system

Abstract

The system according to this embodiment aims to provide appropriate advice to users regarding ethical dilemmas and decision-making situations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and an advice unit. The collection unit collects information on ethical dilemmas and decision-making faced by the user. The analysis unit analyzes the information collected by the collection unit and provides advice from philosophical, cultural, and legal perspectives. The advice unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

[0003]

Patent Document 1

Summary of Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the support for providing appropriate advice for the ethical dilemmas and decision-making faced by users is not sufficient.

[0005] The system according to the embodiment aims to provide appropriate advice for the ethical dilemmas and decision-making faced by users.

Means for Solving the Problems

[0006] ​The system according to this embodiment comprises a data collection unit, an analysis unit, and an advisory unit. The data collection unit collects information related to ethical dilemmas and decision-making faced by the user. The analysis unit analyzes the information collected by the data collection unit and provides advice from philosophical, cultural, and legal perspectives. The advisory unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate advice to users regarding ethical dilemmas and decision-making situations they face. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The ethical advice system according to an embodiment of the present invention is a system that provides advice from philosophical, cultural, and legal perspectives to ethical dilemmas and decision-making situations that users face in their daily lives and business settings. This ethical advice system provides real-time ethical advice and risk assessments tailored to the user's values ​​and industry characteristics, helping users make appropriate judgments. For example, the ethical advice system receives information about ethical dilemmas and decision-making situations that users face. This includes ethical issues related to the development of new products in business, or ethical judgments regarding emergency surgery in a medical setting. This information is input into the ethical advice system. Next, the ethical advice system analyzes the input information and provides advice from philosophical, cultural, and legal perspectives. For example, it may provide up-to-date information on environmental regulations and sustainable development goals, or present options for balancing profit and ethical responsibility. It also provides cultural backgrounds and ethical customs of the target country or region, predicts how business activities will be received locally, and provides advice to mitigate risks. Furthermore, the ethical advice system provides real-time ethical advice and risk assessments tailored to the user's values ​​and industry characteristics. For example, in emergency surgeries in medical settings, it provides legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. This system enables users to make appropriate judgments regarding ethical dilemmas and decisions they face in their daily lives and business situations. For instance, when checking content posted on social media, it analyzes posts, points out potential problems and misleading expressions, and suggests improvements to expression and constructive communication methods. Furthermore, in applying ethical guidelines in AI development, it provides ethical guidelines regarding algorithm design and data use, assesses the risk of bias and discrimination, and proposes countermeasures. This ethical advice system is expected to support users' decision-making and promote ethical behavior throughout society by providing quick and appropriate advice on various ethical issues they face.This allows the ethical advice system to provide appropriate advice to users regarding ethical dilemmas and decision-making situations they face.

[0029] The ethical advice system according to this embodiment comprises a collection unit, an analysis unit, and an advice unit. The collection unit collects information related to ethical dilemmas and decision-making faced by the user. For example, the collection unit collects information such as ethical issues related to the development of new products in business or ethical judgments regarding emergency surgery in a medical setting. For example, the collection unit stores the information entered by the user in a database and makes it searchable as needed. The collection unit can also collect publicly available information on the internet and expert opinions to provide information useful for the user's decision-making. For example, to collect ethical issues related to the development of new products in business, the collection unit searches industry news articles and academic papers to collect relevant information. The collection unit can also collect medical guidelines and expert opinions to collect ethical judgments regarding emergency surgery in a medical setting. The analysis unit analyzes the information collected by the collection unit and provides advice from philosophical, cultural, and legal perspectives. For example, the analysis unit provides the latest information on environmental regulations and sustainable development goals. For example, the analysis unit investigates and provides the latest laws and regulations regarding environmental regulations. The analytics department can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. For example, the analytics department researches and provides users with the latest laws regarding environmental regulations. The analytics department can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. The analytics department provides information on the cultural background and ethical practices of the target country or region and predicts how business activities will be received locally. For example, the analytics department researches and provides users with information on the cultural background and ethical practices of the target country or region. For example, the analytics department researches and provides users with information on the cultural background and ethical practices of the target country or region. The advisory department provides users with appropriate advice based on the analysis results obtained by the analytics department. For example, the advisory department presents legal and ethical guidelines and advises on appropriate actions. For example, the advisory department presents legal and ethical guidelines and advises on appropriate actions. The advisory department provides information that considers the best interests of patients, quickly analyzes risks and benefits, and supports decision-making.The advisory unit, for example, provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. This allows the ethical advisory system according to the embodiment to provide appropriate advice to users facing ethical dilemmas and decision-making challenges.

