system

Abstract

The system according to the embodiment aims to efficiently detect fraudulent documents and information submitted at the time of application. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an examination unit. The collection unit collects documents or information submitted at the time of application. The analysis unit analyzes the information collected by the collection unit and compares it with past data. The determination unit determines the possibility of fraud based on the results of the analysis by the analysis unit. The examination unit performs a detailed examination based on the information determined by the determination unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently detect fraudulent documents and information submitted at the time of application.

[0005] The system according to the embodiment aims to efficiently detect fraudulent documents and information submitted at the time of application. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an examination unit. The collection unit collects documents or information submitted at the time of application. The analysis unit analyzes the information collected by the collection unit and compares it with past data. The determination unit determines the possibility of fraud based on the results of the analysis by the analysis unit. The examination unit performs a detailed examination based on the information determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect fraudulent documents and information submitted at the time of application. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A fraudulent application detection system according to an embodiment of the present invention efficiently detects fraudulent applications for mobile phones and other devices. This fraudulent application detection system utilizes information based on past data and a generation AI to detect approved lines based on fraudulent applications. First, documents and information submitted at the time of application are collected. Next, the generation AI analyzes the collected information and compares it with past data. The generation AI, which has learned the patterns and characteristics of past fraudulent applications, determines whether the submitted information indicates fraud. For example, if documents are falsified or contain false information, the generation AI detects the anomaly. Furthermore, if the generation AI determines that there is a possibility of fraud, it notifies the review team. The review team then conducts a detailed review based on the information provided by the generation AI and makes a final decision on whether the fraudulent application should be approved. This reduces the number of cases where fraud is not detected during the review process. This system allows the review team to efficiently detect fraudulent applications and take appropriate action. For example, utilizing information based on past data improves the accuracy of the review and increases the detection rate of fraudulent applications. Furthermore, the generation AI automatically performs analysis, reducing the burden on the review team. This allows the fraudulent application detection system to detect fraudulent applications by efficiently collecting, analyzing, judging, and reviewing documents and information submitted at the time of application.

[0029] The fraudulent application detection system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an examination unit. The collection unit collects documents or information submitted when applying. Examples of documents or information submitted when applying include, but are not limited to, application forms, identification documents, and financial information. The collection unit, for example, collects documents through an online form. The collection unit can also collect documents by mail or email. The collection unit can also scan documents submitted in person by applicants and convert them into digital data. For example, the collection unit collects application forms through an online form and saves them as digital data. The collection unit can also scan documents sent by mail and convert them into digital data. The analysis unit analyzes the information collected by the collection unit and compares it with past data. For example, the analysis unit analyzes the information using data mining technology. The analysis unit can also analyze the information using statistical analysis technology. The analysis unit can also analyze the information using a machine learning algorithm. For example, the analysis unit extracts patterns of past fraudulent applications using data mining technology and compares them with the submitted information. The analysis unit can also detect abnormal values ​​in the submitted information using statistical analysis techniques. The determination unit determines the possibility of fraud based on the results of the analysis by the analysis unit. The determination unit can, for example, use an anomaly detection algorithm to determine the possibility of fraud. The determination unit can also use a rule-based system to determine the possibility of fraud. The determination unit can also use a machine learning algorithm to determine the possibility of fraud. For example, the determination unit can use an anomaly detection algorithm to detect anomalies in the submitted information and determine the possibility of fraud. The determination unit can also use a rule-based system to check the consistency of the submitted information. The review unit performs a detailed review based on the information determined by the review unit. The review unit can, for example, perform manual verification. The review unit can also request additional documents. The review unit can also perform a detailed review using a machine learning algorithm. For example, the review unit can perform manual verification to confirm the authenticity of the submitted information. The review unit can also request additional documents to supplement the submitted information.As a result, the fraudulent application detection system according to the embodiment can detect fraudulent applications by efficiently collecting, analyzing, determining, and examining documents and information submitted at the time of application.

