system

Abstract

The system of the embodiment aims to promote countermeasures against AI bias, including cultural considerations specific to Asia, and provide a highly reliable AI system. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a standard setting unit, an authentication unit, an authentication result provision unit, a technical know-how collection unit, a know-how sharing unit, and a sharing result provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit. The standard setting unit sets authentication standards. The authentication unit performs authentication based on the standards set by the standard setting unit. The authentication result provision unit provides the authentication results obtained by the authentication unit. The technical know-how collection unit collects technical know-how. The know-how sharing unit shares the know-how collected by the technical know-how collection unit. The sharing result provision unit provides the sharing results obtained by the know-how sharing unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technologies lacked sufficient awareness and countermeasures for the issue of AI bias, and faced the challenge of lacking cultural considerations, particularly those specific to Asia.

[0005] The system of the embodiment aims to promote countermeasures against AI bias, including cultural considerations specific to Asia, and provide a highly reliable AI system. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a criteria setting unit, an authentication unit, an authentication result provision unit, a technical know-how collection unit, a know-how sharing unit, and a sharing result provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit. The criteria setting unit sets authentication criteria. The authentication unit performs authentication based on the criteria set by the criteria setting unit. The authentication result provision unit provides the authentication results obtained by the authentication unit. The technical know-how collection unit collects technical know-how. The know-how sharing unit shares the know-how collected by the technical know-how collection unit. The sharing result provision unit provides the sharing results obtained by the know-how sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can promote countermeasures against AI bias, including cultural considerations specific to Asia, and provide a highly reliable AI system. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​bias countermeasure system according to an embodiment of the present invention promotes efforts to address bias issues specifically in Asia. This system aims to improve AI ethics by leveraging Asia's strengths through the sharing of bias awareness and technical know-how through a consortium of companies from various industries. For example, the AI ​​bias countermeasure system designs and implements a feedback system tailored to Asia to support the use of AI images with cultural sensitivity. Next, a certification mark is awarded to businesses that have established appropriate bias countermeasure systems, which can be used as a tool to demonstrate trustworthiness to consumers. Furthermore, bias awareness and technical know-how are shared through the consortium. This allows participating companies to share the latest bias countermeasure technologies and knowledge and learn from each other. This allows the AI ​​bias countermeasure system to improve AI ethics throughout Asia and gain consumer trust.

[0029] An AI bias countermeasure system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a standard setting unit, an authentication unit, a technical know-how collection unit, a know-how sharing unit, and a shared result provision unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit can collect feedback that reflects diverse Asian cultures and values. The collection unit can also collect data using natural language processing technology or image recognition technology. For example, the collection unit can automatically analyze text feedback and extract important information using natural language processing technology. The collection unit can also estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and the use of machine learning algorithms. For example, the analysis unit can analyze the collected feedback and provide data for reducing AI bias. The analysis unit can also analyze data from multiple perspectives using natural language processing technology or image recognition technology. For example, the analysis unit can analyze text data and extract important information using natural language processing technology. Furthermore, the analysis unit can estimate a user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. The provision unit provides feedback based on the analysis result obtained by the analysis unit. Examples of feedback include, but are not limited to, text comments and numerical evaluations. For example, the provision unit can provide feedback based on the analysis result. The provision unit can also hold regular workshops or seminars and provide feedback. For example, the provision unit can hold regular online workshops and provide feedback to participants. The standard setting unit sets certification standards. Examples of certification standards include, but are not limited to, security standards and quality standards.The standard setting unit may, for example, set certification standards for businesses that have established anti-bias systems. The standard setting unit may also optimize the standards by taking into account regional characteristics. For example, in Japan, the standard setting unit may set certification standards that emphasize politeness and quality. The authentication unit performs certification based on the standards set by the standard setting unit. Examples of certification include, but are not limited to, a certification process and certification conditions. The authentication unit may, for example, perform certification based on the set standards. The authentication unit may also improve the accuracy of the authentication by referring to past authentication data. For example, the authentication unit may analyze past authentication data and identify areas for improvement to improve the accuracy of the authentication. The technical know-how collection unit collects technical know-how. Examples of technical know-how include, but are not limited to, technical documents and experimental data. The technical know-how collection unit may, for example, collect technical know-how. The technical know-how collection unit may also estimate a user's emotions and prioritize the data to be collected based on the estimated user's emotions. For example, when a user is feeling stressed, the technical know-how collection unit may prioritize collecting data of high importance to reduce the user's burden. The know-how sharing unit shares the know-how collected by the technical know-how collection unit. Know-how sharing includes, but is not limited to, sharing on an online platform, distributing documents, and the like. The know-how sharing unit can, for example, share the collected know-how. The know-how sharing unit can also improve the accuracy of sharing by referring to past sharing data. For example, the know-how sharing unit can analyze past sharing data and identify improvements to improve the accuracy of sharing. The sharing result providing unit provides the sharing results obtained by the know-how sharing unit. The sharing results include, but are not limited to, a list of shared information, the effects of sharing, and the like. The sharing result providing unit can, for example, provide the sharing results. The sharing result providing unit can also estimate the user's emotions and adjust the display method of the sharing results based on the estimated user's emotions. For example, if the user is nervous, the sharing result providing unit can provide a simple, highly visible display method.As a result, the AI ​​bias countermeasure system according to the embodiment can function in a coordinated manner across the processes of data collection, analysis, feedback provision, certification standard setting, certification, and know-how sharing.

[0030] The collection unit can collect feedback that reflects multiple Asian cultures and values. The multiple Asian cultures and values ​​include, but are not limited to, the cultures and values ​​of Japan, China, India, and the like. For example, the collection unit can collect feedback that reflects Japanese culture and values. The collection unit can also collect feedback that reflects Chinese culture and values. Furthermore, the collection unit can collect feedback that reflects Indian culture and values. This makes it possible to collect feedback that takes Asian cultures and values ​​into consideration.

[0031] The analysis unit can analyze the collected feedback and provide data for reducing bias in the AI. Examples of AI bias include, but are not limited to, gender bias and racial bias. For example, the analysis unit can analyze the collected feedback and provide data for reducing gender bias. The analysis unit can also provide data for reducing racial bias. Furthermore, the analysis unit can provide data for reducing other biases. This makes it possible to provide data for reducing bias in the AI.

[0032] The providing unit can provide feedback based on the analysis results. The analysis results include, but are not limited to, for example, statistical data, graphs, reports, etc. The providing unit can provide feedback based on, for example, statistical data. The providing unit can also provide feedback based on graphs. Furthermore, the providing unit can also provide feedback based on reports. This makes it possible to provide feedback based on the analysis results.

[0033] The standard setting unit can set certification standards for businesses that have established anti-bias systems. Anti-bias systems include, but are not limited to, ensuring data diversity and validating algorithms. For example, the standard setting unit can set certification standards based on ensuring data diversity. The standard setting unit can also set certification standards based on validating algorithms. Furthermore, the standard setting unit can set certification standards based on other anti-bias systems. This makes it possible to set certification standards for businesses that have established appropriate anti-bias systems.

[0034] The authentication unit can perform authentication based on set criteria. Criteria include, but are not limited to, security criteria and quality criteria. For example, the authentication unit can perform authentication based on security criteria. The authentication unit can also perform authentication based on quality criteria. Furthermore, the authentication unit can perform authentication based on other criteria. This makes it possible to perform authentication based on set criteria.

[0035] The providing unit can provide an authentication result. The authentication result includes, for example, a pass / fail result of authentication, an evaluation score, etc., but is not limited to these examples. The providing unit can provide, for example, a pass / fail result of authentication. The providing unit can also provide an evaluation score. Furthermore, the providing unit can also provide other authentication results. This makes it possible to provide the authentication result.

