system

Abstract

The system according to this embodiment aims to efficiently detect and promptly report criminal activity on the internet. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects data from the internet. The analysis unit analyzes the data collected by the collection unit. The detection unit detects criminal activity based on the data analyzed by the analysis unit. The notification unit notifies the criminal activity detected by the detection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently detect criminal acts on the Internet and report them promptly.

[0005] The system according to the embodiment aims to efficiently detect criminal acts on the Internet and report them promptly.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a reporting unit. The collection unit collects data on the Internet. The analysis unit analyzes the data collected by the collection unit. The detection unit detects criminal acts based on the data analyzed by the analysis unit. The reporting unit reports the criminal acts detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently detect and promptly report criminal activity on the internet. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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] [[ID=第十三条]] 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The crime detection system according to an embodiment of the present invention is a system that collects data from the internet, uses AI to analyze it to detect criminal activity, and automatically reports it. The crime detection system contributes to a safer society by collecting data from the internet, using AI to analyze it to detect criminal activity, and automatically reporting it. For example, the crime detection system collects data from the internet. In this case, data is collected from various internet platforms such as websites, social networking services (SNS), and forums. For example, SNS posts and comments, forum threads, etc., are targeted. This allows for the collection of a wide range of data. Next, the crime detection system uses AI to analyze the collected data. The AI ​​analyzes the collected data and detects criminal activity. For example, criminal activity can be identified based on specific keywords, phrases, images, etc. This allows for the rapid detection of criminal activity from a vast amount of data. Next, the crime detection system automatically reports the detected criminal activity. The AI ​​automatically reports the detected criminal activity to the relevant authorities and administrators. For example, if criminal activity is detected, the information can be notified to the police or internet service providers. This enables a rapid response. This helps to reduce and prevent online crime. By using AI, it becomes possible to analyze vast amounts of data that cannot be handled manually, enabling the rapid detection and reporting of criminal activity. This can create a safer internet environment and improve the safety of society as a whole. As a result, the criminal activity detection system can quickly detect and report criminal activity on the internet.

[0029] The criminal activity detection system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a reporting unit. The collection unit collects data from the internet. The collection unit collects data from, for example, websites, social networking services (SNS), forums, etc. The collection unit can collect, for example, SNS posts and comments, forum threads, etc. The collection unit can collect website data using, for example, a web crawler. The collection unit can collect SNS data using, for example, an API. The collection unit can collect data by, for example, scraping forum threads. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes, for example, specific keywords or phrases, images, etc. The analysis unit can analyze text data using, for example, text mining techniques. The analysis unit can analyze image data using, for example, image recognition techniques. The analysis unit can analyze data using, for example, machine learning algorithms. The detection unit detects criminal activity based on the data analyzed by the analysis unit. The detection unit identifies, for example, criminal activity. The detection unit can detect, for example, fraudulent activity. The detection unit can, for example, detect hacking activities. The detection unit can, for example, detect illegal transactions. The reporting unit reports criminal activities detected by the detection unit. The reporting unit reports detected criminal activities to the relevant authorities or administrators. The reporting unit can report using, for example, email. The reporting unit can report using, for example, telephone. The reporting unit can report using, for example, a dedicated application. Thus, the criminal activity detection system according to the embodiment can quickly detect and report criminal activities on the internet.

[0030] The data collection unit collects data from the internet. For example, it collects data from websites, social networking services (SNS), forums, and other similar sources. Specifically, the unit uses a web crawler to automatically collect website data. The web crawler traverses a specified list of URLs, analyzes the HTML structure of the pages, and extracts the necessary information. For example, it can collect the latest news articles from news sites and filter articles containing keywords related to crime. For SNS data collection, it utilizes APIs provided by each SNS. Through these APIs, it collects posts and comments related to specific hashtags and keywords and stores them in a database in real time. For forum thread collection, it employs scraping techniques. Scraping is a technique that analyzes the HTML structure of web pages and extracts specific elements (e.g., thread titles and post content). This allows for the collection of discussions and user comments on forums, which can then be used as data to detect signs of criminal activity. By combining these data collection methods, the unit can collect a wide range of data from diverse sources, improving the overall accuracy and reliability of the system. Furthermore, the unit can dynamically adjust the frequency and scope of data collection, enabling flexible responses to specific events and situations. For example, during large-scale events or disasters, the frequency of collecting relevant data can be increased, allowing for a quicker understanding of the situation.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes specific keywords, phrases, and images. Specifically, it analyzes text data using text mining techniques. Text mining is a technique that extracts useful information from text data using natural language processing techniques. For example, by extracting crime-related keywords and phrases from social media posts and comments, and performing frequency analysis and co-occurrence network analysis, it is possible to detect signs of criminal activity. Image recognition technology is used for analyzing image data. Image recognition technology is a technique that detects specific objects or scenes within an image using machine learning algorithms. For example, it can detect images indicating illegal goods transactions or violent acts. By combining these techniques, the analysis unit can analyze the collected data from multiple angles and quickly and accurately grasp signs of criminal activity. Furthermore, the analysis unit analyzes data using machine learning algorithms. Machine learning algorithms are techniques that learn from large amounts of data and detect patterns and anomalies. For example, by learning from past crime data and detecting similar patterns from newly collected data, it is possible to predict potential criminal activity. The analysis unit can utilize these technologies to efficiently analyze the collected data and improve the overall accuracy and reliability of the system.

