system

Abstract

The system according to this embodiment aims to generate empathetic responses in interactions with users. [Solution] The system according to the embodiment comprises a reception unit, an emotion analysis unit, and a response generation unit. The reception unit receives user input in natural language. The emotion analysis unit analyzes the input received by the reception unit. The response generation unit generates a response based on the input analyzed by the emotion analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to generate an empathetic response in the interaction with a user, and there are problems in improving user satisfaction.

[0005] The system according to an embodiment aims to generate an empathetic response in the interaction with a user.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, an emotion analysis unit, and a response generation unit. The reception unit receives the input of the user in natural language. The emotion analysis unit analyzes the input received by the reception unit. The response generation unit generates a response based on the input analyzed by the emotion analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can generate empathetic responses in interactions with the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is an advanced solution that leverages natural language processing, emotional intelligence, and machine learning to enable robots to interact empathetically with users like humans. This system provides personalized interactive experiences in various industries such as retail, healthcare, education, and business. The system provides real-time data insights, multilingual support, customer service, employee support, and assistance with training tasks. With 24 / 7 availability and seamless integration into existing systems, the system improves engagement, operational efficiency, and user satisfaction. For example, the system accepts user input in natural language. For instance, if a user inputs "I want to check the stock of a product," the system understands the request and generates an appropriate response. Next, the system leverages emotional intelligence to analyze the user's emotions and provide an empathetic response. For example, if a user is dissatisfied, the system recognizes that emotion and takes appropriate action. Furthermore, the system uses machine learning to learn user behavior patterns and provide a personalized experience. For example, based on past conversation history, it can suggest products and services that the user might prefer. The system also provides real-time data insights to quickly respond to user needs. The system is multilingual and can accommodate users who speak different languages. This allows for consistent service delivery to a global user base. In this way, the system can be used for various purposes, such as customer service, employee support, and training task assistance, improving engagement, operational efficiency, and user satisfaction. The system enables empathetic dialogue by receiving user input in natural language, performing sentiment analysis, and generating responses.

[0029] The system according to this embodiment comprises a reception unit, an emotion analysis unit, and a response generation unit. The reception unit receives user input in natural language. For example, the reception unit receives content entered by the user as text. The reception unit can also receive voice input. For example, if a user says "I want to check the stock of the product" by voice, the reception unit converts the voice into text and receives it. Furthermore, the reception unit can receive user input in real time. For example, it can receive input in chat format. The emotion analysis unit analyzes the input received by the reception unit. For example, the emotion analysis unit analyzes the content of the user's input using natural language processing technology. The emotion analysis unit utilizes emotional intelligence to analyze the user's emotions. For example, the emotion analysis unit uses an algorithm to estimate emotions from the content of the user's input. The emotion analysis unit can classify the user's emotions. For example, the emotion analysis unit classifies emotions such as joy, sadness, and anger from the content of the user's input. The response generation unit generates a response based on the input analyzed by the emotion analysis unit. The response generation unit generates a response that corresponds to the user's emotions, for example. Based on the emotions analyzed by the emotion analysis unit, the response generation unit generates an empathetic response. For example, if the user is feeling dissatisfied, the response generation unit generates a response that empathizes with that emotion. The response generation unit generates a response that provides appropriate information based on the user's input. For example, if the user inputs that they want to check the stock of a product, the response generation unit generates a response that provides stock information. Thus, the system according to this embodiment can engage in empathetic dialogue by receiving user input in natural language, performing emotion analysis, and generating a response.

[0030] The reception desk accepts user input in natural language. For example, it accepts text input from users. Specifically, it receives text messages entered by users in a chat window in real time and incorporates them into the system. The reception desk can also accept voice input. For example, if a user says "I want to check the product inventory" by voice, the reception desk converts that voice into text and accepts it. Speech recognition technology is used to process voice input. Speech recognition technology analyzes the user's speech and converts the voice signal into text data. This allows voice input to be treated the same as text input. Furthermore, the reception desk can accept user input in real time. For example, it can accept input in chat format. In chat format, the text entered by the user is immediately sent to the system, and the system processes that input in real time. This allows for smooth interaction with the user and enables quick responses. To accurately accept user input, the reception desk can also check the consistency and grammar of the input content. For example, if the input content contains typos or omissions, the reception desk can have a function to automatically correct them. This ensures that user input is accurately captured by the system, allowing subsequent processing to proceed smoothly.