[0036] The collection unit can collect technical know-how. The technical know-how includes, for example, technical documents, experimental data, etc., but is not limited to these examples. The collection unit can collect, for example, technical documents. The collection unit can also collect experimental data. Furthermore, the collection unit can also collect other technical know-how. This makes it possible to collect technical know-how.

[0037] The sharing unit can share the collected know-how. The know-how includes, for example, technical documents, experimental data, etc., but is not limited to these examples. The sharing unit can share, for example, technical documents. The sharing unit can also share experimental data. Furthermore, the sharing unit can share other know-how. This makes it possible to share the collected know-how.

[0038] The providing unit can provide the sharing result. The sharing result includes, for example, a list of shared information, an effect of sharing, and the like, but is not limited to these examples. The providing unit can provide, for example, a list of shared information. The providing unit can also provide the effect of sharing. Furthermore, the providing unit can also provide other sharing results. This makes it possible to provide the sharing result.

[0039] The collection unit can optimize the collection method by taking into account the characteristics of each region. Examples of characteristics of each region include, but are not limited to, cultural background, economic situation, etc. For example, in Japan, the collection unit may emphasize polite feedback and adopt a polite question format. In India, the collection unit may collect feedback that supports various languages ​​and take into account the linguistic characteristics of each region. Furthermore, in China, the collection unit may take into account the cultural background and avoid certain social taboos when collecting feedback. This makes it possible to optimize the collection method based on the characteristics of each region.

[0040] The collection unit can analyze the user's past feedback history and select the optimal collection means. Past feedback history includes, for example, past comments, evaluation scores, etc., but is not limited to these examples. For example, if the user has preferred text feedback in the past, the collection unit can prioritize feedback collection in text format. Furthermore, if the user has provided a lot of audio feedback in the past, the collection unit can recommend feedback collection in audio format. Furthermore, if the user has frequently used image feedback in the past, the collection unit can collect feedback in image format. This makes it possible to select the optimal collection means based on the user's past feedback history.

[0041] The collection unit can collect data using natural language processing technology and image recognition technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Image recognition technology includes, but is not limited to, object detection and face recognition. The collection unit can automatically analyze text feedback and extract important information using natural language processing technology. The collection unit can also use image recognition technology to read emotions and intentions from images provided by users and reflect them in feedback. Furthermore, the collection unit can combine natural language processing technology and image recognition technology to collect comprehensive feedback from both text and images. This enables data collection using natural language processing technology and image recognition technology.

[0042] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. For example, if the user is in a specific area, the collection unit prioritizes collection of feedback related to that area. Furthermore, if the user is moving, the collection unit can also collect the most relevant data based on the user's current location. Furthermore, if the user is participating in a specific event, the collection unit can also prioritize collection of feedback related to the event. This makes it possible to collect highly relevant data based on the user's geographical location information.

[0043] The collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the collection unit can collect feedback on places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data based on the user's social media activity.

[0044] The collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, past comments, evaluation scores, and the like. For example, the collection unit can preferentially provide feedback formats that the user has used favorably in the past. The collection unit can also analyze the content of the user's past feedback and suggest an optimal collection method. Furthermore, the collection unit can customize and provide the collection method based on the user's past feedback history. This makes it possible to customize the collection method based on the user's past feedback.

[0045] The analysis unit can analyze the collected feedback and optimize the analysis algorithm when providing data to reduce AI bias. Examples of AI bias include, but are not limited to, gender bias and racial bias. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. The analysis unit, for example, analyzes the feedback data and optimizes an algorithm that detects and corrects specific bias. The analysis unit can also develop an analysis algorithm that takes diverse cultures and values ​​into account to reduce bias. Furthermore, the analysis unit can adjust the analysis algorithm to ensure diversity in the feedback data. This makes it possible to provide data to reduce AI bias.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, for example, the reliability and impact of the data. For example, the analysis unit can perform a detailed analysis on data with high importance to provide highly accurate results. The analysis unit can also perform a simplified analysis on data with low importance to provide quick results. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data.

[0047] The analysis unit can analyze data from multiple angles using natural language processing technology and image recognition technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Image recognition technology includes, but is not limited to, object detection and face recognition. The analysis unit, for example, uses natural language processing technology to analyze text data and extract important information. The analysis unit can also use image recognition technology to analyze image data and read emotions and intentions. Furthermore, the analysis unit can combine natural language processing technology and image recognition technology to perform comprehensive analysis of both text and images. This enables multifaceted data analysis using natural language processing technology and image recognition technology.

[0048] The analysis unit can determine the priority of analysis based on the time of data submission. The time of data submission includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit can prioritize analysis of the most recent data and provide results quickly. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can optimally adjust the analysis schedule based on the time of data submission. This makes it possible to determine the priority of analysis based on the time of data submission.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data. Examples of the relevance of the data include, but are not limited to, correlation and co-occurrence of the data. For example, the analysis unit prioritizes analysis of highly relevant data to provide highly accurate results. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0050] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the analysis unit can explain the analysis results in simple terms to users with less technical expertise. The analysis unit can also provide the analysis results using detailed technical terms to users with more technical expertise. Furthermore, the analysis unit can customize the way the analysis results are presented according to the user's level of expertise. This allows the use of technical terms in the analysis results to be adjusted according to the user's level of expertise.

[0051] The providing unit can adjust the level of detail of the feedback when providing feedback based on the analysis results. Examples of the level of detail of the feedback include, but are not limited to, detailed comments, concise comments, and the like. For example, the providing unit can provide detailed feedback for analysis results with high importance. The providing unit can also provide simplified feedback for analysis results with low importance. Furthermore, the providing unit can optimally allocate feedback resources according to the importance of the analysis results. This makes it possible to adjust the level of detail of the feedback based on the analysis results.

[0052] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback history. The past feedback history includes, but is not limited to, for example, past comments, evaluation scores, etc. The providing unit can, for example, analyze feedback provided by the user in the past and provide more appropriate feedback for similar situations. The providing unit can also provide individually customized feedback based on the user's past feedback history. Furthermore, the providing unit can also improve the accuracy of the feedback by referring to the content of the user's past feedback. This makes it possible to improve the accuracy of feedback based on the user's past feedback history.

[0053] The providing unit can hold regular workshops and seminars and provide feedback. Examples of regular workshops and seminars include, but are not limited to, the frequency of holding the workshops and seminars and the target participants. For example, the providing unit can hold regular online workshops and provide feedback to participants. The providing unit can also provide feedback in a seminar format, providing an opportunity for participants to ask questions directly. Furthermore, the providing unit can record the contents of the workshops and seminars and provide feedback by making the content available for viewing at a later date. This makes it possible to provide feedback through regular workshops and seminars.

[0054] The providing unit can determine the priority of feedback based on the time of data submission. The time of data submission includes, for example, the submission date, the submission time, etc., but is not limited to these examples. The providing unit, for example, can provide feedback preferentially based on the latest data. The providing unit can also provide feedback later for data that was submitted earlier. Furthermore, the providing unit can optimally adjust the feedback schedule based on the submission time. This makes it possible to determine the priority of feedback based on the time of data submission.

[0055] The providing unit can adjust the order of feedback based on the relevance of the data. The relevance of the data includes, but is not limited to, for example, correlation and co-occurrence of the data. For example, the providing unit can provide feedback preferentially based on highly relevant data. The providing unit can also provide feedback later for less relevant data. Furthermore, the providing unit can optimally allocate feedback resources based on the relevance of the data. This makes it possible to adjust the order of feedback based on the relevance of the data.