[0032] The detection unit detects criminal activity based on data analyzed by the analysis unit. Specifically, it identifies criminal activity based on information provided by the analysis unit. For example, to detect fraudulent activity, it analyzes the frequency and patterns of specific keywords and phrases to identify posts and comments that show signs of fraud. To detect hacking activity, it analyzes technical terms and specific behavioral patterns to identify data that shows signs of hacking. To detect illegal transactions, it analyzes keywords and images related to the transaction to identify data indicating the transaction of illegal goods. Based on these analysis results, the detection unit can quickly identify data that is likely to be criminal activity and notify the relevant authorities and administrators. Furthermore, the detection unit can improve the accuracy of criminal activity detection using machine learning algorithms. For example, by learning from past criminal data and detecting similar patterns from newly collected data, it can predict potential criminal activity. This allows the detection unit to detect criminal activity in real time and respond quickly.

[0033] The reporting unit reports criminal activity detected by the detection unit. Specifically, it reports detected criminal activity to the relevant authorities and administrators. For example, when reporting by email, it automatically generates an email containing detailed information about the detected criminal activity and sends it to the specified address. When reporting by telephone, it uses an automated voice response system to provide detailed information about the detected criminal activity by voice. When reporting using a dedicated application, it generates a notification within the application and provides information to the relevant authorities and administrators in real time. By combining these reporting methods, the reporting unit can transmit information quickly and reliably, supporting a rapid response to criminal activity. Furthermore, the reporting unit can collect feedback and optimize the reporting process to continuously improve the accuracy and effectiveness of reports. For example, it can review and improve the content of reports based on feedback from the authorities and administrators that receive the reports. In addition, the reporting unit can reliably transmit information using multiple communication methods. For example, it can use SMS and push notifications in addition to sending emails to ensure that important information is delivered reliably. This allows the reporting unit to report criminal activity quickly and reliably, supporting a rapid response by the relevant authorities and administrators.

[0034] The data collection unit can collect data from websites, social networking services (SNS), forums, and other sources. For example, the data collection unit can use a web crawler to collect website data. For example, the data collection unit can use an API to collect SNS data. For example, the data collection unit can scrape forum threads to collect data. This allows for the collection of a wide range of data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can control a web crawler using AI to efficiently collect data.

[0035] The analysis unit can analyze specific keywords, phrases, images, etc. For example, the analysis unit can analyze text data using text mining techniques. For example, the analysis unit can extract specific keywords or phrases and detect criminal activity based on them. For example, the analysis unit can analyze image data using image recognition techniques. For example, the analysis unit can extract features from specific images and detect criminal activity based on them. For example, the analysis unit can analyze data using machine learning algorithms. For example, the analysis unit can learn patterns related to criminal activity and detect criminal activity based on them. This allows for the rapid detection of criminal activity from a vast amount of data. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not. For example, the analysis unit can analyze text data using generative AI and detect criminal activity.

[0036] The detection unit can identify criminal activity. For example, the detection unit can identify fraudulent activity. For example, the detection unit can identify fraudulent activity based on keywords or phrases related to fraudulent activity. For example, the detection unit can identify hacking activity. For example, the detection unit can identify hacking activity based on keywords or phrases related to hacking activity. For example, the detection unit can identify illegal transactions. For example, the detection unit can identify illegal transactions based on keywords or phrases related to illegal transactions. This makes it possible to accurately identify criminal activity. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can identify and report fraudulent activity using AI.

