System

Abstract

An object of a system according to an embodiment is to enable a fraud victim to receive quick and appropriate assistance.SOLUTION: A system includes a registration unit, an analysis unit, a support unit, and a notification unit. The registration unit receives registration information of a fraud victim. The analysis unit analyzes the information received by the registration unit. The support unit determines a support content based on the information analyzed by the analysis unit. The notification unit notifies the support content determined by the support unit.SELECTED DRAWING: Figure 1

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 the drawback of making it difficult for fraud victims to receive appropriate support through a complex process, making it difficult to respond quickly.

[0005] The system according to the embodiment aims to enable victims of fraud to receive prompt and appropriate assistance. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an analysis unit, a support unit, and a notification unit. The registration unit accepts registration information of fraud victims. The analysis unit analyzes the information accepted by the registration unit. The support unit determines support content based on the information analyzed by the analysis unit. The notification unit notifies the support content determined by the support unit. [Effects of the Invention]

[0007] The system according to the embodiment allows victims of fraud to receive prompt and appropriate assistance. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The crowdfunding and support network system according to an embodiment of the present invention is a system that uses generative AI and the power of the community to support people who have been victims of fraud and help them get back on their feet. As a result, the crowdfunding and support network system can provide multifaceted support to fraud victims and help them get back on their feet.

[0029] The crowdfunding and support network system according to the embodiment includes a registration unit, an analysis unit, a support unit, and a notification unit. The registration unit accepts registration information from fraud victims. For example, fraud victims can register information such as their name, address, details of the crime, and supporting documents on the platform. The registration unit can also collect information through an online form. For example, victims complete registration by entering the necessary information into a form on a website and submitting it. The analysis unit analyzes the information accepted by the registration unit. For example, a generation AI can analyze the details of the crime using data mining technology and classify and organize the necessary support content. The analysis unit can also analyze the victim's situation in detail using text analysis technology. For example, the analysis unit can analyze the details of the crime described by the victim using natural language processing technology to determine the priority of support. The support unit determines the support content based on the information analyzed by the analysis unit. For example, the generation AI can suggest support content such as psychological support, legal advice, and financial assistance depending on the victim's situation. The support unit can also determine the optimal support content taking into account the victim's emotional state. For example, if the victim is experiencing severe anxiety, mental health support can be provided as a priority. The notification unit notifies the victim of the support details determined by the support unit. For example, the notification unit may notify the victim of the support details via email or SMS. The notification unit may also notify the victim of the support details in real time via app notifications. For example, the victim may check the support details through a smartphone app. As a result, the crowdfunding and support network system according to the embodiment can provide multifaceted support to fraud victims and help them get back on their feet. For example, the victim may become mentally stable, obtain information to proceed with legal procedures, and receive financial support, allowing them to live an independent life again.

[0030] The analysis unit can analyze the victim's past behavioral history or social media posts to identify the background and cause of the damage. For example, the analysis unit uses the generation AI to analyze the victim's past behavioral history based on the information the victim registered on the platform. For example, it analyzes what websites the victim visited and what emails the victim received to identify the fraud method and the background of the damage. In addition, to analyze social media posts, the generation AI collects the content of the victim's posts and uses text analysis technology to identify the background and cause of the damage. For example, it analyzes the content posted by the victim on Twitter or Facebook to identify the fraud method and the background of the damage. This allows for more appropriate support by identifying the background and cause of the damage.

[0031] The analysis unit can automatically search for similar cases of victimization based on the victim's registered information and propose support plans that refer to past successful cases. For example, the analysis unit allows the generation AI to automatically search for similar cases of victimization based on the victim's registered information. For example, it can search for information on other victims who have fallen victim to the same fraud method and propose support plans that refer to those successful cases. The analysis unit also collects past successful cases and builds a system that allows the generation AI to propose support plans based on these. For example, it can propose support plans based on support methods that have been effective in the past. This makes it possible to propose support plans that refer to past successful cases.

[0032] The analysis unit anonymizes the registered information of victims and stores it in a database, which can be used for data analysis to prevent future fraud. The analysis unit, for example, anonymizes the registered information of victims and stores it in a database. For example, personal information is deleted, and only details of the victim's injury and the need for support are stored. The analysis unit also builds a system in which the generative AI performs data analysis for fraud prevention based on the anonymized data. For example, pattern recognition technology can be used to identify fraud methods and help prevent future fraud. This can be used for data analysis to prevent future fraud.

[0033] The support department can analyze the community members' past donation history or support activities and propose the most effective support method. For example, the support department analyzes the community members' past donation history and proposes the most effective support method. For example, it proposes a similar support method based on past successful donation patterns. The support department also builds a system to analyze the support activities of community members and propose the most effective support method. For example, it evaluates the effectiveness of volunteer activities and proposes the optimal support method. This makes it possible to analyze the community members' past donation history and support activities and propose the most effective support method.

