system

Abstract

The system according to this embodiment aims to analyze conversations at service counters and contribute to local culture and improve the quality of service at service counters. [Solution] The system according to the embodiment comprises a collection unit, a real-time analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects conversations at the counter. The real-time analysis unit analyzes the conversation data collected by the collection unit in real time. The analysis unit analyzes the data analyzed by the real-time analysis unit and extracts information about local culture and history. The generation unit creates materials based on the information extracted by the analysis unit. The provision unit provides the materials created by the generation 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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to take effective measures against nuisance behaviors at the windows of public facilities, and there is room for improvement in contributing to local culture and improving the soundness of window responses.

[0005] The system according to the embodiment aims to analyze conversations at the window and contribute to local culture and improve the soundness of window responses.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a real-time analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects conversations at the counter. The real-time analysis unit analyzes the conversation data collected by the collection unit in real time. The analysis unit analyzes the data analyzed by the real-time analysis unit and extracts information about local culture and history. The generation unit creates materials based on the information extracted by the analysis unit. The provision unit provides the materials created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze conversations at service counters and contribute to local culture while improving the quality of service at service counters. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention does not aim to eliminate disruptive individuals at public facility counters by utilizing generative AI, but rather to re-evaluate them as local storytellers through the analysis and collection of their conversations, thereby providing solace to their unfulfilled desires, contributing to local culture, and improving the quality of counter service. This system collects and analyzes the content of disruptive individuals' speech, extracts information on local culture and history, and creates and provides materials to achieve solace to disruptive individuals and contribute to local culture. For example, the system collects conversations at counters, and the generative AI analyzes them in real time. If a disruptive individual is talking about local traditional events or historical events, the generative AI extracts that information and provides it as materials. As a result, disruptive individuals recognize that their stories contribute to local culture and history, and their unfulfilled desires are alleviated. In addition, by entrusting the generative AI with the analysis of disruptive individuals' conversations, the workload of counter staff is reduced, and the quality of counter service is improved. In this way, the system can achieve solace to disruptive individuals and contribute to local culture.

[0029] The system according to the embodiment comprises a collection unit, a real-time analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects conversations at the counter. The collection unit collects, for example, the content of what the disruptive person says. The collection unit can use a generation AI to analyze the content of the conversation in real time and record what the disruptive person says. The real-time analysis unit analyzes the conversation data collected by the collection unit in real time. The real-time analysis unit can use a generation AI to analyze the collected conversation data and extract information about local culture and history. The real-time analysis unit can also use a generation AI to analyze the conversation data and extract important information. The analysis unit analyzes the data analyzed by the real-time analysis unit and extracts information about local culture and history. The analysis unit can use a generation AI to analyze the collected conversation data and identify information about local culture and history. The analysis unit can also use a generation AI to analyze the conversation data and extract information about local culture and history. The generation unit creates materials based on the information extracted by the analysis unit. The generation unit can, for example, use a generation AI to organize the extracted information and create materials. The generation unit can also use a generation AI to create materials based on the extracted information. The provision unit provides the materials created by the generation unit. The provision unit can, for example, provide materials created using a generation AI. The provision unit can also provide materials created using a generation AI. As a result, the system according to the embodiment can achieve emotional relief for those who engage in nuisance behavior and contribute to local culture.

[0030] The data collection unit collects conversations at service counters. Specifically, it uses microphones and recording devices installed at the counters to collect audio data of conversations. This allows for accurate recording of what the disruptive party says. The data collection unit can use generative AI to analyze the content of conversations in real time and record what the disruptive party says. The generative AI uses speech recognition technology to convert the collected audio data into text data. This allows the content of the speech to be saved as text information and used for subsequent analysis and interpretation. Furthermore, the data collection unit centrally manages the collected data and can collaborate with other departments as needed. For example, the collected data is stored on a cloud server and made accessible to the real-time analysis unit and the data interpretation unit. In addition, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The real-time analysis unit analyzes conversation data collected by the collection unit in real time. Specifically, it uses generative AI to analyze the collected conversation data and extract information about local culture and history. The generative AI uses natural language processing technology to understand the content of the conversation data and identify important information. For example, it extracts place names and historical events mentioned in the conversation and analyzes their relationships. The generative AI can also understand the context of the conversation and analyze the intentions and emotions of the person committing the nuisance. As a result, the real-time analysis unit can analyze the collected data quickly and accurately and extract important information. Furthermore, the real-time analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, based on past conversation data, it can predict the tendency for nuisance behavior to occur in specific areas and time periods and plan future countermeasures. In addition, the real-time analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the real-time analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The analysis department further analyzes the data analyzed by the real-time analysis department to extract information about local culture and history. Specifically, it uses generative AI to analyze collected conversation data and identify information about local culture and history. The generative AI utilizes natural language processing technology to deeply understand the content of conversations and extract relevant information. For example, it extracts information about specific cultural events or historical figures mentioned in conversations and analyzes their background and relevance. The generative AI can also understand the context of conversations and analyze the intent and emotions of the disruptive party's statements. This allows the analysis department to analyze the collected data in detail and extract valuable information about local culture and history. Furthermore, the analysis department can also use historical data and statistical information to analyze long-term trends and patterns. For example, based on past conversation data, it can predict the occurrence of cultural events in specific areas and time periods and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0033] The generation unit creates materials based on the information extracted by the analysis unit. Specifically, it uses generation AI to organize the extracted information and create materials. The generation AI uses natural language generation technology to organize the extracted information in an easy-to-understand manner and compile it into documents and reports. For example, it can create tourist guides and historical commentaries based on information about local culture and history. The generation AI can also create presentation materials and digital content based on the extracted information. This allows the generation unit to effectively utilize the extracted information and create materials in various formats. Furthermore, the generation unit can evaluate the quality of the created materials and make corrections and improvements as needed. For example, it can use generation AI to check the content of the created materials and correct errors and unclear parts. The generation unit can also collect user feedback and continuously improve the content and format of the materials. This allows the generation unit to create and provide high-quality materials to users.

[0034] The provisioning department provides materials created by the generation department. Specifically, it provides materials created using generation AI. The provisioning department has means to deliver the created materials to users quickly and effectively. For example, it distributes materials created using generation AI via email or cloud storage. The provisioning department can also publish the created materials through websites and applications, making them accessible to users. This allows the provisioning department to provide the created materials to a wide range of users and promote information sharing and utilization. Furthermore, the provisioning department can collect user feedback and continuously improve the delivery methods and content of the materials. For example, it can review the delivery methods and provide a more user-friendly interface based on user opinions and requests. The provisioning department can also monitor the status of material delivery and adjust the delivery methods as needed. This allows the provisioning department to deliver information to users quickly and reliably, improving the reliability and usability of the entire system.

[0035] The collection unit can collect the content of the nuisance perpetrator's speech. For example, the collection unit collects the content of the nuisance perpetrator's speech. The collection unit can use a generation AI to analyze the content of the conversation in real time and record the content of the nuisance perpetrator's speech. This makes it possible to analyze the conversation data by collecting the content of the nuisance perpetrator's speech. Some or all of the above processing in the collection unit may be performed using the generation AI or not. For example, the collection unit can input the content of the nuisance perpetrator's speech into the generation AI and have the generation AI perform the collection of the speech content.