[0037] The reporting unit can report detected criminal activity to the relevant authorities or administrators. The reporting unit can report using, for example, email. The reporting unit can, for example, send details of detected criminal activity to the relevant authorities or administrators via email. The reporting unit can report using, for example, telephone. The reporting unit can, for example, communicate details of detected criminal activity to the relevant authorities or administrators by telephone. The reporting unit can report using, for example, a dedicated application. The reporting unit can, for example, notify the relevant authorities or administrators of details of detected criminal activity through a dedicated application. This enables a rapid response. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to automatically generate the content of the report and send it to the relevant authorities or administrators.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can prioritize data collection from specific platforms based on past data collection history. This enables efficient data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze past data collection history and select the optimal collection method.

[0039] The data collection unit can filter data based on specific regions and time zones during data collection. For example, the data collection unit can prioritize data collection from specific regions to detect region-specific criminal activity. For example, the data collection unit can target and collect data on criminal activity occurring during specific time zones. For example, the data collection unit can filter data based on regions and time zones to collect it efficiently. This enables efficient data collection by filtering data based on specific regions and time zones. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to filter data based on specific regions and time zones to collect it efficiently.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can collect highly relevant data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can analyze the user's movement patterns and prioritize the collection of highly relevant data. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media posts and collect relevant data. For example, the data collection unit can adjust the timing of data collection by considering the user's social media activity time. For example, the data collection unit can analyze the user's social media friendships and collect relevant data. This makes it possible to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can analyze the user's social media activity and collect relevant data using AI.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can classify the data category using AI and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis by referring to past data. For example, the analysis unit may optimally allocate analysis resources based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to evaluate the data collection timing and determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the relevance of the data and adjust the order of analysis.

[0046] The detection unit can improve detection accuracy by considering the interrelationships of data during detection. For example, the detection unit analyzes the interrelationships of data and prioritizes the detection of highly relevant data. For example, the detection unit can reduce false positives by considering the interrelationships of data. For example, the detection unit can optimize the detection algorithm based on the interrelationships of data. This improves detection accuracy by considering the interrelationships of data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can analyze the interrelationships of data using AI to improve detection accuracy.

[0047] The detection unit can perform detection while considering the attribute information of the data submitter. For example, the detection unit can improve the accuracy of detection based on the attribute information of the data submitter. For example, the detection unit can adjust the detection criteria by referring to the past behavioral history of the data submitter. For example, the detection unit can reduce false positives by considering the attribute information of the data submitter. As a result, the accuracy of detection is improved by considering the attribute information of the data submitter. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can improve the accuracy of detection by analyzing the attribute information of the data submitter using AI.

[0048] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit can prioritize the detection of criminal activity occurring in a specific area. For example, the detection unit can improve the accuracy of detection based on geographical distribution. For example, the detection unit can reduce false positives by considering geographical distribution. As a result, the accuracy of detection is improved by considering the geographical distribution of the data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can analyze the geographical distribution of the data using AI to improve the accuracy of detection.

[0049] The detection unit can improve detection accuracy by referring to relevant literature during detection. For example, the detection unit optimizes the detection algorithm based on relevant literature. For example, the detection unit can reduce false positives by referring to relevant literature. For example, the detection unit can adjust the detection criteria based on relevant literature. This improves detection accuracy by referring to relevant literature for the data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can improve detection accuracy by referring to relevant literature using AI.

[0050] The reporting department can select the optimal reporting method by referring to past reporting history when a report is made. For example, the reporting department can select the optimal reporting method based on past reporting history. For example, the reporting department can analyze past reporting history and optimize the frequency and timing of reports. For example, the reporting department can prioritize a specific reporting method by referring to past reporting history. This enables efficient reporting by selecting the optimal reporting method based on past reporting history. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can use AI to analyze past reporting history and select the optimal reporting method.

[0051] The reporting department can apply different reporting methods depending on the category of the criminal activity when a report is made. For example, in the case of cybercrime, the reporting department may report to a specialized cybercrime prevention department. For example, in the case of fraud, the reporting department may report to a financial institution or consumer protection agency. For example, in the case of harassment, the reporting department may report to the appropriate law enforcement agency or manager. This allows for a swift and appropriate response by applying the appropriate reporting method according to the category of the criminal activity. Some or all of the above processing in the reporting department may be performed using AI or not. For example, the reporting department may use AI to classify the category of criminal activity and apply the appropriate reporting method.

[0052] The reporting unit can adjust the order of reports based on the location where the crime occurred. For example, the reporting unit can select the most appropriate reporting destination based on the location where the crime occurred. For example, the reporting unit can adjust the order of reports according to the urgency of the location. For example, the reporting unit can select an appropriate reporting method considering the characteristics of the location. This allows for a quick and appropriate response by adjusting the order of reports based on the location where the crime occurred. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to analyze the location of the crime and adjust the order of reports.

