system

Abstract

The system according to this embodiment aims to detect illegal job postings on social media and job search websites at an early stage and to take countermeasures. [Solution] The system according to the embodiment comprises a communication unit, a determination unit, and a reporting unit. The communication unit actively communicates with accounts that are recruiting using specific keywords. The determination unit makes a determination based on the accounts detected by the communication unit. The reporting unit makes a report based on the results determined by the determination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to detect dark part-time job recruitment on SNS or part-time job recruitment sites at an early stage and take countermeasures.

[0005] The system according to the embodiment aims to detect dark part-time job recruitment on SNS or part-time job recruitment sites at an early stage and take countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a communication unit, a determination unit, and a reporting unit. The communication unit proactively communicates with accounts that are recruiting using specific keywords. The determination unit makes a determination based on the accounts detected by the communication unit. The reporting unit makes a report based on the determination made by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect illegal job postings on social media and job search websites at an early stage and take countermeasures. [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 applicable 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 AI ​​agent system according to an embodiment of the present invention is a system that enables businesses to proactively check and block illegal job postings on social media and job recruitment sites. This AI agent system proactively communicates with accounts posting job openings using specific keywords, communicates and applies on behalf of the user, and reports to the business as "OK" if there are no problems, and as "NG" if there are problems. For example, if the user is directed to an app such as Signal, it will be reported as "NG". This mechanism allows businesses to smoothly freeze accounts or contact the police. This enables the detection of a large number of problematic accounts early on, leading to improved service security. For example, the AI ​​agent system proactively communicates with accounts posting job openings using specific keywords. In this process, the AI ​​agent system interacts naturally and detects the perpetrator. For example, it sends a message such as, "I'm interested. What kind of work is it?" This initiates communication between the AI ​​agent system and the perpetrator. Next, the AI ​​agent system communicates and applies on behalf of the user. For example, if it receives a message such as, "This is a legitimate job. Please enter Signal for details!", the AI ​​agent system analyzes the content and determines whether there is a problem. If there are no problems, the system reports to the service provider with a "Judgment OK"; if there are problems, it reports to the service provider with a "Judgment NG." Furthermore, if the AI ​​agent system determines that an account is "NG," the service provider can smoothly freeze the account or contact the police. For example, if the AI ​​agent system directs a user to an app such as Signal, it reports it as "NG," and the service provider automatically freezes the account. In this way, the service provider can respond quickly. This mechanism allows the AI ​​agent system to detect a large number of problematic accounts quickly and efficiently, leading to improved service security. For example, because the AI ​​agent system can make judgments on a large number of accounts in a short time, it can help prevent crime.Furthermore, the AI ​​agent system can actively converse with perpetrators to understand the specific details of the crime and the methods used to lure them in. This allows businesses to effectively prevent illegal job postings. The AI ​​agent system enables businesses to proactively check and block illegal job postings on social media and job recruitment sites.

[0029] The AI ​​agent system according to this embodiment comprises a communication unit, a judgment unit, and a reporting unit. The communication unit proactively communicates with accounts that are recruiting using specific keywords. For example, the communication unit automatically detects posts containing specific keywords and sends a message to those accounts. For example, the communication unit can send a message such as, "I'm interested. What kind of work is it?" The communication unit can also send multiple messages to accounts containing specific keywords to continue communication. For example, the communication unit can send messages such as, "Please tell me more," or "What are the conditions?" Furthermore, the communication unit can send multiple messages to accounts containing specific keywords to continue communication. For example, the communication unit can send messages such as, "Please tell me more," or "What are the conditions?" The judgment unit makes a judgment based on the accounts detected by the communication unit. For example, the judgment unit analyzes the content of the messages sent by the communication unit and determines whether there is a problem. For example, if the judgment unit receives a message such as, "This is a legitimate job. Please send a signal for details!", it can analyze its content and determine whether there is a problem. Furthermore, the Judgment Unit can analyze the content of messages sent by the Communication Unit and determine whether there is a problem. For example, if the Judgment Unit receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there is a problem. The Reporting Unit makes reports based on the results determined by the Judgment Unit. For example, if the Judgment Unit determines the case to be "OK", the Reporting Unit will report that result to the business operator. For example, the Reporting Unit can report that "This account is fine." Also, if the Judgment Unit determines the case to be "NG", the Reporting Unit can report that result to the business operator.For example, the reporting department can submit a report stating, "This account has a problem." This allows the AI ​​agent system, according to the embodiment, to proactively communicate with, evaluate, and report on accounts recruiting with specific keywords, thereby enabling early detection of problematic accounts and improving service security.

[0030] The Communications Department proactively communicates with accounts that are recruiting using specific keywords. Specifically, the Communications Department uses natural language processing technology to automatically detect posts containing specific keywords. For example, it monitors posts containing keywords such as "job postings," "recruitment," and "work" on platforms such as social media and bulletin boards in real time and sends messages to those accounts. The content of the messages is generated based on pre-set templates. For example, messages such as "I'm interested. What kind of work is it?" or "Please tell me more" are automatically sent. Furthermore, the Communications Department can use an AI chatbot to send multiple messages and continue communication. The chatbot generates appropriate responses in response to the user's responses and continues the conversation. For example, it can ask specific questions such as "What are the conditions?" or "Where is the work location?" to elicit detailed information from the user. This allows the Communications Department to efficiently and effectively interact with users and collect necessary information. In addition, the Communications Department can store the collected information in a database and use it for subsequent processing. For example, by saving user profile information and conversation history and referring to it in the next conversation, it can achieve more personalized communication. This allows the communications department to build trust with users and improve the quality of services.

[0031] The Judgment Unit makes a judgment based on the accounts detected by the Communication Unit. Specifically, the Judgment Unit uses natural language processing technology to analyze the content of messages sent by the Communication Unit. For example, it analyzes the text of the message and checks whether it contains specific keywords or phrases. Furthermore, the Judgment Unit uses machine learning algorithms to determine whether the content of the message is problematic. For example, it uses a model based on past data to detect messages that may be spam or fraudulent. Specifically, if it receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether it may be fraudulent. The Judgment Unit considers not only the content of the message but also information about the sender's account. For example, it evaluates reliability based on information such as the account creation date, posting history, and number of followers. This allows the Judgment Unit to make more accurate judgments. The Judgment Unit can also store the judgment results in a database and use them for subsequent processing. For example, it provides information to the Reporting Unit to make appropriate reports based on the judgment results. This allows the Judgment Unit to effectively analyze the information collected by the Communication Unit and detect problematic accounts at an early stage.

[0032] The reporting department provides reports based on the results determined by the judgment department. Specifically, if the judgment department determines the result as "OK," the reporting department reports the result to the business operator. For example, it can report, "This account is fine." Conversely, if the judgment department determines the result as "NG," the reporting department can report the result to the business operator. For example, it can report, "This account has problems." The reporting department has a system in place to automatically generate reports and notify the business operator. For example, it can provide real-time reports via email or a dashboard. Furthermore, the reporting department can save the report content in a database and use it for subsequent analysis and improvement. For example, it can analyze the characteristics of problematic accounts based on the report content and formulate future countermeasures. The reporting department also has a feedback loop to continuously improve the accuracy and effectiveness of the reports. For example, it can review the report content and judgment criteria based on feedback from the business operator and improve the accuracy of the entire system. This allows the reporting department to report judgment results quickly and accurately, supporting the business operator's decision-making. Furthermore, the reporting department can reliably transmit information using multiple reporting methods. For example, by using not only email notifications but also SMS and push notifications in combination, important information can be reliably delivered. This allows the reporting department to report quickly and reliably to the service provider, contributing to improved service security.