[0034] The support unit can have the generation AI evaluate the support content provided by community members and provide feedback. For example, the support unit can have the generation AI evaluate the support content provided by community members and provide feedback. For example, the support unit can evaluate the effectiveness of the support content and suggest areas for improvement. The support unit can also build a system for the generation AI to evaluate the support content and provide feedback. For example, evaluation criteria for the support content can be set and the generation AI can automatically evaluate it. This allows the support content provided by community members to be evaluated and feedback to be provided, thereby improving the quality of support.

[0035] The support department can build a platform for sharing the support content provided by community members with other members and for providing support jointly. The support department, for example, builds a platform for sharing the support content provided by community members with other members. For example, the support content is posted, and other members provide comments and feedback. The support department also builds a system for the generation AI to share the support content and for providing support jointly. For example, it provides an online platform with a support content sharing function. This makes it possible to build a platform for sharing the support content provided by community members and for providing support jointly.

[0036] The support unit can have the generation AI automatically evaluate the support content provided by community members and display it in a ranking format. For example, the support unit can have the generation AI automatically evaluate the support content provided by community members and display it in a ranking format. For example, the support unit can evaluate the effectiveness of the support content and create a ranking. The support unit can also build a system for the generation AI to evaluate the support content and display it in a ranking format. For example, the support unit can introduce a scoring system for the support content and have the generation AI automatically evaluate it. This improves the quality of support by evaluating the support content provided by community members and displaying it in a ranking format.

[0037] The support department can use the generating AI to analyze the victim's past mental health history and propose the optimal counseling plan. For example, the support department will use the generating AI to analyze the victim's past mental health history and propose the optimal counseling plan. For example, the proposal will be based on counseling methods that have been effective in the past. The support department will also build a system that allows the generating AI to analyze the victim's mental health history and propose the optimal counseling plan. For example, the frequency and content of counseling will be adjusted. This will allow the victim's past mental health history to be analyzed and the optimal counseling plan to be proposed.

[0038] The support department can use generative AI to analyze the emotional state of victims and build an online community to provide optimal mental health support. For example, the support department can use generative AI to analyze the emotional state of victims and build an online community to provide optimal mental health support. For example, the support department can create a community where people who have suffered similar victimization can gather. The support department can also provide a system for using generative AI to analyze the emotional state of victims and build an online community to provide optimal mental health support. For example, the support department can provide an online platform with forum and chat functions. This can help build an online community to analyze the emotional state of victims and provide optimal mental health support.

[0039] The support department can use the generative AI to analyze the emotional state of the victim and develop an application to provide optimal mental health support. For example, the support department develops an application using the generative AI to analyze the emotional state of the victim and provide optimal mental health support. For example, an app that provides counseling or relaxation methods according to the emotional state. The support department also provides a system for developing an application using the generative AI to analyze the emotional state of the victim and provide optimal mental health support. For example, the support department provides an application with a user interface and notification functions. This makes it possible to develop an application using the generative AI to analyze the emotional state of the victim and provide optimal mental health support.

[0040] The support department can use the generative AI to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure. For example, the support department uses the generative AI to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure. For example, it proposes legal procedures according to the victim's situation. The support department also builds a system for the generative AI to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure. For example, it evaluates legal risks and proposes the optimal procedure. This makes it possible to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure.

[0041] The support department can monitor the legal situation of victims in real time and update legal advice as needed. For example, the support department can monitor the legal situation of victims in real time and update legal advice as needed. For example, it can update advice according to the progress of legal proceedings. The support department also builds a system that enables the generative AI to monitor the legal situation of victims in real time and update legal advice. For example, it can detect changes in legal risks and adjust advice. This makes it possible to monitor the legal situation of victims in real time and update legal advice as needed.

[0042] The support department can use generative AI to analyze the legal situation of victims and build an online platform for proposing optimal legal procedures. For example, the support department builds an online platform for generative AI to analyze the legal situation of victims and propose optimal legal procedures. For example, it proposes legal procedures according to the victim's situation. The support department also provides a system for building an online platform for generative AI to analyze the legal situation of victims and propose optimal legal procedures. For example, it provides an online platform with a user interface and data sharing functions. This makes it possible to build an online platform for analyzing the legal situation of victims and propose optimal legal procedures.

[0043] The support department can develop an application that uses generative AI to analyze the legal situation of a victim and propose the most appropriate legal procedure. For example, the support department develops an application that uses generative AI to analyze the legal situation of a victim and propose the most appropriate legal procedure. For example, it proposes legal procedures that suit the victim's situation. The support department also provides a system for developing an application that uses generative AI to analyze the legal situation of a victim and propose the most appropriate legal procedure. For example, it provides an application that has a user interface and notification functions. This makes it possible to develop an application that analyzes the legal situation of a victim and propose the most appropriate legal procedure.

