system

Abstract

The system according to this embodiment aims to streamline and improve the quality of refugee application procedures. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, and a translation unit. The collection unit collects information on the asylum seeker's home country and personal circumstances. The analysis unit analyzes the information collected by the collection unit. The creation unit creates materials based on the analysis results obtained by the analysis unit. The translation unit translates the materials created by the creation unit into Japanese.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in the refugee application procedure, the burden of proof lies with the refugee side, and Japanese-language submission of documents is required, so there is a problem that the procedure is very difficult.

[0005] The system according to the embodiment aims to streamline the refugee application procedure and improve the quality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, and a translation unit. The collection unit collects information on the asylum seeker's home country and personal circumstances. The analysis unit analyzes the information collected by the collection unit. The creation unit creates materials based on the analysis results obtained by the analysis unit. The translation unit translates the materials created by the creation unit into Japanese. [Effects of the Invention]

[0007] The system according to this embodiment can streamline and improve the quality of refugee application procedures. [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 controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 refugee application support system according to an embodiment of the present invention is a system that utilizes the Generative AI Enterprise version to streamline and improve the quality of refugee application procedures in Japan. Refugee application procedures in Japan are extremely difficult because the burden of proof lies with the refugee and documents must be submitted in Japanese. Currently, the Refugee Assistance Association provides support, but the workload is heavy, and there are limits to the number of cases it can handle. The Generative AI Enterprise version excels at multilingual support, extensive information gathering, and document creation, and can handle confidential information and personal information protection, thus contributing to the efficiency and quality improvement of refugee support. For example, the refugee application support system uses the Generative AI Enterprise version to collect information on the refugee applicant's home country situation and personal circumstances. Specifically, it collects information on the applicant's home country situation (politics, religion, ethnicity, etc.) from international organizations, news media, NPOs, etc., and collects attribute information of the applicant (affiliated organizations, place of birth, SNS activity, etc.). This allows for the creation of documents that prove the likelihood that the applicant is at risk of persecution. Next, the refugee application support system uses the Generative AI Enterprise version to create application documents based on the collected information. The generation AI analyzes a wide range of information, creates easy-to-understand explanatory materials about the applicant's home country and personal circumstances, and translates them into Japanese. This allows for the creation of application documents that administrative officials can easily review. Furthermore, the refugee application support system learns from past application information, accepts confidential information from international organizations, and protects refugees' personal information. This reduces the burden on support staff at the Refugee Assistance Association and improves the quality of support. In the future, the enterprise version of the generation AI is envisioned for use by government agencies and other countries. This will enable the streamlining and improvement of refugee application procedures globally. In short, the refugee application support system can streamline and improve the quality of refugee application procedures.

[0029] The refugee application support system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, and a translation unit. The collection unit collects information on the refugee applicant's home country situation and personal circumstances. The collection unit collects information on the applicant's home country situation from, for example, international organizations, news media, and NPOs. The collection unit collects, for example, reports from international organizations and articles from news media to understand the political and social situation in the applicant's home country. The collection unit collects, for example, information from NPOs to understand the human rights situation and the reality of persecution in the applicant's home country. The collection unit collects attribute information of the applicant. The collection unit collects, for example, information on the applicant's birthplace and affiliated organizations. The collection unit collects, for example, information on the applicant's SNS activities and occupation. The collection unit collects, for example, information on the applicant's health status and family structure. The analysis unit analyzes the collected information and creates materials that prove the likelihood that the applicant is at risk of persecution. The analysis unit statistically analyzes the collected information and extracts data that indicates the applicant is at risk of persecution. The analysis department, for example, performs text mining on the collected information to extract evidence indicating that the applicant is at risk of persecution. The analysis department, for example, analyzes the collected information using machine learning algorithms to extract patterns indicating that the applicant is at risk of persecution. The creation department analyzes a wide range of information and creates easy-to-understand explanatory materials about the applicant's situation in their home country and personal circumstances. The creation department, for example, creates documents explaining the applicant's situation in their home country and personal circumstances based on the collected information. The creation department, for example, creates charts and graphs explaining the applicant's situation in their home country and personal circumstances based on the collected information. The creation department, for example, creates presentation materials explaining the applicant's situation in their home country and personal circumstances based on the collected information. The translation department translates the created materials into Japanese. The translation department, for example, translates the created documents into Japanese. The translation department, for example, translates the created charts and graphs into Japanese. The translation department, for example, translates the created presentation materials into Japanese. As a result, the refugee application support system according to this embodiment can streamline and improve the quality of the refugee application process.

[0030] The collection department gathers information on the situation in the applicant's home country and their personal circumstances. For example, it collects information on the applicant's home country from international organizations, news media, and NGOs. Specifically, it collects reports from international organizations and news articles to understand the political and social situation in the applicant's home country. Reports from international organizations include detailed reports published by the United Nations and other international human rights organizations, which are important sources of information showing the reality of human rights abuses and political persecution in the applicant's home country. News articles reflect the latest events and social trends, helping to understand the current situation in the applicant's home country in real time. The collection department also collects information from NGOs to understand the human rights situation and the reality of persecution in the applicant's home country. NGOs provide specific examples and testimonies obtained through their activities on the ground, revealing the detailed background of the crisis the applicant is facing. Furthermore, the collection department collects attribute information on the applicant themselves. For example, it collects information on the applicant's birthplace and affiliated organizations to determine whether the applicant's affiliation with a particular region or organization is a cause of persecution. Information on applicants' social media activity and occupation is also collected to assess their activities and whether they may be subject to persecution. Information on applicants' health status and family structure is also important, as this information is necessary to understand the applicant's overall situation. The data collection department centrally manages the data obtained from these diverse sources, making it available for efficient use by the analysis and creation departments. The collected information is stored in a database and made readily accessible when needed. This allows the data collection department to gain a comprehensive understanding of asylum seekers' situations and provide a foundation for supporting their application process.

[0031] The analysis department analyzes the collected information and creates documentation to prove the likelihood that the applicant is at risk of persecution. Specifically, it statistically analyzes the collected information to extract data indicating that the applicant is at risk of persecution. The statistical analysis reveals the frequency and patterns of persecution cases in the applicant's home country and indicates the possibility that the applicant is facing a similar crisis. It also performs text mining on the collected information to extract evidence that the applicant is at risk of persecution. Text mining extracts relevant keywords and phrases from news articles and reports to find specific examples and testimonies that support the applicant's situation. Furthermore, it analyzes the collected information using machine learning algorithms to extract patterns indicating that the applicant is at risk of persecution. Machine learning algorithms can process large amounts of data quickly and find complex patterns and correlations. For example, based on the applicant's attribute information and the situation in their home country, it can identify specific conditions and characteristics that predict a high risk of persecution. Based on these analysis results, the analysis department creates documentation to prove that the applicant is at risk of persecution. This includes statistical data, graphs, text mining results, and predictions from machine learning algorithms. This allows the analysis department to objectively and scientifically verify the applicant's situation and support the legitimacy of their asylum application. Furthermore, by utilizing past data and case studies, the analysis department can gain a deeper understanding of the applicant's situation and contribute to future risk assessments and the development of countermeasures.