[0030] The data collection unit collects information related to ethical dilemmas and decision-making situations faced by users. Specifically, the unit stores user-entered information in a database, making it searchable as needed. For example, it collects information on ethical issues related to new product development in business and ethical judgments regarding emergency surgery in medical settings. The data collection unit stores user-entered information in a database, making it searchable as needed. The data collection unit can also collect publicly available information on the internet and expert opinions to provide information that is useful for user decision-making. For example, to collect information on ethical issues related to new product development in business, it searches industry news articles and academic papers to collect relevant information. Furthermore, to collect information on ethical judgments regarding emergency surgery in medical settings, the data collection unit can also collect medical guidelines and expert opinions. The data collection unit centrally manages this information, enabling it to quickly provide users with the information they need. For example, the data collection unit stores user-entered information in a database, making it searchable as needed. The data collection unit can also collect publicly available information on the internet and expert opinions to provide information that is useful for user decision-making. This allows the data collection unit to provide users with appropriate information to address ethical dilemmas and decision-making challenges they face.

[0031] The Analysis Department analyzes the information collected by the Data Collection Department and provides advice from philosophical, cultural, and legal perspectives. Specifically, it provides up-to-date information on environmental regulations and sustainable development goals. For example, it researches and provides users with the latest laws and regulations on environmental regulations. It can also provide up-to-date information on sustainable development goals and advise users on how to conduct sustainable business activities. The Analysis Department provides information on the cultural background and ethical practices of the target country or region and predicts how business activities will be received locally. For example, it researches and provides users with information on the cultural background and ethical practices of the target country or region. Based on the collected information, the Analysis Department provides appropriate advice to users regarding ethical dilemmas and decision-making. For example, it researches and provides users with the latest laws and regulations on environmental regulations. It can also provide up-to-date information on sustainable development goals and advise users on how to conduct sustainable business activities. Furthermore, the Analysis Department researches and provides users with information on the cultural background and ethical practices of the target country or region. This allows the Analysis Department to provide appropriate advice to users regarding ethical dilemmas and decision-making.

[0032] The advisory department provides appropriate advice to users based on the analysis results obtained by the analysis department. Specifically, it presents legal and ethical guidelines and advises on appropriate actions. For example, it presents legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. For example, it provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. The advisory department provides appropriate advice to users regarding ethical dilemmas and decision-making. For example, it presents legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. In this way, the advisory department can provide appropriate advice to users regarding ethical dilemmas and decision-making.

[0033] The data collection unit can collect information on ethical issues related to the development of new products in business and ethical judgments regarding emergency surgery in medical settings. For example, to collect information on ethical issues related to the development of new products in business, the data collection unit can search industry news articles and academic papers and collect relevant information. The data collection unit can also collect medical guidelines and expert opinions to gather information on ethical judgments regarding emergency surgery in medical settings. For example, to collect information on ethical issues related to the development of new products in business, the data collection unit can search industry news articles and academic papers and collect relevant information. The data collection unit can also collect medical guidelines and expert opinions to gather information on ethical judgments regarding emergency surgery in medical settings. This allows the collection unit to collect information on ethical issues in business and medical settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input industry news articles and academic papers into a generating AI and have the generating AI collect the relevant information.

[0034] The analysis unit can provide up-to-date information on environmental regulations and sustainable development goals. For example, the analysis unit can research and provide the latest laws and regulations concerning environmental regulations. The analysis unit can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. For example, the analysis unit can research and provide the latest laws concerning environmental regulations. The analysis unit can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. This allows the analysis unit to provide up-to-date information on environmental regulations and sustainable development goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the latest laws and regulations concerning environmental regulations into a generating AI and have the generating AI provide the relevant information.

[0035] The analysis unit can provide information on the cultural background and ethical customs of the target country or region, and predict how business activities will be received locally. For example, the analysis unit can research and provide information on the cultural background and ethical customs of the target country or region. The analysis unit can research and provide information on the cultural background and ethical customs of the target country or region, and predict how business activities will be received locally. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the cultural background and ethical customs of the target country or region into a generating AI and have the generating AI provide the relevant information.

[0036] The advisory unit can provide legal and ethical guidelines and advise on appropriate actions. For example, the advisory unit can provide legal and ethical guidelines and advise on appropriate actions. This allows the advisory unit to provide legal and ethical guidelines and advise on appropriate actions. Some or all of the above-described processes in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input legal and ethical guidelines into a generating AI and have the generating AI provide advice on appropriate actions.

[0037] The advisory unit can provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. For example, the advisory unit can provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. For example, the advisory unit can provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. This allows the advisory unit to provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. Some or all of the above-described processes in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information that considers the patient's best interests into a generating AI and have the generating AI perform a risk and benefit analysis.

[0038] The data collection unit can analyze the user's past ethical decision-making history and select the optimal information collection method. For example, the data collection unit may prioritize using information collection methods that the user has frequently used in the past. For example, the data collection unit may prioritize collecting specific information sources from the user's past decision-making history. For example, the data collection unit may analyze the user's past decision-making history and propose the most effective information collection method. This allows the optimal information collection method to be selected by analyzing the user's past decision-making history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past decision-making history data into a generating AI and have the generating AI select the optimal information collection method.