[0030] The collection unit can analyze the applicant's past application history and select the optimal collection method. The collection unit, for example, analyzes the format of documents previously submitted by the applicant and collects them in a similar format. The collection unit can, for example, prioritize collection of frequently used information from the applicant's past application history. The collection unit can, for example, select the most efficient collection method based on the applicant's past application history. This enables efficient information collection by selecting the optimal collection method based on the past application history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the applicant's past application history data into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting documents and information, the collection unit can filter the information based on the applicant's current situation and areas of interest. For example, the collection unit prioritizes collecting information related to the applicant's current occupation and industry. For example, the collection unit can filter and collect highly relevant information based on the applicant's areas of interest. For example, the collection unit can collect necessary information based on the applicant's current situation (e.g., job hunting). In this way, highly relevant information can be collected by filtering information based on the applicant's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's current situation data into the generation AI and have the generation AI perform information filtering.

[0032] When collecting documents and information, the collection unit can prioritize collecting highly relevant information based on the applicant's geographical location information. The collection unit, for example, prioritizes collecting region-specific information based on the applicant's current location. The collection unit can, for example, filter and collect the most relevant information based on the applicant's geographical location information. The collection unit can, for example, prioritize collecting information regarding region regulations and laws, taking into account the applicant's geographical location information. This allows for efficient collection of region-specific information by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's geographical location information data to the generation AI and have the generation AI perform information filtering.

[0033] The collection unit can analyze the applicant's social media activity and collect relevant information when collecting documents and information. For example, the collection unit can collect information related to topics of interest from the applicant's social media activity. For example, the collection unit can analyze the applicant's social media activity and prioritize the collection of reliable information. For example, the collection unit can filter and collect the most relevant information based on the applicant's social media activity. This allows information related to the applicant to be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the applicant's social media data into the generation AI and have the generation AI collect relevant information.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information with high importance. For example, the analysis unit can perform a simplified analysis on information with low importance. For example, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] 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 financial analysis algorithm to financial information. For example, the analysis unit can apply an analysis algorithm that takes privacy protection into consideration to personal information. For example, the analysis unit can apply a technical analysis algorithm to technical information. By applying an analysis algorithm depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can, for example, postpone analysis of information that was submitted earlier. The analysis unit can, for example, gradually adjust the priority of analysis according to the time of submission. This allows the most recent information to be analyzed preferentially by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit can postpone analysis of information with low relevance. For example, the analysis unit can gradually adjust the order of analysis according to the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0038] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of information when making a judgment. The judgment unit, for example, analyzes the interrelationships of submitted information and checks consistency. The judgment unit can, for example, identify elements that increase the possibility of fraud based on the interrelationships of information. The judgment unit can, for example, improve the accuracy of the judgment by taking into account the interrelationships of information. As a result, the accuracy of the judgment is improved by taking into account the interrelationships of information. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input interrelationship data of information to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0039] The determination unit can make a determination taking into account the applicant's attribute information. The determination unit makes a determination based on attribute information such as the applicant's age and occupation, for example. The determination unit can make a determination taking into account the applicant's past behavioral history, for example. The determination unit can evaluate the possibility of fraud based on the applicant's attribute information, for example. This enables an appropriate determination by taking into account the applicant's attribute information. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the applicant's attribute information data into the generation AI and have the generation AI perform the determination.

[0040] The determination unit can make a determination taking into account the geographic distribution of the information. For example, the determination unit analyzes the geographic distribution of the submitted information and identifies a fraud pattern specific to a region. For example, the determination unit can improve the accuracy of the determination based on the geographic distribution. For example, the determination unit can evaluate the fraud risk for each region taking into account the geographic distribution. As a result, by taking the geographic distribution into account, it is possible to identify a fraud pattern specific to a region. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input geographic distribution data of the information to a generation AI and have the generation AI perform the determination.