[0036] The real-time analysis unit can analyze collected conversation data in real time. For example, the real-time analysis unit analyzes collected conversation data in real time. The real-time analysis unit can use a generative AI to analyze the collected conversation data and extract information about local culture and history. This enables immediate information extraction by analyzing the collected conversation data in real time. Some or all of the above-described processes in the real-time analysis unit may be performed using a generative AI or not. For example, the real-time analysis unit can input collected conversation data into a generative AI and have the generative AI perform the analysis in real time.

[0037] The analysis unit can analyze collected conversation data and extract information about local culture and history. For example, the analysis unit can analyze collected conversation data and extract information about local culture and history. The analysis unit can use generative AI to analyze collected conversation data and identify information about local culture and history. This allows for contributions to local culture by analyzing conversation data and extracting information about local culture and history. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input collected conversation data into a generative AI and have the generative AI extract information about local culture and history.

[0038] The generation unit can create documents based on the extracted information. For example, the generation unit can create documents based on the extracted information. The generation unit can use a generation AI to organize the extracted information and create documents. This makes it possible to organize and provide information by creating documents based on the extracted information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI create the documents.

[0039] The provisioning unit can provide the created materials. For example, the provisioning unit can provide the created materials. The provisioning unit can provide the created materials using a generative AI. This makes it possible to share information and contribute to local culture by providing the created materials. Some or all of the above processing in the provisioning unit may be performed using a generative AI or not. For example, the provisioning unit can input the created materials into a generative AI and have the generative AI perform the provision of the materials.

[0040] The collection unit can prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. For example, the collection unit can prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. The collection unit can use a generative AI to prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is talking about historical events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is providing information about local culture, the collection unit can prioritize collecting that conversation. This makes it possible to collect important information by prioritizing the collection of conversations that contain specific keywords. Some or all of the above processing in the collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the collection unit can input the content of a conversation into a generative AI and have the generative AI collect conversations that contain specific keywords.

[0041] The collection unit can determine the priority of conversations to collect based on their length and frequency. For example, the collection unit can prioritize collecting conversations based on their length and frequency. The collection unit can use generative AI to determine the priority of conversations to collect based on their length and frequency. For example, it can prioritize collecting long conversations to obtain detailed information. The collection unit can prioritize collecting frequently occurring conversations and analyze patterns. Even short conversations can be prioritized if they contain important information. This enables efficient information gathering by prioritizing conversations based on their length and frequency. Some or all of the above-described processes in the collection unit may be performed using generative AI, or without it. For example, the collection unit can input conversation length and frequency data into the generative AI and have the generative AI determine the priority of conversations to collect.

[0042] The collection unit can prioritize the collection of conversations that are highly relevant, taking into account the user's geographical location information. For example, the collection unit can prioritize the collection of conversations that are highly relevant, taking into account the user's geographical location information. The collection unit can use a generation AI to prioritize the collection of conversations that are highly relevant, taking into account the user's geographical location information. For example, if a nuisance perpetrator provides information about a specific region, the collection unit can prioritize the collection of that conversation. If a nuisance perpetrator provides information about a specific place, the collection unit can prioritize the collection of that conversation. If a nuisance perpetrator provides information about a specific event, the collection unit can prioritize the collection of that conversation. This enables region-specific information collection by prioritizing the collection of conversations that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the collection unit can input the user's geographical location information into a generation AI and have the generation AI perform the collection of highly relevant conversations.

[0043] The collection unit can analyze a user's social media activity and collect relevant conversations when collecting conversations. For example, the collection unit can analyze a user's social media activity and collect relevant conversations when collecting conversations. The collection unit can use generative AI to analyze a user's social media activity and collect relevant conversations when collecting conversations. For example, it can collect what a nuisance perpetrator is talking about on social media and prioritize collecting relevant conversations. The collection unit can collect information that a nuisance perpetrator is sharing on social media and prioritize collecting relevant conversations. The collection unit can collect information about accounts that a nuisance perpetrator follows on social media and prioritize collecting relevant conversations. This enables broader information gathering by analyzing a user's social media activity and collecting relevant conversations. Some or all of the above processing in the collection unit may be performed using generative AI or not. For example, the collection unit can input data on a user's social media activity into a generative AI and have the generative AI collect relevant conversations.

[0044] The real-time analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, the real-time analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. The real-time analysis unit can use generative AI to prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the real-time analysis unit can prioritize the analysis of that conversation if the nuisance perpetrator is talking about historical events. The real-time analysis unit can prioritize the analysis of conversations if the nuisance perpetrator is providing information about local culture. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the real-time analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the real-time analysis unit can input the content of a conversation into the generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0045] The real-time analysis unit can determine the priority of conversations to analyze based on their length and frequency. For example, the real-time analysis unit can determine the priority of conversations to analyze based on their length and frequency. The real-time analysis unit can use generative AI to determine the priority of conversations to analyze based on their length and frequency. For example, it can prioritize analyzing long conversations to obtain detailed information. The real-time analysis unit can prioritize analyzing frequently occurring conversations to analyze patterns. Even short conversations can be prioritized if they contain important information. This enables efficient information analysis by determining the priority of conversations to analyze based on their length and frequency. Some or all of the above-described processes in the real-time analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the real-time analysis unit can input conversation length and frequency data into the generative AI and have the generative AI determine the priority of conversations to analyze.

[0046] The real-time analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information. For example, the real-time analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information. The real-time analysis unit can use a generation AI to prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information. For example, if a nuisance perpetrator provides information about a specific region, the unit will prioritize the analysis of that conversation. The real-time analysis unit can prioritize the analysis of conversations that a nuisance perpetrator provides information about a specific location. The real-time analysis unit can prioritize the analysis of conversations that a nuisance perpetrator provides information about a specific event. This enables region-specific information analysis by prioritizing the analysis of conversations that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the real-time analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the real-time analysis unit can input the user's geographical location information into a generation AI and have the generation AI perform the analysis of highly relevant conversations.

[0047] The real-time analysis unit can analyze a user's social media activity and analyze related conversations when analyzing a conversation. For example, the real-time analysis unit can analyze a user's social media activity and analyze related conversations when analyzing a conversation. The real-time analysis unit can use generative AI to analyze a user's social media activity and analyze related conversations when analyzing a conversation. For example, it can analyze what a nuisance perpetrator is talking about on social media and prioritize the analysis of related conversations. The real-time analysis unit can analyze information shared by a nuisance perpetrator on social media and prioritize the analysis of related conversations. The real-time analysis unit can analyze information from accounts followed by a nuisance perpetrator on social media and prioritize the analysis of related conversations. This enables broader information analysis by analyzing a user's social media activity and analyzing related conversations. Some or all of the above processing in the real-time analysis unit may be performed using generative AI or not. For example, the real-time analysis unit can input data on a user's social media activity into a generative AI and have the generative AI perform the analysis of related conversations.