[0039] The data collection unit can filter information on ethical dilemmas based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize collecting information related to projects the user is currently working on. For example, the data collection unit can filter highly relevant information based on the user's areas of interest. For example, the data collection unit can quickly provide necessary information according to the user's work situation. This allows the system to provide highly relevant information by filtering information based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work situation and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering information on ethical dilemmas. For example, if the user is in a specific region, the data collection unit will prioritize the collection of ethical information related to that region. For example, if the user is on the move, the data collection unit will provide highly relevant information based on the user's current location. For example, if the user is entering a specific country, the data collection unit will prioritize the collection of information on the ethical customs and regulations of that country. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information when gathering information on ethical dilemmas. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the content of the user's social media posts and provide relevant ethical information. For example, the data collection unit can analyze the activities of the user's social media followers and friends and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the ethical issues. For example, the analysis unit performs a detailed analysis for ethical issues of high importance. For example, the analysis unit performs a concise analysis for ethical issues of low importance. For example, the analysis unit adjusts the level of detail of its analysis in stages according to importance. This allows for detailed analysis of important issues by adjusting the level of detail of the analysis based on the importance of the ethical issues. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of ethical issues into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the ethical issue during analysis. For example, for environmental issues, the analysis unit applies an environmental impact assessment algorithm. For example, for medical ethics, the analysis unit applies an algorithm based on medical ethics guidelines. For example, for business ethics, the analysis unit applies an algorithm based on corporate ethics standards. By applying different analysis algorithms depending on the category of the ethical issue, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ethical issue category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on when the ethical issues occurred. For example, the analysis unit may prioritize the analysis of recently occurring ethical issues. For example, it may postpone the analysis of older ethical issues. For example, the analysis unit may adjust the priority of analysis in stages according to the timing of occurrence. This allows for prioritizing the analysis of more important issues by determining the priority of analysis based on when the ethical issues occurred. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of ethical issues into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of ethical issues during the analysis process. For example, the analysis unit may prioritize the analysis of ethical issues directly related to the user's work. For example, the analysis unit may postpone the analysis of less relevant ethical issues. For example, the analysis unit may adjust the order of analysis in stages according to relevance. This allows for prioritizing the analysis of more relevant issues by adjusting the order of analysis based on the relevance of ethical issues. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of ethical issues into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The advisory unit can adjust the level of detail of its advice based on the importance of the ethical issue. For example, the advisory unit provides detailed advice for highly important ethical issues. For example, it provides concise advice for less important ethical issues. For example, the advisory unit adjusts the level of detail of its advice in stages according to importance. This allows the advisory unit to provide detailed advice for important issues by adjusting the level of detail of its advice based on the importance of the ethical issue. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input data on the importance of ethical issues into a generating AI and have the generating AI perform the adjustment of the level of detail of its advice.

[0047] The advisory unit can apply different advisory algorithms depending on the category of the ethical issue when providing advice. For example, for environmental issues, the advisory unit provides advice based on environmental impact assessments. For example, for medical ethics, the advisory unit provides advice based on medical ethics guidelines. For example, for business ethics, the advisory unit provides advice based on corporate ethical standards. By applying different advisory algorithms depending on the category of the ethical issue, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input ethical issue category data into a generating AI and have the generating AI execute the application of advisory algorithms.

[0048] The advisory unit can determine the priority of advice based on when the ethical problem occurred. For example, the advisory unit may prioritize advice on recently occurring ethical problems. For example, it may postpone advice on older ethical problems. For example, the advisory unit may adjust the priority of advice in stages according to the timing of occurrence. This allows for prioritizing advice on more important issues by determining the priority of advice based on when the ethical problem occurred. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input data on the timing of ethical problem occurrences into a generating AI and have the generating AI perform the determination of advice priorities.

[0049] The advisory unit can adjust the order of advice based on the relevance of the ethical issues when providing advice. For example, the advisory unit will prioritize advice on ethical issues directly related to the user's work. For example, the advisory unit will postpone advice on less relevant ethical issues. For example, the advisory unit will adjust the order of advice in stages according to relevance. This allows for prioritizing advice on more relevant issues by adjusting the order of advice based on the relevance of the ethical issues. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input relevance data of ethical issues into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can analyze a user's past behavioral history and refer to their past responses to similar ethical dilemmas. For example, if a user previously made an ethical decision regarding emergency surgery in a medical setting, the details of that decision can be collected and applied to the current situation. Similarly, the unit can analyze how a user handled ethical issues related to new product development in the past and use that information to guide decisions in similar situations. Furthermore, it can collect data on how a user dealt with ethical issues related to their social media posts in the past and use that information to help check their current posts. This allows the data collection unit to leverage a user's past behavioral history to provide more appropriate information.