[0033] The communications department can proactively communicate with accounts that are recruiting using specific keywords. For example, the communications department can automatically detect posts containing specific keywords and send messages to those accounts. For example, the communications department can send messages such as, "I'm interested. What kind of work is it?" The communications department can also send multiple messages to accounts containing specific keywords to continue communication. For example, the communications department can send messages such as, "Please tell me more," or "What are the conditions?" Furthermore, the communications department can send multiple messages to accounts containing specific keywords to continue communication. For example, the communications department can send messages such as, "Please tell me more," or "What are the conditions?" This allows for the early detection of problematic accounts by proactively communicating with accounts recruiting using specific keywords. Specific keywords include, but are not limited to, keywords related to fraud or illegal activities. Methods of proactive communication include, but are not limited to, the frequency of message sending and the means of communication used. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not using generative AI. For example, the communications department can input posts containing specific keywords into a message generation AI, which can then automatically generate and send a message.

[0034] The judgment unit can communicate and apply on behalf of the user and report any problems to the service provider as "NG". For example, the judgment unit can analyze the content of messages sent by the communication unit and determine whether there are any problems. For example, if the judgment unit receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there are any problems. The judgment unit can also analyze the content of messages sent by the communication unit and determine whether there are any problems. For example, if the judgment unit receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there are any problems. This allows for the rapid detection of problematic accounts by communicating and applying on behalf of the user and reporting any problems to the service provider as "NG". Methods for communicating and applying on behalf of the user include, but are not limited to, the content of messages and application procedures. Criteria for determining "NG" include, but are not limited to, criteria for detecting illegal activities and means of reporting. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without the use of a generative AI. For example, the judgment unit can input the content of the message sent by the communication unit into the generation AI, which can then automatically analyze it and determine whether there is a problem.

[0035] The reporting department can submit reports to facilitate the freezing of accounts or contact with the police for businesses. For example, if the judgment department determines an account to be "OK," the reporting department will report that result to the business. For example, the reporting department may submit a report stating, "This account is not problematic." Conversely, if the judgment department determines an account to be "NG," the reporting department can also report that result to the business. For example, the reporting department may submit a report stating, "This account is problematic." This allows businesses to respond quickly to problematic accounts by submitting reports that facilitate the freezing of accounts or contact with the police for businesses. The content of reports that facilitate the freezing of accounts or contact with the police for businesses may include, but are not limited to, details of illegal activities and methods for presenting evidence. Some or all of the above processing in the reporting department may be performed using, for example, a generating AI, or not using a generating AI. For example, the reporting department can input the result determined by the judgment department into a generating AI, which can then automatically generate the report content and submit it to the business.

[0036] The communications department can initiate communication with the perpetrator. For example, the communications department can automatically detect posts containing specific keywords and send messages to those accounts. For example, the communications department could send a message such as, "I'm interested. What kind of work is it?" The communications department can also send multiple messages to accounts containing specific keywords and continue the communication. For example, the communications department could send messages such as, "Please tell me more," or "What are the conditions?" By initiating communication with the perpetrator, it becomes possible to understand the specific details of the crime and the methods used to lure the victim. Methods for initiating communication with the perpetrator include, but are not limited to, the content of the messages and the platform used. Some or all of the above processing in the communications department may be performed using, for example, a generative AI, or not using a generative AI. For example, the communications department can input posts containing specific keywords into a generative AI, which can then automatically generate and send messages.

[0037] The judgment unit can report as "NG" if it is redirected to an app such as Signal. For example, the judgment unit can analyze the content of a message sent by the communication unit, and if it is redirected to an app such as Signal, it can analyze the content and report as "NG". This allows for the rapid detection of problematic accounts by reporting as "NG" when redirected to an app such as Signal. Apps such as Signal include, but are not limited to, Signal and Telegram. Criteria for determining "NG" include, but are not limited to, criteria for detecting illegal activity and means of reporting. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input the content of a message sent by the communication unit into a generating AI, the generating AI can automatically analyze it, and if it is redirected to an app such as Signal, it can report as "NG".

[0038] The reporting department can provide reports to enable businesses to take prompt action. For example, if the judgment department determines an account to be "OK," the reporting department will report that result to the business. For example, the reporting department may report, "This account is not problematic." The reporting department can also report if the judgment department determines an account to be "NG." For example, the reporting department may report, "This account is problematic." This allows businesses to take prompt action by providing reports that enable them to respond quickly to problematic accounts. The content of reports that enable businesses to take prompt action may include, but are not limited to, details of illegal activities and methods for presenting evidence. Some or all of the above processing in the reporting department may be performed using, for example, a generating AI, or not using a generating AI. For example, the reporting department can input the result determined by the judgment department into a generating AI, which can then automatically generate the report content and report it to the business.

[0039] The judgment unit can grasp the specific details of the crime and the methods used to induce the victim. For example, the judgment unit can analyze the content of messages sent by the communication unit to grasp the specific details of the crime and the methods used to induce the victim. For example, the judgment unit can analyze how the perpetrator is trying to induce the user and grasp the methods used. This allows for the rapid detection of problematic accounts by grasping the specific details of the crime and the methods used to induce the victim. Methods for grasping the specific details of the crime and the methods used to induce the victim include, but are not limited to, analysis of message content and behavioral patterns. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the content of messages sent by the communication unit into a generative AI, which will automatically analyze it to grasp the specific details of the crime and the methods used to induce the victim.

[0040] The communications department can analyze past communication history and select the optimal communication method. For example, the communications department can select the optimal message format based on communication methods that have been effective in the past. For example, the communications department can select a communication method that is effective for a specific time period from past history. The communications department can also analyze past history and select the optimal response method for a specific keyword. In this way, the optimal communication method can be selected by analyzing past communication history. Methods for analyzing past communication history include, but are not limited to, text mining and behavioral pattern analysis. Criteria for selecting the optimal communication method include, but are not limited to, past success stories and user reactions. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not using generative AI. For example, the communications department can input past communication history into generative AI, which can then automatically analyze it and select the optimal communication method.

[0041] The communications department can filter messages based on account attribute information during communication. For example, the communications department can select appropriate message content based on the account's age and gender. For example, the communications department can select an appropriate communication method based on the account's past behavioral history. The communications department can also select appropriate message content based on the account's geographical location information. This allows for more effective communication by filtering based on account attribute information. Account attribute information includes, but is not limited to, age, gender, and interests. The criteria for filtering include, but is not limited to, account attribute information and past behavioral history. Some or all of the above processing in the communications department may be performed using, for example, a generative AI, or not using a generative AI. For example, the communications department can input account attribute information into a generative AI, which can then automatically filter and select appropriate message content.

[0042] The communications department can prioritize communication with highly relevant accounts by considering the geographical location of the accounts during communication. For example, the communications department can prioritize communication with nearby accounts based on the geographical location of the accounts. For example, the communications department can prioritize communication with accounts that are highly active in a particular area based on the geographical location of the accounts. Furthermore, the communications department can prioritize communication with accounts that have a high crime risk in a particular area based on the geographical location of the accounts. This makes communication more effective by prioritizing communication with highly relevant accounts by considering the geographical location of the accounts. The geographical location of an account includes, but is not limited to, countries, regions, and cities. The criteria for prioritizing communication with highly relevant accounts include, but is not limited to, common interests and past interactions. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not using generative AI. For example, the communications department can input the geographical location of accounts into a generative AI, which can automatically select highly relevant accounts and prioritize communication with them.