[0056] The providing unit can adjust the use of technical terms in the feedback according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the providing unit can provide feedback in simple language to a user with little technical expertise. The providing unit can also provide feedback using detailed technical terms to a user with a wealth of technical expertise. Furthermore, the providing unit can customize the way the feedback is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the feedback according to the user's level of expertise.

[0057] The standard setting unit can adjust the level of detail of the certification standards when setting certification standards for businesses that have an appropriate anti-bias system in place. Anti-bias systems include, but are not limited to, ensuring data diversity and algorithm verification. For example, the standard setting unit sets detailed certification standards for businesses that have sufficient anti-bias measures in place. The standard setting unit can also set simplified certification standards for businesses that have insufficient anti-bias measures in place. Furthermore, the standard setting unit can adjust the level of detail of the certification standards depending on the business's anti-bias system. This makes it possible to adjust the level of detail of the certification standards for businesses that have an appropriate anti-bias system in place.

[0058] The criterion setting unit can improve the accuracy of the criteria by referring to past authentication data. Past authentication data includes, for example, past authentication results, evaluation scores, etc., but is not limited to these examples. For example, the criterion setting unit analyzes the past authentication data and identifies areas for improvement to improve the accuracy of the criteria. The criterion setting unit can also review the authentication criteria based on the past authentication data to improve the accuracy. Furthermore, the criterion setting unit can also optimize the scope of application of the authentication criteria by referring to the past authentication data. This makes it possible to improve the accuracy of the criteria based on the past authentication data.

[0059] The standard setting unit can optimize the standards by taking into account the characteristics of each region. Examples of characteristics of each region include, but are not limited to, cultural background and economic situation. For example, in Japan, the standard setting unit can set certification standards that emphasize politeness and quality. In India, the standard setting unit can also set certification standards that accommodate diverse languages ​​and cultures. Furthermore, in China, the standard setting unit can also set certification standards that take into account the characteristics of each region and avoid specific social taboos. This makes it possible to optimize the standards based on the characteristics of each region.

[0060] The criteria setting unit can determine the priority of criteria based on the time of data submission. The time of data submission includes, for example, the submission date, the submission time, etc., but is not limited to these examples. The criteria setting unit, for example, prioritizes the authentication criteria based on the latest data. The criteria setting unit can also set the authentication criteria later for data submitted earlier. Furthermore, the criteria setting unit can optimally adjust the schedule of the authentication criteria based on the time of submission. This makes it possible to determine the priority of criteria based on the time of data submission.

[0061] The criterion setting unit can adjust the order of criteria based on the relevance of data. Data relevance includes, but is not limited to, for example, correlation and co-occurrence of data. For example, the criterion setting unit can prioritize authentication criteria based on highly relevant data. The criterion setting unit can also set authentication criteria later for data with low relevance. Furthermore, the criterion setting unit can optimally allocate resources for authentication criteria based on the relevance of data. This makes it possible to adjust the order of criteria based on the relevance of data.

[0062] The criteria setting unit may adjust the use of technical terms in the criteria according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the criteria setting unit may explain the authentication criteria in simple terms to a user with little expertise. The criteria setting unit may also provide the authentication criteria using detailed technical terms to a user with extensive expertise. Furthermore, the criteria setting unit may customize the way the authentication criteria are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the criteria according to the user's level of expertise.

[0063] The authentication unit can adjust the level of detail of authentication when performing authentication based on set criteria. Criteria include, but are not limited to, security criteria, quality criteria, and the like. For example, the authentication unit can perform detailed authentication for criteria with high importance to provide highly accurate results. The authentication unit can also perform simplified authentication for criteria with low importance to provide quick results. Furthermore, the authentication unit can optimally allocate authentication resources according to the importance of the criteria. This makes it possible to adjust the level of detail of authentication based on set criteria.

[0064] The authentication unit can improve the accuracy of authentication by referring to past authentication data. Past authentication data includes, for example, past authentication results, evaluation scores, etc., but is not limited to these examples. The authentication unit, for example, analyzes the past authentication data and identifies areas for improvement to improve authentication accuracy. The authentication unit can also review the authentication process based on the past authentication data to improve accuracy. Furthermore, the authentication unit can also optimize the scope of authentication application by referring to the past authentication data. This makes it possible to improve authentication accuracy based on past authentication data.

[0065] The authentication unit can optimize the authentication method by taking into account the characteristics of each region. Examples of characteristics of each region include, but are not limited to, cultural background and economic situation. For example, in Japan, the authentication unit can adopt an authentication method that emphasizes politeness and quality. In India, the authentication unit can also adopt an authentication method that is compatible with diverse languages ​​and cultures. Furthermore, in China, the authentication unit can adopt an authentication method that takes into account the characteristics of each region and avoids specific social taboos. This makes it possible to optimize the authentication method based on the characteristics of each region.

[0066] The authentication unit can determine the priority of authentication based on the time of data submission. The time of data submission includes, but is not limited to, for example, the submission date and submission time. The authentication unit, for example, prioritizes authentication based on the most recent data. The authentication unit can also postpone authentication of data that was submitted earlier. Furthermore, the authentication unit can optimally adjust the authentication schedule based on the submission time. This makes it possible to determine the priority of authentication based on the time of data submission.

[0067] The authentication unit can adjust the order of authentication based on the relevance of data. The relevance of data includes, but is not limited to, for example, correlation and co-occurrence of data. For example, the authentication unit prioritizes authentication based on highly related data. The authentication unit can also postpone authentication of data with low relevance. Furthermore, the authentication unit can optimally allocate authentication resources based on the relevance of data. This makes it possible to adjust the order of authentication based on the relevance of data.

[0068] The authentication unit can adjust the use of technical terms for authentication according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the authentication unit can explain the authentication result in simple terms to a user with little technical expertise. The authentication unit can also provide the authentication result using detailed technical terms to a user with extensive technical expertise. Furthermore, the authentication unit can customize the way in which the authentication result is presented according to the user's level of expertise. This allows the use of technical terms for authentication to be adjusted according to the user's level of expertise.

[0069] The sharing unit can adjust the level of detail of the sharing when sharing the collected know-how. The level of detail of the sharing includes, for example, a detailed explanation, a concise explanation, and the like, but is not limited to these examples. For example, the sharing unit can share detailed information for know-how of high importance and provide highly accurate information. Furthermore, the sharing unit can share simplified information for know-how of low importance and provide information quickly. Furthermore, the sharing unit can optimally allocate sharing resources according to the importance of the know-how. This makes it possible to adjust the level of detail of the collected know-how.

[0070] The sharing unit can improve the accuracy of sharing by referring to past shared data. Past shared data includes, for example, past shared content, evaluation results, etc., but is not limited to these examples. The sharing unit, for example, analyzes the past shared data and identifies improvements to improve the accuracy of sharing. The sharing unit can also review the sharing process based on the past shared data to improve accuracy. Furthermore, the sharing unit can also optimize the scope of sharing by referring to the past shared data. This makes it possible to improve the accuracy of sharing based on the past shared data.

[0071] The common section can optimize the sharing method by taking into account the characteristics of each region. Examples of the characteristics of each region include, but are not limited to, cultural background and economic situation. For example, in Japan, the common section may share know-how with an emphasis on politeness and quality. In India, the common section may also share know-how that is compatible with diverse languages ​​and cultures. Furthermore, in China, the common section may consider the characteristics of each region and share know-how that avoids specific social taboos. This makes it possible to optimize the sharing method based on the characteristics of each region.

[0072] The sharing unit can determine the priority of sharing based on the time of data submission. The time of data submission includes, but is not limited to, for example, the submission date and the submission time. The sharing unit, for example, prioritizes sharing of know-how based on the latest data. The sharing unit can also postpone sharing of know-how for data that was submitted earlier. Furthermore, the sharing unit can optimally adjust the know-how sharing schedule based on the time of submission. This makes it possible to determine the priority of sharing based on the time of data submission.

