system

Abstract

The system according to this embodiment aims to automatically collect and report criminal information, and to facilitate information aggregation and lower the barrier to reporting. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, and a labeling unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The notification unit notifies the information analyzed by the analysis unit. The labeling unit labels and pages the information notified by the notification 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 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 prior art, since the collection and reporting of criminal information are performed manually, there are problems that it takes time for information aggregation and reporting, and the psychological hurdle is high.

[0005] The system according to the embodiment aims to automatically collect and report criminal information and lower the threshold for information aggregation and reporting.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, and a labeling unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The notification unit notifies the information analyzed by the analysis unit. The labeling unit labels and pages the information notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically collect and report crime information, and can facilitate information aggregation and lower the barrier to reporting. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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] 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 information aggregation system according to an embodiment of the present invention is a system in which an AI agent automatically aggregates "fragments of crime information" captured by individuals' smartphones, dashcams, and surveillance cameras, and even makes a report. This crime information aggregation system can automatically update crime information and lower the psychological barrier for ordinary citizens to report to the police. Specifically, two agents are created: a "reporting AI agent" app that aggregates information from users and makes a report, and an "autonomous violation diagnosis AI agent" that aggregates reports and labels and pages the content. The information is aggregated on a platform and provided to the user. First, the AI ​​agent automatically collects "fragments of crime information" captured by individuals' smartphones, dashcams, and surveillance cameras. For example, this includes information such as suspicious behavior in the city and vehicle license plates. This information is analyzed by the AI ​​agent to identify signs and patterns of crime. Next, the analyzed information is reported to the police through the "reporting AI agent" app. This app is designed so that users can easily provide information, and the report is completed simply by uploading photos and videos. The AI ​​agent automatically formats the provided information and sends it to the police in an appropriate format. Furthermore, reported information is aggregated by an "autonomous violation diagnosis AI agent" and labeled and compiled into content pages. This agent automatically classifies the reported information and organizes it by type, location, and time of the crime. This allows users to easily search and view crime information. The AI ​​agent also detects signs of crime based on the collected information and issues alerts to prevent it. For example, if suspicious behavior is frequent in a particular area, it will warn residents of that area. In this way, it contributes to crime prevention. This system lowers the psychological barrier for individuals to report crimes to the police and streamlines the collection and reporting of crime information. In addition, because it can detect signs of crime based on the collected information and prevent it, the safety of the area is improved. As a result, the crime information aggregation system allows the AI ​​agent to automatically aggregate "fragments of crime information" captured by individuals' smartphones, dashcams, and surveillance cameras, and even report them.

[0029] The crime information aggregation system according to the embodiment comprises a collection unit, an analysis unit, a reporting unit, and a labeling unit. The collection unit collects information. The collection unit can collect information from, for example, personal smartphones, dashcams, and surveillance cameras. The collection unit can collect information such as suspicious behavior in the city and vehicle license plates. The collection unit can also collect information using sensors, for example. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, data mining techniques. The analysis unit can also analyze the information using, for example, machine learning algorithms. The analysis unit can identify signs and patterns of crime based on the collected information. The reporting unit reports the information analyzed by the analysis unit. The reporting unit can, for example, automatically format the information provided by the user and send it to the police in an appropriate format. The reporting unit can report the information by methods such as email, SMS, and app notifications. The reporting unit can also, for example, convert the format of the information and send it to the police. The labeling unit labels and creates pages from information reported by the reporting unit. The labeling unit can, for example, automatically classify the content of reports and organize information such as the type, location, and time of the crime. The labeling unit can organize information using methods such as tagging, categorizing, and page layout. The labeling unit can, for example, detect signs of crime based on the collected information and issue alerts to prevent crimes from occurring. As a result, the crime information aggregation system according to this embodiment can efficiently collect, analyze, report, and label / create pages from information.

[0030] The data collection unit collects information. For example, it can collect information from personal smartphones, dashcams, and surveillance cameras. Specifically, GPS data, photos, videos, and audio recordings are collected from personal smartphones. This allows for a detailed understanding of the situation at a specific location and time. Dashcams collect video and audio recordings taken while the vehicle is in motion, recording traffic accidents and suspicious vehicle movements. Surveillance cameras collect video from public places and commercial facilities, monitoring criminal activity and suspicious behavior. Furthermore, the data collection unit can also collect information using sensors. For example, audio sensors can detect unusual sounds or shouts, and vibration sensors can detect suspicious movements. These sensors are installed at building entrances, windows, parking lots, etc., and collect data in real time. The collected information is transmitted to a central database, making it accessible to the analysis unit. The data collection unit centrally manages the information obtained from these diverse devices and sensors and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, allowing the analysis unit and reporting unit to access it in real time. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Specifically, it can use data mining techniques to extract useful patterns and relationships from large amounts of data. For example, it can analyze the frequency, timing, and type of crime in a specific area to understand crime trends. The analysis unit can also analyze information using machine learning algorithms. Machine learning algorithms build models based on past data and analyze newly collected data to identify signs and patterns of crime. For example, they can detect suspicious behavior from surveillance camera footage and track the movements of specific individuals or vehicles. They can also analyze audio data to detect unusual sounds or shouts, enabling early detection of emergencies. Furthermore, the analysis unit can identify signs and patterns of crime based on the collected information. For example, it can analyze suspicious behavior and vehicle movements in a specific area to predict places and times when crimes are likely to occur. This allows the analysis unit to quickly and accurately analyze collected data and grasp the risk of crime in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas or time periods based on past crime data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The reporting unit reports information analyzed by the analysis unit. For example, the reporting unit can automatically format user-provided information and send it to the police in an appropriate format. Specifically, it converts the format of collected information to enable the police to respond quickly. For instance, it converts surveillance camera footage and dashcam footage into an appropriate format and sends it to the police. The reporting unit can also report information via email, SMS, and app notifications. This allows the police to receive information quickly and take appropriate action. Furthermore, the reporting unit can convert the format of information and send it to the police. For example, converting audio data to text and sending it to the police enables a quicker response. By combining these reporting methods, the reporting unit can reliably transmit information and support a rapid response. Additionally, the reporting unit can collect user feedback and continuously improve the accuracy and effectiveness of reports. For example, it can review and improve report content based on feedback from the police who receive the reports. The reporting unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the reporting department to provide information to users quickly and reliably, minimizing the risk of crime.