[0043] The communications department can analyze an account's social media activity and communicate with relevant accounts during communication. For example, the communications department can prioritize communication with relevant accounts based on the account's social media activity. For example, the communications department can analyze social media activity and prioritize communication with accounts related to specific keywords. Furthermore, the communications department can prioritize communication with accounts belonging to specific groups based on social media activity. This enables more effective communication by analyzing an account's social media activity and communicating with relevant accounts. An account's social media activity includes, but is not limited to, posts and follower activity. Criteria for communicating with relevant accounts include, but is not limited to, shared interests and past interactions. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not. For example, the communications department can input an account's social media activity into a generative AI, which can automatically analyze it, select relevant accounts, and communicate with them.

[0044] The determination unit can improve the accuracy of its determination by considering the relationships between accounts. For example, the determination unit can prioritize determining highly relevant accounts based on their relationships. For example, the determination unit can analyze the relationships between accounts and prioritize determining accounts belonging to a specific group. The determination unit can also prioritize determining accounts related to a specific keyword based on their relationships. This improves the accuracy of the determination by considering the relationships between accounts, enabling more appropriate determinations. Relationships between accounts include, but are not limited to, following relationships and message exchanges. Methods for improving the accuracy of the determination include, but are not limited to, the use of machine learning algorithms and data augmentation. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the determination unit can input account relationship data into a generative AI, which can then automatically analyze and improve the accuracy of the determination.

[0045] The judgment unit can make a judgment by considering the account's attribute information. For example, the judgment unit can set appropriate judgment criteria based on the account's age and gender. For example, the judgment unit can set appropriate judgment criteria based on the account's past behavioral history. Furthermore, the judgment unit can set appropriate judgment criteria based on the account's geographical location information. This makes it possible to make a more appropriate judgment by considering the account's attribute information. Account attribute information includes, but is not limited to, age, gender, and interests. Criteria for making a judgment include, but is not limited to, analysis of behavioral patterns and analysis of message content. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the judgment unit can input the account's attribute information into a generative AI, which can then automatically analyze and make a judgment.

[0046] The determination unit can make determinations while considering the geographical distribution of accounts. For example, the determination unit can prioritize determining accounts that are highly active in a particular region based on their geographical distribution. For example, the determination unit can prioritize determining accounts that have a high crime risk in a particular region based on their geographical distribution. Furthermore, the determination unit can prioritize determining accounts that are less active in a particular region based on their geographical distribution. This allows for more appropriate determinations by considering the geographical distribution of accounts. The geographical distribution of accounts includes, but is not limited to, countries, regions, and cities. The criteria for making determinations include, but is not limited to, analysis of behavioral patterns and analysis of message content. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or without a generative AI. For example, the determination unit can input the geographical distribution data of accounts into a generative AI, which can then automatically analyze and make a determination.

[0047] The judgment unit can improve the accuracy of its judgment by referring to the relevant literature for the account during the judgment process. For example, the judgment unit can set appropriate judgment criteria based on the relevant literature for the account. For example, the judgment unit can refer to the relevant literature and prioritize the judgment of accounts related to specific keywords. The judgment unit can also prioritize the judgment of accounts belonging to specific groups based on the relevant literature. This makes it possible to make more appropriate judgments by improving the accuracy of the judgment by referring to the relevant literature for the account. The relevant literature for an account includes, but is not limited to, past research papers and technical reports. Methods for improving the accuracy of the judgment include, but is not limited to, the use of machine learning algorithms and data augmentation. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judgment unit can input the relevant literature data for the account into a generative AI, which can then automatically analyze it and improve the accuracy of the judgment.

[0048] The reporting unit can optimize its reporting algorithm by referring to past reporting data when reporting. For example, the reporting unit can select the optimal reporting method based on past reporting data. For example, the reporting unit can analyze past reporting data and optimize reporting methods related to specific keywords. The reporting unit can also optimize reporting methods for accounts belonging to a specific group based on past reporting data. This makes it possible to provide more appropriate reports by optimizing the reporting algorithm by referring to past reporting data. Past reporting data includes, but is not limited to, past reporting content and reporting results. Methods for optimizing the reporting algorithm include, but is not limited to, the use of machine learning algorithms and data augmentation. Some or all of the above processing in the reporting unit may be performed using, for example, generative AI, or not using generative AI. For example, the reporting unit can input past reporting data into a generative AI, which can then automatically analyze and optimize the reporting algorithm.

[0049] The reporting unit can consider account attribute information when making reports. For example, the reporting unit can select an appropriate reporting method based on the account's age and gender. For example, the reporting unit can select an appropriate reporting method based on the account's past behavioral history. Furthermore, the reporting unit can select an appropriate reporting method based on the account's geographical location information. This allows for more appropriate reporting by considering account attribute information. Account attribute information includes, but is not limited to, age, gender, and interests. Criteria for making reports include, but is not limited to, criteria for detecting illegal activity and means of reporting. Some or all of the above processing in the reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting unit can input account attribute information into a generative AI, which can then automatically analyze and make a report.

[0050] The reporting unit can select the most appropriate reporting method when reporting, taking into account the geographical location of the account. For example, the reporting unit can report to accounts that are highly active in a particular region based on their geographical location. For example, the reporting unit can report to accounts that pose a high crime risk in a particular region based on their geographical location. Furthermore, the reporting unit can report to accounts that are less active in a particular region based on their geographical location. By selecting the most appropriate reporting method considering the geographical location of the account, more appropriate reporting becomes possible. The geographical location of an account includes, but is not limited to, countries, regions, and cities. Criteria for selecting the most appropriate reporting method include, but is not limited to, the urgency of the report and the designated recipient. Some or all of the above processing in the reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting unit can input the geographical location of the account into a generative AI, which can then automatically analyze and select the most appropriate reporting method.

[0051] The reporting department can analyze an account's social media activity and suggest reporting methods when submitting a report. For example, the reporting department can suggest the most suitable reporting method based on the account's social media activity. For example, the reporting department can analyze social media activity and suggest reporting methods related to specific keywords. The reporting department can also suggest the most suitable reporting method for accounts belonging to a specific group based on social media activity. This enables more appropriate reporting by analyzing the account's social media activity and suggesting reporting methods. An account's social media activity includes, but is not limited to, posts and follower trends. Criteria for suggesting reporting methods include, but is not limited to, shared interests and past interactions. Some or all of the above processing in the reporting department may be performed using, for example, generative AI, or not using generative AI. For example, the reporting department can input the account's social media activity into generative AI, which can then automatically analyze and suggest the most suitable reporting method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The AI ​​agent system can also be equipped with a behavioral analysis unit that analyzes the user's behavioral history. This unit detects specific patterns based on the user's past behavior and optimizes the content and timing of communication accordingly. For example, it can detect when a user is active during a specific time period and send a message during that time. It can also analyze what messages the user has responded to in the past and send similar messages. Furthermore, the behavioral analysis unit can predict future behavior based on the user's behavioral patterns and communicate accordingly. This enables effective communication that takes the user's behavioral history into account.