[0044] The Support Department can use the Generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the Support Department uses the Generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the Support Department analyzes the victim's income and expenses and proposes the optimal amount of assistance. The Support Department also builds a system for the Generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the Support Department develops an algorithm for collecting and analyzing financial data. This allows the Support Department to analyze the victim's financial situation and propose the optimal financial assistance plan.

[0045] The support department can monitor the victim's financial situation in real time and update the financial assistance plan as needed. For example, the support department can monitor the victim's financial situation in real time and update the financial assistance plan as needed. For example, the support department can adjust the assistance plan according to fluctuations in the victim's income or expenses. The support department also builds a system that enables the generative AI to monitor the victim's financial situation in real time and update the financial assistance plan. For example, it can detect fluctuations in financial data and adjust the assistance plan. This allows the victim's financial situation to be monitored in real time and the financial assistance plan to be updated as needed.

[0046] The support department can use the generative AI to analyze the financial situation of victims and build an online platform for proposing optimal financial assistance plans. For example, the support department builds an online platform for the generative AI to analyze the financial situation of victims and propose optimal financial assistance plans. For example, the support department analyzes the victim's income and expenses and proposes the optimal amount of assistance. The support department also provides a system for building an online platform for the generative AI to analyze the financial situation of victims and propose optimal financial assistance plans. For example, the support department provides an online platform with a user interface and data sharing functions. This makes it possible to build an online platform for analyzing the financial situation of victims and proposing optimal financial assistance plans.

[0047] The support department can develop an application that uses the generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the support department develops an application that uses the generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the support department analyzes the victim's income and expenses and proposes the optimal amount of assistance. The support department also provides a system for developing an application that uses the generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the support department provides an application that has a user interface and notification functions. This makes it possible to develop an application that analyzes the victim's financial situation and proposes the optimal financial assistance plan.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The crowdfunding and support network system can also include a health management unit that monitors the victim's health status. For example, it could provide an application for the victim to record their health status and collect daily health data. The health management unit could also analyze the victim's health data and suggest necessary medical assistance. For example, if the victim's stress level is high, it could recommend relaxation techniques or refer them to a medical institution. This allows for comprehensive management of the victim's health status and the provision of appropriate medical assistance.

[0050] The analysis unit can analyze the victim's hobbies and interests and suggest rehabilitation programs. For example, if the victim is interested in music, music therapy can be suggested. If the victim likes sports, a rehabilitation program through sports can be suggested. Furthermore, if the victim is interested in art, art therapy can be suggested. This makes it possible to provide rehabilitation programs based on the victim's hobbies and interests and support their mental recovery.

[0051] The support department can analyze the victim's financial situation and propose the optimal investment plan. For example, if the victim wishes to build up assets in the future, they can propose a low-risk investment plan. Alternatively, if the victim wishes to earn short-term profits, they can propose a high-risk, high-return investment plan. Furthermore, if the victim wishes to contribute to society, they can also suggest socially responsible investment (SRI). This allows them to provide the optimal investment plan based on the victim's financial situation and support their financial stability.

[0052] The support department can analyze the victim's financial situation and propose the optimal savings plan. For example, if the victim wants to reduce their daily expenses, they can provide specific advice on how to save. If the victim wants to reduce their energy costs, they can suggest the introduction of energy-efficient home appliances. Furthermore, if the victim wants to reduce their food expenses, they can suggest economical recipes and ways to purchase ingredients. In this way, they can provide the optimal savings plan based on the victim's financial situation and reduce their financial burden.

[0053] The support department can analyze the victim's financial situation and propose the optimal debt repayment plan. For example, if the victim is suffering from multiple debts, they can provide advice on creating a repayment plan. If the victim has high-interest debts, they can suggest ways to refinance them to lower interest rates. Furthermore, if the victim is having difficulty repaying their debts, they can suggest debt consolidation methods. This allows them to provide the optimal debt repayment plan based on the victim's financial situation and reduce their financial burden.

[0054] The support department can analyze the victim's financial situation and propose the most suitable insurance plan. For example, if the victim requests health insurance, the department will propose the most suitable insurance plan. If the victim requests life insurance, the department can also propose an insurance plan that takes into account the victim's family's future. Furthermore, if the victim requests property insurance, the department can also propose an insurance plan aimed at protecting property. This allows the department to provide the most suitable insurance plan according to the victim's financial situation and support their financial stability.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The registration department accepts the registration information of fraud victims. For example, fraud victims can register information such as their name, address, details of the crime, and supporting documents on the platform. The registration department can also collect information through an online form. For example, victims can complete registration by entering the required information into a form on the website and submitting it. Step 2: The analysis unit analyzes the information received by the registration unit. For example, the generation AI uses data mining technology to analyze the details of the damage and classify and organize the necessary support content. The analysis unit can also use text analysis technology to analyze the victim's situation in detail. For example, it can use natural language processing technology to analyze the details of the damage written by the victim and determine the priority of support. Step 3: The support unit determines the support content based on the information analyzed by the analysis unit. For example, the generation AI may suggest support content such as psychological support, legal advice, or financial assistance depending on the victim's situation. The support unit can also determine the optimal support content by taking into account the victim's emotional state. For example, if the victim is experiencing strong anxiety, mental health support may be provided as a priority. Step 4: The notification department notifies the victim of the support details decided by the support department. For example, the notification department may notify the victim of the support details via email or SMS. The notification department can also notify the victim of the support details in real time via app notifications. For example, the victim can check the support details through a smartphone app.

