System

Abstract

An object of a system according to an embodiment is to facilitate communication between different cultures.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a problem or a question from a user by text input or voice input. The analysis unit analyzes the information received by the reception unit and generates an answer or advice based on cultural background or habits. The providing unit provides the user with the answer or the advice generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, there is a risk of misunderstandings and uncertainty arising from cultural differences when communicating between different cultures.

[0005] The system according to the embodiment aims to facilitate communication between different cultures. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives issues and questions from users via text input or voice input. The analysis unit analyzes the information received by the reception unit and generates answers and advice based on cultural background and customs. The provision unit provides the answers and advice generated by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication between different cultures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication support system according to an embodiment of the present invention accepts user input of issues or questions via text or voice, analyzes them with a generation AI, and provides answers and advice based on cultural backgrounds and customs. The communication support system allows users to input issues or questions they face in intercultural communication. The generation AI analyzes the input and provides answers and advice based on the appropriate cultural backgrounds and customs. For example, a question might include the appropriate greeting method at a business meeting or taboos in a particular culture. This information is input into the generation AI, which then analyzes the input information. The generation AI generates appropriate answers and advice for the user's question based on an extensive cultural database. For example, in response to a question about the appropriate greeting method at a business meeting, the generation AI provides common greeting methods and important points to note in that culture. The generated answers and advice are then provided to the user. This allows users to take appropriate actions in intercultural communication and reduce misunderstandings and uncertainty. For example, knowing the appropriate greeting method at a business meeting allows users to respond respectfully to others. This facilitates intercultural communication and promotes global business and exchange. For example, in intercultural business negotiations or international conferences, the use of generative AI can reduce misunderstandings due to cultural differences and enable smoother communication. This allows the communication support system to facilitate intercultural communication and promote global business and exchange. For example, in intercultural business negotiations or international conferences, the use of generative AI can reduce misunderstandings due to cultural differences and enable smoother communication.

[0029] A communication support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user inputs of problems or questions via text or voice. Examples of user inputs and questions include, but are not limited to, appropriate greetings at business meetings and cultural taboos. The reception unit receives text via keyboard input, for example. The reception unit can also convert voice input into text using voice recognition technology. For example, the reception unit may record the user's voice using a microphone and convert the recorded voice into text using voice recognition technology. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate answers or advice based on cultural backgrounds and customs. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit generates appropriate answers or advice for the user's questions based on an extensive cultural database. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and performs the analysis using these. For example, the generation AI may respond to the user's questions by providing common greetings and important points in that culture. The providing unit provides the answer or advice generated by the analyzing unit to the user. The answer or advice may be provided, for example, as a text display or as an audio output, but is not limited to these examples. For example, the providing unit displays text on a screen. The providing unit can also provide the answer or advice by audio using a speaker. This allows the communication support system according to the embodiment to facilitate communication between different cultures. For example, if a user knows the appropriate way to greet others in a business meeting, they will be able to respond respectfully to others. This facilitates communication between different cultures and promotes global business and exchange.

[0030] The reception unit can analyze the user's past input history and select an input method. For example, the reception unit can automatically display tasks or questions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest tasks or questions to be used in a specific time period based on the user's past input history. This improves user convenience by providing the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0031] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, if the user inputs a business issue, the reception unit can prioritize displaying questions about related business culture. Furthermore, if the user is traveling, the reception unit can prioritize displaying questions about the culture of the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize displaying questions about the cultural background and customs related to the event. This prioritizes displaying questions according to the user's situation and areas of interest, thereby providing more appropriate answers. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's location information data to the generation AI and have the generation AI perform filtering.

[0032] When receiving input, the reception unit can select a reception means depending on the user's input method. For example, when the user inputs a question by voice, the reception unit converts the question into text using voice recognition technology and transmits it to the analysis unit. Furthermore, when the user inputs a question by text, the reception unit can also generate an appropriate answer using text analysis technology. Furthermore, when the user uploads an image, the reception unit can also generate an answer based on relevant cultural background and customs using image analysis technology. This improves input efficiency by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0033] The analysis unit can analyze the user's question based on a cultural database and generate an answer or advice. The analysis unit can analyze the user's question based on, for example, an extensive cultural database and generate an appropriate answer or advice. The cultural database can include, for example, cultural information by country and historical background, but is not limited to such examples. The analysis unit can perform analysis using, for example, natural language processing technology or a machine learning algorithm. For example, the analysis unit can provide common greetings and points of caution in a particular culture in response to the user's question. This allows for analysis based on an extensive cultural database to provide more accurate answers and advice. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's question data into a generation AI and have the generation AI generate an answer or advice.