[0033] The labeling department labels and creates pages from information reported by the reporting department. For example, the labeling department can automatically classify reported information and organize it by specifying the type, location, and time of the crime. Specifically, it tags and categorizes reported information to facilitate searching and referencing. For instance, it tags information such as the type of crime, location, and time of occurrence and stores it in a database. This allows the police and related agencies to quickly search for necessary information and take appropriate action. Furthermore, the labeling department can detect signs of crime based on the collected information and issue alerts to prevent crime. For example, it analyzes suspicious behavior and vehicle movements in specific areas to predict locations and times where crimes are likely to occur and issues alerts to the police. This allows the police to respond quickly and strive to prevent crimes. The labeling department can organize information using methods such as tagging, categorization, and page layout. For example, it organizes information such as the type, location, and time of the crime based on reported information and stores it in a database. This allows the police and related agencies to quickly search for necessary information and take appropriate action. Furthermore, the labeling unit can detect signs of crime based on the collected information and issue alerts to prevent crime before it occurs. For example, it can analyze suspicious behavior and vehicle movements in a specific area to predict places and times when crime is likely to occur and issue alerts to the police. This allows the police to respond quickly and strive to prevent crime before it happens.

[0034] The analysis unit can identify signs and patterns of crime. For example, the analysis unit can analyze past crime data to identify signs and patterns of crime. For example, the analysis unit can identify signs of crime using abnormal behavior detection algorithms. For example, the analysis unit can identify crime patterns using machine learning algorithms. By identifying signs and patterns of crime, it contributes to crime prevention.

[0035] The reporting system can automatically format the information provided by the user and send it to the police in an appropriate format. For example, the reporting system can convert the format of the information and send it to the police. For example, the reporting system can summarize the information and send it to the police. For example, the reporting system can format the information into a standard report format and send it to the police. This improves the efficiency of reporting by sending the information provided by the user to the police in an appropriate format.

[0036] The labeling unit can automatically classify reported information and organize information such as the type of crime, location, and time. The labeling unit can classify reported information using, for example, machine learning algorithms. It can also classify reported information using, for example, rule-based classification algorithms. Furthermore, the labeling unit can tag and categorize reported information. This organization of reported information makes it easier to search and view crime information.

[0037] The labeling unit can detect signs of crime based on collected information and issue alerts to prevent crime. For example, the labeling unit can detect signs of crime using an abnormal behavior detection algorithm. It can also detect signs of crime using a pattern recognition algorithm. The labeling unit can issue alerts based on collected information. This improves community safety by detecting signs of crime and issuing alerts to prevent it.

[0038] The labeling unit can alert residents of a particular area if suspicious behavior is frequently occurring in that area. For example, the labeling unit can detect unusual movement patterns and alert residents of that area. For example, the labeling unit can analyze the frequency of specific behaviors and alert residents of that area. For example, the labeling unit can issue alerts based on collected information. This improves the safety of a community by alerting residents when suspicious behavior is frequently occurring in that area.

[0039] The data collection unit can analyze the user's past reporting history and select the optimal collection method during collection. For example, the data collection unit can prioritize collecting similar information based on the type of information the user has previously reported. For example, the data collection unit can collect information during the time period when the user previously reported. For example, the data collection unit can collect information around the location where the user previously reported. This allows the optimal collection method to be selected by analyzing the user's past reporting history.

[0040] The data collection unit can filter data based on the user's current location and activity status during collection. For example, if the user is in a specific region, the data collection unit can prioritize collecting information about that region. For example, if the user is on the move, the data collection unit can collect information along their travel route. For example, if the user is performing a specific activity, the data collection unit can collect information related to that activity. This allows for the collection of more relevant information by filtering data based on the user's current location and activity status.