[0041] The judgment unit can improve the accuracy of the judgment by referring to literature related to the information when making the judgment. The judgment unit can, for example, refer to literature related to the submitted information to improve the accuracy of the judgment. The judgment unit can, for example, evaluate the possibility of fraud based on the related literature. The judgment unit can, for example, adjust the judgment criteria by referring to the related literature. As a result, the accuracy of the judgment is improved by referring to the related literature. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using AI, or can be performed without using AI. For example, the judgment unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0042] During the screening process, the screening department can select the optimal screening method by analyzing the applicant's past behavior. The screening department selects the optimal screening method based on, for example, the applicant's past behavioral history. The screening department can, for example, analyze the applicant's past behavioral patterns and identify high-risk behavior. The screening department can, for example, improve the accuracy of the screening by taking into account the applicant's past behavior. This allows the optimal screening method to be selected by analyzing past behavior. Some or all of the above-mentioned processing in the screening department may be performed using, for example, AI, or may be performed without using AI. For example, the screening department can input the applicant's past behavioral data into a generation AI and have the generation AI select the screening method.

[0043] During the screening process, the screening department can customize the screening method based on the applicant's current situation. The screening department can customize the screening method, for example, by taking into account the applicant's current occupation and living situation. The screening department can adjust the screening method, for example, by taking into account the applicant's current economic situation. The screening department can customize the screening method, for example, by taking into account the applicant's current health condition. This enables appropriate screening by customizing the screening method based on the applicant's current situation. Some or all of the above-described processing in the screening department can be performed using AI, for example, or without AI. For example, the screening department can input the applicant's current situation data into the generation AI and have the generation AI customize the screening method.

[0044] During the screening process, the screening department can select the optimal screening method by taking into account the applicant's geographic location information. For example, the screening department selects a region-specific screening method based on the applicant's geographic location information. For example, the screening department can select the most efficient screening method by taking into account the applicant's geographic location information. For example, the screening department can select a screening method that complies with local regulations and laws based on the applicant's geographic location information. In this way, a region-specific screening method can be selected by taking into account the geographic location information. Some or all of the above-described processing in the screening department may be performed using AI, for example, or may be performed without using AI. For example, the screening department can input the applicant's geographic location information data into a generation AI and have the generation AI select a screening method.

[0045] During the screening process, the screening department can analyze the applicant's social media activity and propose screening methods. The screening department can propose screening methods based on reliable information, for example, from the applicant's social media activity. The screening department can, for example, analyze the applicant's social media activity and propose the most appropriate screening method. The screening department can, for example, customize the screening method based on the applicant's social media activity. In this way, appropriate screening methods can be proposed by analyzing social media activity. Some or all of the above-mentioned processing in the screening department may be performed, for example, using AI, or may be performed without using AI. For example, the screening department can input the applicant's social media data into a generation AI and have the generation AI execute the screening method proposal.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The collection unit can analyze the applicant's past application history and select the optimal collection method. For example, it can analyze the format of documents submitted by the applicant in the past and collect information in a similar format. It can also prioritize collection of frequently used information from the applicant's past application history. Furthermore, it can select the most efficient collection method based on the applicant's past application history. This enables efficient information collection by selecting the optimal collection method based on the past application history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's past application history data into the generation AI and have the generation AI select the optimal collection method.

[0048] When collecting documents and information, the collection unit can filter the documents and information based on the applicant's current situation and areas of interest. For example, it can prioritize collection of information related to the applicant's current occupation and industry. It can also filter and collect highly relevant information based on the applicant's areas of interest. Furthermore, it can collect necessary information based on the applicant's current situation (e.g., job hunting). This makes it possible to collect highly relevant information by filtering information based on the applicant's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the applicant's current situation data into the generation AI and have the generation AI perform information filtering.

[0049] When collecting documents and information, the collection unit can prioritize collection of highly relevant information based on the applicant's geographical location information. For example, it can prioritize collection of region-specific information based on the applicant's current location. It can also filter and collect the most relevant information based on the applicant's geographical location information. Furthermore, it can prioritize collection of information regarding regional regulations and laws taking into account the applicant's geographical location information. This allows for efficient collection of region-specific information by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's geographical location information data into the generation AI and have the generation AI perform information filtering.