[0057] (Example 2) The crowdfunding and support network system according to an embodiment of the present invention is a system that uses generative AI and the power of the community to support people who have been victims of fraud and help them get back on their feet. As a result, the crowdfunding and support network system can provide multifaceted support to fraud victims and help them get back on their feet.

[0058] The crowdfunding and support network system according to the embodiment includes a registration unit, an analysis unit, a support unit, and a notification unit. The registration unit accepts registration information from fraud victims. For example, fraud victims can register information such as their name, address, details of the crime, and supporting documents on the platform. The registration unit can also collect information through an online form. For example, victims complete registration by entering the necessary information into a form on a website and submitting it. The analysis unit analyzes the information accepted by the registration unit. For example, a generation AI can analyze the details of the crime using data mining technology and classify and organize the necessary support content. The analysis unit can also analyze the victim's situation in detail using text analysis technology. For example, the analysis unit can analyze the details of the crime described by the victim using natural language processing technology to determine the priority of support. The support unit determines the support content based on the information analyzed by the analysis unit. For example, the generation AI can suggest support content such as psychological support, legal advice, and financial assistance depending on the victim's situation. The support unit can also determine the optimal support content taking into account the victim's emotional state. For example, if the victim is experiencing severe anxiety, mental health support can be provided as a priority. The notification unit notifies the victim of the support details determined by the support unit. For example, the notification unit may notify the victim of the support details via email or SMS. The notification unit may also notify the victim of the support details in real time via app notifications. For example, the victim may check the support details through a smartphone app. As a result, the crowdfunding and support network system according to the embodiment can provide multifaceted support to fraud victims and help them get back on their feet. For example, the victim may become mentally stable, obtain information to proceed with legal procedures, and receive financial support, allowing them to live an independent life again.

[0059] The analysis unit can analyze the victim's past behavioral history or social media posts to identify the background and cause of the damage. For example, the analysis unit uses the generation AI to analyze the victim's past behavioral history based on the information the victim registered on the platform. For example, it analyzes what websites the victim visited and what emails the victim received to identify the fraud method and the background of the damage. In addition, to analyze social media posts, the generation AI collects the content of the victim's posts and uses text analysis technology to identify the background and cause of the damage. For example, it analyzes the content posted by the victim on Twitter or Facebook to identify the fraud method and the background of the damage. This allows for more appropriate support by identifying the background and cause of the damage.

[0060] The analysis unit can analyze the victim's tone of voice or facial expression, evaluate their emotional state in real time, and determine the priority of support. For example, when a victim registers on the platform, the generation AI analyzes the victim's tone of voice to evaluate their emotional state. For example, if the victim is nervous or anxious, the generation AI evaluates their emotional state in real time and determines the priority of support. In addition, to analyze the victim's facial expression, the generation AI uses facial recognition technology to analyze the facial expression and evaluates their emotional state. For example, if the victim is sad or angry, the generation AI evaluates their emotional state in real time and determines the priority of support. This makes it possible to evaluate the victim's emotional state in real time and determine the appropriate priority of support.

[0061] The analysis unit can use the emotion estimation function to analyze the emotional state of the victim and propose the most appropriate support content. For example, the analysis unit can use the emotion estimation function to analyze the emotional state of the victim and propose the most appropriate support content. For example, if the victim is feeling strong anxiety, it can provide mental health support as a priority. The analysis unit also develops an algorithm that enables the generation AI to analyze the emotional state of the victim and propose the most appropriate support content. For example, it can propose support content based on the emotion score. This makes it possible to propose the optimal support content based on the victim's emotional state.

[0062] The analysis unit can automatically search for similar cases of victimization based on the victim's registered information and propose support plans that refer to past successful cases. For example, the analysis unit allows the generation AI to automatically search for similar cases of victimization based on the victim's registered information. For example, it can search for information on other victims who have fallen victim to the same fraud method and propose support plans that refer to those successful cases. The analysis unit also collects past successful cases and builds a system that allows the generation AI to propose support plans based on these. For example, it can propose support plans based on support methods that have been effective in the past. This makes it possible to propose support plans that refer to past successful cases.