[0034] The analysis unit can learn from past data and improve the accuracy of the analysis. For example, the analysis unit can learn from past question and answer pairs and improve the accuracy of the analysis. The analysis unit can also improve the analysis algorithm based on user feedback. The analysis unit can also use past data to generate appropriate answers and advice to user questions. In this way, by learning from past data, the accuracy of the analysis is continuously improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past question and answer pair data into the generation AI and have the generation AI improve the analysis algorithm.

[0035] The providing unit can provide the generated answer or advice to the user by at least one of text display or audio output. The providing unit, for example, displays the generated answer or advice as text on a screen. The providing unit can also provide the answer or advice by audio using a speaker. This helps the user understand by providing the generated answer or advice in an appropriate format. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input text data of the generated answer or advice to the generation AI and have the generation AI convert it into audio data.

[0036] The providing unit can adjust the level of detail to be provided based on the importance of the answer or advice. For example, the providing unit provides detailed information for answers or advice with high importance. The providing unit can also provide concise information for answers or advice with low importance. The providing unit can also adjust the display order according to the importance of the answer or advice. In this way, by providing a level of detail according to the importance of the answer or advice, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input importance data of the generated answer or advice to the generation AI and cause the generation AI to adjust the level of detail.

[0037] The providing unit can apply different providing methods depending on the category of the answer or advice. For example, the providing unit can provide business-related answers or advice in a business document format. The providing unit can also provide travel-related answers or advice in a travel guide format. The providing unit can also provide answers or advice related to daily life in a general advice format. In this way, by applying a providing method according to the category, information is provided in the most optimal format for the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input category data of the generated answer or advice into the generation AI and cause the generation AI to apply the providing method.

[0038] The providing unit can improve the accuracy of the provision by referring to the user's past provision results. The providing unit can adjust the provision method based on, for example, answers and advice the user has received in the past. The providing unit can also improve the accuracy of the provision results by referring to the user's past feedback. The providing unit can also learn the user's past provision results and continuously improve the provision algorithm. In this way, the accuracy of the provision is improved by referring to the past provision results. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the provision algorithm.

[0039] The providing unit can determine the priority of provision based on the time of submission of the answers and advice. The providing unit can determine the priority of provision based on, for example, the time period in which the answers and advice are submitted. The providing unit can also adjust the display order of the provision results depending on the time of submission of the answers and advice. The providing unit can also adjust the level of detail of the provision based on the time of submission of the answers and advice. In this way, determining the priority based on the time of submission realizes timely information provision. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input submission time data of the generated answers and advice into the generation AI and have the generation AI determine the priority.

[0040] The providing unit can adjust the order of provision based on the relevance of the answers and advice. The providing unit adjusts the display order of the provided results based on, for example, the relevance of the answers and advice. The providing unit can also adjust the level of detail of the provision depending on the relevance of the answers and advice. The providing unit can also determine the priority of provision based on the relevance of the answers and advice. In this way, by adjusting the order based on the relevance, information that is most relevant to the user is preferentially provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input relevance data of the generated answers and advice to the generation AI and cause the generation AI to adjust the order.

[0041] The providing unit can adjust the use of technical terminology in the provided results according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide answers or advice that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide concise and easy-to-understand answers or advice. The providing unit can also adjust the level of detail of the provided results according to the user's level of expertise. This allows the use of technical terminology to be adjusted according to the level of expertise, thereby providing information that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can automatically search for relevant news articles and academic papers based on the user's input and provide them as supplemental information in answers and advice. For example, if a user asks a question about business practices in a particular culture, the analysis unit can search for the latest news articles and relevant academic papers and use that information to reinforce the answer. The analysis unit can also search for videos and images related to the user's question and provide them as visual supplemental information, thereby helping the user gain a deeper understanding. Furthermore, the analysis unit can evaluate the reliability of answers to the user's question and prioritize answers from reliable sources, allowing the user to obtain reliable information.

[0044] The reception unit can automatically search for relevant online forums and communities based on the user's input and suggest participation to the user. For example, if a user inputs a question about a particular culture, the reception unit can search for active online forums and communities related to that culture and suggest participation to the user. In addition, if the user expresses interest in a particular topic, the reception unit can suggest webinars and online events related to that topic, allowing the user to interact with other people and obtain more information. Furthermore, the reception unit can recommend related books and articles based on the user's areas of interest, allowing the user to gain deeper knowledge.