[0053] The reporting unit can improve the accuracy of a report by referring to information from relevant law enforcement agencies when a report is made. For example, the reporting unit can optimize the content of the report based on information from relevant law enforcement agencies. For example, the reporting unit can adjust the timing of the report by referring to information from law enforcement agencies. For example, the reporting unit can select the means of reporting based on information from law enforcement agencies. This improves the accuracy of the report by referring to information from relevant law enforcement agencies. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to analyze information from law enforcement agencies and improve the accuracy of the report.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can prioritize data collection from specific platforms based on past data collection history. This enables efficient data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze past data collection history and select the optimal collection method.

[0056] The data collection unit can filter data based on specific regions and time zones during data collection. For example, the data collection unit can prioritize data collection from specific regions to detect region-specific criminal activity. For example, the data collection unit can target and collect data on criminal activity occurring during specific time zones. For example, the data collection unit can filter data based on regions and time zones to collect it efficiently. This enables efficient data collection by filtering data based on specific regions and time zones. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to filter data based on specific regions and time zones to collect it efficiently.

[0057] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can collect highly relevant data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can analyze the user's movement patterns and prioritize the collection of highly relevant data. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant data.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0059] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can classify the data category using AI and apply an appropriate analysis algorithm.

[0060] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis by referring to past data. For example, the analysis unit may optimally allocate analysis resources based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to evaluate the data collection timing and determine the priority of analysis.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects data from the internet. The collection unit collects data from websites, social networking services (SNS), forums, etc. The collection unit can collect, for example, SNS posts and comments, forum threads, etc. The collection unit can collect website data using, for example, a web crawler. The collection unit can collect SNS data using, for example, an API. The collection unit can collect data by, for example, scraping forum threads. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes, for example, specific keywords, phrases, images, etc. The analysis unit can analyze text data using, for example, text mining techniques. The analysis unit can analyze image data using, for example, image recognition techniques. The analysis unit can analyze data using, for example, machine learning algorithms. Step 3: The detection unit detects criminal activity based on the data analyzed by the analysis unit. The detection unit can, for example, identify criminal activity. The detection unit can, for example, detect fraudulent activity. The detection unit can, for example, detect hacking activity. The detection unit can, for example, detect illegal transactions. Step 4: The reporting unit reports the criminal activity detected by the detection unit. The reporting unit reports the detected criminal activity to the relevant authorities or administrators, for example. The reporting unit may report using, for example, email. The reporting unit may report using, for example, telephone. The reporting unit may report using, for example, a dedicated application.

[0063] (Example of form 2) The crime detection system according to an embodiment of the present invention is a system that collects data from the internet, uses AI to analyze it to detect criminal activity, and automatically reports it. The crime detection system contributes to a safer society by collecting data from the internet, using AI to analyze it to detect criminal activity, and automatically reporting it. For example, the crime detection system collects data from the internet. In this case, data is collected from various internet platforms such as websites, social networking services (SNS), and forums. For example, SNS posts and comments, forum threads, etc., are targeted. This allows for the collection of a wide range of data. Next, the crime detection system uses AI to analyze the collected data. The AI ​​analyzes the collected data and detects criminal activity. For example, criminal activity can be identified based on specific keywords, phrases, images, etc. This allows for the rapid detection of criminal activity from a vast amount of data. Next, the crime detection system automatically reports the detected criminal activity. The AI ​​automatically reports the detected criminal activity to the relevant authorities and administrators. For example, if criminal activity is detected, the information can be notified to the police or internet service providers. This enables a rapid response. This helps to reduce and prevent online crime. By using AI, it becomes possible to analyze vast amounts of data that cannot be handled manually, enabling the rapid detection and reporting of criminal activity. This can create a safer internet environment and improve the safety of society as a whole. As a result, the criminal activity detection system can quickly detect and report criminal activity on the internet.

[0064] The criminal activity detection system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a reporting unit. The collection unit collects data from the internet. The collection unit collects data from, for example, websites, social networking services (SNS), forums, etc. The collection unit can collect, for example, SNS posts and comments, forum threads, etc. The collection unit can collect website data using, for example, a web crawler. The collection unit can collect SNS data using, for example, an API. The collection unit can collect data by, for example, scraping forum threads. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes, for example, specific keywords or phrases, images, etc. The analysis unit can analyze text data using, for example, text mining techniques. The analysis unit can analyze image data using, for example, image recognition techniques. The analysis unit can analyze data using, for example, machine learning algorithms. The detection unit detects criminal activity based on the data analyzed by the analysis unit. The detection unit identifies, for example, criminal activity. The detection unit can detect, for example, fraudulent activity. The detection unit can, for example, detect hacking activities. The detection unit can, for example, detect illegal transactions. The reporting unit reports criminal activities detected by the detection unit. The reporting unit reports detected criminal activities to the relevant authorities or administrators. The reporting unit can report using, for example, email. The reporting unit can report using, for example, telephone. The reporting unit can report using, for example, a dedicated application. Thus, the criminal activity detection system according to the embodiment can quickly detect and report criminal activities on the internet.