[0063] The analysis unit anonymizes the registered information of victims and stores it in a database, which can be used for data analysis to prevent future fraud. The analysis unit, for example, anonymizes the registered information of victims and stores it in a database. For example, personal information is deleted, and only details of the victim's injury and the need for support are stored. The analysis unit also builds a system in which the generative AI performs data analysis for fraud prevention based on the anonymized data. For example, pattern recognition technology can be used to identify fraud methods and help prevent future fraud. This can be used for data analysis to prevent future fraud.

[0064] The analysis unit uses the emotion estimation function to monitor the victim's emotional state in real time and provide support at the appropriate time. The analysis unit, for example, uses the emotion estimation function to monitor the victim's emotional state in real time. For example, if the victim is feeling strong anxiety, it will provide immediate mental health support. The analysis unit also builds a system in which the generation AI monitors the victim's emotional state in real time and provides support at the appropriate time. For example, it detects changes in the victim's emotional state and adjusts the timing of support. This makes it possible to monitor the victim's emotional state in real time and provide support at the appropriate time.

[0065] The support department can analyze the community members' past donation history or support activities and propose the most effective support method. For example, the support department analyzes the community members' past donation history and proposes the most effective support method. For example, it proposes a similar support method based on past successful donation patterns. The support department also builds a system to analyze the support activities of community members and propose the most effective support method. For example, it evaluates the effectiveness of volunteer activities and proposes the optimal support method. This makes it possible to analyze the community members' past donation history and support activities and propose the most effective support method.

[0066] The support unit can have the generation AI evaluate the support content provided by community members and provide feedback. For example, the support unit can have the generation AI evaluate the support content provided by community members and provide feedback. For example, the support unit can evaluate the effectiveness of the support content and suggest areas for improvement. The support unit can also build a system for the generation AI to evaluate the support content and provide feedback. For example, evaluation criteria for the support content can be set and the generation AI can automatically evaluate it. This allows the support content provided by community members to be evaluated and feedback to be provided, thereby improving the quality of support.

[0067] The support unit can use the emotion estimation function to analyze the emotional state of community members and propose a support method that will elicit the most positive emotions. For example, the support unit can use the emotion estimation function to analyze the emotional state of community members and propose a support method that will elicit the most positive emotions. For example, the support unit can prioritize the proposal of a support method with a high emotion score. The support unit also builds a system in which the generation AI analyzes the emotional state of community members and proposes a support method that will elicit the most positive emotions. For example, the support unit can detect changes in the emotional state and adjust the support method. This makes it possible to analyze the emotional state of community members and propose a support method that will elicit the most positive emotions.

[0068] The support department can build a platform for sharing the support content provided by community members with other members and for providing support jointly. The support department, for example, builds a platform for sharing the support content provided by community members with other members. For example, the support content is posted, and other members provide comments and feedback. The support department also builds a system for the generation AI to share the support content and for providing support jointly. For example, it provides an online platform with a support content sharing function. This makes it possible to build a platform for sharing the support content provided by community members and for providing support jointly.

[0069] The support unit can have the generation AI automatically evaluate the support content provided by community members and display it in a ranking format. For example, the support unit can have the generation AI automatically evaluate the support content provided by community members and display it in a ranking format. For example, the support unit can evaluate the effectiveness of the support content and create a ranking. The support unit can also build a system for the generation AI to evaluate the support content and display it in a ranking format. For example, the support unit can introduce a scoring system for the support content and have the generation AI automatically evaluate it. This improves the quality of support by evaluating the support content provided by community members and displaying it in a ranking format.

[0070] The support unit can use the emotion estimation function to monitor the emotional state of community members in real time and propose the optimal support method. For example, the support unit can use the emotion estimation function to monitor the emotional state of community members in real time and propose the optimal support method. For example, it can prioritize the proposal of support methods with a high emotion score. The support unit also builds a system in which the generation AI monitors the emotional state of community members in real time and proposes the optimal support method. For example, it detects changes in the emotional state and adjusts the support method. This makes it possible to monitor the emotional state of community members in real time and propose the optimal support method.

[0071] The support department can use the generating AI to analyze the victim's past mental health history and propose the optimal counseling plan. For example, the support department will use the generating AI to analyze the victim's past mental health history and propose the optimal counseling plan. For example, the proposal will be based on counseling methods that have been effective in the past. The support department will also build a system that allows the generating AI to analyze the victim's mental health history and propose the optimal counseling plan. For example, the frequency and content of counseling will be adjusted. This will allow the victim's past mental health history to be analyzed and the optimal counseling plan to be proposed.