[0045] The analyzer can provide relevant historical background and cultural context based on the user's input. For example, if a user asks about a traditional ritual in a particular culture, the analyzer can provide a detailed explanation of the ritual's historical background and cultural significance. The analyzer can also provide cultural anecdotes and stories related to the user's question, allowing the user to gain a deeper understanding of the culture. Furthermore, the analyzer can provide geographical information related to the answer to the user's question, allowing the user to understand where the culture developed.

[0046] The analysis unit can provide relevant statistical data and research results based on the user's input. For example, if a user asks a question about business practices in a particular culture, the analysis unit can provide the latest statistical data and research results about that culture to reinforce the answer. The analysis unit can also generate graphs and charts related to the user's question to provide visual information. This allows the user to obtain reliable information based on data. Furthermore, the analysis unit can evaluate the reliability of answers to the user's question and prioritize answers from reliable sources. This allows the user to obtain reliable information.

[0047] The reception unit can automatically search for relevant experts and consultants based on the user's input and introduce them to the user. For example, if a user enters a detailed question about a specific culture, the reception unit can search for experts and consultants in that field and introduce them to the user. In addition, if a user expresses interest in a specific topic, the reception unit can provide contact information for experts related to that topic. This allows the user to directly interact with people with specialized knowledge and gain deeper knowledge. Furthermore, the reception unit can suggest webinars and online events by relevant experts based on the user's areas of interest. This allows the user to obtain the latest information.

[0048] The analyzer can provide relevant legal information and regulations based on the user's input. For example, if a user asks a question about business practices in a particular culture, the analyzer can provide legal information and regulations related to that culture to reinforce the answer. The analyzer can also provide legal documents and guidelines relevant to the user's question, thereby providing the user with legally accurate information. Furthermore, the analyzer can evaluate the reliability of answers to the user's question and prioritize answers from reliable sources, thereby providing the user with reliable information.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The reception unit accepts issues or questions from the user via text input or voice input. Issues or questions from users may include appropriate greetings at business meetings or taboos in a particular culture. The reception unit can accept text via keyboard input, and can also convert voice input into text using voice recognition technology. For example, the reception unit can record the user's voice using a microphone and convert it into text using voice recognition technology. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit and generate answers and advice based on cultural background and customs. The analysis is performed using natural language processing technology and machine learning algorithms. For example, it generates appropriate answers and advice to the user's questions based on an extensive cultural database. The generation AI performs the analysis using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The providing unit provides the answer or advice generated by the analysis unit to the user. The answer or advice is provided by displaying text or outputting audio. For example, the answer or advice may be provided by displaying text on a screen or by audio using a speaker.

[0051] (Example 2) A communication support system according to an embodiment of the present invention accepts user input of issues or questions via text or voice, analyzes them with a generation AI, and provides answers and advice based on cultural backgrounds and customs. The communication support system allows users to input issues or questions they face in intercultural communication. The generation AI analyzes the input and provides answers and advice based on the appropriate cultural backgrounds and customs. For example, a question might include the appropriate greeting method at a business meeting or taboos in a particular culture. This information is input into the generation AI, which then analyzes the input information. The generation AI generates appropriate answers and advice for the user's question based on an extensive cultural database. For example, in response to a question about the appropriate greeting method at a business meeting, the generation AI provides common greeting methods and important points to note in that culture. The generated answers and advice are then provided to the user. This allows users to take appropriate actions in intercultural communication and reduce misunderstandings and uncertainty. For example, knowing the appropriate greeting method at a business meeting allows users to respond respectfully to others. This facilitates intercultural communication and promotes global business and exchange. For example, in intercultural business negotiations or international conferences, the use of generative AI can reduce misunderstandings due to cultural differences and enable smoother communication. This allows the communication support system to facilitate intercultural communication and promote global business and exchange. For example, in intercultural business negotiations or international conferences, the use of generative AI can reduce misunderstandings due to cultural differences and enable smoother communication.