[0041] The data collection unit can analyze a user's social media activity during collection and prioritize the collection of relevant information. For example, if a user mentions a specific event on social media, the data collection unit can prioritize the collection of information related to that event. For example, if a user mentions a specific region on social media, the data collection unit can prioritize the collection of information related to that region. For example, if a user mentions a specific person on social media, the data collection unit can prioritize the collection of information related to that person. In this way, by analyzing a user's social media activity, relevant information can be collected preferentially.

[0042] The data collection unit can supplement information by utilizing the sensor data of the user's device during collection. For example, the data collection unit can use the GPS data of the user's smartphone to collect location-based information. For example, the data collection unit can also use the camera data of the user's smartphone to collect visual information. For example, the data collection unit can also use the microphone data of the user's smartphone to collect audio information. In this way, information can be supplemented by utilizing the sensor data of the user's device.

[0043] The analysis unit can analyze current information by referencing past crime data during the analysis process. For example, the analysis unit can identify patterns similar to current information based on past crime data. For example, the analysis unit can also evaluate the credibility of current information based on past crime data. For example, the analysis unit can determine the importance of current information based on past crime data. As a result, the accuracy of the analysis of current information is improved by referring to past crime data.

[0044] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, the analysis unit can apply a vehicle license plate recognition algorithm to traffic violation information. For example, the analysis unit can apply a facial recognition algorithm to suspicious person information. For example, the analysis unit can apply an item feature recognition algorithm to stolen information. By applying different analysis algorithms to each category of information, the accuracy of the analysis is improved.

[0045] The analysis unit can determine the priority of analysis based on when the information was submitted. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can postpone the analysis of older information. For example, the analysis unit can prioritize the analysis of information submitted within a specific period. In this way, by determining the priority of analysis based on when the information was submitted, the latest information can be analyzed first.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. For example, the analysis unit can group relevant information together for analysis. This allows the analysis order to be adjusted based on the relevance of the information, thereby prioritizing the analysis of more relevant information.

[0047] The reporting unit can adjust the level of detail in a report based on the importance of the information. For example, the reporting unit can report highly important information in detail. For example, the reporting unit can report less important information concisely. For example, the reporting unit can report moderately important information with an appropriate level of detail. In this way, by adjusting the level of detail in a report based on the importance of the information, important information can be reported in detail.

[0048] The reporting unit can apply different reporting algorithms depending on the category of information when a report is made. For example, the reporting unit can apply a vehicle license plate recognition algorithm to traffic violation information. For example, the reporting unit can apply a facial recognition algorithm to suspicious person information. For example, the reporting unit can apply an item feature recognition algorithm to theft information. By applying the appropriate reporting algorithm according to the category of information, the accuracy of the report is improved.

[0049] The reporting department can prioritize reports based on when the information was submitted. For example, the reporting department can prioritize reporting the most recent information. For example, the reporting department can postpone reporting older information. For example, the reporting department can prioritize reporting information submitted within a specific period. This allows for prioritizing the reporting of the most recent information by determining the priority of reports based on when the information was submitted.

[0050] The reporting unit can adjust the order of reports based on the relevance of the information when reporting. For example, the reporting unit can prioritize reporting highly relevant information. For example, the reporting unit can postpone reporting less relevant information. For example, the reporting unit can group relevant information together and report it. This allows for prioritizing the reporting of more relevant information by adjusting the order of reports based on the relevance of the information.

[0051] The labeling unit can improve the accuracy of labeling by considering the interrelationships of information during the labeling process. For example, the labeling unit can group related information and label it. For example, the labeling unit can analyze the interrelationships of information and assign appropriate labels. For example, the labeling unit can create a hierarchical structure of labels by considering the interrelationships of information. This improves the accuracy of labeling by considering the interrelationships of information.

[0052] The labeling unit can perform labeling while considering the attribute information of the information submitter. For example, the labeling unit can assign an appropriate label based on the submitter's occupation. For example, the labeling unit can assign an appropriate label based on the submitter's age. For example, the labeling unit can assign an appropriate label based on the submitter's place of residence. This allows for more appropriate labeling by considering the attribute information of the information submitter.

[0053] The labeling unit can perform labeling while considering the geographical distribution of information. For example, the labeling unit can group and label information related to a specific region. The labeling unit can also analyze geographical distribution and assign appropriate labels. Furthermore, the labeling unit can create a hierarchical structure of labels while considering geographical distribution. This allows for more appropriate labeling by considering the geographical distribution of information.

[0054] The labeling unit can improve the accuracy of labeling by referring to relevant literature during the labeling process. For example, the labeling unit can refer to relevant literature and assign appropriate labels. The labeling unit can also evaluate the credibility of information based on relevant literature. Furthermore, the labeling unit can create a hierarchical structure of labels, taking relevant literature into consideration. This improves the accuracy of labeling by referring to relevant literature.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze a user's past reporting history and select the optimal collection method. For example, it can prioritize collecting similar information based on the types of information the user has previously reported. It can also collect information during the time periods when the user previously reported incidents. Furthermore, it can collect information about the area surrounding a location based on the location where the user previously reported incidents. In this way, the optimal collection method can be selected by analyzing the user's past reporting history.