[0072] The support unit can monitor the victim's emotional state in real time and adjust the counseling content as needed. For example, the support unit can monitor the victim's emotional state in real time and adjust the counseling content as needed. For example, if the victim is feeling very anxious, it can suggest relaxation methods. The support unit also builds a system in which the generative AI can monitor the victim's emotional state in real time and adjust the counseling content. For example, it can detect changes in the emotional state and adjust the counseling content. This makes it possible to monitor the victim's emotional state in real time and adjust the counseling content as needed.

[0073] The support unit can use the emotion estimation function to analyze the emotional state of the victim and provide the most effective mental health support. For example, the support unit can use the emotion estimation function to analyze the emotional state of the victim and provide the most effective mental health support. For example, it can prioritize support methods with high emotion scores. The support unit also builds a system in which the generation AI analyzes the emotional state of the victim and provides the most effective mental health support. For example, it can detect changes in the emotional state and adjust the support method. This allows the victim's emotional state to be analyzed and the most effective mental health support to be provided.

[0074] The support department can use generative AI to analyze the emotional state of victims and build an online community to provide optimal mental health support. For example, the support department can use generative AI to analyze the emotional state of victims and build an online community to provide optimal mental health support. For example, the support department can create a community where people who have suffered similar victimization can gather. The support department can also provide a system for using generative AI to analyze the emotional state of victims and build an online community to provide optimal mental health support. For example, the support department can provide an online platform with forum and chat functions. This can help build an online community to analyze the emotional state of victims and provide optimal mental health support.

[0075] The support department can use the generative AI to analyze the emotional state of the victim and develop an application to provide optimal mental health support. For example, the support department develops an application using the generative AI to analyze the emotional state of the victim and provide optimal mental health support. For example, an app that provides counseling or relaxation methods according to the emotional state. The support department also provides a system for developing an application using the generative AI to analyze the emotional state of the victim and provide optimal mental health support. For example, the support department provides an application with a user interface and notification functions. This makes it possible to develop an application using the generative AI to analyze the emotional state of the victim and provide optimal mental health support.

[0076] The support unit can use the emotion estimation function to monitor the emotional state of the victim in real time and provide optimal mental health support. For example, the support unit can use the emotion estimation function to monitor the emotional state of the victim in real time and provide optimal mental health support. For example, it can prioritize support methods with high emotion scores. The support unit also builds a system in which the generation AI monitors the emotional state of the victim in real time and provides optimal mental health support. For example, it can detect changes in the emotional state and adjust the support method. This makes it possible to monitor the emotional state of the victim in real time and provide optimal mental health support.

[0077] The support department can use the generative AI to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure. For example, the support department uses the generative AI to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure. For example, it proposes legal procedures according to the victim's situation. The support department also builds a system for the generative AI to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure. For example, it evaluates legal risks and proposes the optimal procedure. This makes it possible to analyze the victim's legal situation and provide customized legal advice to propose the optimal legal procedure.

[0078] The support department can monitor the legal situation of victims in real time and update legal advice as needed. For example, the support department can monitor the legal situation of victims in real time and update legal advice as needed. For example, it can update advice according to the progress of legal proceedings. The support department also builds a system that enables the generative AI to monitor the legal situation of victims in real time and update legal advice. For example, it can detect changes in legal risks and adjust advice. This makes it possible to monitor the legal situation of victims in real time and update legal advice as needed.

[0079] The support unit can use the emotion estimation function to analyze the emotional state of the victim and provide the most effective legal advice. For example, the support unit can use the emotion estimation function to analyze the emotional state of the victim and provide the most effective legal advice. For example, it can prioritize advice with a high emotion score. The support unit also builds a system in which the generative AI analyzes the emotional state of the victim and provides the most effective legal advice. For example, it can detect changes in the emotional state and adjust the advice. This allows the victim's emotional state to be analyzed and the most effective legal advice to be provided.

[0080] The support department can use generative AI to analyze the legal situation of victims and build an online platform for proposing optimal legal procedures. For example, the support department builds an online platform for generative AI to analyze the legal situation of victims and propose optimal legal procedures. For example, it proposes legal procedures according to the victim's situation. The support department also provides a system for building an online platform for generative AI to analyze the legal situation of victims and propose optimal legal procedures. For example, it provides an online platform with a user interface and data sharing functions. This makes it possible to build an online platform for analyzing the legal situation of victims and propose optimal legal procedures.

[0081] The support department can develop an application that uses generative AI to analyze the legal situation of a victim and propose the most appropriate legal procedure. For example, the support department develops an application that uses generative AI to analyze the legal situation of a victim and propose the most appropriate legal procedure. For example, it proposes legal procedures that suit the victim's situation. The support department also provides a system for developing an application that uses generative AI to analyze the legal situation of a victim and propose the most appropriate legal procedure. For example, it provides an application that has a user interface and notification functions. This makes it possible to develop an application that analyzes the legal situation of a victim and propose the most appropriate legal procedure.