[0065] The data collection unit collects data from the internet. For example, it collects data from websites, social networking services (SNS), forums, and other similar sources. Specifically, the unit uses a web crawler to automatically collect website data. The web crawler traverses a specified list of URLs, analyzes the HTML structure of the pages, and extracts the necessary information. For example, it can collect the latest news articles from news sites and filter articles containing keywords related to crime. For SNS data collection, it utilizes APIs provided by each SNS. Through these APIs, it collects posts and comments related to specific hashtags and keywords and stores them in a database in real time. For forum thread collection, it employs scraping techniques. Scraping is a technique that analyzes the HTML structure of web pages and extracts specific elements (e.g., thread titles and post content). This allows for the collection of discussions and user comments on forums, which can then be used as data to detect signs of criminal activity. By combining these data collection methods, the unit can collect a wide range of data from diverse sources, improving the overall accuracy and reliability of the system. Furthermore, the unit can dynamically adjust the frequency and scope of data collection, enabling flexible responses to specific events and situations. For example, during large-scale events or disasters, the frequency of collecting relevant data can be increased, allowing for a quicker understanding of the situation.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes specific keywords, phrases, and images. Specifically, it analyzes text data using text mining techniques. Text mining is a technique that extracts useful information from text data using natural language processing techniques. For example, by extracting crime-related keywords and phrases from social media posts and comments, and performing frequency analysis and co-occurrence network analysis, it is possible to detect signs of criminal activity. Image recognition technology is used for analyzing image data. Image recognition technology is a technique that detects specific objects or scenes within an image using machine learning algorithms. For example, it can detect images indicating illegal goods transactions or violent acts. By combining these techniques, the analysis unit can analyze the collected data from multiple angles and quickly and accurately grasp signs of criminal activity. Furthermore, the analysis unit analyzes data using machine learning algorithms. Machine learning algorithms are techniques that learn from large amounts of data and detect patterns and anomalies. For example, by learning from past crime data and detecting similar patterns from newly collected data, it is possible to predict potential criminal activity. The analysis unit can utilize these technologies to efficiently analyze the collected data and improve the overall accuracy and reliability of the system.

[0067] The detection unit detects criminal activity based on data analyzed by the analysis unit. Specifically, it identifies criminal activity based on information provided by the analysis unit. For example, to detect fraudulent activity, it analyzes the frequency and patterns of specific keywords and phrases to identify posts and comments that show signs of fraud. To detect hacking activity, it analyzes technical terms and specific behavioral patterns to identify data that shows signs of hacking. To detect illegal transactions, it analyzes keywords and images related to the transaction to identify data indicating the transaction of illegal goods. Based on these analysis results, the detection unit can quickly identify data that is likely to be criminal activity and notify the relevant authorities and administrators. Furthermore, the detection unit can improve the accuracy of criminal activity detection using machine learning algorithms. For example, by learning from past criminal data and detecting similar patterns from newly collected data, it can predict potential criminal activity. This allows the detection unit to detect criminal activity in real time and respond quickly.

[0068] The reporting unit reports criminal activity detected by the detection unit. Specifically, it reports detected criminal activity to the relevant authorities and administrators. For example, when reporting by email, it automatically generates an email containing detailed information about the detected criminal activity and sends it to the specified address. When reporting by telephone, it uses an automated voice response system to provide detailed information about the detected criminal activity by voice. When reporting using a dedicated application, it generates a notification within the application and provides information to the relevant authorities and administrators in real time. By combining these reporting methods, the reporting unit can transmit information quickly and reliably, supporting a rapid response to criminal activity. Furthermore, the reporting unit can collect feedback and optimize the reporting process to continuously improve the accuracy and effectiveness of reports. For example, it can review and improve the content of reports based on feedback from the authorities and administrators that receive the reports. In addition, the reporting unit can reliably transmit information using multiple communication methods. For example, it can use SMS and push notifications in addition to sending emails to ensure that important information is delivered reliably. This allows the reporting unit to report criminal activity quickly and reliably, supporting a rapid response by the relevant authorities and administrators.