[0057] The data collection unit can filter data based on the user's current location and activity status during collection. For example, if a user is in a specific area, it can prioritize collecting information about that area. If a user is on the move, it can also collect information along their travel route. Furthermore, if a user is engaged in a specific activity, it can collect information related to that activity. This allows for the collection of more relevant information by filtering based on the user's current location and activity status.

[0058] The analysis unit can analyze current information by referencing past crime data during the analysis process. For example, it can identify patterns similar to current information based on past crime data. It can also evaluate the credibility of current information based on past crime data. Furthermore, it can determine the importance of current information based on past crime data. As a result, referencing past crime data improves the accuracy of the analysis of current information.

[0059] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, a vehicle license plate recognition algorithm can be applied to traffic violation information. A facial recognition algorithm can also be applied to suspicious person information. Furthermore, an item feature recognition algorithm can be applied to theft information. By applying different analysis algorithms to each category of information, the accuracy of the analysis is improved.

[0060] The reporting unit can adjust the level of detail in a report based on the importance of the information. For example, highly important information can be reported in detail. Less important information can be reported concisely. Furthermore, information of moderate importance can be reported with an appropriate level of detail. This allows important information to be reported in detail by adjusting the level of detail based on the importance of the information.

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

[0062] Step 1: The collection unit collects information. The collection unit can collect information from sources such as personal smartphones, dashcams, and surveillance cameras. It can also collect information on suspicious behavior in urban areas and vehicle license plates. Furthermore, it can collect information using sensors. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze the collected information using data mining techniques. It can also analyze the information using machine learning algorithms. Furthermore, it can identify signs and patterns of crime based on the collected information. Step 3: The reporting unit reports the information analyzed by the analysis unit. The reporting unit can, for example, automatically format the information provided by the user and send it to the police in an appropriate format. It can also report information via methods such as email, SMS, and app notifications. Furthermore, it can convert the format of the information and send it to the police. Step 4: The labeling unit labels and creates pages from the information reported by the reporting unit. The labeling unit can, for example, automatically classify the content of reports and organize information such as the type of crime, location, and time. It can also organize information using methods such as tagging, categorizing, and page layout. Furthermore, based on the collected information, it can detect signs of crime and issue alerts to prevent it from happening.

[0063] (Example of form 2) The crime information aggregation system according to an embodiment of the present invention is a system in which an AI agent automatically aggregates "fragments of crime information" captured by individuals' smartphones, dashcams, and surveillance cameras, and even makes a report. This crime information aggregation system can automatically update crime information and lower the psychological barrier for ordinary citizens to report to the police. Specifically, two agents are created: a "reporting AI agent" app that aggregates information from users and makes a report, and an "autonomous violation diagnosis AI agent" that aggregates reports and labels and pages the content. The information is aggregated on a platform and provided to the user. First, the AI ​​agent automatically collects "fragments of crime information" captured by individuals' smartphones, dashcams, and surveillance cameras. For example, this includes information such as suspicious behavior in the city and vehicle license plates. This information is analyzed by the AI ​​agent to identify signs and patterns of crime. Next, the analyzed information is reported to the police through the "reporting AI agent" app. This app is designed so that users can easily provide information, and the report is completed simply by uploading photos and videos. The AI ​​agent automatically formats the provided information and sends it to the police in an appropriate format. Furthermore, reported information is aggregated by an "autonomous violation diagnosis AI agent" and labeled and compiled into content pages. This agent automatically classifies the reported information and organizes it by type, location, and time of the crime. This allows users to easily search and view crime information. The AI ​​agent also detects signs of crime based on the collected information and issues alerts to prevent it. For example, if suspicious behavior is frequent in a particular area, it will warn residents of that area. In this way, it contributes to crime prevention. This system lowers the psychological barrier for individuals to report crimes to the police and streamlines the collection and reporting of crime information. In addition, because it can detect signs of crime based on the collected information and prevent it, the safety of the area is improved. As a result, the crime information aggregation system allows the AI ​​agent to automatically aggregate "fragments of crime information" captured by individuals' smartphones, dashcams, and surveillance cameras, and even report them.

[0064] The crime information aggregation system according to the embodiment comprises a collection unit, an analysis unit, a reporting unit, and a labeling unit. The collection unit collects information. The collection unit can collect information from, for example, personal smartphones, dashcams, and surveillance cameras. The collection unit can collect information such as suspicious behavior in the city and vehicle license plates. The collection unit can also collect information using sensors, for example. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, data mining techniques. The analysis unit can also analyze the information using, for example, machine learning algorithms. The analysis unit can identify signs and patterns of crime based on the collected information. The reporting unit reports the information analyzed by the analysis unit. The reporting unit can, for example, automatically format the information provided by the user and send it to the police in an appropriate format. The reporting unit can report the information by methods such as email, SMS, and app notifications. The reporting unit can also, for example, convert the format of the information and send it to the police. The labeling unit labels and creates pages from information reported by the reporting unit. The labeling unit can, for example, automatically classify the content of reports and organize information such as the type, location, and time of the crime. The labeling unit can organize information using methods such as tagging, categorizing, and page layout. The labeling unit can, for example, detect signs of crime based on the collected information and issue alerts to prevent crimes from occurring. As a result, the crime information aggregation system according to this embodiment can efficiently collect, analyze, report, and label / create pages from information.