[0082] The support unit can use the emotion estimation function to monitor the emotional state of the victim in real time and provide the most appropriate legal advice. For example, the support unit can use the emotion estimation function to monitor the emotional state of the victim in real time and provide the most appropriate legal advice. For example, it can prioritize advice with a high emotion score. The support unit also builds a system in which the generation AI can monitor the emotional state of the victim in real time and provide the most appropriate legal advice. For example, it can detect changes in the emotional state and adjust the advice. This makes it possible to monitor the emotional state of the victim in real time and provide the most appropriate legal advice.

[0083] The Support Department can use the Generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the Support Department uses the Generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the Support Department analyzes the victim's income and expenses and proposes the optimal amount of assistance. The Support Department also builds a system for the Generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the Support Department develops an algorithm for collecting and analyzing financial data. This allows the Support Department to analyze the victim's financial situation and propose the optimal financial assistance plan.

[0084] The support department can monitor the victim's financial situation in real time and update the financial assistance plan as needed. For example, the support department can monitor the victim's financial situation in real time and update the financial assistance plan as needed. For example, the support department can adjust the assistance plan according to fluctuations in the victim's income or expenses. The support department also builds a system that enables the generative AI to monitor the victim's financial situation in real time and update the financial assistance plan. For example, it can detect fluctuations in financial data and adjust the assistance plan. This allows the victim's financial situation to be monitored in real time and the financial assistance plan to be updated as needed.

[0085] The support department can use the emotion estimation function to analyze the emotional state of the victim and provide the most effective financial assistance. For example, the support department can use the emotion estimation function to analyze the emotional state of the victim and provide the most effective financial assistance. For example, it can prioritize assistance plans with high emotion scores. The support department also builds a system in which the generation AI analyzes the emotional state of the victim and provides the most effective financial assistance. For example, it can detect changes in the emotional state and adjust the assistance plan. This makes it possible to analyze the emotional state of the victim and provide the most effective financial assistance.

[0086] The support department can use the generative AI to analyze the financial situation of victims and build an online platform for proposing optimal financial assistance plans. For example, the support department builds an online platform for the generative AI to analyze the financial situation of victims and propose optimal financial assistance plans. For example, the support department analyzes the victim's income and expenses and proposes the optimal amount of assistance. The support department also provides a system for building an online platform for the generative AI to analyze the financial situation of victims and propose optimal financial assistance plans. For example, the support department provides an online platform with a user interface and data sharing functions. This makes it possible to build an online platform for analyzing the financial situation of victims and proposing optimal financial assistance plans.

[0087] The support department can develop an application that uses the generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the support department develops an application that uses the generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the support department analyzes the victim's income and expenses and proposes the optimal amount of assistance. The support department also provides a system for developing an application that uses the generative AI to analyze the victim's financial situation and propose the optimal financial assistance plan. For example, the support department provides an application that has a user interface and notification functions. This makes it possible to develop an application that analyzes the victim's financial situation and proposes the optimal financial assistance plan.

[0088] The support department can use the emotion estimation function to monitor the emotional state of the victim in real time and provide the most appropriate financial assistance. For example, the support department can use the emotion estimation function to monitor the emotional state of the victim in real time and provide the most appropriate financial assistance. For example, it can prioritize assistance plans with high emotion scores. The support department also builds a system in which the generation AI monitors the emotional state of the victim in real time and provides the most appropriate financial assistance. For example, it can detect changes in the emotional state and adjust the assistance plan. This makes it possible to monitor the emotional state of the victim in real time and provide the most appropriate financial assistance.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The crowdfunding and support network system can also include a health management unit that monitors the victim's health status. For example, it could provide an application for the victim to record their health status and collect daily health data. The health management unit could also analyze the victim's health data and suggest necessary medical assistance. For example, if the victim's stress level is high, it could recommend relaxation techniques or refer them to a medical institution. This allows for comprehensive management of the victim's health status and the provision of appropriate medical assistance.

[0091] The analysis unit can analyze the victim's hobbies and interests and suggest rehabilitation programs. For example, if the victim is interested in music, music therapy can be suggested. If the victim likes sports, a rehabilitation program through sports can be suggested. Furthermore, if the victim is interested in art, art therapy can be suggested. This makes it possible to provide rehabilitation programs based on the victim's hobbies and interests and support their mental recovery.

[0092] The analysis unit can estimate the victim's emotional state and suggest online support groups for the victim to join. For example, if the victim feels lonely, it can suggest online support groups where the victim can connect with other victims who have had similar experiences. If the victim feels anxious, it can also suggest groups where they can share information about relaxation and mental health. Furthermore, if the victim wants to feel more positive, it can also suggest groups that involve positive activities. This allows victims to join support groups that suit their emotional state and receive psychological support.