[0054] The AI ​​agent system can also be equipped with a geographic information unit that considers the user's geographic location when communicating. For example, if the user is in a specific area, the geographic information unit can provide information relevant to that area. For instance, it could send a message such as, "There's a recommended cafe nearby." Furthermore, if the user is traveling, the geographic information unit can provide information about their travel destination. For example, it could send a message such as, "Here are some tourist attractions in this area." This enables effective communication based on the user's geographic location.

[0055] The AI ​​agent system can also include a social media analysis unit that analyzes the user's social media activity and communicates based on that analysis. For example, if a user frequently posts about a particular topic, the social media analysis unit can provide information related to that topic. For instance, it could send a message such as, "Here's some news related to your recent posts." Furthermore, if a user belongs to a specific group, the social media analysis unit can provide information related to that group. For example, it could send a message such as, "Here's some information about a topic being discussed in your group." This enables effective communication based on the user's social media activity.

[0056] The AI ​​agent system can also include a history analysis unit that analyzes the user's past communication history and optimizes the content of reports based on this analysis. For example, the history analysis unit can select the most effective reporting method based on past effective reporting methods. It can also select a reporting method that was effective in a specific situation based on past history. Furthermore, the history analysis unit can analyze past history and select the most effective reporting method for specific keywords. This enables effective reporting that takes past communication history into account.

[0057] The AI ​​agent system can also include a geographic reporting unit that adjusts the content of reports based on the user's geographic location. For example, if the user is in a specific region, the geographic reporting unit can report information relevant to that region. For instance, it could report, "Here's the latest information on the current situation in your area." Furthermore, if the user is traveling, the geographic reporting unit can report information about their travel destination. For example, it could report, "Here are some points to be aware of at your travel destination." This enables effective reporting based on the user's geographic location.

[0058] The AI ​​agent system can also include a social media reporting unit that analyzes the user's social media activity and optimizes the content of reports based on this analysis. For example, if a user frequently posts about a particular topic, the social media reporting unit can report information related to that topic. For instance, it could report, "Here's the latest information related to your recent posts." Furthermore, if the user belongs to a specific group, the social media reporting unit can report information related to that group. This enables effective reporting based on the user's social media activity.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The communications department proactively communicates with accounts that are recruiting using specific keywords. For example, it automatically detects posts containing specific keywords and sends messages to those accounts. Specifically, it can send messages such as "I'm interested. What kind of work is it?", "Please tell me more," and "What are the conditions?" to continue communication. Step 2: The judgment unit makes a judgment based on the accounts detected by the communications unit. For example, it analyzes the content of messages sent by the communications unit and determines whether there is a problem. Specifically, if it receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there is a problem. Step 3: The reporting department makes a report based on the results determined by the judgment department. For example, if the judgment department determines the result to be "OK," the reporting department reports that result to the business operator. Specifically, they can report something like, "This account is fine." Also, if the judgment department determines the result to be "NG," the reporting department can report that result to the business operator. Specifically, they can report something like, "This account has problems."

[0061] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that enables businesses to proactively check and block illegal job postings on social media and job recruitment sites. This AI agent system proactively communicates with accounts posting job openings using specific keywords, communicates and applies on behalf of the user, and reports to the business as "OK" if there are no problems, and as "NG" if there are problems. For example, if the user is directed to an app such as Signal, it will be reported as "NG". This mechanism allows businesses to smoothly freeze accounts or contact the police. This enables the detection of a large number of problematic accounts early on, leading to improved service security. For example, the AI ​​agent system proactively communicates with accounts posting job openings using specific keywords. In this process, the AI ​​agent system interacts naturally and detects the perpetrator. For example, it sends a message such as, "I'm interested. What kind of work is it?" This initiates communication between the AI ​​agent system and the perpetrator. Next, the AI ​​agent system communicates and applies on behalf of the user. For example, if it receives a message such as, "This is a legitimate job. Please enter Signal for details!", the AI ​​agent system analyzes the content and determines whether there is a problem. If there are no problems, the system reports to the service provider with a "Judgment OK"; if there are problems, it reports to the service provider with a "Judgment NG." Furthermore, if the AI ​​agent system determines that an account is "NG," the service provider can smoothly freeze the account or contact the police. For example, if the AI ​​agent system directs a user to an app such as Signal, it reports it as "NG," and the service provider automatically freezes the account. In this way, the service provider can respond quickly. This mechanism allows the AI ​​agent system to detect a large number of problematic accounts quickly and efficiently, leading to improved service security. For example, because the AI ​​agent system can make judgments on a large number of accounts in a short time, it can help prevent crime.Furthermore, the AI ​​agent system can actively converse with perpetrators to understand the specific details of the crime and the methods used to lure them in. This allows businesses to effectively prevent illegal job postings. The AI ​​agent system enables businesses to proactively check and block illegal job postings on social media and job recruitment sites.

[0062] The AI ​​agent system according to this embodiment comprises a communication unit, a judgment unit, and a reporting unit. The communication unit proactively communicates with accounts that are recruiting using specific keywords. For example, the communication unit automatically detects posts containing specific keywords and sends a message to those accounts. For example, the communication unit can send a message such as, "I'm interested. What kind of work is it?" The communication unit can also send multiple messages to accounts containing specific keywords to continue communication. For example, the communication unit can send messages such as, "Please tell me more," or "What are the conditions?" Furthermore, the communication unit can send multiple messages to accounts containing specific keywords to continue communication. For example, the communication unit can send messages such as, "Please tell me more," or "What are the conditions?" The judgment unit makes a judgment based on the accounts detected by the communication unit. For example, the judgment unit analyzes the content of the messages sent by the communication unit and determines whether there is a problem. For example, if the judgment unit receives a message such as, "This is a legitimate job. Please send a signal for details!", it can analyze its content and determine whether there is a problem. Furthermore, the Judgment Unit can analyze the content of messages sent by the Communication Unit and determine whether there is a problem. For example, if the Judgment Unit receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there is a problem. The Reporting Unit makes reports based on the results determined by the Judgment Unit. For example, if the Judgment Unit determines the case to be "OK", the Reporting Unit will report that result to the business operator. For example, the Reporting Unit can report that "This account is fine." Also, if the Judgment Unit determines the case to be "NG", the Reporting Unit can report that result to the business operator.For example, the reporting department can submit a report stating, "This account has a problem." This allows the AI ​​agent system, according to the embodiment, to proactively communicate with, evaluate, and report on accounts recruiting with specific keywords, thereby enabling early detection of problematic accounts and improving service security.

[0063] The Communications Department proactively communicates with accounts that are recruiting using specific keywords. Specifically, the Communications Department uses natural language processing technology to automatically detect posts containing specific keywords. For example, it monitors posts containing keywords such as "job postings," "recruitment," and "work" on platforms such as social media and bulletin boards in real time and sends messages to those accounts. The content of the messages is generated based on pre-set templates. For example, messages such as "I'm interested. What kind of work is it?" or "Please tell me more" are automatically sent. Furthermore, the Communications Department can use an AI chatbot to send multiple messages and continue communication. The chatbot generates appropriate responses in response to the user's responses and continues the conversation. For example, it can ask specific questions such as "What are the conditions?" or "Where is the work location?" to elicit detailed information from the user. This allows the Communications Department to efficiently and effectively interact with users and collect necessary information. In addition, the Communications Department can store the collected information in a database and use it for subsequent processing. For example, by saving user profile information and conversation history and referring to it in the next conversation, it can achieve more personalized communication. This allows the communications department to build trust with users and improve the quality of services.