[0069] The data collection unit can collect data from websites, social networking services (SNS), forums, and other sources. For example, the data collection unit can use a web crawler to collect website data. For example, the data collection unit can use an API to collect SNS data. For example, the data collection unit can scrape forum threads to collect data. This allows for the collection of a wide range of data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can control a web crawler using AI to efficiently collect data.

[0070] The analysis unit can analyze specific keywords, phrases, images, etc. For example, the analysis unit can analyze text data using text mining techniques. For example, the analysis unit can extract specific keywords or phrases and detect criminal activity based on them. For example, the analysis unit can analyze image data using image recognition techniques. For example, the analysis unit can extract features from specific images and detect criminal activity based on them. For example, the analysis unit can analyze data using machine learning algorithms. For example, the analysis unit can learn patterns related to criminal activity and detect criminal activity based on them. This allows for the rapid detection of criminal activity from a vast amount of data. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not. For example, the analysis unit can analyze text data using generative AI and detect criminal activity.

[0071] The detection unit can identify criminal activity. For example, the detection unit can identify fraudulent activity. For example, the detection unit can identify fraudulent activity based on keywords or phrases related to fraudulent activity. For example, the detection unit can identify hacking activity. For example, the detection unit can identify hacking activity based on keywords or phrases related to hacking activity. For example, the detection unit can identify illegal transactions. For example, the detection unit can identify illegal transactions based on keywords or phrases related to illegal transactions. This makes it possible to accurately identify criminal activity. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can identify and report fraudulent activity using AI.

[0072] The reporting unit can report detected criminal activity to the relevant authorities or administrators. The reporting unit can report using, for example, email. The reporting unit can, for example, send details of detected criminal activity to the relevant authorities or administrators via email. The reporting unit can report using, for example, telephone. The reporting unit can, for example, communicate details of detected criminal activity to the relevant authorities or administrators by telephone. The reporting unit can report using, for example, a dedicated application. The reporting unit can, for example, notify the relevant authorities or administrators of details of detected criminal activity through a dedicated application. This enables a rapid response. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to automatically generate the content of the report and send it to the relevant authorities or administrators.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. 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, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can estimate the user's emotions using an emotion engine and adjust the timing of data collection.

[0074] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can prioritize data collection from specific platforms based on past data collection history. This enables efficient data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze past data collection history and select the optimal collection method.

[0075] The data collection unit can filter data based on specific regions and time zones during data collection. For example, the data collection unit can prioritize data collection from specific regions to detect region-specific criminal activity. For example, the data collection unit can target and collect data on criminal activity occurring during specific time zones. For example, the data collection unit can filter data based on regions and time zones to collect it efficiently. This enables efficient data collection by filtering data based on specific regions and time zones. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to filter data based on specific regions and time zones to collect it efficiently.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This makes it possible to prioritize the collection of more important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can estimate the user's emotions using an emotion engine and determine the priority of data to collect.

[0077] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can collect highly relevant data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can analyze the user's movement patterns and prioritize the collection of highly relevant data. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant data.

[0078] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media posts and collect relevant data. For example, the data collection unit can adjust the timing of data collection by considering the user's social media activity time. For example, the data collection unit can analyze the user's social media friendships and collect relevant data. This makes it possible to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can analyze the user's social media activity and collect relevant data using AI.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This makes it possible to provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can estimate the user's emotions using an emotion engine and adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can classify the data category using AI and apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it becomes possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can estimate the user's emotions using an emotion engine and adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis by referring to past data. For example, the analysis unit may optimally allocate analysis resources based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to evaluate the data collection timing and determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the relevance of the data and adjust the order of analysis.

[0085] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, if the user is tense, the detection unit can perform detection using strict criteria. For example, if the user is relaxed, the detection unit can perform detection using flexible criteria. For example, if the user is in a hurry, the detection unit can set criteria for rapid detection. This allows for more appropriate detection by adjusting the detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can estimate the user's emotions using an emotion engine and adjust the detection criteria.

[0086] The detection unit can improve detection accuracy by considering the interrelationships of data during detection. For example, the detection unit analyzes the interrelationships of data and prioritizes the detection of highly relevant data. For example, the detection unit can reduce false positives by considering the interrelationships of data. For example, the detection unit can optimize the detection algorithm based on the interrelationships of data. This improves detection accuracy by considering the interrelationships of data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can analyze the interrelationships of data using AI to improve detection accuracy.