[0093] The support department can estimate the victim's emotional state and provide customized mental health support based on the emotion. For example, if the victim is feeling highly stressed, it can suggest counseling or relaxation methods to reduce stress. If the victim is feeling sad, it can also provide grief counseling. Furthermore, if the victim is feeling angry, it can also provide anger management support. This makes it possible to provide customized mental health support according to the victim's emotional state and support their mental recovery.

[0094] The support department can estimate the victim's emotional state and provide customized legal advice based on their emotions. For example, if the victim is feeling anxious, it can provide a detailed explanation of the progress of the legal proceedings to reassure them. If the victim is feeling angry, it can provide advice on how to remain calm. Furthermore, if the victim is confused, it can provide a concise explanation of the legal proceedings to help them understand. This allows the provision of customized legal advice based on the victim's emotional state, allowing the legal proceedings to proceed smoothly.

[0095] The support department can analyze the victim's financial situation and propose the optimal investment plan. For example, if the victim wishes to build up assets in the future, they can propose a low-risk investment plan. Alternatively, if the victim wishes to earn short-term profits, they can propose a high-risk, high-return investment plan. Furthermore, if the victim wishes to contribute to society, they can also suggest socially responsible investment (SRI). This allows them to provide the optimal investment plan based on the victim's financial situation and support their financial stability.

[0096] The support department can analyze the victim's financial situation and propose the optimal savings plan. For example, if the victim wants to reduce their daily expenses, they can provide specific advice on how to save. If the victim wants to reduce their energy costs, they can suggest the introduction of energy-efficient home appliances. Furthermore, if the victim wants to reduce their food expenses, they can suggest economical recipes and ways to purchase ingredients. In this way, they can provide the optimal savings plan based on the victim's financial situation and reduce their financial burden.

[0097] The support department can analyze the victim's financial situation and propose the optimal debt repayment plan. For example, if the victim is suffering from multiple debts, they can provide advice on creating a repayment plan. If the victim has high-interest debts, they can suggest ways to refinance them to lower interest rates. Furthermore, if the victim is having difficulty repaying their debts, they can suggest debt consolidation methods. This allows them to provide the optimal debt repayment plan based on the victim's financial situation and reduce their financial burden.

[0098] The support department can analyze the victim's financial situation and propose the most suitable insurance plan. For example, if the victim requests health insurance, the department will propose the most suitable insurance plan. If the victim requests life insurance, the department can also propose an insurance plan that takes into account the victim's family's future. Furthermore, if the victim requests property insurance, the department can also propose an insurance plan aimed at protecting property. This allows the department to provide the most suitable insurance plan according to the victim's financial situation and support their financial stability.

[0099] The support department can estimate the victim's emotional state and provide customized financial advice based on that emotion. For example, if the victim is feeling anxious, it can provide specific financial advice to provide reassurance. If the victim is confused, it can organize their financial situation and explain it in an easy-to-understand way. Furthermore, if the victim wants to feel more positive, it can provide advice on future asset formation. This allows the department to provide customized financial advice based on the victim's emotional state and support their financial stability.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The registration department accepts the registration information of fraud victims. For example, fraud victims can register information such as their name, address, details of the crime, and supporting documents on the platform. The registration department can also collect information through an online form. For example, victims can complete registration by entering the required information into a form on the website and submitting it. Step 2: The analysis unit analyzes the information received by the registration unit. For example, the generation AI uses data mining technology to analyze the details of the damage and classify and organize the necessary support content. The analysis unit can also use text analysis technology to analyze the victim's situation in detail. For example, it can use natural language processing technology to analyze the details of the damage written by the victim and determine the priority of support. Step 3: The support unit determines the support content based on the information analyzed by the analysis unit. For example, the generation AI may suggest support content such as psychological support, legal advice, or financial assistance depending on the victim's situation. The support unit can also determine the optimal support content by taking into account the victim's emotional state. For example, if the victim is experiencing strong anxiety, mental health support may be provided as a priority. Step 4: The notification department notifies the victim of the support details decided by the support department. For example, the notification department may notify the victim of the support details via email or SMS. The notification department can also notify the victim of the support details in real time via app notifications. For example, the victim can check the support details through a smartphone app.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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. [Explanation of symbols]

[0169] 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 registration department that accepts registration information of fraud victims; an analysis unit that analyzes the information received by the registration unit; a support unit that determines support content based on the information analyzed by the analysis unit; a notification unit that notifies the user of the support content determined by the support unit. A system characterized by:

2. The analysis unit Based on the victim's registered information, the system automatically searches for similar cases of damage and proposes support plans based on successful cases.

2. The system of claim 1.

3. The support unit Analyze community members' donation history or support activities to suggest the most effective ways to support them 2. The system of claim 1.

4. The support unit Generative AI is used to analyze the victim's mental health history and propose the best counseling plan.

2. The system of claim 1.

5. The support unit Using generative AI to analyze victims' legal situations and provide customized legal advice to recommend the best legal course of action 2. The system of claim 1.

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