[0064] The Judgment Unit makes a judgment based on the accounts detected by the Communication Unit. Specifically, the Judgment Unit uses natural language processing technology to analyze the content of messages sent by the Communication Unit. For example, it analyzes the text of the message and checks whether it contains specific keywords or phrases. Furthermore, the Judgment Unit uses machine learning algorithms to determine whether the content of the message is problematic. For example, it uses a model based on past data to detect messages that may be spam or fraudulent. Specifically, if it receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether it may be fraudulent. The Judgment Unit considers not only the content of the message but also information about the sender's account. For example, it evaluates reliability based on information such as the account creation date, posting history, and number of followers. This allows the Judgment Unit to make more accurate judgments. The Judgment Unit can also store the judgment results in a database and use them for subsequent processing. For example, it provides information to the Reporting Unit to make appropriate reports based on the judgment results. This allows the Judgment Unit to effectively analyze the information collected by the Communication Unit and detect problematic accounts at an early stage.

[0065] The reporting department provides reports based on the results determined by the judgment department. Specifically, if the judgment department determines the result as "OK," the reporting department reports the result to the business operator. For example, it can report, "This account is fine." Conversely, if the judgment department determines the result as "NG," the reporting department can report the result to the business operator. For example, it can report, "This account has problems." The reporting department has a system in place to automatically generate reports and notify the business operator. For example, it can provide real-time reports via email or a dashboard. Furthermore, the reporting department can save the report content in a database and use it for subsequent analysis and improvement. For example, it can analyze the characteristics of problematic accounts based on the report content and formulate future countermeasures. The reporting department also has a feedback loop to continuously improve the accuracy and effectiveness of the reports. For example, it can review the report content and judgment criteria based on feedback from the business operator and improve the accuracy of the entire system. This allows the reporting department to report judgment results quickly and accurately, supporting the business operator's decision-making. Furthermore, the reporting department can reliably transmit information using multiple reporting methods. For example, by using not only email notifications but also SMS and push notifications in combination, important information can be reliably delivered. This allows the reporting department to report quickly and reliably to the service provider, contributing to improved service security.

[0066] The communications department can proactively communicate with accounts that are recruiting using specific keywords. For example, the communications department can automatically detect posts containing specific keywords and send messages to those accounts. For example, the communications department can send messages such as, "I'm interested. What kind of work is it?" The communications department can also send multiple messages to accounts containing specific keywords to continue communication. For example, the communications department can send messages such as, "Please tell me more," or "What are the conditions?" Furthermore, the communications department can send multiple messages to accounts containing specific keywords to continue communication. For example, the communications department can send messages such as, "Please tell me more," or "What are the conditions?" This allows for the early detection of problematic accounts by proactively communicating with accounts recruiting using specific keywords. Specific keywords include, but are not limited to, keywords related to fraud or illegal activities. Methods of proactive communication include, but are not limited to, the frequency of message sending and the means of communication used. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not using generative AI. For example, the communications department can input posts containing specific keywords into a message generation AI, which can then automatically generate and send a message.

[0067] The judgment unit can communicate and apply on behalf of the user and report any problems to the service provider as "NG". For example, the judgment unit can analyze the content of messages sent by the communication unit and determine whether there are any problems. For example, if the judgment unit receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there are any problems. The judgment unit can also analyze the content of messages sent by the communication unit and determine whether there are any problems. For example, if the judgment unit receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there are any problems. This allows for the rapid detection of problematic accounts by communicating and applying on behalf of the user and reporting any problems to the service provider as "NG". Methods for communicating and applying on behalf of the user include, but are not limited to, the content of messages and application procedures. Criteria for determining "NG" include, but are not limited to, criteria for detecting illegal activities and means of reporting. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without the use of a generative AI. For example, the judgment unit can input the content of the message sent by the communication unit into the generation AI, which can then automatically analyze it and determine whether there is a problem.

[0068] The reporting department can submit reports to facilitate the freezing of accounts or contact with the police for businesses. For example, if the judgment department determines an account to be "OK," the reporting department will report that result to the business. For example, the reporting department may submit a report stating, "This account is not problematic." Conversely, if the judgment department determines an account to be "NG," the reporting department can also report that result to the business. For example, the reporting department may submit a report stating, "This account is problematic." This allows businesses to respond quickly to problematic accounts by submitting reports that facilitate the freezing of accounts or contact with the police for businesses. The content of reports that facilitate the freezing of accounts or contact with the police for businesses may include, but are not limited to, details of illegal activities and methods for presenting evidence. Some or all of the above processing in the reporting department may be performed using, for example, a generating AI, or not using a generating AI. For example, the reporting department can input the result determined by the judgment department into a generating AI, which can then automatically generate the report content and submit it to the business.

[0069] The communications department can initiate communication with the perpetrator. For example, the communications department can automatically detect posts containing specific keywords and send messages to those accounts. For example, the communications department could send a message such as, "I'm interested. What kind of work is it?" The communications department can also send multiple messages to accounts containing specific keywords and continue the communication. For example, the communications department could send messages such as, "Please tell me more," or "What are the conditions?" By initiating communication with the perpetrator, it becomes possible to understand the specific details of the crime and the methods used to lure the victim. Methods for initiating communication with the perpetrator include, but are not limited to, the content of the messages and the platform used. Some or all of the above processing in the communications department may be performed using, for example, a generative AI, or not using a generative AI. For example, the communications department can input posts containing specific keywords into a generative AI, which can then automatically generate and send messages.

[0070] The judgment unit can report as "NG" if it is redirected to an app such as Signal. For example, the judgment unit can analyze the content of a message sent by the communication unit, and if it is redirected to an app such as Signal, it can analyze the content and report as "NG". This allows for the rapid detection of problematic accounts by reporting as "NG" when redirected to an app such as Signal. Apps such as Signal include, but are not limited to, Signal and Telegram. Criteria for determining "NG" include, but are not limited to, criteria for detecting illegal activity and means of reporting. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input the content of a message sent by the communication unit into a generating AI, the generating AI can automatically analyze it, and if it is redirected to an app such as Signal, it can report as "NG".

[0071] The reporting department can provide reports to enable businesses to take prompt action. For example, if the judgment department determines an account to be "OK," the reporting department will report that result to the business. For example, the reporting department may report, "This account is not problematic." The reporting department can also report if the judgment department determines an account to be "NG." For example, the reporting department may report, "This account is problematic." This allows businesses to take prompt action by providing reports that enable them to respond quickly to problematic accounts. The content of reports that enable businesses to take prompt action may include, but are not limited to, details of illegal activities and methods for presenting evidence. Some or all of the above processing in the reporting department may be performed using, for example, a generating AI, or not using a generating AI. For example, the reporting department can input the result determined by the judgment department into a generating AI, which can then automatically generate the report content and report it to the business.