[0032] The drafting department analyzes a wide range of information and creates easy-to-understand explanatory materials about the applicant's situation in their home country and their personal circumstances. Specifically, based on the collected information, they create documents explaining the applicant's situation in their home country and their personal circumstances. These documents detail the political and social situation and the reality of human rights abuses in the applicant's home country. They also include the applicant's personal background and specific examples of persecution. Based on the collected information, the drafting department creates charts and graphs explaining the applicant's situation in their home country and their personal circumstances. These charts and graphs include statistical data, graphs, and maps to facilitate understanding by visually conveying the information. For example, they create maps and graphs showing the distribution and frequency of persecution cases in the applicant's home country to visually demonstrate the severity of the crisis the applicant is facing. Furthermore, based on the collected information, the drafting department creates presentation materials explaining the applicant's situation in their home country and their personal circumstances. These presentation materials are organized in a slide format and used during the application process. This allows for effective communication of the applicant's situation. When creating these materials, the drafting department emphasizes ensuring the accuracy and consistency of the information and explaining the applicant's situation objectively and clearly. Furthermore, the drafting department takes care to protect the applicant's privacy and ensure that confidential information is handled appropriately. This allows the drafting department to provide materials that accurately and effectively convey the applicant's situation and support the asylum application process.

[0033] The Translation Department translates the prepared materials into Japanese. Specifically, it translates the prepared documents into Japanese. These documents include detailed explanations of the applicant's situation in their home country and their personal circumstances, and by accurately translating them into Japanese, the department provides the information necessary for the application process. The Translation Department takes care to accurately translate specialized terminology and expressions, ensuring that the meaning and nuances of the information are not lost. The Translation Department also translates the prepared charts and graphs into Japanese. These charts and graphs include statistical data, graphs, and maps, and by translating them into Japanese, the department can accurately convey visual information. For example, maps and graphs showing the distribution and frequency of persecution cases in the applicant's home country are translated into Japanese to visually demonstrate the severity of the crisis the applicant is facing. Furthermore, the Translation Department translates the prepared presentation materials into Japanese. These presentation materials are organized in slide format, and by translating them into Japanese, they are used during the application process. When translating these materials, the Translation Department emphasizes ensuring the accuracy and consistency of the information and explaining the applicant's situation objectively and clearly. Furthermore, the translation department takes care to protect applicants' privacy and ensure that confidential information is handled appropriately. This allows the translation department to provide materials that accurately and effectively convey the applicant's situation and support the asylum application process.

[0034] The data collection unit can collect information about the applicant's home country from international organizations, news media, NGOs, etc. For example, the data collection unit can collect reports from international organizations to understand the political and social situation in the applicant's home country. For example, the data collection unit can collect articles from news media to understand recent events and incidents in the applicant's home country. For example, the data collection unit can collect information from NGOs to understand the human rights situation and the reality of persecution in the applicant's home country. This allows for the efficient collection of information about the applicant's home country. Some or all of the above processing in the data collection unit may be performed using or without a generating AI. For example, the data collection unit can input reports from international organizations into a generating AI, which can analyze the contents of the reports and extract the necessary information.

[0035] The data collection unit can collect attribute information of the applicant. For example, the data collection unit can collect information about the applicant's place of birth and affiliated organizations. For example, the data collection unit can collect information about the applicant's social media activity and occupation. For example, the data collection unit can collect information about the applicant's health status and family structure. This allows for the efficient collection of attribute information of the applicant. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the applicant's social media activity into a generating AI, which can then analyze the content of the social media activity and extract the necessary information.

[0036] The analysis department can analyze the collected information and create documentation to prove the likelihood that the applicant is at risk of persecution. For example, the analysis department can statistically analyze the collected information and extract data indicating that the applicant is at risk of persecution. For example, the analysis department can text-mine the collected information and extract evidence indicating that the applicant is at risk of persecution. For example, the analysis department can analyze the collected information using machine learning algorithms and extract patterns indicating that the applicant is at risk of persecution. This allows for the efficient creation of documentation to prove the likelihood that the applicant is at risk of persecution. Some or all of the above processes in the analysis department may be performed using or without generative AI. For example, the analysis department can input the collected information into a generative AI, which can then analyze the information and create documentation to prove the likelihood.

[0037] The creation unit can analyze a wide range of information and create easy-to-understand explanatory materials about the applicant's country of origin and personal circumstances. For example, the creation unit can create documents explaining the applicant's country of origin and personal circumstances based on the collected information. For example, the creation unit can create charts and graphs explaining the applicant's country of origin and personal circumstances based on the collected information. For example, the creation unit can create presentation materials explaining the applicant's country of origin and personal circumstances based on the collected information. This makes it possible to create easy-to-understand explanatory materials about the applicant's country of origin and personal circumstances. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input the collected information into a generation AI, and the generation AI can analyze the information and create explanatory materials.

[0038] The translation unit can translate the created materials into Japanese. For example, the translation unit can translate a document into Japanese. For example, the translation unit can translate a chart or graph into Japanese. For example, the translation unit can translate a presentation into Japanese. This allows the created materials to be translated into Japanese. Some or all of the above-described processes in the translation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the translation unit can input the created materials into a generative AI, and the generative AI can translate the materials into Japanese.

[0039] The data collection unit can analyze the applicant's past application history during collection and select the optimal information collection method. For example, the data collection unit can analyze materials previously submitted by the applicant and select a method to efficiently collect similar information. For example, the data collection unit can identify the types of information needed from the applicant's past application history and optimize the collection method. For example, the data collection unit can determine the priority of information collection based on the applicant's past application history. This allows the data collection unit to select the optimal information collection method based on the applicant's past application history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the applicant's past application history data into a generation AI, which can then select the optimal information collection method.