[0065] The data collection unit collects information. For example, it can collect information from personal smartphones, dashcams, and surveillance cameras. Specifically, GPS data, photos, videos, and audio recordings are collected from personal smartphones. This allows for a detailed understanding of the situation at a specific location and time. Dashcams collect video and audio recordings taken while the vehicle is in motion, recording traffic accidents and suspicious vehicle movements. Surveillance cameras collect video from public places and commercial facilities, monitoring criminal activity and suspicious behavior. Furthermore, the data collection unit can also collect information using sensors. For example, audio sensors can detect unusual sounds or shouts, and vibration sensors can detect suspicious movements. These sensors are installed at building entrances, windows, parking lots, etc., and collect data in real time. The collected information is transmitted to a central database, making it accessible to the analysis unit. The data collection unit centrally manages the information obtained from these diverse devices and sensors and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, allowing the analysis unit and reporting unit to access it in real time. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Specifically, it can use data mining techniques to extract useful patterns and relationships from large amounts of data. For example, it can analyze the frequency, timing, and type of crime in a specific area to understand crime trends. The analysis unit can also analyze information using machine learning algorithms. Machine learning algorithms build models based on past data and analyze newly collected data to identify signs and patterns of crime. For example, they can detect suspicious behavior from surveillance camera footage and track the movements of specific individuals or vehicles. They can also analyze audio data to detect unusual sounds or shouts, enabling early detection of emergencies. Furthermore, the analysis unit can identify signs and patterns of crime based on the collected information. For example, it can analyze suspicious behavior and vehicle movements in a specific area to predict places and times when crimes are likely to occur. This allows the analysis unit to quickly and accurately analyze collected data and grasp the risk of crime in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas or time periods based on past crime data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The reporting unit reports information analyzed by the analysis unit. For example, the reporting unit can automatically format user-provided information and send it to the police in an appropriate format. Specifically, it converts the format of collected information to enable the police to respond quickly. For instance, it converts surveillance camera footage and dashcam footage into an appropriate format and sends it to the police. The reporting unit can also report information via email, SMS, and app notifications. This allows the police to receive information quickly and take appropriate action. Furthermore, the reporting unit can convert the format of information and send it to the police. For example, converting audio data to text and sending it to the police enables a quicker response. By combining these reporting methods, the reporting unit can reliably transmit information and support a rapid response. Additionally, the reporting unit can collect user feedback and continuously improve the accuracy and effectiveness of reports. For example, it can review and improve report content based on feedback from the police who receive the reports. The reporting unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the reporting department to provide information to users quickly and reliably, minimizing the risk of crime.

[0068] The labeling department labels and creates pages from information reported by the reporting department. For example, the labeling department can automatically classify reported information and organize it by specifying the type, location, and time of the crime. Specifically, it tags and categorizes reported information to facilitate searching and referencing. For instance, it tags information such as the type of crime, location, and time of occurrence and stores it in a database. This allows the police and related agencies to quickly search for necessary information and take appropriate action. Furthermore, the labeling department can detect signs of crime based on the collected information and issue alerts to prevent crime. For example, it analyzes suspicious behavior and vehicle movements in specific areas to predict locations and times where crimes are likely to occur and issues alerts to the police. This allows the police to respond quickly and strive to prevent crimes. The labeling department can organize information using methods such as tagging, categorization, and page layout. For example, it organizes information such as the type, location, and time of the crime based on reported information and stores it in a database. This allows the police and related agencies to quickly search for necessary information and take appropriate action. Furthermore, the labeling unit can detect signs of crime based on the collected information and issue alerts to prevent crime before it occurs. For example, it can analyze suspicious behavior and vehicle movements in a specific area to predict places and times when crime is likely to occur and issue alerts to the police. This allows the police to respond quickly and strive to prevent crime before it happens.

[0069] The analysis unit can identify signs and patterns of crime. For example, the analysis unit can analyze past crime data to identify signs and patterns of crime. For example, the analysis unit can identify signs of crime using abnormal behavior detection algorithms. For example, the analysis unit can identify crime patterns using machine learning algorithms. By identifying signs and patterns of crime, it contributes to crime prevention.

[0070] The reporting system can automatically format the information provided by the user and send it to the police in an appropriate format. For example, the reporting system can convert the format of the information and send it to the police. For example, the reporting system can summarize the information and send it to the police. For example, the reporting system can format the information into a standard report format and send it to the police. This improves the efficiency of reporting by sending the information provided by the user to the police in an appropriate format.

[0071] The labeling unit can automatically classify reported information and organize information such as the type of crime, location, and time. The labeling unit can classify reported information using, for example, machine learning algorithms. It can also classify reported information using, for example, rule-based classification algorithms. Furthermore, the labeling unit can tag and categorize reported information. This organization of reported information makes it easier to search and view crime information.

[0072] The labeling unit can detect signs of crime based on collected information and issue alerts to prevent crime. For example, the labeling unit can detect signs of crime using an abnormal behavior detection algorithm. It can also detect signs of crime using a pattern recognition algorithm. The labeling unit can issue alerts based on collected information. This improves community safety by detecting signs of crime and issuing alerts to prevent it.