[0052] The information collection unit can prioritize information based on the user's current work situation and areas of interest. For example, it can prioritize collecting information related to the project the user is currently working on. It filters highly relevant information based on the user's areas of interest. It quickly provides the necessary information according to the user's work situation. In this way, by filtering information based on the user's work situation and areas of interest, it can provide highly relevant information.

[0053] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize the collection of ethical information related to that region. If the user is on the move, it will provide highly relevant information based on their current location. If the user is traveling to a specific country, it will prioritize the collection of information regarding the ethical customs and regulations of that country. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant information.

[0054] The analysis unit can adjust the level of detail of the analysis based on the importance of the ethical issues. For example, it can perform a detailed analysis for highly important ethical issues and a concise analysis for less important ones. The level of detail of the analysis can be adjusted in stages according to importance. This allows for detailed analysis of important issues by adjusting the level of detail based on the importance of the ethical issues.

[0055] The advisory department can apply different advisory algorithms depending on the category of the ethical issue. For example, for environmental issues, it provides advice based on environmental impact assessments. For medical ethics, it provides advice based on medical ethics guidelines. For business ethics, it provides advice based on corporate ethical standards. By applying different advisory algorithms depending on the category of the ethical issue, it is possible to provide more appropriate advice.

[0056] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect information related to topics the user has shown interest in on social media. It can analyze the content of a user's social media posts and provide relevant ethical information. It can also analyze the activities of a user's social media followers and friends and collect relevant information. In this way, relevant information can be collected by analyzing a user's social media activity.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit gathers information about ethical dilemmas and decision-making situations faced by users. For example, it collects information on ethical issues related to the development of new products in business or ethical judgments regarding emergency surgery in medical settings. The data collection unit stores the information entered by users in a database and makes it searchable as needed. It can also collect publicly available information from the internet and expert opinions to provide information that can help users make decisions. Step 2: The analysis unit analyzes the information collected by the collection unit and provides advice from philosophical, cultural, and legal perspectives. For example, it provides up-to-date information on environmental regulations and sustainable development goals, and researches and provides users with information on the cultural background and ethical practices of the target country or region. Step 3: The advisory unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit. For example, it provides legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly performs risk and benefit analysis, and supports decision-making.

[0059] (Example of form 2) The ethical advice system according to an embodiment of the present invention is a system that provides advice from philosophical, cultural, and legal perspectives to ethical dilemmas and decision-making situations that users face in their daily lives and business settings. This ethical advice system provides real-time ethical advice and risk assessments tailored to the user's values ​​and industry characteristics, helping users make appropriate judgments. For example, the ethical advice system receives information about ethical dilemmas and decision-making situations that users face. This includes ethical issues related to the development of new products in business, or ethical judgments regarding emergency surgery in a medical setting. This information is input into the ethical advice system. Next, the ethical advice system analyzes the input information and provides advice from philosophical, cultural, and legal perspectives. For example, it may provide up-to-date information on environmental regulations and sustainable development goals, or present options for balancing profit and ethical responsibility. It also provides cultural backgrounds and ethical customs of the target country or region, predicts how business activities will be received locally, and provides advice to mitigate risks. Furthermore, the ethical advice system provides real-time ethical advice and risk assessments tailored to the user's values ​​and industry characteristics. For example, in emergency surgeries in medical settings, it provides legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. This system enables users to make appropriate judgments regarding ethical dilemmas and decisions they face in their daily lives and business situations. For instance, when checking content posted on social media, it analyzes posts, points out potential problems and misleading expressions, and suggests improvements to expression and constructive communication methods. Furthermore, in applying ethical guidelines in AI development, it provides ethical guidelines regarding algorithm design and data use, assesses the risk of bias and discrimination, and proposes countermeasures. This ethical advice system is expected to support users' decision-making and promote ethical behavior throughout society by providing quick and appropriate advice on various ethical issues they face.This allows the ethical advice system to provide appropriate advice to users regarding ethical dilemmas and decision-making situations they face.