[0040] The data collection unit can filter information based on the applicant's current living situation and areas of interest during the information collection process. For example, the data collection unit considers the applicant's current living situation and prioritizes the collection of highly relevant information. For example, the data collection unit filters necessary information based on the applicant's areas of interest. For example, the data collection unit adjusts the scope of information collection according to the applicant's living situation and areas of interest. This allows information to be filtered based on the applicant's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the applicant's living situation data into a generating AI, which can then filter the information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the applicant's geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant information based on the applicant's current location. For example, the data collection unit filters necessary information by considering the applicant's geographical location information. For example, the data collection unit adjusts the scope of data collection based on the applicant's geographical location information. This allows for the priority collection of highly relevant information by considering the applicant's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generating AI, or without using a generating AI. For example, the data collection unit can input the applicant's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0042] The data collection unit can analyze the applicant's social media activity during information collection and collect relevant information. For example, the data collection unit can analyze the applicant's social media activity and prioritize the collection of relevant information. For example, the data collection unit can filter the necessary information based on the applicant's social media activity. For example, the data collection unit can adjust the scope of information collection considering the applicant's social media activity. This allows the data collection unit to collect relevant information based on the applicant's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input the applicant's social media activity data into a generative AI, which can then collect the relevant information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the information collected during the analysis. For example, the analysis unit may prioritize the analysis of important information and add detailed explanations. The analysis unit may adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit may summarize less important information concisely and focus on important information. This allows the level of detail of the analysis to be adjusted based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit may input the collected information into a generative AI, which can then evaluate the importance of the information and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific algorithm to political information to perform a detailed analysis. For example, the analysis unit can apply a different algorithm to religious information to perform an appropriate analysis. For example, the analysis unit can apply yet another algorithm to ethnic information to perform a precise analysis. This allows the application of an appropriate analysis algorithm depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the category of information into a generative AI, which can then select and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit prioritizes the analysis of the latest information and responds quickly. The analysis unit determines the priority of analysis based on when the information was collected. For example, the analysis unit postpones older information and focuses on the latest information. This allows the analysis priority to be determined based on when the information was collected. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input data on when the information was collected into a generative AI, and the generative AI can determine the priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information and adjusts the order accordingly. For example, the analysis unit determines the order of analysis based on the relevance of the information. For example, the analysis unit postpones the analysis of less relevant information and focuses on highly relevant information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information relevance data into a generative AI, and the generative AI can adjust the order.

[0047] The creation unit can improve the accuracy of the document by considering the interrelationships of the information collected during document creation. For example, the creation unit analyzes the interrelationships of the collected information and creates an accurate document. For example, the creation unit improves the accuracy of the document by considering the interrelationships of the information. For example, the creation unit combines interrelated information to create a detailed document. This allows the accuracy of the document to be improved by considering the interrelationships of the collected information. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input the collected information into a generation AI, and the generation AI can analyze the interrelationships of the information to improve the accuracy of the document.

[0048] The creation unit can customize documents by considering the applicant's attribute information when creating them. For example, the creation unit can create individually customized documents based on the applicant's attribute information. For example, the creation unit can create documents that are optimal for the applicant by considering the attribute information. For example, the creation unit can adjust the content of the documents based on the applicant's attribute information. This makes it possible to create customized documents based on the applicant's attribute information. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the applicant's attribute information into a generation AI, and the generation AI can customize the documents.

[0049] The creation unit can create documents while considering the geographical distribution of information. For example, the creation unit can create accurate documents based on the geographical distribution of information. For example, the creation unit can adjust the content of the documents while considering the geographical distribution. For example, the creation unit can create detailed documents based on the geographical distribution. This allows for the creation of accurate documents while considering the geographical distribution of information. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input geographical distribution data of information into a generation AI, and the generation AI can create the documents.

[0050] The creation unit can improve the accuracy of the materials by referring to relevant literature during the creation process. For example, the creation unit can create accurate materials by referring to relevant literature. For example, the creation unit can adjust the content of the materials based on relevant literature. For example, the creation unit can create detailed materials by referring to relevant literature. In this way, the accuracy of the materials can be improved by referring to relevant literature. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input relevant literature into a generation AI, and the generation AI can refer to the literature to improve the accuracy of the materials.

[0051] The translation unit can adjust the level of detail in the translation based on the importance of the source material. For example, the translation unit prioritizes translating important material in detail. The translation unit adjusts the level of detail in the translation based on the importance of the source material. For example, the translation unit translates less important material concisely and focuses on important material. This allows the level of detail in the translation to be adjusted based on the importance of the source material. Some or all of the above processes in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input source importance data into a generative AI, which can then adjust the level of detail in the translation.

[0052] The translation unit can apply different translation algorithms depending on the category of the material during translation. For example, the translation unit may apply a specific algorithm to political materials to produce a detailed translation. For example, the translation unit may apply a different algorithm to religious materials to produce an appropriate translation. For example, the translation unit may apply yet another algorithm to ethnic materials to produce an accurate translation. This allows the translation unit to apply an appropriate translation algorithm according to the category of the material. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input the category of the material into a generative AI, which can then select and apply an appropriate translation algorithm.

[0053] The translation department can determine translation priorities based on the submission dates of the materials during the translation process. For example, the translation department can prioritize the translation of urgent materials and respond quickly. The translation department can determine translation priorities based on the submission dates of the materials. For example, the translation department can prioritize the translation of materials with approaching deadlines and respond quickly. This allows the translation department to determine translation priorities based on the submission dates of the materials. Some or all of the above processes in the translation department may be performed using or without a generative AI. For example, the translation department can input data on the submission dates of the materials into a generative AI, which can then determine the priorities.

[0054] The translation unit can adjust the order of translations based on the relevance of the materials during translation. For example, the translation unit may prioritize translating highly relevant materials and adjust the order accordingly. For example, the translation unit may determine the order of translations based on the relevance of the materials. For example, the translation unit may postpone less relevant materials and focus on highly relevant ones. This allows the order of translations to be adjusted based on the relevance of the materials. Some or all of the above processes in the translation unit may be performed using or without a generative AI. For example, the translation unit may input material relevance data into a generative AI, which can then adjust the order.

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

[0056] The data collection unit can analyze the applicant's past application history to select the most suitable information collection method when gathering information on the applicant's country of origin and personal circumstances. For example, it can analyze documents previously submitted by the applicant to select an efficient method for collecting similar information. It can also identify the types of information needed from the applicant's past application history and optimize the collection method. Furthermore, it can determine the priority of information collection based on the applicant's past application history. This allows for the selection of the most suitable information collection method based on the applicant's past application history.

[0057] The analysis department can prioritize the analysis based on when the information was collected when analyzing the collected information and creating documentation to prove the likelihood that the applicant is at risk of persecution. For example, they can prioritize the analysis of the most recent information and respond quickly. They can also prioritize the analysis based on when the information was collected. Furthermore, they can postpone older information and focus on the most recent information. This allows them to prioritize the analysis based on when the information was collected.

[0058] The creation unit can improve the accuracy of the document by considering the interrelationships of the information collected during the document creation process. For example, it can analyze the interrelationships of the collected information and create an accurate document. It can also improve the accuracy of the document by considering the interrelationships of the information. Furthermore, it can combine interrelated information to create a detailed document. This allows for improved accuracy of the document by considering the interrelationships of the collected information.