[0050] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis can be performed for information with high importance. A simplified analysis can be performed for information with low importance. Furthermore, the level of detail of the analysis can be adjusted in stages depending on the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis depending on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0051] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, a specific financial analysis algorithm can be applied to financial information. Furthermore, an analysis algorithm that takes privacy protection into consideration can be applied to personal information. Furthermore, a technical analysis algorithm can be applied to technical information. By applying an analysis algorithm depending on the category of information, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply the analysis algorithm.

[0052] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the information. For example, the latest information can be analyzed preferentially. Also, information that was submitted earlier can be analyzed later. Furthermore, the analysis priority can be adjusted in stages depending on the time of submission. In this way, by determining the analysis priority based on the time of submission of the information, the latest information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of the information to the generation AI and have the generation AI determine the analysis priority.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection department collects documents or information submitted with the application. Documents or information submitted with the application include application forms, identification documents, financial information, etc. The collection department can collect documents through online forms, mail, or email. They can also scan documents submitted in person by the applicant and convert them into digital data. Step 2: The analysis unit analyzes the information collected by the collection unit and compares it with past data. The analysis unit analyzes the information using data mining technology, statistical analysis technology, and machine learning algorithms. For example, it extracts patterns of past fraudulent applications and compares them with the submitted information. It can also detect outliers in the submitted information. Step 3: The determination unit determines the possibility of fraud based on the results of the analysis by the analysis unit. The determination unit determines the possibility of fraud using anomaly detection algorithms, rule-based systems, and machine learning algorithms. For example, the determination unit detects anomalies in the submitted information and determines the possibility of fraud. It can also check the consistency of the submitted information. Step 4: The Inspection Department conducts a detailed review based on the information determined by the Determination Department. The Inspection Department performs a detailed review using manual verification, requests for additional documents, and machine learning algorithms. For example, the department checks the authenticity of the submitted information and requests additional documents to supplement the submitted information.

[0055] (Example 2) A fraudulent application detection system according to an embodiment of the present invention efficiently detects fraudulent applications for mobile phones and other devices. This fraudulent application detection system utilizes information based on past data and a generation AI to detect approved lines based on fraudulent applications. First, documents and information submitted at the time of application are collected. Next, the generation AI analyzes the collected information and compares it with past data. The generation AI, which has learned the patterns and characteristics of past fraudulent applications, determines whether the submitted information indicates fraud. For example, if documents are falsified or contain false information, the generation AI detects the anomaly. Furthermore, if the generation AI determines that there is a possibility of fraud, it notifies the review team. The review team then conducts a detailed review based on the information provided by the generation AI and makes a final decision on whether the fraudulent application should be approved. This reduces the number of cases where fraud is not detected during the review process. This system allows the review team to efficiently detect fraudulent applications and take appropriate action. For example, utilizing information based on past data improves the accuracy of the review and increases the detection rate of fraudulent applications. Furthermore, the generation AI automatically performs analysis, reducing the burden on the review team. This allows the fraudulent application detection system to detect fraudulent applications by efficiently collecting, analyzing, judging, and reviewing documents and information submitted at the time of application.