[0060] The ethical advice system according to this embodiment comprises a collection unit, an analysis unit, and an advice unit. The collection unit collects information related to ethical dilemmas and decision-making faced by the user. For example, the collection unit collects information such as ethical issues related to the development of new products in business or ethical judgments regarding emergency surgery in a medical setting. For example, the collection unit stores the information entered by the user in a database and makes it searchable as needed. The collection unit can also collect publicly available information on the internet and expert opinions to provide information useful for the user's decision-making. For example, to collect ethical issues related to the development of new products in business, the collection unit searches industry news articles and academic papers to collect relevant information. The collection unit can also collect medical guidelines and expert opinions to collect ethical judgments regarding emergency surgery in a medical setting. The analysis unit analyzes the information collected by the collection unit and provides advice from philosophical, cultural, and legal perspectives. For example, the analysis unit provides the latest information on environmental regulations and sustainable development goals. For example, the analysis unit investigates and provides the latest laws and regulations regarding environmental regulations. The analytics department can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. For example, the analytics department researches and provides users with the latest laws regarding environmental regulations. The analytics department can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. The analytics department provides information on the cultural background and ethical practices of the target country or region and predicts how business activities will be received locally. For example, the analytics department researches and provides users with information on the cultural background and ethical practices of the target country or region. For example, the analytics department researches and provides users with information on the cultural background and ethical practices of the target country or region. The advisory department provides users with appropriate advice based on the analysis results obtained by the analytics department. For example, the advisory department presents legal and ethical guidelines and advises on appropriate actions. For example, the advisory department presents legal and ethical guidelines and advises on appropriate actions. The advisory department provides information that considers the best interests of patients, quickly analyzes risks and benefits, and supports decision-making.The advisory unit, for example, provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. This allows the ethical advisory system according to the embodiment to provide appropriate advice to users facing ethical dilemmas and decision-making challenges.

[0061] The data collection unit collects information related to ethical dilemmas and decision-making situations faced by users. Specifically, the unit stores user-entered information in a database, making it searchable as needed. For example, it collects information on ethical issues related to new product development in business and ethical judgments regarding emergency surgery in medical settings. The data collection unit stores user-entered information in a database, making it searchable as needed. The data collection unit can also collect publicly available information on the internet and expert opinions to provide information that is useful for user decision-making. For example, to collect information on ethical issues related to new product development in business, it searches industry news articles and academic papers to collect relevant information. Furthermore, to collect information on ethical judgments regarding emergency surgery in medical settings, the data collection unit can also collect medical guidelines and expert opinions. The data collection unit centrally manages this information, enabling it to quickly provide users with the information they need. For example, the data collection unit stores user-entered information in a database, making it searchable as needed. The data collection unit can also collect publicly available information on the internet and expert opinions to provide information that is useful for user decision-making. This allows the data collection unit to provide users with appropriate information to address ethical dilemmas and decision-making challenges they face.

[0062] The Analysis Department analyzes the information collected by the Data Collection Department and provides advice from philosophical, cultural, and legal perspectives. Specifically, it provides up-to-date information on environmental regulations and sustainable development goals. For example, it researches and provides users with the latest laws and regulations on environmental regulations. It can also provide up-to-date information on sustainable development goals and advise users on how to conduct sustainable business activities. The Analysis Department provides information on the cultural background and ethical practices of the target country or region and predicts how business activities will be received locally. For example, it researches and provides users with information on the cultural background and ethical practices of the target country or region. Based on the collected information, the Analysis Department provides appropriate advice to users regarding ethical dilemmas and decision-making. For example, it researches and provides users with the latest laws and regulations on environmental regulations. It can also provide up-to-date information on sustainable development goals and advise users on how to conduct sustainable business activities. Furthermore, the Analysis Department researches and provides users with information on the cultural background and ethical practices of the target country or region. This allows the Analysis Department to provide appropriate advice to users regarding ethical dilemmas and decision-making.

[0063] The advisory department provides appropriate advice to users based on the analysis results obtained by the analysis department. Specifically, it presents legal and ethical guidelines and advises on appropriate actions. For example, it presents legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. For example, it provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. The advisory department provides appropriate advice to users regarding ethical dilemmas and decision-making. For example, it presents legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly analyzes risks and benefits, and supports decision-making. In this way, the advisory department can provide appropriate advice to users regarding ethical dilemmas and decision-making.

[0064] The data collection unit can collect information on ethical issues related to the development of new products in business and ethical judgments regarding emergency surgery in medical settings. For example, to collect information on ethical issues related to the development of new products in business, the data collection unit can search industry news articles and academic papers and collect relevant information. The data collection unit can also collect medical guidelines and expert opinions to gather information on ethical judgments regarding emergency surgery in medical settings. For example, to collect information on ethical issues related to the development of new products in business, the data collection unit can search industry news articles and academic papers and collect relevant information. The data collection unit can also collect medical guidelines and expert opinions to gather information on ethical judgments regarding emergency surgery in medical settings. This allows the collection unit to collect information on ethical issues in business and medical settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input industry news articles and academic papers into a generating AI and have the generating AI collect the relevant information.

[0065] The analysis unit can provide up-to-date information on environmental regulations and sustainable development goals. For example, the analysis unit can research and provide the latest laws and regulations concerning environmental regulations. The analysis unit can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. For example, the analysis unit can research and provide the latest laws concerning environmental regulations. The analysis unit can also provide the latest information on sustainable development goals and advise users on how to conduct sustainable business activities. This allows the analysis unit to provide up-to-date information on environmental regulations and sustainable development goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the latest laws and regulations concerning environmental regulations into a generating AI and have the generating AI provide the relevant information.