[0059] The translation department can adjust the level of detail in translations based on the importance of the source material. For example, important documents can be prioritized and translated in detail. Furthermore, less important documents can be translated concisely, while emphasis is placed on more important documents. This allows for adjustment of the level of detail in translations based on the importance of the source material.

[0060] The information collection unit can filter information based on the applicant's current living situation and areas of interest. For example, it can prioritize the collection of highly relevant information, taking into account the applicant's current living situation. It can also filter necessary information based on the applicant's areas of interest. Furthermore, it can adjust the scope of information collection according to the applicant's living situation and areas of interest. This allows for information filtering based on the applicant's current living situation and areas of interest.

[0061] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, a specific algorithm can be applied to political information for detailed analysis. Another algorithm can be applied to religious information for appropriate analysis. Furthermore, yet another algorithm can be applied to ethnic information for accurate analysis. This allows for the application of the appropriate analysis algorithm depending on the category of information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection department collects information about the applicant's home country and personal circumstances. For example, it collects information about the applicant's home country from international organizations, news media, and NGOs. The collection department collects reports from international organizations and articles from news media to understand the political and social situation in the applicant's home country. Furthermore, it collects information from NGOs to understand the human rights situation and the reality of persecution in the applicant's home country. The collection department also collects attribute information about the applicant themselves, such as their place of birth, affiliated organizations, social media activity, occupation, health status, and family structure. Step 2: The analysis department analyzes the collected information and creates documentation to prove the likelihood that the applicant is at risk of persecution. For example, it statistically analyzes the collected information to extract data indicating that the applicant is at risk of persecution. Furthermore, it text-mines the collected information to extract evidence indicating that the applicant is at risk of persecution. In addition, it analyzes the collected information using machine learning algorithms to extract patterns indicating that the applicant is at risk of persecution. Step 3: The drafting team analyzes a wide range of information and creates easy-to-understand explanatory materials about the applicant's country of origin and personal circumstances. For example, based on the collected information, they create documents, charts, and presentation materials that explain the applicant's country of origin and personal circumstances. Step 4: The translation department translates the created materials into Japanese. For example, they translate the created documents, charts, and presentation materials into Japanese.

[0064] (Example of form 2) The refugee application support system according to an embodiment of the present invention is a system that utilizes the Generative AI Enterprise version to streamline and improve the quality of refugee application procedures in Japan. Refugee application procedures in Japan are extremely difficult because the burden of proof lies with the refugee and documents must be submitted in Japanese. Currently, the Refugee Assistance Association provides support, but the workload is heavy, and there are limits to the number of cases it can handle. The Generative AI Enterprise version excels at multilingual support, extensive information gathering, and document creation, and can handle confidential information and personal information protection, thus contributing to the efficiency and quality improvement of refugee support. For example, the refugee application support system uses the Generative AI Enterprise version to collect information on the refugee applicant's home country situation and personal circumstances. Specifically, it collects information on the applicant's home country situation (politics, religion, ethnicity, etc.) from international organizations, news media, NPOs, etc., and collects attribute information of the applicant (affiliated organizations, place of birth, SNS activity, etc.). This allows for the creation of documents that prove the likelihood that the applicant is at risk of persecution. Next, the refugee application support system uses the Generative AI Enterprise version to create application documents based on the collected information. The generation AI analyzes a wide range of information, creates easy-to-understand explanatory materials about the applicant's home country and personal circumstances, and translates them into Japanese. This allows for the creation of application documents that administrative officials can easily review. Furthermore, the refugee application support system learns from past application information, accepts confidential information from international organizations, and protects refugees' personal information. This reduces the burden on support staff at the Refugee Assistance Association and improves the quality of support. In the future, the enterprise version of the generation AI is envisioned for use by government agencies and other countries. This will enable the streamlining and improvement of refugee application procedures globally. In short, the refugee application support system can streamline and improve the quality of refugee application procedures.

[0065] The refugee application support system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, and a translation unit. The collection unit collects information on the refugee applicant's home country situation and personal circumstances. The collection unit collects information on the applicant's home country situation from, for example, international organizations, news media, and NPOs. The collection unit collects, for example, reports from international organizations and articles from news media to understand the political and social situation in the applicant's home country. The collection unit collects, for example, information from NPOs to understand the human rights situation and the reality of persecution in the applicant's home country. The collection unit collects attribute information of the applicant. The collection unit collects, for example, information on the applicant's birthplace and affiliated organizations. The collection unit collects, for example, information on the applicant's SNS activities and occupation. The collection unit collects, for example, information on the applicant's health status and family structure. The analysis unit analyzes the collected information and creates materials that prove the likelihood that the applicant is at risk of persecution. The analysis unit statistically analyzes the collected information and extracts data that indicates the applicant is at risk of persecution. The analysis department, for example, performs text mining on the collected information to extract evidence indicating that the applicant is at risk of persecution. The analysis department, for example, analyzes the collected information using machine learning algorithms to extract patterns indicating that the applicant is at risk of persecution. The creation department analyzes a wide range of information and creates easy-to-understand explanatory materials about the applicant's situation in their home country and personal circumstances. The creation department, for example, creates documents explaining the applicant's situation in their home country and personal circumstances based on the collected information. The creation department, for example, creates charts and graphs explaining the applicant's situation in their home country and personal circumstances based on the collected information. The creation department, for example, creates presentation materials explaining the applicant's situation in their home country and personal circumstances based on the collected information. The translation department translates the created materials into Japanese. The translation department, for example, translates the created documents into Japanese. The translation department, for example, translates the created charts and graphs into Japanese. The translation department, for example, translates the created presentation materials into Japanese. As a result, the refugee application support system according to this embodiment can streamline and improve the quality of the refugee application process.

[0066] The collection department gathers information on the situation in the applicant's home country and their personal circumstances. For example, it collects information on the applicant's home country from international organizations, news media, and NGOs. Specifically, it collects reports from international organizations and news articles to understand the political and social situation in the applicant's home country. Reports from international organizations include detailed reports published by the United Nations and other international human rights organizations, which are important sources of information showing the reality of human rights abuses and political persecution in the applicant's home country. News articles reflect the latest events and social trends, helping to understand the current situation in the applicant's home country in real time. The collection department also collects information from NGOs to understand the human rights situation and the reality of persecution in the applicant's home country. NGOs provide specific examples and testimonies obtained through their activities on the ground, revealing the detailed background of the crisis the applicant is facing. Furthermore, the collection department collects attribute information on the applicant themselves. For example, it collects information on the applicant's birthplace and affiliated organizations to determine whether the applicant's affiliation with a particular region or organization is a cause of persecution. Information on applicants' social media activity and occupation is also collected to assess their activities and whether they may be subject to persecution. Information on applicants' health status and family structure is also important, as this information is necessary to understand the applicant's overall situation. The data collection department centrally manages the data obtained from these diverse sources, making it available for efficient use by the analysis and creation departments. The collected information is stored in a database and made readily accessible when needed. This allows the data collection department to gain a comprehensive understanding of asylum seekers' situations and provide a foundation for supporting their application process.