[0073] The labeling unit can alert residents of a particular area if suspicious behavior is frequently occurring in that area. For example, the labeling unit can detect unusual movement patterns and alert residents of that area. For example, the labeling unit can analyze the frequency of specific behaviors and alert residents of that area. For example, the labeling unit can issue alerts based on collected information. This improves the safety of a community by alerting residents when suspicious behavior is frequently occurring in that area.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can immediately collect information and quickly transmit it to the analysis unit. For example, if the user is relaxed, the data collection unit can periodically collect information and transmit it to the analysis unit. For example, if the user is excited, the data collection unit can frequently collect information and transmit it to the analysis unit. This allows for information to be collected at a more appropriate time by adjusting the timing of information 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data collection unit can analyze the user's past reporting history and select the optimal collection method during collection. For example, the data collection unit can prioritize collecting similar information based on the type of information the user has previously reported. For example, the data collection unit can collect information during the time period when the user previously reported. For example, the data collection unit can collect information around the location where the user previously reported. This allows the optimal collection method to be selected by analyzing the user's past reporting history.

[0076] The data collection unit can filter data based on the user's current location and activity status during collection. For example, if the user is in a specific region, the data collection unit can prioritize collecting information about that region. For example, if the user is on the move, the data collection unit can collect information along their travel route. For example, if the user is performing a specific activity, the data collection unit can collect information related to that activity. This allows for the collection of more relevant information by filtering data based on the user's current location and activity status.

[0077] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information of high urgency. For example, if the user is relaxed, the data collection unit can prioritize collecting general information. For example, if the user is excited, the data collection unit can prioritize collecting detailed information. This allows for the collection of more important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can analyze a user's social media activity during collection and prioritize the collection of relevant information. For example, if a user mentions a specific event on social media, the data collection unit can prioritize the collection of information related to that event. For example, if a user mentions a specific region on social media, the data collection unit can prioritize the collection of information related to that region. For example, if a user mentions a specific person on social media, the data collection unit can prioritize the collection of information related to that person. In this way, by analyzing a user's social media activity, relevant information can be collected preferentially.

[0079] The data collection unit can supplement information by utilizing the sensor data of the user's device during collection. For example, the data collection unit can use the GPS data of the user's smartphone to collect location-based information. For example, the data collection unit can also use the camera data of the user's smartphone to collect visual information. For example, the data collection unit can also use the microphone data of the user's smartphone to collect audio information. In this way, information can be supplemented by utilizing the sensor data of the user's device.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can perform a highly accurate analysis and provide detailed information. For example, if the user is relaxed, the analysis unit can perform an analysis with standard accuracy. For example, if the user is excited, the analysis unit can perform a rapid analysis and provide timely information. By adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can analyze current information by referencing past crime data during the analysis process. For example, the analysis unit can identify patterns similar to current information based on past crime data. For example, the analysis unit can also evaluate the credibility of current information based on past crime data. For example, the analysis unit can determine the importance of current information based on past crime data. As a result, the accuracy of the analysis of current information is improved by referring to past crime data.

[0082] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, the analysis unit can apply a vehicle license plate recognition algorithm to traffic violation information. For example, the analysis unit can apply a facial recognition algorithm to suspicious person information. For example, the analysis unit can apply an item feature recognition algorithm to stolen information. By applying different analysis algorithms to each category of information, the accuracy of the analysis is improved.

[0083] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can display the analysis results in detail. For example, if the user is relaxed, the analysis unit can display the analysis results concisely. For example, if the user is excited, the analysis unit can visually highlight the analysis results. By adjusting how the analysis results are displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on when the information was submitted. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can postpone the analysis of older information. For example, the analysis unit can prioritize the analysis of information submitted within a specific period. In this way, by determining the priority of analysis based on when the information was submitted, the latest information can be analyzed first.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. For example, the analysis unit can group relevant information together for analysis. This allows the analysis order to be adjusted based on the relevance of the information, thereby prioritizing the analysis of more relevant information.

[0086] The reporting unit can estimate the user's emotions and adjust the way the report is expressed based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit can describe the report in detail. For example, if the user is relaxed, the reporting unit can describe the report concisely. For example, if the user is excited, the reporting unit can emphasize the report. This allows for more appropriate reporting by adjusting the way the report is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reporting unit can adjust the level of detail in a report based on the importance of the information. For example, the reporting unit can report highly important information in detail. For example, the reporting unit can report less important information concisely. For example, the reporting unit can report moderately important information with an appropriate level of detail. In this way, by adjusting the level of detail in a report based on the importance of the information, important information can be reported in detail.

[0088] The reporting unit can apply different reporting algorithms depending on the category of information when a report is made. For example, the reporting unit can apply a vehicle license plate recognition algorithm to traffic violation information. For example, the reporting unit can apply a facial recognition algorithm to suspicious person information. For example, the reporting unit can apply an item feature recognition algorithm to theft information. By applying the appropriate reporting algorithm according to the category of information, the accuracy of the report is improved.