[0087] The detection unit can perform detection while considering the attribute information of the data submitter. For example, the detection unit can improve the accuracy of detection based on the attribute information of the data submitter. For example, the detection unit can adjust the detection criteria by referring to the past behavioral history of the data submitter. For example, the detection unit can reduce false positives by considering the attribute information of the data submitter. As a result, the accuracy of detection is improved by considering the attribute information of the data submitter. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can improve the accuracy of detection by analyzing the attribute information of the data submitter using AI.

[0088] The detection unit can estimate the user's emotions and adjust the order in which the detection results are displayed based on the estimated emotions. For example, if the user is tense, the detection unit can prioritize displaying important results. For example, if the user is relaxed, the detection unit can display detailed results in a sequential manner. For example, if the user is in a hurry, the detection unit can quickly display results that get straight to the point. By adjusting the order in which the detection results are displayed according to the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can estimate the user's emotions using an emotion engine and adjust the order in which the detection results are displayed.

[0089] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit can prioritize the detection of criminal activity occurring in a specific area. For example, the detection unit can improve the accuracy of detection based on geographical distribution. For example, the detection unit can reduce false positives by considering geographical distribution. As a result, the accuracy of detection is improved by considering the geographical distribution of the data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can analyze the geographical distribution of the data using AI to improve the accuracy of detection.

[0090] The detection unit can improve detection accuracy by referring to relevant literature during detection. For example, the detection unit optimizes the detection algorithm based on relevant literature. For example, the detection unit can reduce false positives by referring to relevant literature. For example, the detection unit can adjust the detection criteria based on relevant literature. This improves detection accuracy by referring to relevant literature for the data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can improve detection accuracy by referring to relevant literature using AI.

[0091] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a simple and quick reporting method. For example, if the user is relaxed, the reporting unit can provide a detailed reporting method. For example, if the user is in a hurry, the reporting unit can provide a quick reporting method. This allows for more appropriate reporting by adjusting the reporting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can estimate the user's emotions using an emotion engine and adjust the reporting method.

[0092] The reporting department can select the optimal reporting method by referring to past reporting history when a report is made. For example, the reporting department can select the optimal reporting method based on past reporting history. For example, the reporting department can analyze past reporting history and optimize the frequency and timing of reports. For example, the reporting department can prioritize a specific reporting method by referring to past reporting history. This enables efficient reporting by selecting the optimal reporting method based on past reporting history. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can use AI to analyze past reporting history and select the optimal reporting method.

[0093] The reporting department can apply different reporting methods depending on the category of the criminal activity when a report is made. For example, in the case of cybercrime, the reporting department may report to a specialized cybercrime prevention department. For example, in the case of fraud, the reporting department may report to a financial institution or consumer protection agency. For example, in the case of harassment, the reporting department may report to the appropriate law enforcement agency or manager. This allows for a swift and appropriate response by applying the appropriate reporting method according to the category of the criminal activity. Some or all of the above processing in the reporting department may be performed using AI or not. For example, the reporting department may use AI to classify the category of criminal activity and apply the appropriate reporting method.

[0094] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit may prioritize high-priority reports. For example, if the user is relaxed, the reporting unit may prioritize detailed reports. For example, if the user is in a hurry, the reporting unit may prioritize methods that allow for quick reporting. This makes it possible to prioritize more important reports by determining the priority of reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can estimate the user's emotions using an emotion engine and determine the priority of reports.

[0095] The reporting unit can adjust the order of reports based on the location where the crime occurred. For example, the reporting unit can select the most appropriate reporting destination based on the location where the crime occurred. For example, the reporting unit can adjust the order of reports according to the urgency of the location. For example, the reporting unit can select an appropriate reporting method considering the characteristics of the location. This allows for a quick and appropriate response by adjusting the order of reports based on the location where the crime occurred. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to analyze the location of the crime and adjust the order of reports.

[0096] The reporting unit can improve the accuracy of a report by referring to information from relevant law enforcement agencies when a report is made. For example, the reporting unit can optimize the content of the report based on information from relevant law enforcement agencies. For example, the reporting unit can adjust the timing of the report by referring to information from law enforcement agencies. For example, the reporting unit can select the means of reporting based on information from law enforcement agencies. This improves the accuracy of the report by referring to information from relevant law enforcement agencies. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to analyze information from law enforcement agencies and improve the accuracy of the report.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. 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, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can estimate the user's emotions using an emotion engine and adjust the timing of data collection.

[0099] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most effective collection method from past data collection history. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can prioritize data collection from specific platforms based on past data collection history. This enables efficient data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze past data collection history and select the optimal collection method.