[0056] The fraudulent application detection system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an examination unit. The collection unit collects documents or information submitted when applying. Examples of documents or information submitted when applying include, but are not limited to, application forms, identification documents, and financial information. The collection unit, for example, collects documents through an online form. The collection unit can also collect documents by mail or email. The collection unit can also scan documents submitted in person by applicants and convert them into digital data. For example, the collection unit collects application forms through an online form and saves them as digital data. The collection unit can also scan documents sent by mail and convert them into digital data. The analysis unit analyzes the information collected by the collection unit and compares it with past data. For example, the analysis unit analyzes the information using data mining technology. The analysis unit can also analyze the information using statistical analysis technology. The analysis unit can also analyze the information using a machine learning algorithm. For example, the analysis unit extracts patterns of past fraudulent applications using data mining technology and compares them with the submitted information. The analysis unit can also detect abnormal values ​​in the submitted information using statistical analysis techniques. The determination unit determines the possibility of fraud based on the results of the analysis by the analysis unit. The determination unit can, for example, use an anomaly detection algorithm to determine the possibility of fraud. The determination unit can also use a rule-based system to determine the possibility of fraud. The determination unit can also use a machine learning algorithm to determine the possibility of fraud. For example, the determination unit can use an anomaly detection algorithm to detect anomalies in the submitted information and determine the possibility of fraud. The determination unit can also use a rule-based system to check the consistency of the submitted information. The review unit performs a detailed review based on the information determined by the review unit. The review unit can, for example, perform manual verification. The review unit can also request additional documents. The review unit can also perform a detailed review using a machine learning algorithm. For example, the review unit can perform manual verification to confirm the authenticity of the submitted information. The review unit can also request additional documents to supplement the submitted information.As a result, the fraudulent application detection system according to the embodiment can detect fraudulent applications by efficiently collecting, analyzing, determining, and examining documents and information submitted at the time of application.

[0057] The collection unit estimates the user's emotions and adjusts the timing of collecting documents and information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to reduce the user's burden. For example, if the user is relaxed, the collection unit can immediately collect documents and information and quickly proceed with processing. For example, if the user is in a hurry, the collection unit can accelerate the collection timing to respond quickly. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0058] The collection unit can analyze the applicant's past application history and select the optimal collection method. The collection unit, for example, analyzes the format of documents previously submitted by the applicant and collects them in a similar format. The collection unit can, for example, prioritize collection of frequently used information from the applicant's past application history. The collection unit can, for example, select the most efficient collection method based on the applicant's past application history. This enables efficient information collection by selecting the optimal collection method based on the past application history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the applicant's past application history data into the generation AI and have the generation AI select the optimal collection method.

[0059] When collecting documents and information, the collection unit can filter the information based on the applicant's current situation and areas of interest. For example, the collection unit prioritizes collecting information related to the applicant's current occupation and industry. For example, the collection unit can filter and collect highly relevant information based on the applicant's areas of interest. For example, the collection unit can collect necessary information based on the applicant's current situation (e.g., job hunting). In this way, highly relevant information can be collected by filtering information based on the applicant's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's current situation data into the generation AI and have the generation AI perform information filtering.

[0060] The collection unit can estimate the user's emotions and determine the priority of documents and information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit postpones the collection of less important information. For example, if the user is relaxed, the collection unit can collect all information evenly. For example, if the user is in a hurry, the collection unit can prioritize the collection of more important information. This enables efficient information collection by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0061] When collecting documents and information, the collection unit can prioritize collecting highly relevant information based on the applicant's geographical location information. The collection unit, for example, prioritizes collecting region-specific information based on the applicant's current location. The collection unit can, for example, filter and collect the most relevant information based on the applicant's geographical location information. The collection unit can, for example, prioritize collecting information regarding region regulations and laws, taking into account the applicant's geographical location information. This allows for efficient collection of region-specific information by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's geographical location information data to the generation AI and have the generation AI perform information filtering.

[0062] The collection unit can analyze the applicant's social media activity and collect relevant information when collecting documents and information. For example, the collection unit can collect information related to topics of interest from the applicant's social media activity. For example, the collection unit can analyze the applicant's social media activity and prioritize the collection of reliable information. For example, the collection unit can filter and collect the most relevant information based on the applicant's social media activity. This allows information related to the applicant to be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the applicant's social media data into the generation AI and have the generation AI collect relevant information.