[0089] The reporting unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit can describe the report in detail. For example, if the user is relaxed, the reporting unit can describe the report concisely. For example, if the user is excited, the reporting unit can emphasize the report. This allows for more appropriate reporting by adjusting the length of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The reporting department can prioritize reports based on when the information was submitted. For example, the reporting department can prioritize reporting the most recent information. For example, the reporting department can postpone reporting older information. For example, the reporting department can prioritize reporting information submitted within a specific period. This allows for prioritizing the reporting of the most recent information by determining the priority of reports based on when the information was submitted.

[0091] The reporting unit can adjust the order of reports based on the relevance of the information when reporting. For example, the reporting unit can prioritize reporting highly relevant information. For example, the reporting unit can postpone reporting less relevant information. For example, the reporting unit can group relevant information together and report it. This allows for prioritizing the reporting of more relevant information by adjusting the order of reports based on the relevance of the information.

[0092] The labeling unit can estimate the user's emotions and adjust the labeling method based on the estimated emotions. For example, if the user is feeling anxious, the labeling unit can assign a detailed label. For example, if the user is relaxed, the labeling unit can assign a concise label. For example, if the user is excited, the labeling unit can assign an emphasized label. This allows for more appropriate labeling by adjusting the labeling 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The labeling unit can improve the accuracy of labeling by considering the interrelationships of information during the labeling process. For example, the labeling unit can group related information and label it. For example, the labeling unit can analyze the interrelationships of information and assign appropriate labels. For example, the labeling unit can create a hierarchical structure of labels by considering the interrelationships of information. This improves the accuracy of labeling by considering the interrelationships of information.

[0094] The labeling unit can perform labeling while considering the attribute information of the information submitter. For example, the labeling unit can assign an appropriate label based on the submitter's occupation. For example, the labeling unit can assign an appropriate label based on the submitter's age. For example, the labeling unit can assign an appropriate label based on the submitter's place of residence. This allows for more appropriate labeling by considering the attribute information of the information submitter.

[0095] The labeling unit can estimate the user's emotions and adjust the order in which the labeling results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the labeling unit can prioritize displaying important information. For example, if the user is relaxed, the labeling unit can prioritize displaying general information. For example, if the user is excited, the labeling unit can prioritize displaying visually highlighted information. This allows for the provision of more appropriate information by adjusting the order in which the labeling results are displayed 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The labeling unit can perform labeling while considering the geographical distribution of information. For example, the labeling unit can group and label information related to a specific region. The labeling unit can also analyze geographical distribution and assign appropriate labels. Furthermore, the labeling unit can create a hierarchical structure of labels while considering geographical distribution. This allows for more appropriate labeling by considering the geographical distribution of information.

[0097] The labeling unit can improve the accuracy of labeling by referring to relevant literature during the labeling process. For example, the labeling unit can refer to relevant literature and assign appropriate labels. The labeling unit can also evaluate the credibility of information based on relevant literature. Furthermore, the labeling unit can create a hierarchical structure of labels, taking relevant literature into consideration. This improves the accuracy of labeling by referring to relevant literature.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling anxious, information can be collected immediately and quickly transmitted to the analysis unit. If the user is relaxed, information can be collected periodically and transmitted to the analysis unit. Furthermore, if the user is excited, information can be collected frequently and transmitted to the analysis unit. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, it can perform a highly accurate analysis and provide detailed information. If the user is relaxed, it can perform an analysis with standard accuracy. Furthermore, if the user is excited, it can perform a rapid analysis and provide timely information. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The reporting unit can estimate the user's emotions and adjust the way the report is expressed based on those emotions. For example, if the user is feeling anxious, the report can be described in detail. If the user is relaxed, the report can be described concisely. Furthermore, if the user is excited, the report can be emphasized. By adjusting the way the report is expressed according to the user's emotions, more appropriate reports can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The labeling unit can estimate the user's emotions and adjust the labeling method based on the estimated emotions. For example, if the user is feeling anxious, a detailed label can be assigned. If the user is relaxed, a concise label can be assigned. Furthermore, if the user is excited, an emphasized label can be assigned. This allows for more appropriate labeling by adjusting the labeling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The labeling unit can estimate the user's emotions and adjust the order in which the labeling results are displayed based on the estimated emotions. For example, if the user is feeling anxious, important information can be displayed preferentially. If the user is relaxed, general information can be displayed preferentially. Furthermore, if the user is excited, visually highlighted information can be displayed preferentially. In this way, by adjusting the order in which the labeling results are displayed according to the user's emotions, more appropriate information can be provided. 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.

[0104] The data collection unit can analyze a user's past reporting history and select the optimal collection method. For example, it can prioritize collecting similar information based on the types of information the user has previously reported. It can also collect information during the time periods when the user previously reported incidents. Furthermore, it can collect information about the area surrounding a location based on the location where the user previously reported incidents. In this way, the optimal collection method can be selected by analyzing the user's past reporting history.

[0105] The data collection unit can filter data based on the user's current location and activity status during collection. For example, if a user is in a specific area, it can prioritize collecting information about that area. If a user is on the move, it can also collect information along their travel route. Furthermore, if a user is engaged in a specific activity, it can collect information related to that activity. This allows for the collection of more relevant information by filtering based on the user's current location and activity status.