[0048] The analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, the analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. The analysis unit can use generative AI to prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is talking about historical events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is providing information about local culture, the analysis unit can prioritize the analysis of that conversation. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the content of a conversation into a generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0049] The analysis unit can determine the priority of conversations to analyze based on their length and frequency. For example, the analysis unit can prioritize the analysis of conversations based on their length and frequency. The analysis unit can use generative AI to determine the priority of conversations to analyze based on their length and frequency. For example, it can prioritize the analysis of longer conversations to obtain detailed information. The analysis unit can prioritize the analysis of frequently occurring conversations to analyze patterns. Even short conversations can be prioritized if they contain important information. This enables efficient information analysis by prioritizing conversations based on their length and frequency. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input conversation length and frequency data into the generative AI and have the generative AI determine the priority of conversations to analyze.

[0050] The analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information, when analyzing conversations. For example, the analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information, when analyzing conversations. The analysis unit can use generative AI to prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information, when analyzing conversations. For example, if a nuisance perpetrator provides information about a specific region, the analysis unit can prioritize the analysis of that conversation. If a nuisance perpetrator provides information about a specific place, the analysis unit can prioritize the analysis of that conversation. If a nuisance perpetrator provides information about a specific event, the analysis unit can prioritize the analysis of that conversation. This enables region-specific information analysis by prioritizing the analysis of conversations that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the user's geographical location information into the generative AI and have the generative AI perform the analysis of highly relevant conversations.

[0051] The analysis unit can analyze a user's social media activity and related conversations when analyzing a conversation. For example, the analysis unit can analyze a user's social media activity and related conversations when analyzing a conversation. The analysis unit can use generative AI to analyze a user's social media activity and related conversations when analyzing a conversation. For example, it can analyze what a nuisance perpetrator is talking about on social media and prioritize the analysis of related conversations. The analysis unit can analyze the information a nuisance perpetrator is sharing on social media and prioritize the analysis of related conversations. The analysis unit can analyze the information of accounts that a nuisance perpetrator follows on social media and prioritize the analysis of related conversations. This enables broader information analysis by analyzing a user's social media activity and related conversations. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input data on a user's social media activity into a generative AI and have the generative AI perform the analysis of related conversations.

[0052] The generation unit can prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. For example, the generation unit can prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. The generation unit can use a generation AI to prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the generation unit can prioritize reflecting that information in the document. If the nuisance perpetrator is talking about historical events, the generation unit can prioritize reflecting that information in the document. If the nuisance perpetrator is providing information about local culture, the generation unit can prioritize reflecting that information in the document. This makes it possible to document important information by prioritizing the reflection of conversations containing specific keywords. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the content of a conversation into a generation AI and have the generation AI perform the task of documenting conversations containing specific keywords.

[0053] The generation unit can determine the priority of conversations to be included in the document based on their length and frequency. For example, the generation unit can determine the priority of conversations to be included in the document based on their length and frequency. The generation unit can use a generation AI to determine the priority of conversations to be included in the document based on their length and frequency. For example, it can prioritize including long conversations in the document to provide detailed information. The generation unit can prioritize including frequently occurring conversations in the document to analyze patterns. The generation unit can also prioritize including short conversations in the document if they contain important information. This enables efficient document generation by determining the priority of conversations to be included in the document based on their length and frequency. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the length and frequency of conversations into a generation AI and have the generation AI determine the priority of conversations to be included in the document.

[0054] The generation unit can prioritize reflecting highly relevant information by considering the user's geographical location when generating materials. For example, the generation unit can prioritize reflecting highly relevant information by considering the user's geographical location when generating materials. The generation unit can use a generation AI to prioritize reflecting highly relevant information by considering the user's geographical location when generating materials. For example, if a nuisance perpetrator provides information about a specific region, that information will be prioritized and reflected in the materials. The generation unit can prioritize reflecting information about a specific location if a nuisance perpetrator provides information about a specific event if a nuisance perpetrator provides information about that event. This makes it possible to generate region-specific materials by prioritizing the reflection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the creation of materials with highly relevant information.

[0055] The generation unit can analyze the user's social media activity and reflect relevant information in the material when generating it. For example, the generation unit can analyze the user's social media activity and reflect relevant information in the material when generating it. The generation unit can use a generation AI to analyze the user's social media activity and reflect relevant information in the material when generating it. For example, it can reflect the content that the nuisance perpetrator is talking about on social media in the material and provide relevant information. The generation unit can reflect the information that the nuisance perpetrator is sharing on social media in the material and provide relevant information. The generation unit can reflect the information of accounts that the nuisance perpetrator is following on social media in the material and provide relevant information. This makes it possible to create a more comprehensive set of information by analyzing the user's social media activity and reflecting relevant information in the material. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the creation of relevant information.

[0056] The service provider can prioritize providing materials containing specific keywords, depending on the content of the materials. For example, the service provider can prioritize providing materials containing specific keywords, depending on the content of the materials. The service provider can use a generative AI to prioritize providing materials containing specific keywords, depending on the content of the materials. For example, if a nuisance perpetrator talks about local traditional events, the service provider can prioritize reflecting that information in the materials and providing it. If a nuisance perpetrator talks about historical events, the service provider can prioritize reflecting that information in the materials and providing it. If a nuisance perpetrator provides information about local culture, the service provider can prioritize reflecting that information in the materials and providing it. This makes it possible to provide important information by prioritizing the provision of materials containing specific keywords. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the content of the materials into a generative AI and have the generative AI provide materials containing specific keywords.

[0057] The service provider can prioritize providing highly relevant materials by considering the user's geographical location when providing materials. For example, the service provider can prioritize providing highly relevant materials by considering the user's geographical location when providing materials. The service provider can use a generation AI to prioritize providing highly relevant materials by considering the user's geographical location when providing materials. For example, if a nuisance perpetrator provides information about a specific region, that information can be prioritized and provided in the materials. If a nuisance perpetrator provides information about a specific location, the service provider can prioritize and provide that information. If a nuisance perpetrator provides information about a specific event, the service provider can prioritize and provide that information. This makes it possible to provide region-specific information by prioritizing the provision of highly relevant materials by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's geographical location information into a generation AI and have the generation AI provide highly relevant materials.

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

[0059] The collection unit can prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is talking about historical events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is providing information about local culture, the collection unit can prioritize collecting that conversation. This makes it possible to collect important information by prioritizing the collection of conversations that contain specific keywords. Some or all of the above processing in the collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the collection unit can input the content of a conversation into a generative AI and have the generative AI perform the collection of conversations that contain specific keywords.

[0060] The real-time analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the unit will prioritize the analysis of that conversation. The real-time analysis unit can prioritize the analysis of conversations if the nuisance perpetrator is talking about historical events. The real-time analysis unit can prioritize the analysis of conversations if the nuisance perpetrator is providing information about local culture. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the real-time analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the real-time analysis unit can input the content of a conversation into a generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0061] The analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is talking about historical events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is providing information about local culture, the analysis unit can prioritize the analysis of that conversation. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the content of a conversation into a generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0062] The generation unit can prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, that information will be prioritized in the document. If the nuisance perpetrator is talking about historical events, that information will be prioritized in the document. If the nuisance perpetrator is providing information about local culture, that information will be prioritized in the document. This makes it possible to document important information by prioritizing the reflection of conversations containing specific keywords. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the content of the conversation into a generation AI and have the generation AI perform the task of documenting conversations containing specific keywords.