[0073] The sharing unit can adjust the order of sharing based on the relevance of the data. The relevance of the data includes, for example, correlation and co-occurrence of the data, but is not limited to these examples. For example, the sharing unit prioritizes sharing of know-how based on highly related data. The sharing unit can also postpone sharing of know-how for less related data. Furthermore, the sharing unit can optimally allocate know-how sharing resources based on the relevance of the data. This makes it possible to adjust the order of sharing based on the relevance of the data.

[0074] The sharing unit can adjust the use of shared terminology according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the sharing unit can share know-how in simple language with users with little expertise. The sharing unit can also share know-how using detailed terminology with users with extensive expertise. Furthermore, the sharing unit can customize the way know-how is shared according to the user's level of expertise. This makes it possible to adjust the use of shared terminology according to the user's level of expertise.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The collection unit can also analyze the user's behavioral patterns and determine the optimal timing for data collection. For example, if the user is active during a specific time period, data collection can be concentrated on that time period. Also, if the user relaxes on weekends, detailed feedback can be collected on weekends. Furthermore, if the user participates in a specific event, related data can be collected during that event. This makes it possible to optimize data collection based on the user's behavioral patterns.

[0077] The analysis unit can also analyze feedback trends and predict future feedback content based on the user's past feedback history. For example, if a user has provided a lot of feedback on a specific topic in the past, new feedback related to that topic can be predicted. Also, if a user has provided a lot of positive feedback in the past, it can predict that future feedback is likely to be positive as well. Furthermore, if a user has provided negative feedback in the past, the cause can be analyzed and improvements can be proposed. This makes it possible to predict future feedback based on the user's past feedback history.

[0078] The providing unit can also provide personalized learning content based on the user's feedback. For example, if the user provides feedback on a specific skill, learning content to improve that skill can be provided. Also, if the user shows interest in a specific topic, learning content related to that topic can be provided. Furthermore, an individually customized learning plan can be proposed based on feedback previously provided by the user. This makes it possible to provide personalized learning content based on the user's feedback.

[0079] The standard setting unit can also review the authentication standards based on the user's feedback. For example, if a user expresses dissatisfaction with a specific authentication standard, the standard setting unit can reevaluate the standard and identify areas for improvement. Also, if a user gives a high rating to a specific authentication standard, the standard setting unit can apply the high rating to other standards. Furthermore, new authentication standards can be set based on the user's feedback. This makes it possible to review the authentication standards based on the user's feedback.

[0080] The authentication unit can also improve the authentication process based on user feedback. For example, if a user expresses dissatisfaction with a particular authentication process, the authentication unit reevaluates the process and identifies areas for improvement. Also, if a user expresses a high evaluation of a particular authentication process, the authentication unit can apply that process to other authentications. Furthermore, a new authentication process can be introduced based on user feedback. This makes it possible to improve the authentication process based on user feedback.

[0081] The processing flow of the first embodiment will be briefly explained below.

[0082] Step 1: The collection unit collects data. The data includes text data, numerical data, image data, etc. The collection unit collects data using natural language processing technology and image recognition technology, and can also estimate the user's emotions and adjust the timing of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis can involve the use of statistical analysis and machine learning algorithms, as well as natural language processing and image recognition techniques to analyze the data from multiple angles. It can also adjust the way the analysis is presented based on the user's sentiment. Step 3: The delivery department provides feedback based on the analysis results obtained by the analysis department. Feedback can include text comments or numerical evaluations, and can also be provided through regular workshops or seminars. Step 4: The Standard Setting Department sets the certification standards, which include security and quality standards, and can also optimize the standards by taking into account the characteristics of each region. Step 5: The certification department certifies the product based on the standards set by the standard setting department. The certification process includes the certification process and the conditions for certification, and the accuracy of the certification can be improved by referring to past certification data. Step 6: The authentication result providing unit provides the authentication result obtained by the authentication unit. The authentication result includes whether the authentication was successful or not and a detailed evaluation. Step 7: The technical know-how collection unit collects technical know-how. The technical know-how includes technical documents and experimental data, and can also determine the priority of the data to be collected based on user sentiment. Step 8: The Know-how Sharing Department shares the know-how collected by the Technical Know-How Collection Department. Know-how sharing can include sharing on online platforms and distributing documents, and can also improve the accuracy of sharing by referring to past sharing data. Step 9: The sharing result providing unit provides the sharing results obtained by the know-how sharing unit. The sharing results include a list of shared information and the effects of sharing, and can also adjust the way the sharing results are displayed based on the user's emotions.

[0083] (Example 2) The AI ​​bias countermeasure system according to an embodiment of the present invention promotes efforts to address bias issues specifically in Asia. This system aims to improve AI ethics by leveraging Asia's strengths through the sharing of bias awareness and technical know-how through a consortium of companies from various industries. For example, the AI ​​bias countermeasure system designs and implements a feedback system tailored to Asia to support the use of AI images with cultural sensitivity. Next, a certification mark is awarded to businesses that have established appropriate bias countermeasure systems, which can be used as a tool to demonstrate trustworthiness to consumers. Furthermore, bias awareness and technical know-how are shared through the consortium. This allows participating companies to share the latest bias countermeasure technologies and knowledge and learn from each other. This allows the AI ​​bias countermeasure system to improve AI ethics throughout Asia and gain consumer trust.

[0084] An AI bias countermeasure system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a standard setting unit, an authentication unit, a technical know-how collection unit, a know-how sharing unit, and a shared result provision unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit can collect feedback that reflects diverse Asian cultures and values. The collection unit can also collect data using natural language processing technology or image recognition technology. For example, the collection unit can automatically analyze text feedback and extract important information using natural language processing technology. The collection unit can also estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and the use of machine learning algorithms. For example, the analysis unit can analyze the collected feedback and provide data for reducing AI bias. The analysis unit can also analyze data from multiple perspectives using natural language processing technology or image recognition technology. For example, the analysis unit can analyze text data and extract important information using natural language processing technology. Furthermore, the analysis unit can estimate a user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. The provision unit provides feedback based on the analysis result obtained by the analysis unit. Examples of feedback include, but are not limited to, text comments and numerical evaluations. For example, the provision unit can provide feedback based on the analysis result. The provision unit can also hold regular workshops or seminars and provide feedback. For example, the provision unit can hold regular online workshops and provide feedback to participants. The standard setting unit sets certification standards. Examples of certification standards include, but are not limited to, security standards and quality standards.The standard setting unit may, for example, set certification standards for businesses that have established anti-bias systems. The standard setting unit may also optimize the standards by taking into account regional characteristics. For example, in Japan, the standard setting unit may set certification standards that emphasize politeness and quality. The authentication unit performs certification based on the standards set by the standard setting unit. Examples of certification include, but are not limited to, a certification process and certification conditions. The authentication unit may, for example, perform certification based on the set standards. The authentication unit may also improve the accuracy of the authentication by referring to past authentication data. For example, the authentication unit may analyze past authentication data and identify areas for improvement to improve the accuracy of the authentication. The technical know-how collection unit collects technical know-how. Examples of technical know-how include, but are not limited to, technical documents and experimental data. The technical know-how collection unit may, for example, collect technical know-how. The technical know-how collection unit may also estimate a user's emotions and prioritize the data to be collected based on the estimated user's emotions. For example, when a user is feeling stressed, the technical know-how collection unit may prioritize collecting data of high importance to reduce the user's burden. The know-how sharing unit shares the know-how collected by the technical know-how collection unit. Know-how sharing includes, but is not limited to, sharing on an online platform, distributing documents, and the like. The know-how sharing unit can, for example, share the collected know-how. The know-how sharing unit can also improve the accuracy of sharing by referring to past sharing data. For example, the know-how sharing unit can analyze past sharing data and identify improvements to improve the accuracy of sharing. The sharing result providing unit provides the sharing results obtained by the know-how sharing unit. The sharing results include, but are not limited to, a list of shared information, the effects of sharing, and the like. The sharing result providing unit can, for example, provide the sharing results. The sharing result providing unit can also estimate the user's emotions and adjust the display method of the sharing results based on the estimated user's emotions. For example, if the user is nervous, the sharing result providing unit can provide a simple, highly visible display method.As a result, the AI ​​bias countermeasure system according to the embodiment can function in a coordinated manner across the processes of data collection, analysis, feedback provision, certification standard setting, certification, and know-how sharing.