[0100] The data collection unit can filter data based on specific regions and time zones during data collection. For example, the data collection unit can prioritize data collection from specific regions to detect region-specific criminal activity. For example, the data collection unit can target and collect data on criminal activity occurring during specific time zones. For example, the data collection unit can filter data based on regions and time zones to collect it efficiently. This enables efficient data collection by filtering data based on specific regions and time zones. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to filter data based on specific regions and time zones to collect it efficiently.

[0101] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This makes it possible to prioritize the collection of more important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can estimate the user's emotions using an emotion engine and determine the priority of data to collect.

[0102] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can collect highly relevant data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can analyze the user's movement patterns and prioritize the collection of highly relevant data. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant data.

[0103] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This makes it possible to provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can estimate the user's emotions using an emotion engine and adjust the presentation of the analysis.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0105] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, highly accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can classify the data category using AI and apply an appropriate analysis algorithm.

[0106] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it becomes possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can estimate the user's emotions using an emotion engine and adjust the length of the analysis.

[0107] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis by referring to past data. For example, the analysis unit may optimally allocate analysis resources based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to evaluate the data collection timing and determine the priority of analysis.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects data from the internet. The collection unit collects data from websites, social networking services (SNS), forums, etc. The collection unit can collect, for example, SNS posts and comments, forum threads, etc. The collection unit can collect website data using, for example, a web crawler. The collection unit can collect SNS data using, for example, an API. The collection unit can collect data by, for example, scraping forum threads. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes, for example, specific keywords, phrases, images, etc. The analysis unit can analyze text data using, for example, text mining techniques. The analysis unit can analyze image data using, for example, image recognition techniques. The analysis unit can analyze data using, for example, machine learning algorithms. Step 3: The detection unit detects criminal activity based on the data analyzed by the analysis unit. The detection unit can, for example, identify criminal activity. The detection unit can, for example, detect fraudulent activity. The detection unit can, for example, detect hacking activity. The detection unit can, for example, detect illegal transactions. Step 4: The reporting unit reports the criminal activity detected by the detection unit. The reporting unit reports the detected criminal activity to the relevant authorities or administrators, for example. The reporting unit may report using, for example, email. The reporting unit may report using, for example, telephone. The reporting unit may report using, for example, a dedicated application.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect data from the internet using the communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects criminal activity based on the analyzed data. The notification unit is implemented in the control unit 46A of the smart device 14 and notifies the relevant authorities or administrators of the detected criminal activity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect data from the internet using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The detection unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and detects criminal activity based on the analyzed data. The notification unit is implemented, for example, in the control unit 46A of the smart glasses 214, and notifies the detected criminal activity to the relevant authorities or administrators. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect data from the internet using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12 and detects criminal activity based on the analyzed data. The notification unit is implemented in the control unit 46A of the headset terminal 314 and notifies the relevant authorities or administrators of the detected criminal activity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect data from the internet using the communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and detects criminal activity based on the analyzed data. The notification unit is implemented, for example, by the control unit 46A of the robot 414, and notifies the relevant authorities or administrators of the detected criminal activity. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A data collection unit that collects data from the internet, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit that detects criminal activity based on data analyzed by the aforementioned analysis unit, The system includes a reporting unit that reports criminal acts detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from websites, social media, forums, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze specific keywords, phrases, images, etc. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is Identifying criminal activity The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, Report detected criminal activity to the relevant authorities or administrators. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on specific regions and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is During detection, the accuracy of the detection is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is During detection, the data submitter's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is It estimates the user's sentiment and adjusts the order in which the detection results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is During detection, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is During detection, we improve detection accuracy by referring to relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, We estimate the user's emotions and adjust the reporting method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, When a report is made, the system will refer to past reporting history to select the most appropriate reporting method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, When reporting a crime, different reporting methods will be applied depending on the category of the criminal act. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, When reporting, the order of reports will be adjusted based on the location where the crime occurred. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, When making a report, we refer to information from relevant law enforcement agencies to improve the accuracy of the report. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from the internet, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit that detects criminal activity based on data analyzed by the aforementioned analysis unit, The system includes a reporting unit that reports criminal acts detected by the aforementioned detection unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from websites, social media, forums, etc. The system according to feature 1.

3. The aforementioned analysis unit, Analyze specific keywords, phrases, images, etc. The system according to feature 1.

4. The detection unit is Identifying criminal activity The system according to feature 1.

5. The aforementioned reporting unit, Report detected criminal activity to the relevant authorities or administrators. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on specific regions and time periods. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system according to feature 1.

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