[0063] The information provider can prioritize providing materials containing specific keywords, depending on the content of the materials. For example, if a nuisance perpetrator talks about local traditional events, that information will be prioritized and provided in the materials. If a nuisance perpetrator talks about historical events, that information will be prioritized and provided in the materials. If a nuisance perpetrator provides information about local culture, that information will be prioritized and provided in the materials. This makes it possible to provide important information by prioritizing the provision of materials containing specific keywords. Some or all of the above processing in the information provider may be performed using a generative AI, or not. For example, the information provider can input the content of the materials into a generative AI and have the generative AI provide materials containing specific keywords.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects conversations at the counter. For example, it can collect the content of what the nuisance perpetrator says. The collection unit can use a generation AI to analyze the content of the conversation in real time and record what the nuisance perpetrator says. Step 2: The real-time analysis unit analyzes the conversation data collected by the collection unit in real time. For example, it can use a generative AI to analyze the collected conversation data and extract information about local culture and history. Step 3: The analysis unit analyzes the data analyzed by the real-time analysis unit and extracts information about the local culture and history. For example, it can use generative AI to analyze the collected conversation data and identify information about the local culture and history. Step 4: The generation unit creates documents based on the information extracted by the analysis unit. For example, a generation AI can be used to organize the extracted information and create documents. Step 5: The providing unit provides the materials created by the generating unit. For example, materials created using a generation AI can be provided.

[0066] (Example of form 2) The system according to an embodiment of the present invention does not aim to eliminate disruptive individuals at public facility counters by utilizing generative AI, but rather to re-evaluate them as local storytellers through the analysis and collection of their conversations, thereby providing solace to their unfulfilled desires, contributing to local culture, and improving the quality of counter service. This system collects and analyzes the content of disruptive individuals' speech, extracts information on local culture and history, and creates and provides materials to achieve solace to disruptive individuals and contribute to local culture. For example, the system collects conversations at counters, and the generative AI analyzes them in real time. If a disruptive individual is talking about local traditional events or historical events, the generative AI extracts that information and provides it as materials. As a result, disruptive individuals recognize that their stories contribute to local culture and history, and their unfulfilled desires are alleviated. In addition, by entrusting the generative AI with the analysis of disruptive individuals' conversations, the workload of counter staff is reduced, and the quality of counter service is improved. In this way, the system can achieve solace to disruptive individuals and contribute to local culture.

[0067] The system according to the embodiment comprises a collection unit, a real-time analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects conversations at the counter. The collection unit collects, for example, the content of what the disruptive person says. The collection unit can use a generation AI to analyze the content of the conversation in real time and record what the disruptive person says. The real-time analysis unit analyzes the conversation data collected by the collection unit in real time. The real-time analysis unit can use a generation AI to analyze the collected conversation data and extract information about local culture and history. The real-time analysis unit can also use a generation AI to analyze the conversation data and extract important information. The analysis unit analyzes the data analyzed by the real-time analysis unit and extracts information about local culture and history. The analysis unit can use a generation AI to analyze the collected conversation data and identify information about local culture and history. The analysis unit can also use a generation AI to analyze the conversation data and extract information about local culture and history. The generation unit creates materials based on the information extracted by the analysis unit. The generation unit can, for example, use a generation AI to organize the extracted information and create materials. The generation unit can also use a generation AI to create materials based on the extracted information. The provision unit provides the materials created by the generation unit. The provision unit can, for example, provide materials created using a generation AI. The provision unit can also provide materials created using a generation AI. As a result, the system according to the embodiment can achieve emotional relief for those who engage in nuisance behavior and contribute to local culture.

[0068] The data collection unit collects conversations at service counters. Specifically, it uses microphones and recording devices installed at the counters to collect audio data of conversations. This allows for accurate recording of what the disruptive party says. The data collection unit can use generative AI to analyze the content of conversations in real time and record what the disruptive party says. The generative AI uses speech recognition technology to convert the collected audio data into text data. This allows the content of the speech to be saved as text information and used for subsequent analysis and interpretation. Furthermore, the data collection unit centrally manages the collected data and can collaborate with other departments as needed. For example, the collected data is stored on a cloud server and made accessible to the real-time analysis unit and the data interpretation unit. In addition, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0069] The real-time analysis unit analyzes conversation data collected by the collection unit in real time. Specifically, it uses generative AI to analyze the collected conversation data and extract information about local culture and history. The generative AI uses natural language processing technology to understand the content of the conversation data and identify important information. For example, it extracts place names and historical events mentioned in the conversation and analyzes their relationships. The generative AI can also understand the context of the conversation and analyze the intentions and emotions of the person committing the nuisance. As a result, the real-time analysis unit can analyze the collected data quickly and accurately and extract important information. Furthermore, the real-time analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, based on past conversation data, it can predict the tendency for nuisance behavior to occur in specific areas and time periods and plan future countermeasures. In addition, the real-time analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the real-time analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0070] The analysis department further analyzes the data analyzed by the real-time analysis department to extract information about local culture and history. Specifically, it uses generative AI to analyze collected conversation data and identify information about local culture and history. The generative AI utilizes natural language processing technology to deeply understand the content of conversations and extract relevant information. For example, it extracts information about specific cultural events or historical figures mentioned in conversations and analyzes their background and relevance. The generative AI can also understand the context of conversations and analyze the intent and emotions of the disruptive party's statements. This allows the analysis department to analyze the collected data in detail and extract valuable information about local culture and history. Furthermore, the analysis department can also use historical data and statistical information to analyze long-term trends and patterns. For example, based on past conversation data, it can predict the occurrence of cultural events in specific areas and time periods and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0071] The generation unit creates materials based on the information extracted by the analysis unit. Specifically, it uses generation AI to organize the extracted information and create materials. The generation AI uses natural language generation technology to organize the extracted information in an easy-to-understand manner and compile it into documents and reports. For example, it can create tourist guides and historical commentaries based on information about local culture and history. The generation AI can also create presentation materials and digital content based on the extracted information. This allows the generation unit to effectively utilize the extracted information and create materials in various formats. Furthermore, the generation unit can evaluate the quality of the created materials and make corrections and improvements as needed. For example, it can use generation AI to check the content of the created materials and correct errors and unclear parts. The generation unit can also collect user feedback and continuously improve the content and format of the materials. This allows the generation unit to create and provide high-quality materials to users.

[0072] The provisioning department provides materials created by the generation department. Specifically, it provides materials created using generation AI. The provisioning department has means to deliver the created materials to users quickly and effectively. For example, it distributes materials created using generation AI via email or cloud storage. The provisioning department can also publish the created materials through websites and applications, making them accessible to users. This allows the provisioning department to provide the created materials to a wide range of users and promote information sharing and utilization. Furthermore, the provisioning department can collect user feedback and continuously improve the delivery methods and content of the materials. For example, it can review the delivery methods and provide a more user-friendly interface based on user opinions and requests. The provisioning department can also monitor the status of material delivery and adjust the delivery methods as needed. This allows the provisioning department to deliver information to users quickly and reliably, improving the reliability and usability of the entire system.