[0052] A communication support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user inputs of problems or questions via text or voice. Examples of user inputs and questions include, but are not limited to, appropriate greetings at business meetings and cultural taboos. The reception unit receives text via keyboard input, for example. The reception unit can also convert voice input into text using voice recognition technology. For example, the reception unit may record the user's voice using a microphone and convert the recorded voice into text using voice recognition technology. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate answers or advice based on cultural backgrounds and customs. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit generates appropriate answers or advice for the user's questions based on an extensive cultural database. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and performs the analysis using these. For example, the generation AI may respond to the user's questions by providing common greetings and important points in that culture. The providing unit provides the answer or advice generated by the analyzing unit to the user. The answer or advice may be provided, for example, as a text display or as an audio output, but is not limited to these examples. For example, the providing unit displays text on a screen. The providing unit can also provide the answer or advice by audio using a speaker. This allows the communication support system according to the embodiment to facilitate communication between different cultures. For example, if a user knows the appropriate way to greet others in a business meeting, they will be able to respond respectfully to others. This facilitates communication between different cultures and promotes global business and exchange.

[0053] The reception unit can estimate the user's emotions and adjust the input method based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input issues or questions. This allows for more appropriate communication by providing an input method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0054] The reception unit can analyze the user's past input history and select an input method. For example, the reception unit can automatically display tasks or questions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest tasks or questions to be used in a specific time period based on the user's past input history. This improves user convenience by providing the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0055] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, if the user inputs a business issue, the reception unit can prioritize displaying questions about related business culture. Furthermore, if the user is traveling, the reception unit can prioritize displaying questions about the culture of the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize displaying questions about the cultural background and customs related to the event. This prioritizes displaying questions according to the user's situation and areas of interest, thereby providing more appropriate answers. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's location information data to the generation AI and have the generation AI perform filtering.

[0056] When receiving input, the reception unit can select a reception means depending on the user's input method. For example, when the user inputs a question by voice, the reception unit converts the question into text using voice recognition technology and transmits it to the analysis unit. Furthermore, when the user inputs a question by text, the reception unit can also generate an appropriate answer using text analysis technology. Furthermore, when the user uploads an image, the reception unit can also generate an answer based on relevant cultural background and customs using image analysis technology. This improves input efficiency by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0057] The analysis unit can analyze the user's question based on a cultural database and generate an answer or advice. The analysis unit can analyze the user's question based on, for example, an extensive cultural database and generate an appropriate answer or advice. The cultural database can include, for example, cultural information by country and historical background, but is not limited to such examples. The analysis unit can perform analysis using, for example, natural language processing technology or a machine learning algorithm. For example, the analysis unit can provide common greetings and points of caution in a particular culture in response to the user's question. This allows for analysis based on an extensive cultural database to provide more accurate answers and advice. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's question data into a generation AI and have the generation AI generate an answer or advice.

[0058] The analysis unit can learn from past data and improve the accuracy of the analysis. For example, the analysis unit can learn from past question and answer pairs and improve the accuracy of the analysis. The analysis unit can also improve the analysis algorithm based on user feedback. The analysis unit can also use past data to generate appropriate answers and advice to user questions. In this way, by learning from past data, the accuracy of the analysis is continuously improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past question and answer pair data into the generation AI and have the generation AI improve the analysis algorithm.

[0059] The providing unit can provide the generated answer or advice to the user by at least one of text display or audio output. The providing unit, for example, displays the generated answer or advice as text on a screen. The providing unit can also provide the answer or advice by audio using a speaker. This helps the user understand by providing the generated answer or advice in an appropriate format. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input text data of the generated answer or advice to the generation AI and have the generation AI convert it into audio data.

[0060] The providing unit can estimate the user's emotions and adjust the expression method of the answers and advice provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows for providing an expression method that matches the user's emotions, thereby providing more appropriate answers and advice. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0061] The providing unit can adjust the level of detail to be provided based on the importance of the answer or advice. For example, the providing unit provides detailed information for answers or advice with high importance. The providing unit can also provide concise information for answers or advice with low importance. The providing unit can also adjust the display order according to the importance of the answer or advice. In this way, by providing a level of detail according to the importance of the answer or advice, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input importance data of the generated answer or advice to the generation AI and cause the generation AI to adjust the level of detail.

[0062] The providing unit can apply different providing methods depending on the category of the answer or advice. For example, the providing unit can provide business-related answers or advice in a business document format. The providing unit can also provide travel-related answers or advice in a travel guide format. The providing unit can also provide answers or advice related to daily life in a general advice format. In this way, by applying a providing method according to the category, information is provided in the most optimal format for the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input category data of the generated answer or advice into the generation AI and cause the generation AI to apply the providing method.