[0066] The analysis unit can provide information on the cultural background and ethical customs of the target country or region, and predict how business activities will be received locally. For example, the analysis unit can research and provide information on the cultural background and ethical customs of the target country or region. The analysis unit can research and provide information on the cultural background and ethical customs of the target country or region, and predict how business activities will be received locally. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the cultural background and ethical customs of the target country or region into a generating AI and have the generating AI provide the relevant information.

[0067] The advisory unit can provide legal and ethical guidelines and advise on appropriate actions. For example, the advisory unit can provide legal and ethical guidelines and advise on appropriate actions. This allows the advisory unit to provide legal and ethical guidelines and advise on appropriate actions. Some or all of the above-described processes in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input legal and ethical guidelines into a generating AI and have the generating AI provide advice on appropriate actions.

[0068] The advisory unit can provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. For example, the advisory unit can provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. For example, the advisory unit can provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. This allows the advisory unit to provide information that considers the patient's best interests, quickly analyze risks and benefits, and support decision-making. Some or all of the above-described processes in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information that considers the patient's best interests into a generating AI and have the generating AI perform a risk and benefit analysis.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of information collection regarding ethical dilemmas based on the estimated user emotions. For example, if the user is stressed, the data collection unit will delay information collection and wait until the user is relaxed. For example, if the user is relaxed, the data collection unit will immediately begin information collection. For example, if the user is in a hurry, the data collection unit will collect information quickly and provide the necessary information in a short time. This allows information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can analyze the user's past ethical decision-making history and select the optimal information collection method. For example, the data collection unit may prioritize using information collection methods that the user has frequently used in the past. For example, the data collection unit may prioritize collecting specific information sources from the user's past decision-making history. For example, the data collection unit may analyze the user's past decision-making history and propose the most effective information collection method. This allows the optimal information collection method to be selected by analyzing the user's past decision-making history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past decision-making history data into a generating AI and have the generating AI select the optimal information collection method.

[0071] The data collection unit can filter information on ethical dilemmas based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize collecting information related to projects the user is currently working on. For example, the data collection unit can filter highly relevant information based on the user's areas of interest. For example, the data collection unit can quickly provide necessary information according to the user's work situation. This allows the system to provide highly relevant information by filtering information based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work situation and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0072] The data collection unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of ethical dilemmas to collect. For example, if the user is stressed, the data collection unit will postpone collecting less important information. For example, if the user is relaxed, the data collection unit will prioritize collecting more important information. For example, if the user is in a hurry, the data collection unit will quickly collect the most important information. This allows for the priority collection of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering information on ethical dilemmas. For example, if the user is in a specific region, the data collection unit will prioritize the collection of ethical information related to that region. For example, if the user is on the move, the data collection unit will provide highly relevant information based on the user's current location. For example, if the user is entering a specific country, the data collection unit will prioritize the collection of information on the ethical customs and regulations of that country. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0074] The data collection unit can analyze the user's social media activity and collect relevant information when gathering information on ethical dilemmas. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the content of the user's social media posts and provide relevant ethical information. For example, the data collection unit can analyze the activities of the user's social media followers and friends and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of its analysis based on the importance of the ethical issues. For example, the analysis unit performs a detailed analysis for ethical issues of high importance. For example, the analysis unit performs a concise analysis for ethical issues of low importance. For example, the analysis unit adjusts the level of detail of its analysis in stages according to importance. This allows for detailed analysis of important issues by adjusting the level of detail of the analysis based on the importance of the ethical issues. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of ethical issues into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the ethical issue during analysis. For example, for environmental issues, the analysis unit applies an environmental impact assessment algorithm. For example, for medical ethics, the analysis unit applies an algorithm based on medical ethics guidelines. For example, for business ethics, the analysis unit applies an algorithm based on corporate ethics standards. By applying different analysis algorithms depending on the category of the ethical issue, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ethical issue category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is excited, the analysis unit provides a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can determine the priority of analysis based on when the ethical issues occurred. For example, the analysis unit may prioritize the analysis of recently occurring ethical issues. For example, it may postpone the analysis of older ethical issues. For example, the analysis unit may adjust the priority of analysis in stages according to the timing of occurrence. This allows for prioritizing the analysis of more important issues by determining the priority of analysis based on when the ethical issues occurred. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the timing of ethical issues into a generating AI and have the generating AI determine the priority of analysis.