[0063] The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information with high importance. For example, the analysis unit can perform a simplified analysis on information with low importance. For example, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] 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 financial analysis algorithm to financial information. For example, the analysis unit can apply an analysis algorithm that takes privacy protection into consideration to personal information. For example, the analysis unit can apply a technical analysis algorithm to technical information. By applying an analysis algorithm depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0066] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result appropriate for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can, for example, postpone analysis of information that was submitted earlier. The analysis unit can, for example, gradually adjust the priority of analysis according to the time of submission. This allows the most recent information to be analyzed preferentially by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit can postpone analysis of information with low relevance. For example, the analysis unit can gradually adjust the order of analysis according to the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0069] The determination unit estimates the user's emotion and adjusts the determination criteria based on the estimated user emotion. For example, if the user is nervous, the determination unit makes a determination using strict criteria. For example, if the user is relaxed, the determination unit can make a determination using flexible criteria. For example, if the user is in a hurry, the determination unit can set criteria for making a quick determination. This enables appropriate determination by adjusting the determination criteria according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the determination unit may be performed using an AI, for example, or without an AI. For example, the determination unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotion.

[0070] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of information when making a judgment. The judgment unit, for example, analyzes the interrelationships of submitted information and checks consistency. The judgment unit can, for example, identify elements that increase the possibility of fraud based on the interrelationships of information. The judgment unit can, for example, improve the accuracy of the judgment by taking into account the interrelationships of information. As a result, the accuracy of the judgment is improved by taking into account the interrelationships of information. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input interrelationship data of information to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0071] The determination unit can make a determination taking into account the applicant's attribute information. The determination unit makes a determination based on attribute information such as the applicant's age and occupation, for example. The determination unit can make a determination taking into account the applicant's past behavioral history, for example. The determination unit can evaluate the possibility of fraud based on the applicant's attribute information, for example. This enables an appropriate determination by taking into account the applicant's attribute information. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the applicant's attribute information data into the generation AI and have the generation AI perform the determination.

[0072] The determination unit estimates the user's emotions and adjusts the order in which the determination results are displayed based on the estimated user emotions. For example, if the user is nervous, the determination unit displays important results first. For example, if the user is relaxed, the determination unit can sequentially display detailed results. For example, if the user is in a hurry, the determination unit can display results that highlight the main points first. This allows the display order of results to be adjusted according to the user's emotions, thereby providing results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the determination unit may be performed using an AI, for example, or without an AI. For example, the determination unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The determination unit can make a determination taking into account the geographic distribution of the information. For example, the determination unit analyzes the geographic distribution of the submitted information and identifies a fraud pattern specific to a region. For example, the determination unit can improve the accuracy of the determination based on the geographic distribution. For example, the determination unit can evaluate the fraud risk for each region taking into account the geographic distribution. As a result, by taking the geographic distribution into account, it is possible to identify a fraud pattern specific to a region. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input geographic distribution data of the information to a generation AI and have the generation AI perform the determination.

[0074] The judgment unit can improve the accuracy of the judgment by referring to literature related to the information when making the judgment. The judgment unit can, for example, refer to literature related to the submitted information to improve the accuracy of the judgment. The judgment unit can, for example, evaluate the possibility of fraud based on the related literature. The judgment unit can, for example, adjust the judgment criteria by referring to the related literature. As a result, the accuracy of the judgment is improved by referring to the related literature. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using AI, or can be performed without using AI. For example, the judgment unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0075] The review unit estimates the user's emotions and adjusts the review method based on the estimated user emotions. For example, if the user is nervous, the review unit may adopt a flexible review method. For example, if the user is relaxed, the review unit may adopt a detailed review method. For example, if the user is in a hurry, the review unit may adopt a quick review method. This allows for appropriate review by adjusting the review method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the review unit may be performed using AI, or may be performed without AI. For example, the review unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0076] During the screening process, the screening department can select the optimal screening method by analyzing the applicant's past behavior. The screening department selects the optimal screening method based on, for example, the applicant's past behavioral history. The screening department can, for example, analyze the applicant's past behavioral patterns and identify high-risk behavior. The screening department can, for example, improve the accuracy of the screening by taking into account the applicant's past behavior. This allows the optimal screening method to be selected by analyzing past behavior. Some or all of the above-mentioned processing in the screening department may be performed using, for example, AI, or may be performed without using AI. For example, the screening department can input the applicant's past behavioral data into a generation AI and have the generation AI select the screening method.