[0063] The providing unit can improve the accuracy of the provision by referring to the user's past provision results. The providing unit can adjust the provision method based on, for example, answers and advice the user has received in the past. The providing unit can also improve the accuracy of the provision results by referring to the user's past feedback. The providing unit can also learn the user's past provision results and continuously improve the provision algorithm. In this way, the accuracy of the provision is improved by referring to the past provision results. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the provision algorithm.

[0064] The providing unit can estimate the user's emotions and adjust the length of the answers or advice provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point answers or advice. Furthermore, if the user is relaxed, the providing unit can provide longer answers or advice with detailed explanations. Furthermore, if the user is excited, the providing unit can provide answers or advice with visually stimulating effects. This allows for providing answers or advice of a length appropriate to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0065] The providing unit can determine the priority of provision based on the time of submission of the answers and advice. The providing unit can determine the priority of provision based on, for example, the time period in which the answers and advice are submitted. The providing unit can also adjust the display order of the provision results depending on the time of submission of the answers and advice. The providing unit can also adjust the level of detail of the provision based on the time of submission of the answers and advice. In this way, determining the priority based on the time of submission realizes timely information provision. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input submission time data of the generated answers and advice into the generation AI and have the generation AI determine the priority.

[0066] The providing unit can adjust the order of provision based on the relevance of the answers and advice. The providing unit adjusts the display order of the provided results based on, for example, the relevance of the answers and advice. The providing unit can also adjust the level of detail of the provision depending on the relevance of the answers and advice. The providing unit can also determine the priority of provision based on the relevance of the answers and advice. In this way, by adjusting the order based on the relevance, information that is most relevant to the user is preferentially provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input relevance data of the generated answers and advice to the generation AI and cause the generation AI to adjust the order.

[0067] The providing unit can adjust the use of technical terminology in the provided results according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide answers or advice that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide concise and easy-to-understand answers or advice. The providing unit can also adjust the level of detail of the provided results according to the user's level of expertise. This allows the use of technical terminology to be adjusted according to the level of expertise, thereby providing information that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input using a generation AI and generates answers or advice based on cultural background and customs. For example, the provision unit is realized by the output device 40 of the smart device 14 and provides the analysis results to the user as text display or voice output. This allows the communication support system to facilitate communication between different cultures. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input using a generation AI to generate answers and advice based on cultural background and customs. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and provides the analysis results to the user as voice output. This allows the communication support system to facilitate communication between different cultures. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input using a generation AI to generate answers and advice based on cultural background and customs. For example, the provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the user with the analysis results as voice output. This enables the communication support system to facilitate communication between different cultures. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input using a generation AI and generates answers and advice based on cultural background and customs. For example, the provision unit is realized by the speaker 240 of the robot 414 and provides the user with the analysis results as voice output. This allows the communication support system to facilitate communication between different cultures.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The analysis unit can automatically search for relevant news articles and academic papers based on the user's input and provide them as supplemental information in answers and advice. For example, if a user asks a question about business practices in a particular culture, the analysis unit can search for the latest news articles and relevant academic papers and use that information to reinforce the answer. The analysis unit can also search for videos and images related to the user's question and provide them as visual supplemental information, thereby helping the user gain a deeper understanding. Furthermore, the analysis unit can evaluate the reliability of answers to the user's question and prioritize answers from reliable sources, allowing the user to obtain reliable information.

[0070] The providing unit can estimate the user's emotions and adjust the tone of the answer or advice based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide an answer in a gentle tone to reassure the user. If the user is excited, the providing unit can provide an answer in an energetic tone to maintain the user's excitement. Furthermore, if the user is feeling down, the providing unit can provide an answer in an encouraging tone to lift the user's spirits. This allows for more effective communication by providing answers or advice in an appropriate tone according to the user's emotions.

[0071] The reception unit can automatically search for relevant online forums and communities based on the user's input and suggest participation to the user. For example, if a user inputs a question about a particular culture, the reception unit can search for active online forums and communities related to that culture and suggest participation to the user. In addition, if the user expresses interest in a particular topic, the reception unit can suggest webinars and online events related to that topic, allowing the user to interact with other people and obtain more information. Furthermore, the reception unit can recommend related books and articles based on the user's areas of interest, allowing the user to gain deeper knowledge.

[0072] The analyzer can provide relevant historical background and cultural context based on the user's input. For example, if a user asks about a traditional ritual in a particular culture, the analyzer can provide a detailed explanation of the ritual's historical background and cultural significance. The analyzer can also provide cultural anecdotes and stories related to the user's question, allowing the user to gain a deeper understanding of the culture. Furthermore, the analyzer can provide geographical information related to the answer to the user's question, allowing the user to understand where the culture developed.

