes it possible to collect investigative records by voice input, resulting in efficient data collection.

[0071] The investigative materials utilization system further includes an automatic classification unit that can automatically classify collected investigative records. For example, a machine learning model can be used to automatically classify the type and importance of the records. The automatic classification unit can also classify the records using a rule-based classification algorithm. Furthermore, the automatic classification unit can set an appropriate security level according to the classified records. This allows an appropriate security level to be set according to the type and importance of the investigative records.

[0072] The investigative materials utilization system can also use emotion estimation functionality to monitor the emotional state of investigators and pause the collection process if stress or fatigue increases. For example, it can analyze an investigator's facial expressions and voice and pause collection if stress levels increase. It can also measure heart rate and galvanic skin response to assess stress levels. Furthermore, if stress levels increase, it can temporarily pause the collection process and encourage the investigator to take a break. This makes it possible to monitor investigator stress and fatigue and pause the collection process at the appropriate time.

[0073] Investigative materials utilization systems can also be cloud-based, allowing data collection over the internet. For example, investigators can upload data directly from the field to the cloud. Data can also be stored securely using cloud services such as AWS, Azure, or Google Cloud. Security measures such as VPN and SSL / TLS can also be implemented to protect data transmission over the internet. This allows cloud-based systems to collect data over the internet.

[0074] The investigative materials utilization system can also use emotion estimation functionality to automatically highlight particularly important information based on the content of collected records, encouraging rapid response. For example, emotion analysis can be used to identify information with high urgency, and the system can automatically notify investigators. Important information can also be displayed in color, allowing investigators to respond quickly. Furthermore, an alert system can be implemented to immediately notify investigators when important information is detected. This allows important information to be automatically highlighted, encouraging rapid response.

[0075] The investigative materials utilization system can also analyze investigative records from different regions and countries to identify global crime trends. For example, it can analyze common crime patterns in specific regions. It can also analyze crime trends using international crime databases. Furthermore, it can use trend analysis algorithms to identify global crime trends. This makes it possible to analyze investigative records from different regions and countries to identify global crime trends.

[0076] The investigative materials utilization system can also use its emotion estimation function to analyze the emotions of victims and witnesses contained in investigative records and clarify the motives and background of crimes. For example, it can analyze the victim's emotional state to identify the motive for the crime. It can also analyze witness testimony to detect changes in emotion. Furthermore, it can use emotion estimation algorithms to evaluate the intensity and type of emotion. This makes it possible to analyze the emotions of victims and witnesses and clarify the motives and background of crimes.

[0077] The investigative materials utilization system can also analyze image data from investigative records and extract characteristics of crimes from visual information. For example, it can identify the modus operandi from crime scene photos. It can also analyze image data using image analysis algorithms. It can also integrate visual information with text data for more accurate analysis. This makes it possible to extract characteristics of crimes from visual information and conduct more multifaceted analysis.

[0078] The investigative materials utilization system can also use its emotion estimation function to collect investigators' emotional responses to the results analyzed by the generative AI, thereby improving the reliability of the analysis results. For example, it can analyze investigators' emotional responses and evaluate the accuracy of the analysis results. It can also analyze investigators' facial expressions and voice to detect changes in emotion. Furthermore, it can incorporate emotional responses into a feedback loop to improve the analysis algorithm. This allows it to collect investigators' emotional responses and improve the reliability of the analysis results.

[0079] The investigative materials utilization system can also unify the data formats of each investigative agency and introduce standardized protocols to ensure data consistency. For example, each investigative agency can input data in the same format. Standardized data formats such as JSON and XML can also be used. Furthermore, API standards can be introduced to unify data exchange protocols. This ensures data consistency and enables efficient data management.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The record collection system collects investigation-related records from multiple investigative agencies. For example, the record collection system uses an API to collect data from the databases of each investigative agency. The record collection system can also use web scraping technology to collect publicly available investigation-related information. It can also collect manually entered data. Step 2: The information security system encrypts the investigation-related records collected from multiple investigation agencies. For example, the information security system encrypts the data using the AES encryption algorithm. It can also protect data transmission and reception using the TLS protocol. It also has the ability to record audit logs and detect unauthorized access. Step 3: The generative AI analyzes the collected investigative records. For example, the generative AI uses text generation AI (e.g., LLM) to analyze the investigative records and extract characteristics of the crime. It can also use multimodal generative AI to integrate and analyze text data and image data. Furthermore, it can use natural language processing technology to understand the context of the investigative records and perform more accurate analysis.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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. Information security systems, a record collection system; Equipped with a generative AI, The information security system includes: Encrypt investigative records collected from multiple investigative agencies, The record collection system includes: Collecting the investigation-related records from multiple investigative agencies; The generated AI is Analyze the collected investigative records A system characterized by:

2. The information security system includes: It will be linked to the systems of each investigative agency in real time, and automatically encrypt and transfer the investigative records to an independent system whenever they are generated.

2. The system of claim 1.

3. The record collection system includes: Incorporating voice recognition technology to enable the collection of investigative records through voice input.

2. The system of claim 1.

4. The record collection system includes: Automate each agency's record collection process so that each such investigative record is immediately uploaded to an independent system as it is generated.

2. The system of claim 1.

5. The generated AI is Analyzing the time-series data of the investigation-related records will clarify the pattern of crime occurrence over time.

2. The system of claim 1.

6. The information security system includes: Monitor the stress levels of system users and issue warnings before security risks arise 2. The system of claim 1.

7. The record collection system includes: Automatically identify particularly sensitive information based on the content of the investigative records and apply additional security measures to it.

2. The system of claim 1.

8. The generated AI is Analyzing the emotions of victims and witnesses contained in the investigative records to clarify the motives and background of the crime 2. The system of claim 1.

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