[0067] The analysis department analyzes the collected information and creates documentation to prove the likelihood that the applicant is at risk of persecution. Specifically, it statistically analyzes the collected information to extract data indicating that the applicant is at risk of persecution. The statistical analysis reveals the frequency and patterns of persecution cases in the applicant's home country and indicates the possibility that the applicant is facing a similar crisis. It also performs text mining on the collected information to extract evidence that the applicant is at risk of persecution. Text mining extracts relevant keywords and phrases from news articles and reports to find specific examples and testimonies that support the applicant's situation. Furthermore, it analyzes the collected information using machine learning algorithms to extract patterns indicating that the applicant is at risk of persecution. Machine learning algorithms can process large amounts of data quickly and find complex patterns and correlations. For example, based on the applicant's attribute information and the situation in their home country, it can identify specific conditions and characteristics that predict a high risk of persecution. Based on these analysis results, the analysis department creates documentation to prove that the applicant is at risk of persecution. This includes statistical data, graphs, text mining results, and predictions from machine learning algorithms. This allows the analysis department to objectively and scientifically verify the applicant's situation and support the legitimacy of their asylum application. Furthermore, by utilizing past data and case studies, the analysis department can gain a deeper understanding of the applicant's situation and contribute to future risk assessments and the development of countermeasures.

[0068] The drafting department analyzes a wide range of information and creates easy-to-understand explanatory materials about the applicant's situation in their home country and their personal circumstances. Specifically, based on the collected information, they create documents explaining the applicant's situation in their home country and their personal circumstances. These documents detail the political and social situation and the reality of human rights abuses in the applicant's home country. They also include the applicant's personal background and specific examples of persecution. Based on the collected information, the drafting department creates charts and graphs explaining the applicant's situation in their home country and their personal circumstances. These charts and graphs include statistical data, graphs, and maps to facilitate understanding by visually conveying the information. For example, they create maps and graphs showing the distribution and frequency of persecution cases in the applicant's home country to visually demonstrate the severity of the crisis the applicant is facing. Furthermore, based on the collected information, the drafting department creates presentation materials explaining the applicant's situation in their home country and their personal circumstances. These presentation materials are organized in a slide format and used during the application process. This allows for effective communication of the applicant's situation. When creating these materials, the drafting department emphasizes ensuring the accuracy and consistency of the information and explaining the applicant's situation objectively and clearly. Furthermore, the drafting department takes care to protect the applicant's privacy and ensure that confidential information is handled appropriately. This allows the drafting department to provide materials that accurately and effectively convey the applicant's situation and support the asylum application process.

[0069] The Translation Department translates the prepared materials into Japanese. Specifically, it translates the prepared documents into Japanese. These documents include detailed explanations of the applicant's situation in their home country and their personal circumstances, and by accurately translating them into Japanese, the department provides the information necessary for the application process. The Translation Department takes care to accurately translate specialized terminology and expressions, ensuring that the meaning and nuances of the information are not lost. The Translation Department also translates the prepared charts and graphs into Japanese. These charts and graphs include statistical data, graphs, and maps, and by translating them into Japanese, the department can accurately convey visual information. For example, maps and graphs showing the distribution and frequency of persecution cases in the applicant's home country are translated into Japanese to visually demonstrate the severity of the crisis the applicant is facing. Furthermore, the Translation Department translates the prepared presentation materials into Japanese. These presentation materials are organized in slide format, and by translating them into Japanese, they are used during the application process. When translating these materials, the Translation Department emphasizes ensuring the accuracy and consistency of the information and explaining the applicant's situation objectively and clearly. Furthermore, the translation department takes care to protect applicants' privacy and ensure that confidential information is handled appropriately. This allows the translation department to provide materials that accurately and effectively convey the applicant's situation and support the asylum application process.

[0070] The data collection unit can collect information about the applicant's home country from international organizations, news media, NGOs, etc. For example, the data collection unit can collect reports from international organizations to understand the political and social situation in the applicant's home country. For example, the data collection unit can collect articles from news media to understand recent events and incidents in the applicant's home country. For example, the data collection unit can collect information from NGOs to understand the human rights situation and the reality of persecution in the applicant's home country. This allows for the efficient collection of information about the applicant's home country. Some or all of the above processing in the data collection unit may be performed using or without a generating AI. For example, the data collection unit can input reports from international organizations into a generating AI, which can analyze the contents of the reports and extract the necessary information.

[0071] The data collection unit can collect attribute information of the applicant. For example, the data collection unit can collect information about the applicant's place of birth and affiliated organizations. For example, the data collection unit can collect information about the applicant's social media activity and occupation. For example, the data collection unit can collect information about the applicant's health status and family structure. This allows for the efficient collection of attribute information of the applicant. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the applicant's social media activity into a generating AI, which can then analyze the content of the social media activity and extract the necessary information.

[0072] The analysis department can analyze the collected information and create documentation to prove the likelihood that the applicant is at risk of persecution. For example, the analysis department can statistically analyze the collected information and extract data indicating that the applicant is at risk of persecution. For example, the analysis department can text-mine the collected information and extract evidence indicating that the applicant is at risk of persecution. For example, the analysis department can analyze the collected information using machine learning algorithms and extract patterns indicating that the applicant is at risk of persecution. This allows for the efficient creation of documentation to prove the likelihood that the applicant is at risk of persecution. Some or all of the above processes in the analysis department may be performed using or without generative AI. For example, the analysis department can input the collected information into a generative AI, which can then analyze the information and create documentation to prove the likelihood.

[0073] The creation unit can analyze a wide range of information and create easy-to-understand explanatory materials about the applicant's country of origin and personal circumstances. For example, the creation unit can create documents explaining the applicant's country of origin and personal circumstances based on the collected information. For example, the creation unit can create charts and graphs explaining the applicant's country of origin and personal circumstances based on the collected information. For example, the creation unit can create presentation materials explaining the applicant's country of origin and personal circumstances based on the collected information. This makes it possible to create easy-to-understand explanatory materials about the applicant's country of origin and personal circumstances. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input the collected information into a generation AI, and the generation AI can analyze the information and create explanatory materials.

[0074] The translation unit can translate the created materials into Japanese. For example, the translation unit can translate a document into Japanese. For example, the translation unit can translate a chart or graph into Japanese. For example, the translation unit can translate a presentation into Japanese. This allows the created materials to be translated into Japanese. Some or all of the above-described processes in the translation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the translation unit can input the created materials into a generative AI, and the generative AI can translate the materials into Japanese.