[0073] The collection unit can collect the content of the nuisance perpetrator's speech. For example, the collection unit collects the content of the nuisance perpetrator's speech. The collection unit can use a generation AI to analyze the content of the conversation in real time and record the content of the nuisance perpetrator's speech. This makes it possible to analyze the conversation data by collecting the content of the nuisance perpetrator's speech. Some or all of the above processing in the collection unit may be performed using the generation AI or not. For example, the collection unit can input the content of the nuisance perpetrator's speech into the generation AI and have the generation AI perform the collection of the speech content.

[0074] The real-time analysis unit can analyze collected conversation data in real time. For example, the real-time analysis unit analyzes collected conversation data in real time. The real-time analysis unit can use a generative AI to analyze the collected conversation data and extract information about local culture and history. This enables immediate information extraction by analyzing the collected conversation data in real time. Some or all of the above-described processes in the real-time analysis unit may be performed using a generative AI or not. For example, the real-time analysis unit can input collected conversation data into a generative AI and have the generative AI perform the analysis in real time.

[0075] The analysis unit can analyze collected conversation data and extract information about local culture and history. For example, the analysis unit can analyze collected conversation data and extract information about local culture and history. The analysis unit can use generative AI to analyze collected conversation data and identify information about local culture and history. This allows for contributions to local culture by analyzing conversation data and extracting information about local culture and history. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input collected conversation data into a generative AI and have the generative AI extract information about local culture and history.

[0076] The generation unit can create documents based on the extracted information. For example, the generation unit can create documents based on the extracted information. The generation unit can use a generation AI to organize the extracted information and create documents. This makes it possible to organize and provide information by creating documents based on the extracted information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI create the documents.

[0077] The provisioning unit can provide the created materials. For example, the provisioning unit can provide the created materials. The provisioning unit can provide the created materials using a generative AI. This makes it possible to share information and contribute to local culture by providing the created materials. Some or all of the above processing in the provisioning unit may be performed using a generative AI or not. For example, the provisioning unit can input the created materials into a generative AI and have the generative AI perform the provision of the materials.

[0078] The collection unit can estimate the user's emotions and adjust the timing of conversation collection based on the estimated emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of conversation collection based on the estimated emotions. The collection unit can use generative AI to estimate the user's emotions and adjust the timing of conversation collection based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the collection unit can temporarily stop collecting conversations and wait until the perpetrator calms down. If the nuisance perpetrator is calm, the collection unit can start collecting conversations and collect detailed information. If the nuisance perpetrator is stressed, the collection unit can refrain from collecting conversations and wait until the perpetrator relaxes. This allows for appropriate information collection by adjusting the timing of conversation collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using generative AI or not. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0079] The collection unit can prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. For example, the collection unit can prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. The collection unit can use a generative AI to prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is talking about historical events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is providing information about local culture, the collection unit can prioritize collecting that conversation. This makes it possible to collect important information by prioritizing the collection of conversations that contain specific keywords. Some or all of the above processing in the collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the collection unit can input the content of a conversation into a generative AI and have the generative AI collect conversations that contain specific keywords.

[0080] The collection unit can determine the priority of conversations to collect based on their length and frequency. For example, the collection unit can prioritize collecting conversations based on their length and frequency. The collection unit can use generative AI to determine the priority of conversations to collect based on their length and frequency. For example, it can prioritize collecting long conversations to obtain detailed information. The collection unit can prioritize collecting frequently occurring conversations and analyze patterns. Even short conversations can be prioritized if they contain important information. This enables efficient information gathering by prioritizing conversations based on their length and frequency. Some or all of the above-described processes in the collection unit may be performed using generative AI, or without it. For example, the collection unit can input conversation length and frequency data into the generative AI and have the generative AI determine the priority of conversations to collect.

[0081] The data collection unit can estimate the user's emotions and determine the priority of conversations to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions and determine the priority of conversations to collect based on the estimated emotions. The data collection unit can use generative AI to estimate the user's emotions and determine the priority of conversations to collect based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the data collection unit can prioritize collecting that conversation to obtain detailed information. If the nuisance perpetrator is calm, the data collection unit can prioritize collecting that conversation to obtain detailed information. If the nuisance perpetrator is stressed, the data collection unit can prioritize collecting that conversation to obtain detailed information. This enables appropriate information collection by determining the priority of conversations to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0082] The collection unit can prioritize the collection of conversations that are highly relevant, taking into account the user's geographical location information. For example, the collection unit can prioritize the collection of conversations that are highly relevant, taking into account the user's geographical location information. The collection unit can use a generation AI to prioritize the collection of conversations that are highly relevant, taking into account the user's geographical location information. For example, if a nuisance perpetrator provides information about a specific region, the collection unit can prioritize the collection of that conversation. If a nuisance perpetrator provides information about a specific place, the collection unit can prioritize the collection of that conversation. If a nuisance perpetrator provides information about a specific event, the collection unit can prioritize the collection of that conversation. This enables region-specific information collection by prioritizing the collection of conversations that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the collection unit can input the user's geographical location information into a generation AI and have the generation AI perform the collection of highly relevant conversations.

[0083] The collection unit can analyze a user's social media activity and collect relevant conversations when collecting conversations. For example, the collection unit can analyze a user's social media activity and collect relevant conversations when collecting conversations. The collection unit can use generative AI to analyze a user's social media activity and collect relevant conversations when collecting conversations. For example, it can collect what a nuisance perpetrator is talking about on social media and prioritize collecting relevant conversations. The collection unit can collect information that a nuisance perpetrator is sharing on social media and prioritize collecting relevant conversations. The collection unit can collect information about accounts that a nuisance perpetrator follows on social media and prioritize collecting relevant conversations. This enables broader information gathering by analyzing a user's social media activity and collecting relevant conversations. Some or all of the above processing in the collection unit may be performed using generative AI or not. For example, the collection unit can input data on a user's social media activity into a generative AI and have the generative AI collect relevant conversations.

[0084] The real-time analysis unit can estimate the user's emotions and adjust the real-time analysis method based on the estimated emotions. For example, the real-time analysis unit can estimate the user's emotions and adjust the real-time analysis method based on the estimated emotions. The real-time analysis unit can use generative AI to estimate the user's emotions and adjust the real-time analysis method based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the real-time analysis unit can temporarily stop analyzing the conversation and wait until the perpetrator calms down. If the nuisance perpetrator is calm, the real-time analysis unit can start analyzing the conversation and analyze detailed information. If the nuisance perpetrator is stressed, the real-time analysis unit can refrain from analyzing the conversation and wait until the perpetrator relaxes. This allows for appropriate information analysis by adjusting the real-time analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the real-time analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the real-time analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The real-time analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, the real-time analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. The real-time analysis unit can use generative AI to prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the real-time analysis unit can prioritize the analysis of that conversation if the nuisance perpetrator is talking about historical events. The real-time analysis unit can prioritize the analysis of conversations if the nuisance perpetrator is providing information about local culture. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the real-time analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the real-time analysis unit can input the content of a conversation into the generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0086] The real-time analysis unit can determine the priority of conversations to analyze based on their length and frequency. For example, the real-time analysis unit can determine the priority of conversations to analyze based on their length and frequency. The real-time analysis unit can use generative AI to determine the priority of conversations to analyze based on their length and frequency. For example, it can prioritize analyzing long conversations to obtain detailed information. The real-time analysis unit can prioritize analyzing frequently occurring conversations to analyze patterns. Even short conversations can be prioritized if they contain important information. This enables efficient information analysis by determining the priority of conversations to analyze based on their length and frequency. Some or all of the above-described processes in the real-time analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the real-time analysis unit can input conversation length and frequency data into the generative AI and have the generative AI determine the priority of conversations to analyze.