[0077] During the screening process, the screening department can customize the screening method based on the applicant's current situation. The screening department can customize the screening method, for example, by taking into account the applicant's current occupation and living situation. The screening department can adjust the screening method, for example, by taking into account the applicant's current economic situation. The screening department can customize the screening method, for example, by taking into account the applicant's current health condition. This enables appropriate screening by customizing the screening method based on the applicant's current situation. Some or all of the above-described processing in the screening department can be performed using AI, for example, or without AI. For example, the screening department can input the applicant's current situation data into the generation AI and have the generation AI customize the screening method.

[0078] The review unit estimates the user's emotions and determines the priority of the review based on the estimated user emotions. For example, if the user is nervous, the review unit sets the priority of the review high. For example, if the user is relaxed, the review unit can perform the review at normal priority. For example, if the user is in a hurry, the review unit can set the priority of the review to highest. This enables appropriate review by determining the priority of the review according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the review unit may be performed using AI, for example, or without AI. For example, the review unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0079] During the screening process, the screening department can select the optimal screening method by taking into account the applicant's geographic location information. For example, the screening department selects a region-specific screening method based on the applicant's geographic location information. For example, the screening department can select the most efficient screening method by taking into account the applicant's geographic location information. For example, the screening department can select a screening method that complies with local regulations and laws based on the applicant's geographic location information. In this way, a region-specific screening method can be selected by taking into account the geographic location information. Some or all of the above-described processing in the screening department may be performed using AI, for example, or may be performed without using AI. For example, the screening department can input the applicant's geographic location information data into a generation AI and have the generation AI select a screening method.

[0080] During the screening process, the screening department can analyze the applicant's social media activity and propose screening methods. The screening department can propose screening methods based on reliable information, for example, from the applicant's social media activity. The screening department can, for example, analyze the applicant's social media activity and propose the most appropriate screening method. The screening department can, for example, customize the screening method based on the applicant's social media activity. In this way, appropriate screening methods can be proposed by analyzing social media activity. Some or all of the above-mentioned processing in the screening department may be performed, for example, using AI, or may be performed without using AI. For example, the screening department can input the applicant's social media data into a generation AI and have the generation AI execute the screening method proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and examination unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects documents and information submitted at the time of application using the camera 42 and microphone 38B of the smart device 14 and converts them into digital data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and compares it with past data. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the possibility of fraud based on the analysis results. The examination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs a detailed examination based on the determination results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and examination unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects documents and information submitted at the time of application using the camera 42 and microphone 238 of the smart glasses 214 and converts the documents and information into digital data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and compares it with past data. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the possibility of fraud based on the analysis results. The examination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs a detailed examination based on the determination results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and examination unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects documents and information submitted at the time of application using the camera 42 and microphone 238 of the headset terminal 314 and converts them into digital data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and compares it with past data. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the possibility of fraud based on the analysis results. The examination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs a detailed examination based on the determination results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and screening unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects documents and information submitted at the time of application using the camera 42 and microphone 238 of the robot 414, and converts the collected information into digital data by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and compares it with past data. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the possibility of fraud based on the analysis results. The screening unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs a detailed screening based on the determination results.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection unit can analyze the applicant's past application history and select the optimal collection method. For example, it can analyze the format of documents submitted by the applicant in the past and collect information in a similar format. It can also prioritize collection of frequently used information from the applicant's past application history. Furthermore, it can select the most efficient collection method based on the applicant's past application history. This enables efficient information collection by selecting the optimal collection method based on the past application history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's past application history data into the generation AI and have the generation AI select the optimal collection method.