[0072] The judgment unit can grasp the specific details of the crime and the methods used to induce the victim. For example, the judgment unit can analyze the content of messages sent by the communication unit to grasp the specific details of the crime and the methods used to induce the victim. For example, the judgment unit can analyze how the perpetrator is trying to induce the user and grasp the methods used. This allows for the rapid detection of problematic accounts by grasping the specific details of the crime and the methods used to induce the victim. Methods for grasping the specific details of the crime and the methods used to induce the victim include, but are not limited to, analysis of message content and behavioral patterns. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the content of messages sent by the communication unit into a generative AI, which will automatically analyze it to grasp the specific details of the crime and the methods used to induce the victim.

[0073] The communication unit can estimate the user's emotions and adjust the timing of communication based on the estimated emotions. For example, if the user is stressed, the communication unit can reduce the frequency of communication and send messages at appropriate times. For example, if the user is relaxed, the communication unit can communicate proactively and elicit detailed information. Also, if the user is in a hurry, the communication unit can send quick, concise messages to ensure efficient communication. By adjusting the timing of communication based on the user's emotions, more effective communication becomes possible. Methods for estimating the user's emotions include, but are not limited to, facial recognition and text analysis. Criteria for adjusting the timing of communication include, but are not limited to, changes in emotions and time of day. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the communication unit may be performed using, for example, generative AI, or without generative AI. For example, the communication department can input user emotion data into a generating AI, which can then automatically estimate the emotion and adjust the timing of communication.

[0074] The communications department can analyze past communication history and select the optimal communication method. For example, the communications department can select the optimal message format based on communication methods that have been effective in the past. For example, the communications department can select a communication method that is effective for a specific time period from past history. The communications department can also analyze past history and select the optimal response method for a specific keyword. In this way, the optimal communication method can be selected by analyzing past communication history. Methods for analyzing past communication history include, but are not limited to, text mining and behavioral pattern analysis. Criteria for selecting the optimal communication method include, but are not limited to, past success stories and user reactions. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not using generative AI. For example, the communications department can input past communication history into generative AI, which can then automatically analyze it and select the optimal communication method.

[0075] The communications department can filter messages based on account attribute information during communication. For example, the communications department can select appropriate message content based on the account's age and gender. For example, the communications department can select an appropriate communication method based on the account's past behavioral history. The communications department can also select appropriate message content based on the account's geographical location information. This allows for more effective communication by filtering based on account attribute information. Account attribute information includes, but is not limited to, age, gender, and interests. The criteria for filtering include, but is not limited to, account attribute information and past behavioral history. Some or all of the above processing in the communications department may be performed using, for example, a generative AI, or not using a generative AI. For example, the communications department can input account attribute information into a generative AI, which can then automatically filter and select appropriate message content.

[0076] The communication unit can estimate the user's emotions and determine communication priorities based on those estimated emotions. For example, if the user is feeling anxious, the communication unit will prioritize their response and send reassuring messages. For example, if the user is excited, the communication unit can respond quickly and provide detailed information. If the user is relaxed, the communication unit can respond with normal priority and send messages at the appropriate time. This enables more effective communication by determining communication priorities based on the user's emotions. Methods for estimating the user's emotions include, but are not limited to, facial recognition and text analysis. Criteria for determining communication priorities include, but are not limited to, the intensity and urgency of emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the communication unit may be performed using, for example, generative AI, or without generative AI. For example, the communications department can input user emotion data into a generating AI, which can then automatically estimate the emotion and determine communication priorities.

[0077] The communications department can prioritize communication with highly relevant accounts by considering the geographical location of the accounts during communication. For example, the communications department can prioritize communication with nearby accounts based on the geographical location of the accounts. For example, the communications department can prioritize communication with accounts that are highly active in a particular area based on the geographical location of the accounts. Furthermore, the communications department can prioritize communication with accounts that have a high crime risk in a particular area based on the geographical location of the accounts. This makes communication more effective by prioritizing communication with highly relevant accounts by considering the geographical location of the accounts. The geographical location of an account includes, but is not limited to, countries, regions, and cities. The criteria for prioritizing communication with highly relevant accounts include, but is not limited to, common interests and past interactions. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not using generative AI. For example, the communications department can input the geographical location of accounts into a generative AI, which can automatically select highly relevant accounts and prioritize communication with them.

[0078] The communications department can analyze an account's social media activity and communicate with relevant accounts during communication. For example, the communications department can prioritize communication with relevant accounts based on the account's social media activity. For example, the communications department can analyze social media activity and prioritize communication with accounts related to specific keywords. Furthermore, the communications department can prioritize communication with accounts belonging to specific groups based on social media activity. This enables more effective communication by analyzing an account's social media activity and communicating with relevant accounts. An account's social media activity includes, but is not limited to, posts and follower activity. Criteria for communicating with relevant accounts include, but is not limited to, shared interests and past interactions. Some or all of the above processing in the communications department may be performed using, for example, generative AI, or not. For example, the communications department can input an account's social media activity into a generative AI, which can automatically analyze it, select relevant accounts, and communicate with them.

[0079] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, if the user is feeling anxious, the judgment unit can make a judgment using strict criteria. For example, if the user is relaxed, the judgment unit can make a judgment using normal criteria. Also, if the user is excited, the judgment unit can make a judgment quickly. By adjusting the judgment criteria based on the user's emotions, a more appropriate judgment becomes possible. Methods for estimating the user's emotions include, but are not limited to, facial recognition and text analysis. Criteria for adjusting the judgment criteria include, but are not limited to, the intensity and urgency of the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input user emotion data into a generating AI, which can then automatically estimate the emotion and adjust the judgment criteria.

[0080] The determination unit can improve the accuracy of its determination by considering the relationships between accounts. For example, the determination unit can prioritize determining highly relevant accounts based on their relationships. For example, the determination unit can analyze the relationships between accounts and prioritize determining accounts belonging to a specific group. The determination unit can also prioritize determining accounts related to a specific keyword based on their relationships. This improves the accuracy of the determination by considering the relationships between accounts, enabling more appropriate determinations. Relationships between accounts include, but are not limited to, following relationships and message exchanges. Methods for improving the accuracy of the determination include, but are not limited to, the use of machine learning algorithms and data augmentation. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the determination unit can input account relationship data into a generative AI, which can then automatically analyze and improve the accuracy of the determination.

[0081] The judgment unit can make a judgment by considering the account's attribute information. For example, the judgment unit can set appropriate judgment criteria based on the account's age and gender. For example, the judgment unit can set appropriate judgment criteria based on the account's past behavioral history. Furthermore, the judgment unit can set appropriate judgment criteria based on the account's geographical location information. This makes it possible to make a more appropriate judgment by considering the account's attribute information. Account attribute information includes, but is not limited to, age, gender, and interests. Criteria for making a judgment include, but is not limited to, analysis of behavioral patterns and analysis of message content. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the judgment unit can input the account's attribute information into a generative AI, which can then automatically analyze and make a judgment.

[0082] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the judgment unit will prioritize displaying important results. For example, if the user is relaxed, the judgment unit can display results in the normal order. Also, if the user is excited, the judgment unit can display results quickly. By adjusting the order in which the judgment results are displayed based on the user's emotions, more appropriate results can be displayed. Methods for estimating the user's emotions include, but are not limited to, facial recognition and text analysis. Criteria for adjusting the order in which the judgment results are displayed include, but are not limited to, the intensity and urgency of the emotion. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using, for example, generative AI, or without generative AI. For example, the judgment unit can input user emotion data into a generating AI, which can automatically estimate the emotion and adjust the order in which the judgment results are displayed.