[0085] The collection unit can collect feedback that reflects multiple Asian cultures and values. The multiple Asian cultures and values ​​include, but are not limited to, the cultures and values ​​of Japan, China, India, and the like. For example, the collection unit can collect feedback that reflects Japanese culture and values. The collection unit can also collect feedback that reflects Chinese culture and values. Furthermore, the collection unit can collect feedback that reflects Indian culture and values. This makes it possible to collect feedback that takes Asian cultures and values ​​into consideration.

[0086] The analysis unit can analyze the collected feedback and provide data for reducing bias in the AI. Examples of AI bias include, but are not limited to, gender bias and racial bias. For example, the analysis unit can analyze the collected feedback and provide data for reducing gender bias. The analysis unit can also provide data for reducing racial bias. Furthermore, the analysis unit can provide data for reducing other biases. This makes it possible to provide data for reducing bias in the AI.

[0087] The providing unit can provide feedback based on the analysis results. The analysis results include, but are not limited to, for example, statistical data, graphs, reports, etc. The providing unit can provide feedback based on, for example, statistical data. The providing unit can also provide feedback based on graphs. Furthermore, the providing unit can also provide feedback based on reports. This makes it possible to provide feedback based on the analysis results.

[0088] The standard setting unit can set certification standards for businesses that have established anti-bias systems. Anti-bias systems include, but are not limited to, ensuring data diversity and validating algorithms. For example, the standard setting unit can set certification standards based on ensuring data diversity. The standard setting unit can also set certification standards based on validating algorithms. Furthermore, the standard setting unit can set certification standards based on other anti-bias systems. This makes it possible to set certification standards for businesses that have established appropriate anti-bias systems.

[0089] The authentication unit can perform authentication based on set criteria. Criteria include, but are not limited to, security criteria and quality criteria. For example, the authentication unit can perform authentication based on security criteria. The authentication unit can also perform authentication based on quality criteria. Furthermore, the authentication unit can perform authentication based on other criteria. This makes it possible to perform authentication based on set criteria.

[0090] The providing unit can provide an authentication result. The authentication result includes, for example, a pass / fail result of authentication, an evaluation score, etc., but is not limited to these examples. The providing unit can provide, for example, a pass / fail result of authentication. The providing unit can also provide an evaluation score. Furthermore, the providing unit can also provide other authentication results. This makes it possible to provide the authentication result.

[0091] The collection unit can collect technical know-how. The technical know-how includes, for example, technical documents, experimental data, etc., but is not limited to these examples. The collection unit can collect, for example, technical documents. The collection unit can also collect experimental data. Furthermore, the collection unit can also collect other technical know-how. This makes it possible to collect technical know-how.

[0092] The sharing unit can share the collected know-how. The know-how includes, for example, technical documents, experimental data, etc., but is not limited to these examples. The sharing unit can share, for example, technical documents. The sharing unit can also share experimental data. Furthermore, the sharing unit can share other know-how. This makes it possible to share the collected know-how.

[0093] The providing unit can provide the sharing result. The sharing result includes, for example, a list of shared information, an effect of sharing, and the like, but is not limited to these examples. The providing unit can provide, for example, a list of shared information. The providing unit can also provide the effect of sharing. Furthermore, the providing unit can also provide other sharing results. This makes it possible to provide the sharing result.

[0094] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed feedback. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of data collection to quickly collect necessary information. This makes it possible to adjust the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The collection unit can optimize the collection method by taking into account the characteristics of each region. Examples of characteristics of each region include, but are not limited to, cultural background, economic situation, etc. For example, in Japan, the collection unit may emphasize polite feedback and adopt a polite question format. In India, the collection unit may collect feedback that supports various languages ​​and take into account the linguistic characteristics of each region. Furthermore, in China, the collection unit may take into account the cultural background and avoid certain social taboos when collecting feedback. This makes it possible to optimize the collection method based on the characteristics of each region.

[0096] The collection unit can analyze the user's past feedback history and select the optimal collection means. Past feedback history includes, for example, past comments, evaluation scores, etc., but is not limited to these examples. For example, if the user has preferred text feedback in the past, the collection unit can prioritize feedback collection in text format. Furthermore, if the user has provided a lot of audio feedback in the past, the collection unit can recommend feedback collection in audio format. Furthermore, if the user has frequently used image feedback in the past, the collection unit can collect feedback in image format. This makes it possible to select the optimal collection means based on the user's past feedback history.

[0097] The collection unit can collect data using natural language processing technology and image recognition technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Image recognition technology includes, but is not limited to, object detection and face recognition. The collection unit can automatically analyze text feedback and extract important information using natural language processing technology. The collection unit can also use image recognition technology to read emotions and intentions from images provided by users and reflect them in feedback. Furthermore, the collection unit can combine natural language processing technology and image recognition technology to collect comprehensive feedback from both text and images. This enables data collection using natural language processing technology and image recognition technology.

[0098] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important data to reduce the burden on the user. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data to improve the quality of feedback. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This enables data prioritization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. For example, if the user is in a specific area, the collection unit prioritizes collection of feedback related to that area. Furthermore, if the user is moving, the collection unit can also collect the most relevant data based on the user's current location. Furthermore, if the user is participating in a specific event, the collection unit can also prioritize collection of feedback related to the event. This makes it possible to collect highly relevant data based on the user's geographical location information.

[0100] The collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the collection unit can collect feedback on places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data based on the user's social media activity.

[0101] The collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, past comments, evaluation scores, and the like. For example, the collection unit can preferentially provide feedback formats that the user has used favorably in the past. The collection unit can also analyze the content of the user's past feedback and suggest an optimal collection method. Furthermore, the collection unit can customize and provide the collection method based on the user's past feedback history. This makes it possible to customize the collection method based on the user's past feedback.

[0102] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide analysis results with detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. This makes it possible to adjust the way the analysis is presented 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 can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The analysis unit can analyze the collected feedback and optimize the analysis algorithm when providing data to reduce AI bias. Examples of AI bias include, but are not limited to, gender bias and racial bias. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. The analysis unit, for example, analyzes the feedback data and optimizes an algorithm that detects and corrects specific bias. The analysis unit can also develop an analysis algorithm that takes diverse cultures and values ​​into account to reduce bias. Furthermore, the analysis unit can adjust the analysis algorithm to ensure diversity in the feedback data. This makes it possible to provide data to reduce AI bias.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, for example, the reliability and impact of the data. For example, the analysis unit can perform a detailed analysis on data with high importance to provide highly accurate results. The analysis unit can also perform a simplified analysis on data with low importance to provide quick results. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data.

[0105] The analysis unit can analyze data from multiple angles using natural language processing technology and image recognition technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Image recognition technology includes, but is not limited to, object detection and face recognition. The analysis unit, for example, uses natural language processing technology to analyze text data and extract important information. The analysis unit can also use image recognition technology to analyze image data and read emotions and intentions. Furthermore, the analysis unit can combine natural language processing technology and image recognition technology to perform comprehensive analysis of both text and images. This enables multifaceted data analysis using natural language processing technology and image recognition technology.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a concise display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The analysis unit can determine the priority of analysis based on the time of data submission. The time of data submission includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit can prioritize analysis of the most recent data and provide results quickly. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can optimally adjust the analysis schedule based on the time of data submission. This makes it possible to determine the priority of analysis based on the time of data submission.