[0073] The providing unit can estimate the user's emotions and adjust the timing of providing answers and advice based on the estimated user emotions. For example, if the user is impatient, the providing unit can provide a quick answer to reduce the user's anxiety. If the user is relaxed, the providing unit can provide an answer including detailed information to allow the user to carefully understand the information. Furthermore, if the user is tired, the providing unit can provide a concise answer that focuses on the main points, reducing the user's burden. This allows for more effective communication by providing answers and advice at appropriate times according to the user's emotions.

[0074] The analysis unit can provide relevant statistical data and research results based on the user's input. For example, if a user asks a question about business practices in a particular culture, the analysis unit can provide the latest statistical data and research results about that culture to reinforce the answer. The analysis unit can also generate graphs and charts related to the user's question to provide visual information. This allows the user to obtain reliable information based on data. Furthermore, the analysis unit can evaluate the reliability of answers to the user's question and prioritize answers from reliable sources. This allows the user to obtain reliable information.

[0075] The providing unit can estimate the user's emotions and adjust the format of the answer or advice based on the estimated user's emotions. For example, if the user prefers visual information, the providing unit can provide the answer using infographics or illustrations. If the user prefers auditory information, the providing unit can provide the answer in the form of an audio message or podcast. Furthermore, if the user prefers text information, the providing unit can provide a detailed text explanation. This allows for more effective communication by providing answers or advice in an appropriate format according to the user's emotions and preferences.

[0076] The reception unit can automatically search for relevant experts and consultants based on the user's input and introduce them to the user. For example, if a user enters a detailed question about a specific culture, the reception unit can search for experts and consultants in that field and introduce them to the user. In addition, if a user expresses interest in a specific topic, the reception unit can provide contact information for experts related to that topic. This allows the user to directly interact with people with specialized knowledge and gain deeper knowledge. Furthermore, the reception unit can suggest webinars and online events by relevant experts based on the user's areas of interest. This allows the user to obtain the latest information.

[0077] The providing unit can estimate the user's emotions and customize the content of answers and advice based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide an answer containing content that gives a sense of security. Also, if the user is excited, the providing unit can provide an answer containing content that maintains the user's excitement. Furthermore, if the user is depressed, the providing unit can provide an answer containing encouraging content. In this way, more effective communication can be achieved by providing answers and advice with appropriate content according to the user's emotions.

[0078] The analyzer can provide relevant legal information and regulations based on the user's input. For example, if a user asks a question about business practices in a particular culture, the analyzer can provide legal information and regulations related to that culture to reinforce the answer. The analyzer can also provide legal documents and guidelines relevant to the user's question, thereby providing the user with legally accurate information. Furthermore, the analyzer can evaluate the reliability of answers to the user's question and prioritize answers from reliable sources, thereby providing the user with reliable information.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The reception unit accepts issues or questions from the user via text input or voice input. Issues or questions from users may include appropriate greetings at business meetings or taboos in a particular culture. The reception unit can accept text via keyboard input, and can also convert voice input into text using voice recognition technology. For example, the reception unit can record the user's voice using a microphone and convert it into text using voice recognition technology. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit and generate answers and advice based on cultural background and customs. The analysis is performed using natural language processing technology and machine learning algorithms. For example, it generates appropriate answers and advice to the user's questions based on an extensive cultural database. The generation AI performs the analysis using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The providing unit provides the answer or advice generated by the analysis unit to the user. The answer or advice is provided by displaying text or outputting audio. For example, the answer or advice may be provided by displaying text on a screen or by audio using a speaker.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] [Explanation of symbols]

[0153] 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 reception unit that receives issues and questions from users by text input or voice input; an analysis unit that analyzes the information received by the reception unit and generates answers and advice based on cultural backgrounds and customs; a providing unit that provides the answer or advice generated by the analysis unit to the user. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the input method based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's past input history and select the input method 2. The system of claim 1.

4. The reception unit As input is received, it filters it based on the user's current situation or interests.

2. The system of claim 1.

5. The reception unit When accepting input, select the acceptance method according to the user's input method.

2. The system of claim 1.

6. The analysis unit Analyzes user questions based on a cultural database and generates answers and advice 2. The system of claim 1.

7. The analysis unit Learn from past data to improve analysis accuracy 2. The system of claim 1.

8. The providing unit Providing the generated answer or advice to the user through at least one of a text display and an audio output 2. The system of claim 1.

Images