[0083] The determination unit can make determinations while considering the geographical distribution of accounts. For example, the determination unit can prioritize determining accounts that are highly active in a particular region based on their geographical distribution. For example, the determination unit can prioritize determining accounts that have a high crime risk in a particular region based on their geographical distribution. Furthermore, the determination unit can prioritize determining accounts that are less active in a particular region based on their geographical distribution. This allows for more appropriate determinations by considering the geographical distribution of accounts. The geographical distribution of accounts includes, but is not limited to, countries, regions, and cities. The criteria for making determinations include, but is not limited to, analysis of behavioral patterns and analysis of message content. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or without a generative AI. For example, the determination unit can input the geographical distribution data of accounts into a generative AI, which can then automatically analyze and make a determination.

[0084] The judgment unit can improve the accuracy of its judgment by referring to the relevant literature for the account during the judgment process. For example, the judgment unit can set appropriate judgment criteria based on the relevant literature for the account. For example, the judgment unit can refer to the relevant literature and prioritize the judgment of accounts related to specific keywords. The judgment unit can also prioritize the judgment of accounts belonging to specific groups based on the relevant literature. This makes it possible to make more appropriate judgments by improving the accuracy of the judgment by referring to the relevant literature for the account. The relevant literature for an account includes, but is not limited to, past research papers and technical reports. Methods for improving the accuracy of the judgment include, but is not limited to, the use of machine learning algorithms and data augmentation. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judgment unit can input the relevant literature data for the account into a generative AI, which can then automatically analyze it and improve the accuracy of the judgment.

[0085] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit can provide a detailed report to reassure them. For example, if the user is relaxed, the reporting unit can report using the normal reporting method. Also, if the user is agitated, the reporting unit can report quickly. This allows for more appropriate reporting by adjusting the reporting method based on the user's emotions. Methods for estimating the user's emotions include, but are not limited to, facial recognition and text analysis. Criteria for adjusting the reporting method include, but are not limited to, the intensity and urgency of the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using, for example, generative AI, or without generative AI. For example, the reporting department can input user emotion data into a generating AI, which can automatically estimate the emotion and adjust the reporting method accordingly.

[0086] The reporting unit can optimize its reporting algorithm by referring to past reporting data when reporting. For example, the reporting unit can select the optimal reporting method based on past reporting data. For example, the reporting unit can analyze past reporting data and optimize reporting methods related to specific keywords. The reporting unit can also optimize reporting methods for accounts belonging to a specific group based on past reporting data. This makes it possible to provide more appropriate reports by optimizing the reporting algorithm by referring to past reporting data. Past reporting data includes, but is not limited to, past reporting content and reporting results. Methods for optimizing the reporting algorithm include, but is not limited to, the use of machine learning algorithms and data augmentation. Some or all of the above processing in the reporting unit may be performed using, for example, generative AI, or not using generative AI. For example, the reporting unit can input past reporting data into a generative AI, which can then automatically analyze and optimize the reporting algorithm.

[0087] The reporting unit can consider account attribute information when making reports. For example, the reporting unit can select an appropriate reporting method based on the account's age and gender. For example, the reporting unit can select an appropriate reporting method based on the account's past behavioral history. Furthermore, the reporting unit can select an appropriate reporting method based on the account's geographical location information. This allows for more appropriate reporting by considering account attribute information. Account attribute information includes, but is not limited to, age, gender, and interests. Criteria for making reports include, but is not limited to, criteria for detecting illegal activity and means of reporting. Some or all of the above processing in the reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting unit can input account attribute information into a generative AI, which can then automatically analyze and make a report.

[0088] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit can prioritize reporting to provide reassurance. For example, if the user is relaxed, the reporting unit can report with normal priority. Also, if the user is agitated, the reporting unit can report quickly. This allows for more appropriate reporting by determining the priority of reports based on the user's emotions. Methods for estimating the user's emotions include, but are not limited to, facial recognition and text analysis. Criteria for determining the priority of reports include, but are not limited to, the intensity and urgency of the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using, for example, generative AI, or without generative AI. For example, the reporting department can input user emotion data into a generating AI, which can automatically estimate the emotion and determine the priority of the report.

[0089] The reporting unit can select the most appropriate reporting method when reporting, taking into account the geographical location of the account. For example, the reporting unit can report to accounts that are highly active in a particular region based on their geographical location. For example, the reporting unit can report to accounts that pose a high crime risk in a particular region based on their geographical location. Furthermore, the reporting unit can report to accounts that are less active in a particular region based on their geographical location. By selecting the most appropriate reporting method considering the geographical location of the account, more appropriate reporting becomes possible. The geographical location of an account includes, but is not limited to, countries, regions, and cities. Criteria for selecting the most appropriate reporting method include, but is not limited to, the urgency of the report and the designated recipient. Some or all of the above processing in the reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting unit can input the geographical location of the account into a generative AI, which can then automatically analyze and select the most appropriate reporting method.

[0090] The reporting department can analyze an account's social media activity and suggest reporting methods when submitting a report. For example, the reporting department can suggest the most suitable reporting method based on the account's social media activity. For example, the reporting department can analyze social media activity and suggest reporting methods related to specific keywords. The reporting department can also suggest the most suitable reporting method for accounts belonging to a specific group based on social media activity. This enables more appropriate reporting by analyzing the account's social media activity and suggesting reporting methods. An account's social media activity includes, but is not limited to, posts and follower trends. Criteria for suggesting reporting methods include, but is not limited to, shared interests and past interactions. Some or all of the above processing in the reporting department may be performed using, for example, generative AI, or not using generative AI. For example, the reporting department can input the account's social media activity into generative AI, which can then automatically analyze and suggest the most suitable reporting method.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The AI ​​agent system can also be equipped with a behavioral analysis unit that analyzes the user's behavioral history. This unit detects specific patterns based on the user's past behavior and optimizes the content and timing of communication accordingly. For example, it can detect when a user is active during a specific time period and send a message during that time. It can also analyze what messages the user has responded to in the past and send similar messages. Furthermore, the behavioral analysis unit can predict future behavior based on the user's behavioral patterns and communicate accordingly. This enables effective communication that takes the user's behavioral history into account.

[0093] The AI ​​agent system can also include an emotion adjustment unit that estimates the user's emotions and adjusts the content of communication based on those emotions. For example, if the user is feeling stressed, the emotion adjustment unit can send a message to help them relax. For instance, it could send a message like, "You must be tired. Why don't you take a short break?" Similarly, if the user is agitated, the emotion adjustment unit can send a message to help them calm down. For example, it could send a message like, "Calm down, let's check again." This enables appropriate communication tailored to the user's emotions.

[0094] The AI ​​agent system can also be equipped with a geographic information unit that considers the user's geographic location when communicating. For example, if the user is in a specific area, the geographic information unit can provide information relevant to that area. For instance, it could send a message such as, "There's a recommended cafe nearby." Furthermore, if the user is traveling, the geographic information unit can provide information about their travel destination. For example, it could send a message such as, "Here are some tourist attractions in this area." This enables effective communication based on the user's geographic location.

[0095] The AI ​​agent system can also include a social media analysis unit that analyzes the user's social media activity and communicates based on that analysis. For example, if a user frequently posts about a particular topic, the social media analysis unit can provide information related to that topic. For instance, it could send a message such as, "Here's some news related to your recent posts." Furthermore, if a user belongs to a specific group, the social media analysis unit can provide information related to that group. For example, it could send a message such as, "Here's some information about a topic being discussed in your group." This enables effective communication based on the user's social media activity.