[0108] The analysis unit can adjust the order of analysis based on the relevance of the data. Examples of the relevance of the data include, but are not limited to, correlation and co-occurrence of the data. For example, the analysis unit prioritizes analysis of highly relevant data to provide highly accurate results. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0109] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the analysis unit can explain the analysis results in simple terms to users with less technical expertise. The analysis unit can also provide the analysis results using detailed technical terms to users with more technical expertise. Furthermore, the analysis unit can customize the way the analysis results are presented according to the user's level of expertise. This allows the use of technical terms in the analysis results to be adjusted according to the user's level of expertise.

[0110] The providing unit can estimate the user's emotions and adjust the way in which feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible feedback. If the user is relaxed, the providing unit can also provide feedback including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide concise feedback that focuses on the main points. This makes it possible to adjust the way in which feedback is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The providing unit can adjust the level of detail of the feedback when providing feedback based on the analysis results. Examples of the level of detail of the feedback include, but are not limited to, detailed comments, concise comments, and the like. For example, the providing unit can provide detailed feedback for analysis results with high importance. The providing unit can also provide simplified feedback for analysis results with low importance. Furthermore, the providing unit can optimally allocate feedback resources according to the importance of the analysis results. This makes it possible to adjust the level of detail of the feedback based on the analysis results.

[0112] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback history. The past feedback history includes, but is not limited to, for example, past comments, evaluation scores, etc. The providing unit can, for example, analyze feedback provided by the user in the past and provide more appropriate feedback for similar situations. The providing unit can also provide individually customized feedback based on the user's past feedback history. Furthermore, the providing unit can also improve the accuracy of the feedback by referring to the content of the user's past feedback. This makes it possible to improve the accuracy of feedback based on the user's past feedback history.

[0113] The providing unit can hold regular workshops and seminars and provide feedback. Examples of regular workshops and seminars include, but are not limited to, the frequency of holding the workshops and seminars and the target participants. For example, the providing unit can hold regular online workshops and provide feedback to participants. The providing unit can also provide feedback in a seminar format, providing an opportunity for participants to ask questions directly. Furthermore, the providing unit can record the contents of the workshops and seminars and provide feedback by making the content available for viewing at a later date. This makes it possible to provide feedback through regular workshops and seminars.

[0114] The providing unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide short, to-the-point feedback. If the user is relaxed, the providing unit can also provide longer feedback with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can also provide quick, concise feedback. This makes it possible to adjust the length of the feedback based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The providing unit can determine the priority of feedback based on the time of data submission. The time of data submission includes, for example, the submission date, the submission time, etc., but is not limited to these examples. The providing unit, for example, can provide feedback preferentially based on the latest data. The providing unit can also provide feedback later for data that was submitted earlier. Furthermore, the providing unit can optimally adjust the feedback schedule based on the submission time. This makes it possible to determine the priority of feedback based on the time of data submission.

[0116] The providing unit can adjust the order of feedback based on the relevance of the data. The relevance of the data includes, but is not limited to, for example, correlation and co-occurrence of the data. For example, the providing unit can provide feedback preferentially based on highly relevant data. The providing unit can also provide feedback later for less relevant data. Furthermore, the providing unit can optimally allocate feedback resources based on the relevance of the data. This makes it possible to adjust the order of feedback based on the relevance of the data.

[0117] The providing unit can adjust the use of technical terms in the feedback according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the providing unit can provide feedback in simple language to a user with little technical expertise. The providing unit can also provide feedback using detailed technical terms to a user with a wealth of technical expertise. Furthermore, the providing unit can customize the way the feedback is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the feedback according to the user's level of expertise.

[0118] The criteria setting unit can estimate the user's emotions and adjust the authentication criteria setting method based on the estimated user emotions. For example, if the user is nervous, the criteria setting unit can set simple and clear authentication criteria. If the user is relaxed, the criteria setting unit can also set authentication criteria that include detailed explanations. Furthermore, if the user is in a hurry, the criteria setting unit can also set authentication criteria that can be quickly understood. This makes it possible to adjust the authentication criteria setting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The standard setting unit can adjust the level of detail of the certification standards when setting certification standards for businesses that have an appropriate anti-bias system in place. Anti-bias systems include, but are not limited to, ensuring data diversity and algorithm verification. For example, the standard setting unit sets detailed certification standards for businesses that have sufficient anti-bias measures in place. The standard setting unit can also set simplified certification standards for businesses that have insufficient anti-bias measures in place. Furthermore, the standard setting unit can adjust the level of detail of the certification standards depending on the business's anti-bias system. This makes it possible to adjust the level of detail of the certification standards for businesses that have an appropriate anti-bias system in place.

[0120] The criterion setting unit can improve the accuracy of the criteria by referring to past authentication data. Past authentication data includes, for example, past authentication results, evaluation scores, etc., but is not limited to these examples. For example, the criterion setting unit analyzes the past authentication data and identifies areas for improvement to improve the accuracy of the criteria. The criterion setting unit can also review the authentication criteria based on the past authentication data to improve the accuracy. Furthermore, the criterion setting unit can also optimize the scope of application of the authentication criteria by referring to the past authentication data. This makes it possible to improve the accuracy of the criteria based on the past authentication data.

[0121] The standard setting unit can optimize the standards by taking into account the characteristics of each region. Examples of characteristics of each region include, but are not limited to, cultural background and economic situation. For example, in Japan, the standard setting unit can set certification standards that emphasize politeness and quality. In India, the standard setting unit can also set certification standards that accommodate diverse languages ​​and cultures. Furthermore, in China, the standard setting unit can also set certification standards that take into account the characteristics of each region and avoid specific social taboos. This makes it possible to optimize the standards based on the characteristics of each region.

[0122] The criteria setting unit can estimate the user's emotions and determine the priority of authentication criteria based on the estimated user's emotions. For example, if the user is nervous, the criteria setting unit can prioritize authentication criteria with high importance. Furthermore, if the user is relaxed, the criteria setting unit can also prioritize detailed authentication criteria. Furthermore, if the user is in a hurry, the criteria setting unit can also prioritize authentication criteria that can be quickly understood. This makes it possible to determine the priority of authentication criteria based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The criteria setting unit can determine the priority of criteria based on the time of data submission. The time of data submission includes, for example, the submission date, the submission time, etc., but is not limited to these examples. The criteria setting unit, for example, prioritizes the authentication criteria based on the latest data. The criteria setting unit can also set the authentication criteria later for data submitted earlier. Furthermore, the criteria setting unit can optimally adjust the schedule of the authentication criteria based on the time of submission. This makes it possible to determine the priority of criteria based on the time of data submission.

[0124] The criterion setting unit can adjust the order of criteria based on the relevance of data. Data relevance includes, but is not limited to, for example, correlation and co-occurrence of data. For example, the criterion setting unit can prioritize authentication criteria based on highly relevant data. The criterion setting unit can also set authentication criteria later for data with low relevance. Furthermore, the criterion setting unit can optimally allocate resources for authentication criteria based on the relevance of data. This makes it possible to adjust the order of criteria based on the relevance of data.

[0125] The criteria setting unit may adjust the use of technical terms in the criteria according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the criteria setting unit may explain the authentication criteria in simple terms to a user with little expertise. The criteria setting unit may also provide the authentication criteria using detailed technical terms to a user with extensive expertise. Furthermore, the criteria setting unit may customize the way the authentication criteria are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the criteria according to the user's level of expertise.