[0075] The data collection unit can estimate the applicant's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the applicant is stressed, the data collection unit adjusts the timing of collection so that the applicant can provide information in a relaxed state. For example, if the applicant is nervous, the data collection unit carefully selects the timing of collection to provide a sense of security. For example, if the applicant is relaxed, the data collection unit starts collection quickly to efficiently gather information. This allows the timing of information collection to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 processing in the data collection unit may be performed using a generative AI or not. For example, the data collection unit can input the applicant's facial expression data into a generative AI, which can estimate emotions and adjust the timing of information collection.

[0076] The data collection unit can analyze the applicant's past application history during collection and select the optimal information collection method. For example, the data collection unit can analyze materials previously submitted by the applicant and select a method to efficiently collect similar information. For example, the data collection unit can identify the types of information needed from the applicant's past application history and optimize the collection method. For example, the data collection unit can determine the priority of information collection based on the applicant's past application history. This allows the data collection unit to select the optimal information collection method based on the applicant's past application history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the applicant's past application history data into a generation AI, which can then select the optimal information collection method.

[0077] The data collection unit can filter information based on the applicant's current living situation and areas of interest during the information collection process. For example, the data collection unit considers the applicant's current living situation and prioritizes the collection of highly relevant information. For example, the data collection unit filters necessary information based on the applicant's areas of interest. For example, the data collection unit adjusts the scope of information collection according to the applicant's living situation and areas of interest. This allows information to be filtered based on the applicant's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the applicant's living situation data into a generating AI, which can then filter the information.

[0078] The data collection unit can estimate the applicant's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the applicant is feeling anxious, the data collection unit will prioritize collecting important information to provide reassurance. For example, if the applicant is relaxed, the data collection unit will adjust the priority to efficiently collect information. For example, if the applicant is tense, the data collection unit will carefully determine the priority of information to collect to provide reassurance. This allows the priority of information to be collected to be determined according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 processing in the data collection unit may be performed using a generative AI or not. For example, the data collection unit can input the applicant's facial expression data into a generative AI, which can estimate emotions and determine the priority of information.

[0079] The data collection unit can prioritize the collection of highly relevant information by considering the applicant's geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant information based on the applicant's current location. For example, the data collection unit filters necessary information by considering the applicant's geographical location information. For example, the data collection unit adjusts the scope of data collection based on the applicant's geographical location information. This allows for the priority collection of highly relevant information by considering the applicant's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generating AI, or without using a generating AI. For example, the data collection unit can input the applicant's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0080] The data collection unit can analyze the applicant's social media activity during information collection and collect relevant information. For example, the data collection unit can analyze the applicant's social media activity and prioritize the collection of relevant information. For example, the data collection unit can filter the necessary information based on the applicant's social media activity. For example, the data collection unit can adjust the scope of information collection considering the applicant's social media activity. This allows the data collection unit to collect relevant information based on the applicant's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input the applicant's social media activity data into a generative AI, which can then collect the relevant information.

[0081] The analysis unit can estimate the applicant's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the applicant is feeling anxious, the analysis unit may adopt a simple and easy-to-understand presentation to provide reassurance. For example, if the applicant is relaxed, the analysis unit may adopt a presentation that includes detailed information. For example, if the applicant is nervous, the analysis unit may carefully select a presentation to provide reassurance. This allows the presentation of the analysis to be adjusted according to the applicant'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 analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the applicant's facial expression data into a generative AI, which can then estimate the emotions and adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the information collected during the analysis. For example, the analysis unit may prioritize the analysis of important information and add detailed explanations. The analysis unit may adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit may summarize less important information concisely and focus on important information. This allows the level of detail of the analysis to be adjusted based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit may input the collected information into a generative AI, which can then evaluate the importance of the information and adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific algorithm to political information to perform a detailed analysis. For example, the analysis unit can apply a different algorithm to religious information to perform an appropriate analysis. For example, the analysis unit can apply yet another algorithm to ethnic information to perform a precise analysis. This allows the application of an appropriate analysis algorithm depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the category of information into a generative AI, which can then select and apply an appropriate analysis algorithm.

[0084] The analysis unit can estimate the applicant's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the applicant is feeling anxious, the analysis unit will perform a concise and to-the-point analysis to provide reassurance. If the applicant is relaxed, the analysis unit will perform a longer analysis that includes detailed information. If the applicant is nervous, the analysis unit will carefully adjust the length of the analysis to provide reassurance. This allows the length of the analysis to be adjusted according to the applicant'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 analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the applicant's facial expression data into a generative AI, which can then estimate emotions and adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit prioritizes the analysis of the latest information and responds quickly. The analysis unit determines the priority of analysis based on when the information was collected. For example, the analysis unit postpones older information and focuses on the latest information. This allows the analysis priority to be determined based on when the information was collected. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input data on when the information was collected into a generative AI, and the generative AI can determine the priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information and adjusts the order accordingly. For example, the analysis unit determines the order of analysis based on the relevance of the information. For example, the analysis unit postpones the analysis of less relevant information and focuses on highly relevant information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information relevance data into a generative AI, and the generative AI can adjust the order.

[0087] The creation unit can estimate the applicant's emotions and adjust the method of creating the materials based on the estimated emotions. For example, if the applicant is feeling anxious, the creation unit will create simple and easy-to-understand materials to provide reassurance. For example, if the applicant is relaxed, the creation unit will create materials that include detailed information. For example, if the applicant is nervous, the creation unit will carefully select the method of creating the materials to provide reassurance. This allows the method of creating the materials to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 processing in the creation unit may be performed using a generative AI or not. For example, the creation unit can input the applicant's facial expression data into a generative AI, which can estimate emotions and adjust the method of creating the materials.

[0088] The creation unit can improve the accuracy of the document by considering the interrelationships of the information collected during document creation. For example, the creation unit analyzes the interrelationships of the collected information and creates an accurate document. For example, the creation unit improves the accuracy of the document by considering the interrelationships of the information. For example, the creation unit combines interrelated information to create a detailed document. This allows the accuracy of the document to be improved by considering the interrelationships of the collected information. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can input the collected information into a generation AI, and the generation AI can analyze the interrelationships of the information to improve the accuracy of the document.

[0089] The creation unit can customize documents by considering the applicant's attribute information when creating them. For example, the creation unit can create individually customized documents based on the applicant's attribute information. For example, the creation unit can create documents that are optimal for the applicant by considering the attribute information. For example, the creation unit can adjust the content of the documents based on the applicant's attribute information. This makes it possible to create customized documents based on the applicant's attribute information. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the applicant's attribute information into a generation AI, and the generation AI can customize the documents.