[0106] The analysis unit can analyze current information by referencing past crime data during the analysis process. For example, it can identify patterns similar to current information based on past crime data. It can also evaluate the credibility of current information based on past crime data. Furthermore, it can determine the importance of current information based on past crime data. As a result, referencing past crime data improves the accuracy of the analysis of current information.

[0107] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, a vehicle license plate recognition algorithm can be applied to traffic violation information. A facial recognition algorithm can also be applied to suspicious person information. Furthermore, an item feature recognition algorithm can be applied to theft information. By applying different analysis algorithms to each category of information, the accuracy of the analysis is improved.

[0108] The reporting unit can adjust the level of detail in a report based on the importance of the information. For example, highly important information can be reported in detail. Less important information can be reported concisely. Furthermore, information of moderate importance can be reported with an appropriate level of detail. This allows important information to be reported in detail by adjusting the level of detail based on the importance of the information.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects information. The collection unit can collect information from sources such as personal smartphones, dashcams, and surveillance cameras. It can also collect information on suspicious behavior in urban areas and vehicle license plates. Furthermore, it can collect information using sensors. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze the collected information using data mining techniques. It can also analyze the information using machine learning algorithms. Furthermore, it can identify signs and patterns of crime based on the collected information. Step 3: The reporting unit reports the information analyzed by the analysis unit. The reporting unit can, for example, automatically format the information provided by the user and send it to the police in an appropriate format. It can also report information via methods such as email, SMS, and app notifications. Furthermore, it can convert the format of the information and send it to the police. Step 4: The labeling unit labels and creates pages from the information reported by the reporting unit. The labeling unit can, for example, automatically classify the content of reports and organize information such as the type of crime, location, and time. It can also organize information using methods such as tagging, categorizing, and page layout. Furthermore, based on the collected information, it can detect signs of crime and issue alerts to prevent it from happening.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, and labeling unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify signs and patterns of crime. The reporting unit is implemented in the identification processing unit 290 of the data processing unit 12 and automatically formats the analyzed information and transmits it to the police in an appropriate format. The labeling unit is implemented in the identification processing unit 290 of the data processing unit 12 and labels the reported information into pages so that users can easily search and view them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, and labeling 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 collects information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify signs and patterns of crime. The reporting unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and automatically formats the analyzed information and transmits it to the police in an appropriate format. The labeling unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and labels the reported information into pages so that users can easily search and view them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, and labeling unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify signs and patterns of crime. The reporting unit is implemented in the identification processing unit 290 of the data processing unit 12 and automatically formats the analyzed information and transmits it to the police in an appropriate format. The labeling unit is implemented in the identification processing unit 290 of the data processing unit 12 and labels the reported information into pages so that users can easily search and view them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, and labeling unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify signs and patterns of crime. The reporting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically formats the analyzed information and transmits it to the police in an appropriate format. The labeling unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and labels the reported information into pages so that users can easily search and view them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A notification unit that notifies the information analyzed by the aforementioned analysis unit, A labeling unit that labels and pages the information reported by the aforementioned reporting unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, Identifying signs and patterns of crime The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reporting unit, The system automatically formats the information provided by the user and sends it to the police in the appropriate format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The labeling section is, The system automatically categorizes reports and organizes information such as the type of crime, location, and time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The labeling section is, Based on the collected information, it detects signs of crime and issues alerts to prevent it before it happens. The system described in Appendix 1, characterized by the features described herein. (Note 6) The labeling section is, If suspicious activity is frequently occurring in a particular area, residents of that area will be warned. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes the user's past reporting history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current location and activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. 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 prioritizes collecting relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system utilizes sensor data from the user's device to supplement the information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, past crime data is referenced to analyze current information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the way reports are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting unit, When reporting, adjust the level of detail in the report based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting unit, When a report is submitted, different reporting algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the length of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, When a report is submitted, priority is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, When reporting, the order of reports will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The labeling section is, It estimates the user's emotions and adjusts the labeling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The labeling section is, When labeling, consider the interrelationships between pieces of information to improve labeling accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The labeling section is, When labeling, the labeling process takes into account the attribute information of the information submitter. The system described in Appendix 1, characterized by the features described herein. (Note 28) The labeling section is, It estimates the user's emotions and adjusts the order in which the labeling results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The labeling section is, When labeling, consider the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The labeling section is, When labeling, refer to relevant literature to improve the accuracy of the labeling. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A notification unit that notifies the information analyzed by the aforementioned analysis unit, A labeling unit that labels and pages the information reported by the aforementioned reporting unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Identifying signs and patterns of crime The system according to feature 1.

3. The aforementioned reporting unit, The system automatically formats the information provided by the user and sends it to the police in the appropriate format. The system according to feature 1.

4. The labeling section is, The system automatically categorizes reports and organizes information such as the type of crime, location, and time. The system according to feature 1.

5. The labeling section is, Based on the collected information, it detects signs of crime and issues alerts to prevent it before it happens. The system according to feature 1.

6. The labeling section is, If suspicious activity is frequently occurring in a particular area, residents of that area will be warned. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes the user's past reporting history to select the most suitable collection method. The system according to feature 1.

Images