[0126] The authentication unit can estimate the user's emotions and adjust the authentication presentation method based on the estimated user emotions. For example, if the user is nervous, the authentication unit provides a simple, highly visible authentication result. If the user is relaxed, the authentication unit can also provide an authentication result that includes detailed information. Furthermore, if the user is in a hurry, the authentication unit can also provide a concise authentication result that focuses on the main points. This makes it possible to adjust the authentication presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] The authentication unit can adjust the level of detail of authentication when performing authentication based on set criteria. Criteria include, but are not limited to, security criteria, quality criteria, and the like. For example, the authentication unit can perform detailed authentication for criteria with high importance to provide highly accurate results. The authentication unit can also perform simplified authentication for criteria with low importance to provide quick results. Furthermore, the authentication unit can optimally allocate authentication resources according to the importance of the criteria. This makes it possible to adjust the level of detail of authentication based on set criteria.

[0128] The authentication unit can improve the accuracy of authentication by referring to past authentication data. Past authentication data includes, for example, past authentication results, evaluation scores, etc., but is not limited to these examples. The authentication unit, for example, analyzes the past authentication data and identifies areas for improvement to improve authentication accuracy. The authentication unit can also review the authentication process based on the past authentication data to improve accuracy. Furthermore, the authentication unit can also optimize the scope of authentication application by referring to the past authentication data. This makes it possible to improve authentication accuracy based on past authentication data.

[0129] The authentication unit can optimize the authentication method by taking into account the characteristics of each region. Examples of characteristics of each region include, but are not limited to, cultural background and economic situation. For example, in Japan, the authentication unit can adopt an authentication method that emphasizes politeness and quality. In India, the authentication unit can also adopt an authentication method that is compatible with diverse languages ​​and cultures. Furthermore, in China, the authentication unit can adopt an authentication method that takes into account the characteristics of each region and avoids specific social taboos. This makes it possible to optimize the authentication method based on the characteristics of each region.

[0130] The authentication unit can estimate the user's emotions and determine the authentication priority based on the estimated user's emotions. For example, if the user is nervous, the authentication unit can prioritize authentication with high importance. Also, if the user is relaxed, the authentication unit can prioritize detailed authentication. Furthermore, if the user is in a hurry, the authentication unit can prioritize authentication that can be quickly understood. This makes it possible to determine the authentication priority based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0131] The authentication unit can determine the priority of authentication based on the time of data submission. The time of data submission includes, but is not limited to, for example, the submission date and submission time. The authentication unit, for example, prioritizes authentication based on the most recent data. The authentication unit can also postpone authentication of data that was submitted earlier. Furthermore, the authentication unit can optimally adjust the authentication schedule based on the submission time. This makes it possible to determine the priority of authentication based on the time of data submission.

[0132] The authentication unit can adjust the order of authentication based on the relevance of data. The relevance of data includes, but is not limited to, for example, correlation and co-occurrence of data. For example, the authentication unit prioritizes authentication based on highly related data. The authentication unit can also postpone authentication of data with low relevance. Furthermore, the authentication unit can optimally allocate authentication resources based on the relevance of data. This makes it possible to adjust the order of authentication based on the relevance of data.

[0133] The authentication unit can adjust the use of technical terms for authentication according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the authentication unit can explain the authentication result in simple terms to a user with little technical expertise. The authentication unit can also provide the authentication result using detailed technical terms to a user with extensive technical expertise. Furthermore, the authentication unit can customize the way in which the authentication result is presented according to the user's level of expertise. This allows the use of technical terms for authentication to be adjusted according to the user's level of expertise.

[0134] The sharing unit can estimate the user's emotions and adjust the way in which the know-how sharing is expressed based on the estimated user's emotions. For example, if the user is nervous, the sharing unit can provide a simple, highly visible know-how sharing. If the user is relaxed, the sharing unit can also provide a know-how sharing that includes detailed information. Furthermore, if the user is in a hurry, the sharing unit can also provide a concise know-how sharing that focuses on the main points. This makes it possible to adjust the way in which the know-how sharing is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0135] The sharing unit can adjust the level of detail of the sharing when sharing the collected know-how. The level of detail of the sharing includes, for example, a detailed explanation, a concise explanation, and the like, but is not limited to these examples. For example, the sharing unit can share detailed information for know-how of high importance and provide highly accurate information. Furthermore, the sharing unit can share simplified information for know-how of low importance and provide information quickly. Furthermore, the sharing unit can optimally allocate sharing resources according to the importance of the know-how. This makes it possible to adjust the level of detail of the collected know-how.

[0136] The sharing unit can improve the accuracy of sharing by referring to past shared data. Past shared data includes, for example, past shared content, evaluation results, etc., but is not limited to these examples. The sharing unit, for example, analyzes the past shared data and identifies improvements to improve the accuracy of sharing. The sharing unit can also review the sharing process based on the past shared data to improve accuracy. Furthermore, the sharing unit can also optimize the scope of sharing by referring to the past shared data. This makes it possible to improve the accuracy of sharing based on the past shared data.

[0137] The common section can optimize the sharing method by taking into account the characteristics of each region. Examples of the characteristics of each region include, but are not limited to, cultural background and economic situation. For example, in Japan, the common section may share know-how with an emphasis on politeness and quality. In India, the common section may also share know-how that is compatible with diverse languages ​​and cultures. Furthermore, in China, the common section may consider the characteristics of each region and share know-how that avoids specific social taboos. This makes it possible to optimize the sharing method based on the characteristics of each region.

[0138] The sharing unit can estimate the user's emotions and determine the priority of know-how sharing based on the estimated user's emotions. For example, if the user is nervous, the sharing unit prioritizes sharing of highly important know-how. Furthermore, if the user is relaxed, the sharing unit can prioritize sharing of detailed know-how. Furthermore, if the user is in a hurry, the sharing unit can prioritize sharing of know-how that can be quickly understood. This makes it possible to determine the priority of know-how sharing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0139] The sharing unit can determine the priority of sharing based on the time of data submission. The time of data submission includes, but is not limited to, for example, the submission date and the submission time. The sharing unit, for example, prioritizes sharing of know-how based on the latest data. The sharing unit can also postpone sharing of know-how for data that was submitted earlier. Furthermore, the sharing unit can optimally adjust the know-how sharing schedule based on the time of submission. This makes it possible to determine the priority of sharing based on the time of data submission.

[0140] The sharing unit can adjust the order of sharing based on the relevance of the data. The relevance of the data includes, for example, correlation and co-occurrence of the data, but is not limited to these examples. For example, the sharing unit prioritizes sharing of know-how based on highly related data. The sharing unit can also postpone sharing of know-how for less related data. Furthermore, the sharing unit can optimally allocate know-how sharing resources based on the relevance of the data. This makes it possible to adjust the order of sharing based on the relevance of the data.