[0080] The analysis unit can adjust the order of analysis based on the relevance of ethical issues during the analysis process. For example, the analysis unit may prioritize the analysis of ethical issues directly related to the user's work. For example, the analysis unit may postpone the analysis of less relevant ethical issues. For example, the analysis unit may adjust the order of analysis in stages according to relevance. This allows for prioritizing the analysis of more relevant issues by adjusting the order of analysis based on the relevance of ethical issues. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of ethical issues into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is nervous, the advice unit will provide simple and easy-to-understand advice. For example, if the user is relaxed, the advice unit will provide detailed advice. For example, if the user is in a hurry, the advice unit will provide concise advice. By adjusting the way it expresses advice based on the user's emotions, it is possible to provide more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The advisory unit can adjust the level of detail of its advice based on the importance of the ethical issue. For example, the advisory unit provides detailed advice for highly important ethical issues. For example, it provides concise advice for less important ethical issues. For example, the advisory unit adjusts the level of detail of its advice in stages according to importance. This allows the advisory unit to provide detailed advice for important issues by adjusting the level of detail of its advice based on the importance of the ethical issue. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input data on the importance of ethical issues into a generating AI and have the generating AI perform the adjustment of the level of detail of its advice.

[0083] The advisory unit can apply different advisory algorithms depending on the category of the ethical issue when providing advice. For example, for environmental issues, the advisory unit provides advice based on environmental impact assessments. For example, for medical ethics, the advisory unit provides advice based on medical ethics guidelines. For example, for business ethics, the advisory unit provides advice based on corporate ethical standards. By applying different advisory algorithms depending on the category of the ethical issue, more appropriate advice can be provided. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input ethical issue category data into a generating AI and have the generating AI execute the application of advisory algorithms.

[0084] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit will provide short, concise advice. For example, if the user is relaxed, the advice unit will provide detailed advice. For example, if the user is excited, the advice unit will provide visually stimulating advice. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The advisory unit can determine the priority of advice based on when the ethical problem occurred. For example, the advisory unit may prioritize advice on recently occurring ethical problems. For example, it may postpone advice on older ethical problems. For example, the advisory unit may adjust the priority of advice in stages according to the timing of occurrence. This allows for prioritizing advice on more important issues by determining the priority of advice based on when the ethical problem occurred. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input data on the timing of ethical problem occurrences into a generating AI and have the generating AI perform the determination of advice priorities.

[0086] The advisory unit can adjust the order of advice based on the relevance of the ethical issues when providing advice. For example, the advisory unit will prioritize advice on ethical issues directly related to the user's work. For example, the advisory unit will postpone advice on less relevant ethical issues. For example, the advisory unit will adjust the order of advice in stages according to relevance. This allows for prioritizing advice on more relevant issues by adjusting the order of advice based on the relevance of the ethical issues. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input relevance data of ethical issues into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can analyze a user's past behavioral history and refer to their past responses to similar ethical dilemmas. For example, if a user previously made an ethical decision regarding emergency surgery in a medical setting, the details of that decision can be collected and applied to the current situation. Similarly, the unit can analyze how a user handled ethical issues related to new product development in the past and use that information to guide decisions in similar situations. Furthermore, it can collect data on how a user dealt with ethical issues related to their social media posts in the past and use that information to help check their current posts. This allows the data collection unit to leverage a user's past behavioral history to provide more appropriate information.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is stressed, it will prioritize analyzing high-priority ethical issues. If the user is relaxed, it will perform a detailed analysis including less important issues. If the user is in a hurry, it will perform a quick analysis and provide concise results. By adjusting the analysis priority based on the user's emotions, it is possible to provide more appropriate analysis results.

[0090] The advice unit can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is tense, it will delay advice until the user is relaxed. If the user is relaxed, it will provide advice immediately. If the user is in a hurry, it will provide advice quickly and deliver the necessary information in a short amount of time. By adjusting the timing of advice based on the user's emotions, it is possible to provide advice at a more appropriate time.

[0091] The information collection unit can prioritize information based on the user's current work situation and areas of interest. For example, it can prioritize collecting information related to the project the user is currently working on. It filters highly relevant information based on the user's areas of interest. It quickly provides the necessary information according to the user's work situation. In this way, by filtering information based on the user's work situation and areas of interest, it can provide highly relevant information.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it provides simple and easy-to-understand analysis results. If the user is relaxed, it provides detailed analysis results. If the user is in a hurry, it provides concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide more appropriate analysis results.

[0093] The advice function can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, it provides simple and easy-to-understand advice. If the user is relaxed, it provides detailed advice. If the user is in a hurry, it provides concise advice. By adjusting the way advice is presented based on the user's emotions, it can provide more appropriate advice.

[0094] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize the collection of ethical information related to that region. If the user is on the move, it will provide highly relevant information based on their current location. If the user is traveling to a specific country, it will prioritize the collection of information regarding the ethical customs and regulations of that country. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant information.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the ethical issues. For example, it can perform a detailed analysis for highly important ethical issues and a concise analysis for less important ones. The level of detail of the analysis can be adjusted in stages according to importance. This allows for detailed analysis of important issues by adjusting the level of detail based on the importance of the ethical issues.