[0096] The AI ​​agent system can also include an emotion reporting unit that estimates the user's emotions and adjusts the content of the report based on the estimated emotions. For example, if the user is feeling anxious, the emotion reporting unit can provide a reassuring report. For instance, it could report, "The problem has been resolved. Please rest assured." Conversely, if the user is agitated, the emotion reporting unit can provide a calming report. For example, it could report, "Let's organize the situation and consider the next steps." This enables the system to provide appropriate reports tailored to the user's emotions.

[0097] The AI ​​agent system can also include a history analysis unit that analyzes the user's past communication history and optimizes the content of reports based on this analysis. For example, the history analysis unit can select the most effective reporting method based on past effective reporting methods. It can also select a reporting method that was effective in a specific situation based on past history. Furthermore, the history analysis unit can analyze past history and select the most effective reporting method for specific keywords. This enables effective reporting that takes past communication history into account.

[0098] The AI ​​agent system may also include an emotion display unit that estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the emotion display unit may prioritize displaying important results. For instance, it could display a message such as, "We will inform you of the most important information first." Conversely, if the user is relaxed, the emotion display unit can display the results in the normal order. This enables the display of results that are appropriate to the user's emotions.

[0099] The AI ​​agent system can also include a geographic reporting unit that adjusts the content of reports based on the user's geographic location. For example, if the user is in a specific region, the geographic reporting unit can report information relevant to that region. For instance, it could report, "Here's the latest information on the current situation in your area." Furthermore, if the user is traveling, the geographic reporting unit can report information about their travel destination. For example, it could report, "Here are some points to be aware of at your travel destination." This enables effective reporting based on the user's geographic location.

[0100] The AI ​​agent system can also include an emotion-prioritizing unit that estimates the user's emotions and determines communication priorities based on those emotions. For example, if the user is feeling anxious, the emotion-prioritizing unit will prioritize their response and send reassuring messages. For instance, it might send a message such as, "Please rest assured, we will respond immediately." Conversely, if the user is relaxed, the emotion-prioritizing unit can respond with normal priority. This enables appropriate communication tailored to the user's emotions.

[0101] The AI ​​agent system can also include a social media reporting unit that analyzes the user's social media activity and optimizes the content of reports based on this analysis. For example, if a user frequently posts about a particular topic, the social media reporting unit can report information related to that topic. For instance, it could report, "Here's the latest information related to your recent posts." Furthermore, if the user belongs to a specific group, the social media reporting unit can report information related to that group. This enables effective reporting based on the user's social media activity.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The communications department proactively communicates with accounts that are recruiting using specific keywords. For example, it automatically detects posts containing specific keywords and sends messages to those accounts. Specifically, it can send messages such as "I'm interested. What kind of work is it?", "Please tell me more," and "What are the conditions?" to continue communication. Step 2: The judgment unit makes a judgment based on the accounts detected by the communications unit. For example, it analyzes the content of messages sent by the communications unit and determines whether there is a problem. Specifically, if it receives a message such as "This is a legitimate case. Please send a signal for details!", it can analyze its content and determine whether there is a problem. Step 3: The reporting department makes a report based on the results determined by the judgment department. For example, if the judgment department determines the result to be "OK," the reporting department reports that result to the business operator. Specifically, they can report something like, "This account is fine." Also, if the judgment department determines the result to be "NG," the reporting department can report that result to the business operator. Specifically, they can report something like, "This account has problems."

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the communication unit, determination unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the smart device 14, which automatically detects posts containing specific keywords and sends a message to the account. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the content of the message sent by the communication unit and determines whether there is a problem. The reporting unit is implemented by the control unit 46A of the smart device 14, which reports to the service provider based on the result determined by the determination unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the communication unit, determination unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the smart glasses 214, which automatically detects posts containing specific keywords and sends a message to the account. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the content of the message sent by the communication unit and determines whether there is a problem. The reporting unit is implemented by the control unit 46A of the smart glasses 214, which reports to the service provider based on the result determined by the determination unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the communication unit, determination unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the headset terminal 314, which automatically detects posts containing specific keywords and sends a message to the account. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the content of the message sent by the communication unit and determines whether there is a problem. The reporting unit is implemented by the control unit 46A of the headset terminal 314, which reports to the service provider based on the results determined by the determination unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the communication unit, determination unit, and reporting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the robot 414, which automatically detects posts containing specific keywords and sends a message to the account. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the content of the message sent by the communication unit and determines whether there is a problem. The reporting unit is implemented by the control unit 46A of the robot 414, which reports to the business operator based on the result determined by the determination unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] (Note 1) The communications department proactively communicates with accounts that are recruiting using specific keywords, A determination unit that makes a determination based on the account detected by the aforementioned communication unit, The system includes a reporting unit that provides a report based on the result determined by the determination unit. A system characterized by the following features. (Note 2) The aforementioned communications department, Proactively communicate with accounts that are recruiting using specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, It handles communication and applications on behalf of the user, and reports any problems to the service provider as "NG". The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting department, The business operator submits a report to facilitate account suspension and contact with the police. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications department, Start communication with the perpetrator. The system described in Appendix 1, characterized by the features described herein. (Note 6) The determination unit, If you are redirected to an app like Signal, report it as "NG". The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reporting department, The business operator will submit a report to enable them to respond quickly. The system described in Appendix 1, characterized by the features described herein. (Note 8) The determination unit, To understand the specific details of the crime and the methods used to lure the victim into a trap. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned communications department, It estimates the user's emotions and adjusts the timing of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned communications department, Analyze past communication history and select the optimal communication method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned communications department, Filtering is performed during communication based on account attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned communications department, It estimates the user's emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned communications department, When communicating, the system prioritizes communication with accounts that are more relevant, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned communications department, When communicating, analyze the account's social media activity and communicate with relevant accounts. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, When making a determination, we improve the accuracy of the determination by considering the interrelationships between accounts. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The determination is made by taking into account the account's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When making a determination, the geographical distribution of accounts will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, During the assessment process, we refer to relevant literature related to the account to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting department, We estimate the user's emotions and adjust the reporting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting department, When reporting, the reporting algorithm is optimized by referring to past reporting data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting department, When reporting, account attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting department, The system estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting department, When reporting, the most suitable reporting method will be selected, taking into account the account's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting department, When reporting, we will analyze the account's social media activity and suggest reporting methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 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 communications department proactively communicates with accounts that are recruiting using specific keywords, A determination unit that makes a determination based on the account detected by the aforementioned communication unit, The system includes a reporting unit that provides a report based on the result determined by the determination unit. A system characterized by the following features.

2. The determination unit, We handle communication and applications on behalf of the user, and report any problems to the service provider. The system according to feature 1.

3. The aforementioned reporting department, The business operator submits a report to facilitate account suspension and contact with the police. The system according to feature 1.

4. The aforementioned communications department, Start communication with the perpetrator. The system according to feature 1.

5. The determination unit, If you are redirected to an app such as Signals, report it as a problem. The system according to feature 1.

6. The aforementioned reporting department, The business operator will submit a report to enable them to respond quickly. The system according to feature 1.

7. The determination unit, To understand the specific details of the crime and the methods used to lure the victim into a trap. The system according to feature 1.

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