[0087] The real-time analysis unit can estimate the user's emotions and determine the priority of real-time analysis based on the estimated user emotions. For example, the real-time analysis unit can estimate the user's emotions and determine the priority of real-time analysis based on the estimated user emotions. The real-time analysis unit can use generative AI to estimate the user's emotions and determine the priority of real-time analysis based on the estimated user emotions. For example, if the nuisance perpetrator is agitated, the unit prioritizes analyzing that conversation to obtain detailed information. If the nuisance perpetrator is calm, the real-time analysis unit prioritizes analyzing that conversation to obtain detailed information. If the nuisance perpetrator is stressed, the real-time analysis unit prioritizes analyzing that conversation to obtain detailed information. This enables appropriate information analysis by determining the priority of real-time analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the real-time analysis unit may be performed using generative AI or not. For example, the real-time analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The real-time analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information. For example, the real-time analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information. The real-time analysis unit can use a generation AI to prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information. For example, if a nuisance perpetrator provides information about a specific region, the unit will prioritize the analysis of that conversation. The real-time analysis unit can prioritize the analysis of conversations that a nuisance perpetrator provides information about a specific location. The real-time analysis unit can prioritize the analysis of conversations that a nuisance perpetrator provides information about a specific event. This enables region-specific information analysis by prioritizing the analysis of conversations that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the real-time analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the real-time analysis unit can input the user's geographical location information into a generation AI and have the generation AI perform the analysis of highly relevant conversations.

[0089] The real-time analysis unit can analyze a user's social media activity and analyze related conversations when analyzing a conversation. For example, the real-time analysis unit can analyze a user's social media activity and analyze related conversations when analyzing a conversation. The real-time analysis unit can use generative AI to analyze a user's social media activity and analyze related conversations when analyzing a conversation. For example, it can analyze what a nuisance perpetrator is talking about on social media and prioritize the analysis of related conversations. The real-time analysis unit can analyze information shared by a nuisance perpetrator on social media and prioritize the analysis of related conversations. The real-time analysis unit can analyze information from accounts followed by a nuisance perpetrator on social media and prioritize the analysis of related conversations. This enables broader information analysis by analyzing a user's social media activity and analyzing related conversations. Some or all of the above processing in the real-time analysis unit may be performed using generative AI or not. For example, the real-time analysis unit can input data on a user's social media activity into a generative AI and have the generative AI perform the analysis of related conversations.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. The analysis unit can use generative AI to estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the nuisance is agitated, the analysis unit can temporarily stop analyzing the conversation and wait until the nuisance calms down. If the nuisance is calm, the analysis unit can start analyzing the conversation and analyze detailed information. If the nuisance is stressed, the analysis unit can refrain from analyzing the conversation and wait until the nuisance relaxes. This allows for appropriate information analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, the analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. The analysis unit can use generative AI to prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is talking about historical events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is providing information about local culture, the analysis unit can prioritize the analysis of that conversation. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the content of a conversation into a generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0092] The analysis unit can determine the priority of conversations to analyze based on their length and frequency. For example, the analysis unit can prioritize the analysis of conversations based on their length and frequency. The analysis unit can use generative AI to determine the priority of conversations to analyze based on their length and frequency. For example, it can prioritize the analysis of longer conversations to obtain detailed information. The analysis unit can prioritize the analysis of frequently occurring conversations to analyze patterns. Even short conversations can be prioritized if they contain important information. This enables efficient information analysis by prioritizing conversations based on their length and frequency. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input conversation length and frequency data into the generative AI and have the generative AI determine the priority of conversations to analyze.

[0093] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. The analysis unit can use generative AI to estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the analysis unit can prioritize analyzing that conversation to obtain detailed information. If the nuisance perpetrator is calm, the analysis unit can prioritize analyzing that conversation to obtain detailed information. If the nuisance perpetrator is stressed, the analysis unit can prioritize analyzing that conversation to obtain detailed information. This enables appropriate information analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information, when analyzing conversations. For example, the analysis unit can prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information, when analyzing conversations. The analysis unit can use generative AI to prioritize the analysis of conversations that are highly relevant, taking into account the user's geographical location information, when analyzing conversations. For example, if a nuisance perpetrator provides information about a specific region, the analysis unit can prioritize the analysis of that conversation. If a nuisance perpetrator provides information about a specific place, the analysis unit can prioritize the analysis of that conversation. If a nuisance perpetrator provides information about a specific event, the analysis unit can prioritize the analysis of that conversation. This enables region-specific information analysis by prioritizing the analysis of conversations that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the user's geographical location information into the generative AI and have the generative AI perform the analysis of highly relevant conversations.

[0095] The analysis unit can analyze a user's social media activity and related conversations when analyzing a conversation. For example, the analysis unit can analyze a user's social media activity and related conversations when analyzing a conversation. The analysis unit can use generative AI to analyze a user's social media activity and related conversations when analyzing a conversation. For example, it can analyze what a nuisance perpetrator is talking about on social media and prioritize the analysis of related conversations. The analysis unit can analyze the information a nuisance perpetrator is sharing on social media and prioritize the analysis of related conversations. The analysis unit can analyze the information of accounts that a nuisance perpetrator follows on social media and prioritize the analysis of related conversations. This enables broader information analysis by analyzing a user's social media activity and related conversations. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input data on a user's social media activity into a generative AI and have the generative AI perform the analysis of related conversations.

[0096] The generation unit can estimate the user's emotions and adjust the method of generating materials based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the method of generating materials based on the estimated user emotions. The generation unit can use a generation AI to estimate the user's emotions and adjust the method of generating materials based on the estimated user emotions. For example, if the nuisance perpetrator is agitated, the generation unit can generate concise and visually calming materials. If the nuisance perpetrator is calm, the generation unit can generate detailed and informative materials. If the nuisance perpetrator is stressed, the generation unit can generate materials with a relaxing design. This makes it possible to generate appropriate materials by adjusting the method of generating materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0097] The generation unit can prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. For example, the generation unit can prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. The generation unit can use a generation AI to prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the generation unit can prioritize reflecting that information in the document. If the nuisance perpetrator is talking about historical events, the generation unit can prioritize reflecting that information in the document. If the nuisance perpetrator is providing information about local culture, the generation unit can prioritize reflecting that information in the document. This makes it possible to document important information by prioritizing the reflection of conversations containing specific keywords. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the content of a conversation into a generation AI and have the generation AI perform the task of documenting conversations containing specific keywords.

[0098] The generation unit can determine the priority of conversations to be included in the document based on their length and frequency. For example, the generation unit can determine the priority of conversations to be included in the document based on their length and frequency. The generation unit can use a generation AI to determine the priority of conversations to be included in the document based on their length and frequency. For example, it can prioritize including long conversations in the document to provide detailed information. The generation unit can prioritize including frequently occurring conversations in the document to analyze patterns. The generation unit can also prioritize including short conversations in the document if they contain important information. This enables efficient document generation by determining the priority of conversations to be included in the document based on their length and frequency. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the length and frequency of conversations into a generation AI and have the generation AI determine the priority of conversations to be included in the document.