[0090] The creation unit can estimate the applicant's emotions and adjust the way the materials are displayed based on the estimated emotions. For example, if the applicant is feeling anxious, the creation unit may adopt a simple and highly visible display method to provide a sense of security. For example, if the applicant is relaxed, the creation unit may adopt a display method that includes detailed information. For example, if the applicant is nervous, the creation unit may carefully select a display method to provide a sense of security. This allows the display method of the materials to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, 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 creation unit may be performed using a generative AI or not. For example, the creation unit can input the applicant's facial expression data into a generative AI, which can estimate the emotions and adjust the display method of the materials.

[0091] The creation unit can create documents while considering the geographical distribution of information. For example, the creation unit can create accurate documents based on the geographical distribution of information. For example, the creation unit can adjust the content of the documents while considering the geographical distribution. For example, the creation unit can create detailed documents based on the geographical distribution. This allows for the creation of accurate documents while considering the geographical distribution of information. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input geographical distribution data of information into a generation AI, and the generation AI can create the documents.

[0092] The creation unit can improve the accuracy of the materials by referring to relevant literature during the creation process. For example, the creation unit can create accurate materials by referring to relevant literature. For example, the creation unit can adjust the content of the materials based on relevant literature. For example, the creation unit can create detailed materials by referring to relevant literature. In this way, the accuracy of the materials can be improved by referring to relevant literature. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input relevant literature into a generation AI, and the generation AI can refer to the literature to improve the accuracy of the materials.

[0093] The translation unit can estimate the applicant's emotions and adjust the translation's expression based on the estimated emotions. For example, if the applicant is feeling anxious, the translation unit may adopt a simple and easy-to-understand expression to provide reassurance. For example, if the applicant is relaxed, the translation unit may adopt an expression that includes detailed information. For example, if the applicant is nervous, the translation unit may carefully select an expression to provide reassurance. This allows the translation's expression to be adjusted according to the applicant'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 translation unit may be performed using or without a generative AI. For example, the translation unit can input the applicant's facial expression data into a generative AI, which can estimate the emotions and adjust the translation's expression.

[0094] The translation unit can adjust the level of detail in the translation based on the importance of the source material. For example, the translation unit prioritizes translating important material in detail. The translation unit adjusts the level of detail in the translation based on the importance of the source material. For example, the translation unit translates less important material concisely and focuses on important material. This allows the level of detail in the translation to be adjusted based on the importance of the source material. Some or all of the above processes in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input source importance data into a generative AI, which can then adjust the level of detail in the translation.

[0095] The translation unit can apply different translation algorithms depending on the category of the material during translation. For example, the translation unit may apply a specific algorithm to political materials to produce a detailed translation. For example, the translation unit may apply a different algorithm to religious materials to produce an appropriate translation. For example, the translation unit may apply yet another algorithm to ethnic materials to produce an accurate translation. This allows the translation unit to apply an appropriate translation algorithm according to the category of the material. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input the category of the material into a generative AI, which can then select and apply an appropriate translation algorithm.

[0096] The translation unit can estimate the applicant's emotions and adjust the length of the translation based on the estimated emotions. For example, if the applicant is feeling anxious, the translation unit will produce a concise and to-the-point translation to provide reassurance. If the applicant is relaxed, the translation unit will produce a longer translation that includes detailed information. If the applicant is nervous, the translation unit will carefully adjust the length of the translation to provide reassurance. This allows the translation length to be adjusted according to the applicant'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 translation unit may be performed using or without a generative AI. For example, the translation unit can input the applicant's facial expression data into a generative AI, which can estimate emotions and adjust the length of the translation.

[0097] The translation department can determine translation priorities based on the submission dates of the materials during the translation process. For example, the translation department can prioritize the translation of urgent materials and respond quickly. The translation department can determine translation priorities based on the submission dates of the materials. For example, the translation department can prioritize the translation of materials with approaching deadlines and respond quickly. This allows the translation department to determine translation priorities based on the submission dates of the materials. Some or all of the above processes in the translation department may be performed using or without a generative AI. For example, the translation department can input data on the submission dates of the materials into a generative AI, which can then determine the priorities.

[0098] The translation unit can adjust the order of translations based on the relevance of the materials during translation. For example, the translation unit may prioritize translating highly relevant materials and adjust the order accordingly. For example, the translation unit may determine the order of translations based on the relevance of the materials. For example, the translation unit may postpone less relevant materials and focus on highly relevant ones. This allows the order of translations to be adjusted based on the relevance of the materials. Some or all of the above processes in the translation unit may be performed using or without a generative AI. For example, the translation unit may input material relevance data into a generative AI, which can then adjust the order.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can analyze the applicant's past application history to select the most suitable information collection method when gathering information on the applicant's country of origin and personal circumstances. For example, it can analyze documents previously submitted by the applicant to select an efficient method for collecting similar information. It can also identify the types of information needed from the applicant's past application history and optimize the collection method. Furthermore, it can determine the priority of information collection based on the applicant's past application history. This allows for the selection of the most suitable information collection method based on the applicant's past application history.

[0101] The information collection unit can estimate the applicant's emotions and adjust the timing of information collection based on those estimates. For example, if the applicant is feeling stressed, the timing of collection can be adjusted so that they can provide information in a relaxed state. If the applicant is feeling anxious, the timing of collection can be carefully selected to provide reassurance. Furthermore, if the applicant is relaxed, collection can be started quickly to efficiently gather information. In this way, the timing of information collection can be adjusted according to the applicant's emotions.

[0102] The analysis department can prioritize the analysis based on when the information was collected when analyzing the collected information and creating documentation to prove the likelihood that the applicant is at risk of persecution. For example, they can prioritize the analysis of the most recent information and respond quickly. They can also prioritize the analysis based on when the information was collected. Furthermore, they can postpone older information and focus on the most recent information. This allows them to prioritize the analysis based on when the information was collected.

[0103] The analysis department can estimate the applicant's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the applicant is feeling anxious, a simple and easy-to-understand presentation can be used to provide reassurance. If the applicant is relaxed, a presentation including detailed information can be used. Furthermore, if the applicant is feeling tense, the presentation can be carefully selected to provide reassurance. In this way, the presentation of the analysis can be adjusted according to the applicant's emotions.

[0104] The creation unit can improve the accuracy of the document by considering the interrelationships of the information collected during the document creation process. For example, it can analyze the interrelationships of the collected information and create an accurate document. It can also improve the accuracy of the document by considering the interrelationships of the information. Furthermore, it can combine interrelated information to create a detailed document. This allows for improved accuracy of the document by considering the interrelationships of the collected information.

[0105] The preparation unit can estimate the applicant's emotions and adjust the method of preparing the materials based on those estimates. For example, if the applicant is feeling anxious, the system can create simple and easy-to-understand materials to provide reassurance. If the applicant is relaxed, the system can create materials that include detailed information. Furthermore, if the applicant is nervous, the system can carefully select the method of preparing the materials to provide reassurance. In this way, the method of preparing the materials can be adjusted according to the applicant's emotions.