[0083] The collection unit can estimate the user's emotions and adjust the timing of document and information collection based on the estimated user emotions. For example, if the user is stressed, the collection timing can be delayed to reduce the user's burden. Furthermore, if the user is relaxed, documents and information can be collected immediately and processed quickly. Furthermore, if the user is in a hurry, the collection timing can be accelerated to respond quickly. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0084] When collecting documents and information, the collection unit can filter the documents and information based on the applicant's current situation and areas of interest. For example, it can prioritize collection of information related to the applicant's current occupation and industry. It can also filter and collect highly relevant information based on the applicant's areas of interest. Furthermore, it can collect necessary information based on the applicant's current situation (e.g., job hunting). This makes it possible to collect highly relevant information by filtering information based on the applicant's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the applicant's current situation data into the generation AI and have the generation AI perform information filtering.

[0085] The collection unit can estimate the user's emotions and prioritize the documents and information to be collected based on the estimated user emotions. For example, if the user is stressed, collecting less important information can be postponed. Also, if the user is relaxed, all information can be collected equally. Furthermore, if the user is in a hurry, collecting more important information can be prioritized. This enables efficient information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0086] When collecting documents and information, the collection unit can prioritize collection of highly relevant information based on the applicant's geographical location information. For example, it can prioritize collection of region-specific information based on the applicant's current location. It can also filter and collect the most relevant information based on the applicant's geographical location information. Furthermore, it can prioritize collection of information regarding regional regulations and laws taking into account the applicant's geographical location information. This allows for efficient collection of region-specific information by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the applicant's geographical location information data into the generation AI and have the generation AI perform information filtering.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result that focuses on the main points can be provided. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0088] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis can be performed for information with high importance. A simplified analysis can be performed for information with low importance. Furthermore, the level of detail of the analysis can be adjusted in stages depending on the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis depending on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, a specific financial analysis algorithm can be applied to financial information. Furthermore, an analysis algorithm that takes privacy protection into consideration can be applied to personal information. Furthermore, a technical analysis algorithm can be applied to technical information. By applying an analysis algorithm depending on the category of information, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, a short and concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is excited, a visually stimulating analysis result can be provided. By adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0091] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the information. For example, the latest information can be analyzed preferentially. Also, information that was submitted earlier can be analyzed later. Furthermore, the analysis priority can be adjusted in stages depending on the time of submission. In this way, by determining the analysis priority based on the time of submission of the information, the latest information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of the information to the generation AI and have the generation AI determine the analysis priority.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection department collects documents or information submitted with the application. Documents or information submitted with the application include application forms, identification documents, financial information, etc. The collection department can collect documents through online forms, mail, or email. They can also scan documents submitted in person by the applicant and convert them into digital data. Step 2: The analysis unit analyzes the information collected by the collection unit and compares it with past data. The analysis unit analyzes the information using data mining technology, statistical analysis technology, and machine learning algorithms. For example, it extracts patterns of past fraudulent applications and compares them with the submitted information. It can also detect outliers in the submitted information. Step 3: The determination unit determines the possibility of fraud based on the results of the analysis by the analysis unit. The determination unit determines the possibility of fraud using anomaly detection algorithms, rule-based systems, and machine learning algorithms. For example, the determination unit detects anomalies in the submitted information and determines the possibility of fraud. It can also check the consistency of the submitted information. Step 4: The Inspection Department conducts a detailed review based on the information determined by the Determination Department. The Inspection Department performs a detailed review using manual verification, requests for additional documents, and machine learning algorithms. For example, the department checks the authenticity of the submitted information and requests additional documents to supplement the submitted information.

[0094] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects documents or information submitted at the time of application; an analysis unit that analyzes the information collected by the collection unit and compares it with past data; a determination unit that determines the possibility of fraud based on the results of the analysis by the analysis unit; and an examination unit that performs a detailed examination based on the information determined by the determination unit. A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of collecting documents and information based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze the applicant's past application history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit When collecting documents and information, filter based on the applicant's current situation and areas of interest 2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and prioritize the documents and information to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit When collecting documents and information, prioritize the collection of relevant information based on the applicant's geographic location 2. The system of claim 1.

7. The collecting unit When collecting documents and information, analyze the applicant's social media activity and collect relevant information.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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