[0099] The generation unit can estimate the user's emotions and determine the priority of material generation based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and determine the priority of material generation based on the estimated user emotions. The generation unit can use a generation AI to estimate the user's emotions and determine the priority of material generation based on the estimated user emotions. For example, if the nuisance perpetrator is agitated, the generation unit can prioritize reflecting that conversation in the material and provide detailed information. If the nuisance perpetrator is calm, the generation unit can prioritize reflecting that conversation in the material and provide detailed information. If the nuisance perpetrator is stressed, the generation unit can prioritize reflecting that conversation in the material and provide detailed information. This makes it possible to generate appropriate materials by determining the priority of material generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0100] The generation unit can prioritize reflecting highly relevant information by considering the user's geographical location when generating materials. For example, the generation unit can prioritize reflecting highly relevant information by considering the user's geographical location when generating materials. The generation unit can use a generation AI to prioritize reflecting highly relevant information by considering the user's geographical location when generating materials. For example, if a nuisance perpetrator provides information about a specific region, that information will be prioritized and reflected in the materials. The generation unit can prioritize reflecting information about a specific location if a nuisance perpetrator provides information about a specific event if a nuisance perpetrator provides information about that event. This makes it possible to generate region-specific materials by prioritizing the reflection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the creation of materials with highly relevant information.

[0101] The generation unit can analyze the user's social media activity and reflect relevant information in the material when generating it. For example, the generation unit can analyze the user's social media activity and reflect relevant information in the material when generating it. The generation unit can use a generation AI to analyze the user's social media activity and reflect relevant information in the material when generating it. For example, it can reflect the content that the nuisance perpetrator is talking about on social media in the material and provide relevant information. The generation unit can reflect the information that the nuisance perpetrator is sharing on social media in the material and provide relevant information. The generation unit can reflect the information of accounts that the nuisance perpetrator is following on social media in the material and provide relevant information. This makes it possible to create a more comprehensive set of information by analyzing the user's social media activity and reflecting relevant information in the material. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the creation of relevant information.

[0102] The service provider can estimate the user's emotions and adjust the method of providing materials based on the estimated emotions. For example, the service provider can estimate the user's emotions and adjust the method of providing materials based on the estimated emotions. The service provider can use generative AI to estimate the user's emotions and adjust the method of providing materials based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the service provider can provide concise and visually calming materials. If the nuisance perpetrator is calm, the service provider can provide detailed and informative materials. If the nuisance perpetrator is stressed, the service provider can provide materials with a relaxing design. This allows for the provision of appropriate materials by adjusting the method of providing materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0103] The service provider can prioritize providing materials containing specific keywords, depending on the content of the materials. For example, the service provider can prioritize providing materials containing specific keywords, depending on the content of the materials. The service provider can use a generative AI to prioritize providing materials containing specific keywords, depending on the content of the materials. For example, if a nuisance perpetrator talks about local traditional events, the service provider can prioritize reflecting that information in the materials and providing it. If a nuisance perpetrator talks about historical events, the service provider can prioritize reflecting that information in the materials and providing it. If a nuisance perpetrator provides information about local culture, the service provider can prioritize reflecting that information in the materials and providing it. This makes it possible to provide important information by prioritizing the provision of materials containing specific keywords. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the content of the materials into a generative AI and have the generative AI provide materials containing specific keywords.

[0104] The service provider can estimate the user's emotions and determine the priority of providing materials based on the estimated emotions. For example, the service provider can estimate the user's emotions and determine the priority of providing materials based on the estimated emotions. The service provider can use generative AI to estimate the user's emotions and determine the priority of providing materials based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the service provider can prioritize reflecting that conversation in the materials. If the nuisance perpetrator is calm, the service provider can prioritize reflecting that conversation in the materials. If the nuisance perpetrator is stressed, the service provider can prioritize reflecting that conversation in the materials. This makes it possible to provide appropriate materials by determining the priority of providing materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0105] The service provider can prioritize providing highly relevant materials by considering the user's geographical location when providing materials. For example, the service provider can prioritize providing highly relevant materials by considering the user's geographical location when providing materials. The service provider can use a generation AI to prioritize providing highly relevant materials by considering the user's geographical location when providing materials. For example, if a nuisance perpetrator provides information about a specific region, that information can be prioritized and provided in the materials. If a nuisance perpetrator provides information about a specific location, the service provider can prioritize and provide that information. If a nuisance perpetrator provides information about a specific event, the service provider can prioritize and provide that information. This makes it possible to provide region-specific information by prioritizing the provision of highly relevant materials by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's geographical location information into a generation AI and have the generation AI provide highly relevant materials.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The collection unit can estimate the user's emotions and adjust the conversation collection method based on the estimated user emotions. For example, if the nuisance perpetrator is agitated, the collection unit can temporarily stop collecting conversations and wait until the perpetrator calms down. If the nuisance perpetrator is calm, the collection unit can start collecting conversations and collect detailed information. If the nuisance perpetrator is stressed, the collection unit can refrain from collecting conversations and wait until the perpetrator relaxes. This allows for appropriate information collection by adjusting the conversation collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the collection unit may be performed using a generative AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The real-time analysis unit can estimate the user's emotions and adjust the real-time analysis method based on the estimated user emotions. For example, if the harasser is agitated, the real-time analysis unit can temporarily stop analyzing the conversation and wait until the harasser calms down. If the harasser is calm, the real-time analysis unit can start analyzing the conversation and analyze detailed information. If the harasser is stressed, the real-time analysis unit can refrain from analyzing the conversation and wait until the harasser relaxes. This allows for appropriate information analysis by adjusting the real-time analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the real-time analysis unit may be performed using a generative AI or not. For example, the real-time analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the analysis unit can temporarily stop analyzing the conversation and wait until the perpetrator calms down. If the nuisance perpetrator is calm, the analysis unit can start analyzing the conversation and analyze detailed information. If the nuisance perpetrator is stressed, the analysis unit can refrain from analyzing the conversation and wait until the perpetrator relaxes. This allows for appropriate information analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The generation unit can estimate the user's emotions and adjust the method of generating materials based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the generation unit can generate concise and visually calming materials. If the nuisance perpetrator is calm, the generation unit can generate detailed and informative materials. If the nuisance perpetrator is stressed, the generation unit can generate materials with a relaxing design. This allows for the generation of appropriate materials by adjusting the method of generating materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] The service provider can estimate the user's emotions and adjust the way materials are provided based on the estimated emotions. For example, if the nuisance perpetrator is agitated, the service provider can provide concise, visually calming materials. If the nuisance perpetrator is calm, the service provider can provide detailed, informational materials. If the nuisance perpetrator is stressed, the service provider can provide materials with a relaxing design. This allows for the provision of appropriate materials by adjusting the method of provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The collection unit can prioritize collecting conversations that contain specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is talking about historical events, the collection unit can prioritize collecting that conversation. If the nuisance perpetrator is providing information about local culture, the collection unit can prioritize collecting that conversation. This makes it possible to collect important information by prioritizing the collection of conversations that contain specific keywords. Some or all of the above processing in the collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the collection unit can input the content of a conversation into a generative AI and have the generative AI perform the collection of conversations that contain specific keywords.