[0141] The sharing unit can adjust the use of shared terminology according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the sharing unit can share know-how in simple language with users with little expertise. The sharing unit can also share know-how using detailed terminology with users with extensive expertise. Furthermore, the sharing unit can customize the way know-how is shared according to the user's level of expertise. This makes it possible to adjust the use of shared terminology according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, standard setting unit, authentication unit, technical know-how collection unit, know-how sharing unit, and shared result provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs multifaceted analysis of the collected data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides feedback based on the analysis results. The standard setting unit and authentication unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and set and authenticate certification standards. The technical know-how collection unit and know-how sharing unit are realized, for example, by the control unit 46A of the smart device 14 and collect and share technical know-how. The shared result provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides shared results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, standard setting unit, authentication unit, technical know-how collection unit, know-how sharing unit, and sharing result provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs multifaceted analysis of the collected data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides feedback based on the analysis results. The standard setting unit and authentication unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and set and authenticate certification standards. The technical know-how collection unit and know-how sharing unit are realized, for example, by the control unit 46A of the smart glasses 214 and collect and share technical know-how. The sharing result provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides sharing results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, standard setting unit, authentication unit, technical know-how collection unit, know-how sharing unit, and sharing result provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs multifaceted analysis of the collected data. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides feedback based on the analysis results. The standard setting unit and authentication unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and set authentication standards and perform authentication. The technical know-how collection unit and know-how sharing unit are realized, for example, by the control unit 46A of the headset terminal 314 and perform collection and sharing of technical know-how. The shared result providing unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides the shared result. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, standard setting unit, authentication unit, technical know-how collection unit, know-how sharing unit, and shared result provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs multifaceted analysis of the collected data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides feedback based on the analysis results. The standard setting unit and authentication unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and set authentication standards and perform authentication. The technical know-how collection unit and know-how sharing unit are realized, for example, by the control unit 46A of the robot 414 and collect and share technical know-how. The shared result provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides shared results.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] The collection unit can also analyze the user's behavioral patterns and determine the optimal timing for data collection. For example, if the user is active during a specific time period, data collection can be concentrated on that time period. Also, if the user relaxes on weekends, detailed feedback can be collected on weekends. Furthermore, if the user participates in a specific event, related data can be collected during that event. This makes it possible to optimize data collection based on the user's behavioral patterns.

[0144] The analysis unit can also analyze feedback trends and predict future feedback content based on the user's past feedback history. For example, if a user has provided a lot of feedback on a specific topic in the past, new feedback related to that topic can be predicted. Also, if a user has provided a lot of positive feedback in the past, it can predict that future feedback is likely to be positive as well. Furthermore, if a user has provided negative feedback in the past, the cause can be analyzed and improvements can be proposed. This makes it possible to predict future feedback based on the user's past feedback history.

[0145] The providing unit can also provide personalized learning content based on the user's feedback. For example, if the user provides feedback on a specific skill, learning content to improve that skill can be provided. Also, if the user shows interest in a specific topic, learning content related to that topic can be provided. Furthermore, an individually customized learning plan can be proposed based on feedback previously provided by the user. This makes it possible to provide personalized learning content based on the user's feedback.

[0146] The standard setting unit can also review the authentication standards based on the user's feedback. For example, if a user expresses dissatisfaction with a specific authentication standard, the standard setting unit can reevaluate the standard and identify areas for improvement. Also, if a user gives a high rating to a specific authentication standard, the standard setting unit can apply the high rating to other standards. Furthermore, new authentication standards can be set based on the user's feedback. This makes it possible to review the authentication standards based on the user's feedback.

[0147] The authentication unit can also improve the authentication process based on user feedback. For example, if a user expresses dissatisfaction with a particular authentication process, the authentication unit reevaluates the process and identifies areas for improvement. Also, if a user expresses a high evaluation of a particular authentication process, the authentication unit can apply that process to other authentications. Furthermore, a new authentication process can be introduced based on user feedback. This makes it possible to improve the authentication process based on user feedback.

[0148] The collection unit can also estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, data can be collected in the form of simple questions to reduce the burden on the user. Alternatively, if the user is relaxed, data can be collected in the form of detailed questions to obtain more in-depth feedback. Furthermore, if the user is in a hurry, data can be collected in a format that allows for quick responses. This makes it possible to adjust the data collection method based on the user's emotions.

[0149] The analysis unit can also estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, simple, highly visible analysis results can be provided. If the user is relaxed, analysis results including detailed information can be provided. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. This makes it possible to adjust the way in which the analysis results are presented based on the user's emotions.

[0150] The providing unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, positive feedback can be provided preferentially to improve the user's motivation. Also, if the user is relaxed, constructive feedback can be provided to encourage the user's growth. Furthermore, if the user is in a hurry, concise and to the point feedback can be provided. This makes it possible to adjust the content of the feedback based on the user's emotions.

[0151] The criteria setting unit can also estimate the user's emotions and adjust the explanation method for the authentication criteria based on the estimated user's emotions. For example, if the user is nervous, a simple and clear explanation can be provided. If the user is relaxed, a detailed explanation can be provided. Furthermore, if the user is in a hurry, an explanation can be provided in a format that can be quickly understood. This makes it possible to adjust the explanation method for the authentication criteria based on the user's emotions.

[0152] The authentication unit can also estimate the user's emotions and adjust the notification method of the authentication result based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible notification can be provided. If the user is relaxed, a notification containing detailed information can be provided. Furthermore, if the user is in a hurry, a concise notification that focuses on the main points can be provided. This makes it possible to adjust the notification method of the authentication result based on the user's emotions.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The collection unit collects data. The data includes text data, numerical data, image data, etc. The collection unit collects data using natural language processing technology and image recognition technology, and can also estimate the user's emotions and adjust the timing of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis can involve the use of statistical analysis and machine learning algorithms, as well as natural language processing and image recognition techniques to analyze the data from multiple angles. It can also adjust the way the analysis is presented based on the user's sentiment. Step 3: The delivery department provides feedback based on the analysis results obtained by the analysis department. Feedback can include text comments or numerical evaluations, and can also be provided through regular workshops or seminars. Step 4: The Standard Setting Department sets the certification standards, which include security and quality standards, and can also optimize the standards by taking into account the characteristics of each region. Step 5: The certification department certifies the product based on the standards set by the standard setting department. The certification process includes the certification process and the conditions for certification, and the accuracy of the certification can be improved by referring to past certification data. Step 6: The authentication result providing unit provides the authentication result obtained by the authentication unit. The authentication result includes whether the authentication was successful or not and a detailed evaluation. Step 7: The technical know-how collection unit collects technical know-how. The technical know-how includes technical documents and experimental data, and can also determine the priority of the data to be collected based on user sentiment. Step 8: The Know-how Sharing Department shares the know-how collected by the Technical Know-How Collection Department. Know-how sharing can include sharing on online platforms and distributing documents, and can also improve the accuracy of sharing by referring to past sharing data. Step 9: The sharing result providing unit provides the sharing results obtained by the know-how sharing unit. The sharing results include a list of shared information and the effects of sharing, and can also adjust the way the sharing results are displayed based on the user's emotions.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 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.

[0193] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0195] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0198] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0199] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0200] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0202] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0203] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0205] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0210] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0211] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0212] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0216] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0217] 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.

[0218] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0219] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0220] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0221] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0223] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0224] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0225] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0226] [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides feedback based on the analysis result obtained by the analyzing unit; a standard setting unit that sets certification standards; an authentication unit that performs authentication based on the criteria set by the criteria setting unit; an authentication result providing unit that provides the authentication result obtained by the authentication unit; a technical know-how collection department that collects technical know-how; a know-how sharing unit that shares the know-how collected by the technical know-how collecting unit; a sharing result providing unit that provides the sharing result obtained by the know-how sharing unit; Equipped with A system characterized by:

2. The collecting unit Collecting feedback that reflects multiple Asian cultures and values 2. The system of claim 1.

3. The analysis unit Analyze the collected feedback and provide data to reduce AI bias 2. The system of claim 1.

4. The providing unit Providing feedback based on analysis results 2. The system of claim 1.

5. The reference setting unit Establish certification standards for businesses with anti-bias systems 2. The system of claim 1.

6. The authentication unit Certify based on set standards 2. The system of claim 1.

7. The providing unit Providing authentication results 2. The system of claim 1.

8. The collecting unit Gathering technical know-how 2. The system of claim 1.

9. The know-how sharing unit: Share the collected know-how 2. The system of claim 1.

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