[0096] The advisory department can apply different advisory algorithms depending on the category of the ethical issue. For example, for environmental issues, it provides advice based on environmental impact assessments. For medical ethics, it provides advice based on medical ethics guidelines. For business ethics, it provides advice based on corporate ethical standards. By applying different advisory algorithms depending on the category of the ethical issue, it is possible to provide more appropriate advice.

[0097] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect information related to topics the user has shown interest in on social media. It can analyze the content of a user's social media posts and provide relevant ethical information. It can also analyze the activities of a user's social media followers and friends and collect relevant information. In this way, relevant information can be collected by analyzing a user's social media activity.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit gathers information about ethical dilemmas and decision-making situations faced by users. For example, it collects information on ethical issues related to the development of new products in business or ethical judgments regarding emergency surgery in medical settings. The data collection unit stores the information entered by users in a database and makes it searchable as needed. It can also collect publicly available information from the internet and expert opinions to provide information that can help users make decisions. Step 2: The analysis unit analyzes the information collected by the collection unit and provides advice from philosophical, cultural, and legal perspectives. For example, it provides up-to-date information on environmental regulations and sustainable development goals, and researches and provides users with information on the cultural background and ethical practices of the target country or region. Step 3: The advisory unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit. For example, it provides legal and ethical guidelines and advises on appropriate actions. It also provides information that considers the patient's best interests, quickly performs risk and benefit analysis, and supports decision-making.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and advisory unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14, which stores user-inputted information in the database 24 and makes it searchable as needed. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and provides advice from philosophical, cultural, and legal perspectives. The advisory unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and advisory unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214, which stores the information entered by the user in the database 24 and makes it searchable as needed. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and provides advice from philosophical, cultural, and legal perspectives. The advisory unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides appropriate advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and advisory unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314, which stores the information entered by the user in the database 24 and makes it searchable as needed. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and provides advice from philosophical, cultural, and legal perspectives. The advisory unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and advisory unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414, which stores user-inputted information in the database 24 and makes it searchable as needed. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and provides advice from philosophical, cultural, and legal perspectives. The advisory unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A collection unit that collects information on ethical dilemmas and decision-making that users face, The analysis unit analyzes the information collected by the aforementioned collection unit and provides advice from philosophical, cultural, and legal perspectives. The system includes an advisory unit that provides appropriate advice to the user based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information on ethical issues related to the development of new products in business, and ethical judgments regarding emergency surgery in medical settings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We provide the latest information on environmental regulations and sustainable development goals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We provide information on the cultural background and ethical practices of the target country or region, and predict how business activities will be received locally. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advisory unit, Provide legal and ethical guidelines and advise on appropriate actions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advisory unit, We provide information that considers the patient's best interests, enabling rapid risk-benefit analysis and supporting decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information gathering regarding ethical dilemmas based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past ethical decision-making history and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information on ethical dilemmas, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate user emotions and prioritize the ethical dilemmas to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information on ethical dilemmas, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information on ethical dilemmas, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the ethical issues. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied depending on the category of the ethical issue. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the ethical issues arose. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of ethical issues. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advisory unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advisory unit, When providing advice, adjust the level of detail based on the importance of the ethical issues. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advisory unit, When providing advice, different advisory algorithms are applied depending on the category of the ethical issue. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advisory unit, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advisory unit, When providing advice, prioritize the advice based on when the ethical issue arose. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advisory unit, When providing advice, the order of advice will be adjusted based on the relevance of the ethical issues. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information on ethical dilemmas and decision-making that users face, The analysis unit analyzes the information collected by the aforementioned collection unit and provides advice from philosophical, cultural, and legal perspectives. The system includes an advisory unit that provides appropriate advice to the user based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect information on ethical issues related to the development of new products in business, and ethical judgments regarding emergency surgery in medical settings. The system according to feature 1.

3. The aforementioned analysis unit, We provide the latest information on environmental regulations and sustainable development goals. The system according to feature 1.

4. The aforementioned analysis unit, We provide information on the cultural background and ethical practices of the target country or region, and predict how business activities will be received locally. The system according to feature 1.

5. The aforementioned advisory unit, Provide legal and ethical guidelines and advise on appropriate actions. The system according to feature 1.

6. The aforementioned advisory unit, We provide information that considers the patient's best interests, enabling rapid risk-benefit analysis and supporting decision-making. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past ethical decision-making history and select the optimal information gathering method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information on ethical dilemmas, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is We estimate user emotions and prioritize the ethical dilemmas to collect based on those estimated emotions. The system according to feature 1.

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