[0113] The real-time analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the unit will prioritize the analysis of that conversation. The real-time analysis unit can prioritize the analysis of conversations if the nuisance perpetrator is talking about historical events. The real-time analysis unit can prioritize the analysis of conversations if the nuisance perpetrator is providing information about local culture. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the real-time analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the real-time analysis unit can input the content of a conversation into a generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0114] The analysis unit can prioritize the analysis of conversations containing specific keywords, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is talking about historical events, the analysis unit can prioritize the analysis of that conversation. If the nuisance perpetrator is providing information about local culture, the analysis unit can prioritize the analysis of that conversation. This makes it possible to analyze important information by prioritizing the analysis of conversations containing specific keywords. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the content of a conversation into a generative AI and have the generative AI perform the analysis of conversations containing specific keywords.

[0115] The generation unit can prioritize reflecting conversations containing specific keywords in the document, depending on the content of the conversation. For example, if the nuisance perpetrator is talking about local traditional events, that information will be prioritized in the document. If the nuisance perpetrator is talking about historical events, that information will be prioritized in the document. If the nuisance perpetrator is providing information about local culture, that information will be prioritized in the document. This makes it possible to document important information by prioritizing the reflection of conversations containing specific keywords. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the content of the conversation into a generation AI and have the generation AI perform the task of documenting conversations containing specific keywords.

[0116] The information provider can prioritize providing materials containing specific keywords, depending on the content of the materials. For example, if a nuisance perpetrator talks about local traditional events, that information will be prioritized and provided in the materials. If a nuisance perpetrator talks about historical events, that information will be prioritized and provided in the materials. If a nuisance perpetrator provides information about local culture, that information will be prioritized and provided in the materials. This makes it possible to provide important information by prioritizing the provision of materials containing specific keywords. Some or all of the above processing in the information provider may be performed using a generative AI, or not. For example, the information provider can input the content of the materials into a generative AI and have the generative AI provide materials containing specific keywords.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The collection unit collects conversations at the counter. For example, it can collect the content of what the nuisance perpetrator says. The collection unit can use a generation AI to analyze the content of the conversation in real time and record what the nuisance perpetrator says. Step 2: The real-time analysis unit analyzes the conversation data collected by the collection unit in real time. For example, it can use a generative AI to analyze the collected conversation data and extract information about local culture and history. Step 3: The analysis unit analyzes the data analyzed by the real-time analysis unit and extracts information about the local culture and history. For example, it can use generative AI to analyze the collected conversation data and identify information about the local culture and history. Step 4: The generation unit creates documents based on the information extracted by the analysis unit. For example, a generation AI can be used to organize the extracted information and create documents. Step 5: The providing unit provides the materials created by the generating unit. For example, materials created using a generation AI can be provided.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, real-time analysis unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects conversations at the counter using the camera 42 and microphone 38B of the smart device 14. The real-time analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the analyzed data, it extracts information about local culture and history. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the extracted information, it creates materials. The provision unit is implemented in real time by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, real-time analysis unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects conversations at the counter using the camera 42 and microphone 238 of the smart glasses 214. The real-time analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected conversation data in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and extracts information about local culture and history based on the analyzed data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates materials based on the extracted information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the created materials. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, real-time analysis unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects conversations at the counter using the camera 42 and microphone 238 of the headset terminal 314. The real-time analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the analyzed data, information about local culture and history is extracted. The generation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the extracted information, materials are created. The provision unit is implemented in real time by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, real-time analysis unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects conversations at the counter using the camera 42 and microphone 238 of the robot 414. The real-time analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected conversation data in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and extracts information about local culture and history based on the analyzed data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates materials based on the extracted information. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the created materials. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The collection department collects conversations at the counter, A real-time analysis unit analyzes the conversation data collected by the aforementioned collection unit in real time, The analysis unit analyzes the data analyzed by the aforementioned real-time analysis unit and extracts information about the local culture and history. A generation unit that creates materials based on the information extracted by the analysis unit, The system includes a providing unit that provides materials created by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the content of what the person engaging in disruptive behavior says. The system described in Appendix 1, characterized by the features described herein. (Note 3) The real-time analysis unit described above is: The collected conversation data is analyzed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The collected conversation data is analyzed to extract information about local culture and history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Create a document based on the extracted information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the created materials The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Based on the content of the conversation, prioritize collecting conversations that contain specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Prioritize the conversations to collect based on their length and frequency. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of conversations to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting conversations, the system prioritizes collecting highly relevant conversations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting conversations, the system analyzes users' social media activity and collects relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The real-time analysis unit described above is: It estimates the user's emotions and adjusts the real-time analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The real-time analysis unit described above is: Based on the content of the conversation, prioritize analyzing conversations that contain specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 15) The real-time analysis unit described above is: Prioritize the conversations to analyze based on their length and frequency. The system described in Appendix 1, characterized by the features described herein. (Note 16) The real-time analysis unit described above is: It estimates the user's emotions and determines the priority of real-time analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The real-time analysis unit described above is: When analyzing conversations, the system prioritizes analyzing conversations that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The real-time analysis unit described above is: When analyzing conversations, the system analyzes the user's social media activity and identifies relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is Based on the content of the conversation, prioritize analyzing conversations that contain specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is Prioritize the conversations to analyze based on their length and frequency. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When analyzing conversations, the system prioritizes analyzing highly relevant conversations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When analyzing conversations, we analyze users' social media activity and analyze relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the method of generating materials based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is Based on the content of the conversation, prioritize including conversations containing specific keywords in the document. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is Based on the length and frequency of the conversations, prioritize which conversations to include in the document. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and determines the priority of generating materials based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating materials, the system prioritizes reflecting highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating materials, the system analyzes users' social media activity and incorporates relevant information into the materials. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we provide materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, Depending on the content of the materials, we will prioritize providing materials that contain specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of providing materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing materials, we prioritize providing highly relevant materials by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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 collection department collects conversations at the counter, A real-time analysis unit analyzes the conversation data collected by the aforementioned collection unit in real time, The analysis unit analyzes the data analyzed by the aforementioned real-time analysis unit and extracts information about the local culture and history. A generation unit that creates materials based on the information extracted by the analysis unit, The system includes a providing unit that provides materials created by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect the content of what the person engaging in disruptive behavior says. The system according to feature 1.

3. The real-time analysis unit described above is: The collected conversation data is analyzed in real time. The system according to feature 1.

4. The aforementioned analysis unit is The collected conversation data is analyzed to extract information about local culture and history. The system according to feature 1.

5. The generating unit is Create a document based on the extracted information. The system according to feature 1.

6. The aforementioned supply unit is, Provide the created materials The system according to feature 1.

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

8. The aforementioned collection unit is Based on the content of the conversation, prioritize collecting conversations that contain specific keywords. The system according to feature 1.

9. The aforementioned collection unit is Prioritize the conversations to collect based on their length and frequency. The system according to feature 1.

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