[0106] The translation department can adjust the level of detail in translations based on the importance of the source material. For example, important documents can be prioritized and translated in detail. Furthermore, less important documents can be translated concisely, while emphasis is placed on more important documents. This allows for adjustment of the level of detail in translations based on the importance of the source material.

[0107] The translation department can estimate the applicant's emotions and adjust the translation's expression based on those estimates. For example, if the applicant is feeling anxious, a simple and easy-to-understand expression can be used to provide reassurance. If the applicant is relaxed, an expression containing detailed information can be used. Furthermore, if the applicant is nervous, the expression can be carefully selected to provide reassurance. This allows the translation's expression to be adjusted according to the applicant's emotions.

[0108] The information collection unit can filter information based on the applicant's current living situation and areas of interest. For example, it can prioritize the collection of highly relevant information, taking into account the applicant's current living situation. It can also filter necessary information based on the applicant's areas of interest. Furthermore, it can adjust the scope of information collection according to the applicant's living situation and areas of interest. This allows for information filtering based on the applicant's current living situation and areas of interest.

[0109] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, a specific algorithm can be applied to political information for detailed analysis. Another algorithm can be applied to religious information for appropriate analysis. Furthermore, yet another algorithm can be applied to ethnic information for accurate analysis. This allows for the application of the appropriate analysis algorithm depending on the category of information.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection department collects information about the applicant's home country and personal circumstances. For example, it collects information about the applicant's home country from international organizations, news media, and NGOs. The collection department collects reports from international organizations and articles from news media to understand the political and social situation in the applicant's home country. Furthermore, it collects information from NGOs to understand the human rights situation and the reality of persecution in the applicant's home country. The collection department also collects attribute information about the applicant themselves, such as their place of birth, affiliated organizations, social media activity, occupation, health status, and family structure. Step 2: The analysis department analyzes the collected information and creates documentation to prove the likelihood that the applicant is at risk of persecution. For example, it statistically analyzes the collected information to extract data indicating that the applicant is at risk of persecution. Furthermore, it text-mines the collected information to extract evidence indicating that the applicant is at risk of persecution. In addition, it analyzes the collected information using machine learning algorithms to extract patterns indicating that the applicant is at risk of persecution. Step 3: The drafting team analyzes a wide range of information and creates easy-to-understand explanatory materials about the applicant's country of origin and personal circumstances. For example, based on the collected information, they create documents, charts, and presentation materials that explain the applicant's country of origin and personal circumstances. Step 4: The translation department translates the created materials into Japanese. For example, they translate the created documents, charts, and presentation materials into Japanese.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and translation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the smart device 14 and analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs statistical analysis and text mining on the collected information. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates explanatory materials based on the collected information. The translation unit is implemented in the control unit 46A of the smart device 14 and translates the created materials into Japanese. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, creation unit, and translation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs statistical analysis and text mining on the collected information. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates explanatory materials based on the collected information. The translation unit is implemented in the control unit 46A of the smart glasses 214 and translates the created materials into Japanese. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and translation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs statistical analysis and text mining on the collected information. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates explanatory materials based on the collected information. The translation unit is implemented in the control unit 46A of the headset terminal 314 and translates the created materials into Japanese. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, creation unit, and translation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and communication I / F 44 of the robot 414 and analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs statistical analysis and text mining on the collected information. The creation unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates explanatory materials based on the collected information. The translation unit is implemented in the control unit 46A of the robot 414 and translates the created materials into Japanese. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The collection department collects information on the situation in the home country and the personal circumstances of asylum seekers, An analysis unit analyzes the information collected by the aforementioned collection unit, A creation unit that creates materials based on the analysis results obtained by the aforementioned analysis unit, The system includes a translation unit that translates the materials created by the creation unit into Japanese. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about the applicant's home country from international organizations, news media, NGOs, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect personal attribute information of the applicant. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The collected information is analyzed, and documentation is created to prove the likelihood that the applicant is at risk of persecution. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Analyze a wide range of information and create easy-to-understand explanatory materials about the situation in the person's home country and their personal circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned translation department, Translate the created document into Japanese. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the applicant's emotions and adjusts the timing of information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During the data collection process, the applicant's past application history is analyzed to select the most appropriate information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During the information gathering process, filtering is performed based on the applicant's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the applicant's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, the geographical location of the applicant will be taken into consideration, and highly relevant information will be collected preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During the information gathering process, we analyze the applicant's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the applicant's emotions and adjust the way the analysis is presented based on the estimated emotions of the applicant. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Adjust the level of detail of the analysis based on the importance of the information collected during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Apply different analysis algorithms depending on the category of information during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The length of the analysis is adjusted based on the estimated emotions of the applicant, and the emotions of the applicant are estimated. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Prioritize analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Adjust the order of analysis based on the relevance of the information during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, We estimate the applicant's emotions and adjust the method of preparing the documents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, Improve the accuracy of the document by considering the interrelationships of the information collected during its creation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, Customize the documents when creating them, taking into account the applicant's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, The system estimates the applicant's emotions and adjusts how the materials are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating documents, consider the geographical distribution of information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, Referencing relevant literature during document creation improves the accuracy of the document. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned translation department, The system estimates the applicant's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned translation department, Adjust the level of detail in the translation based on the importance of the source material. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned translation department, Apply different translation algorithms depending on the category of the document during translation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned translation department, The system estimates the applicant's emotions and adjusts the translation length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned translation department, Prioritize translations based on the submission date of the materials during the translation process. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned translation department, The order of translations is adjusted based on the relevance of the materials during translation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 information on the situation in the home country and the personal circumstances of asylum seekers, An analysis unit analyzes the information collected by the aforementioned collection unit, A creation unit that creates materials based on the analysis results obtained by the aforementioned analysis unit, The system includes a translation unit that translates the materials created by the creation unit into Japanese. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about the applicant's home country from international organizations, news media, NGOs, etc. The system according to feature 1.

3. The aforementioned collection unit is Collect personal attribute information of the applicant. The system according to feature 1.

4. The aforementioned analysis unit is The collected information is analyzed, and documentation is created to prove the likelihood that the applicant is at risk of persecution. The system according to feature 1.

5. The aforementioned creation unit, Analyze a wide range of information and create easy-to-understand explanatory materials about the situation in the person's home country and their personal circumstances. The system according to feature 1.

6. The aforementioned translation department, Translate the created document into Japanese. The system according to feature 1.

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

8. The aforementioned collection unit is During the data collection process, the applicant's past application history is analyzed to select the most appropriate information collection method. The system according to feature 1.

9. The aforementioned collection unit is During the information gathering process, filtering is performed based on the applicant's current living situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the applicant's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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