[0031] The sentiment analysis unit analyzes the input received by the reception unit. The sentiment analysis unit analyzes user input using, for example, natural language processing (NLP) technology. NLP is a technology that analyzes text data and understands its grammatical structure and meaning. The sentiment analysis unit utilizes emotional intelligence to analyze the user's emotions. Emotional intelligence is an algorithm that estimates emotions from text data and can classify the user's emotions. For example, the sentiment analysis unit classifies emotions such as joy, sadness, and anger from the user's input. The sentiment analysis unit can use machine learning models for emotion classification. Machine learning models have the ability to learn from large amounts of text data and accurately classify emotions. The sentiment analysis unit can also analyze user input and evaluate the intensity and nuance of emotions. For example, if a user inputs "very happy," the sentiment analysis unit highly values ​​the intensity of that emotion and uses this information to generate an appropriate response. The sentiment analysis unit analyzes the user's emotions in real time and reflects this in the system's overall response generation process. This allows the emotion analysis unit to accurately understand the user's emotions and provide a foundation for empathetic dialogue. Furthermore, the emotion analysis unit can perform more accurate emotion analysis by utilizing past dialogue history and user profile information. As a result, the emotion analysis unit can deeply understand the user's emotions and generate responses that meet their individual needs.

[0032] The response generation unit generates a response based on the input analyzed by the emotion analysis unit. For example, the response generation unit generates a response that corresponds to the user's emotions. Specifically, it generates an empathetic response based on the emotions analyzed by the emotion analysis unit. For example, if the user is feeling dissatisfied, the response generation unit generates a response that empathizes with that emotion. The response generation unit generates a response that provides appropriate information based on the user's input. For example, if the user inputs that they want to check the stock of a product, the response generation unit generates a response that provides stock information. The response generation unit uses natural language generation technology to generate a response to the user in natural language. Natural language generation technology is a technology that generates text data and can provide users with responses that are easy to understand and friendly. The response generation unit can adjust the tone and style of the response according to the user's emotions and input. For example, if the user is angry, the response generation unit will respond in a calm tone and choose words that will soothe the user's emotions. The response generation unit can also search for relevant information based on the user's input and generate an appropriate response. For example, if the user asks a question about a specific product, the response generation unit will search for detailed information about that product and provide it to the user. This allows the response generation unit to quickly generate appropriate responses that meet user needs, facilitating smooth communication with users. Furthermore, the response generation unit can collect user feedback and continuously improve the quality of its responses. As a result, the response generation unit can consistently provide high-quality responses and improve user satisfaction.

[0033] The system includes a Real-Time Data Insights unit that responds quickly to user needs. For example, the Real-Time Data Insights unit analyzes user input in real time to identify user needs. The Real-Time Data Insights unit utilizes user input and behavioral history as data collection methods. For example, it identifies current needs based on keywords the user has previously searched for and purchase history. The Real-Time Data Insights unit uses analytical algorithms to quickly identify user needs. For example, it uses machine learning algorithms to learn user behavior patterns and predict needs. Based on user needs, the Real-Time Data Insights unit provides appropriate information. For example, if a user inputs "I'm looking for a new smartphone," the Real-Time Data Insights unit suggests the optimal smartphone based on the user's past purchase and search history. This improves user satisfaction by responding quickly to user needs. Some or all of the above-described processes in the Real-Time Data Insights unit may be performed using AI, or not. For example, the Real-Time Data Insights unit can input user input into a generating AI and have the generating AI identify user needs.

[0034] The system includes a multilingual support unit that accommodates users who speak different languages. The multilingual support unit, for example, translates user input into different languages. It uses translation algorithms to translate user input in real time. For example, it uses a machine translation algorithm to translate user input from English to Japanese. The multilingual support unit can be configured to support multiple languages, such as English, Japanese, French, and Spanish. Based on the user's language settings, the multilingual support unit generates responses in the appropriate language. For example, if the user selects Japanese, the multilingual support unit generates a response in Japanese. This allows for consistent service to a global user base by accommodating users who speak different languages. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input user input into a generating AI and have the generating AI perform the translation.

[0035] The system includes a personalization unit that learns user behavior patterns and provides a personalized experience. The personalization unit learns, for example, the user's past behavior history. The personalization unit learns user behavior patterns using machine learning algorithms. For example, the personalization unit learns user preferences based on products the user has previously purchased and pages they have viewed. The personalization unit makes personalized suggestions based on the learned behavior patterns. For example, the personalization unit suggests products related to products the user has previously purchased. The personalization unit learns user behavior patterns in real time and provides a personalized experience. For example, the personalization unit provides relevant information based on the page the user is currently viewing. This improves user engagement by learning user behavior patterns and providing a personalized experience. Some or all of the above processing in the personalization unit may be performed using, for example, AI, or not using AI. For example, the personalization unit can input the user's behavior history into a generating AI and have the generating AI generate personalized suggestions.

[0036] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk may suggest relevant input methods by referring to content the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI select the optimal reception method.

[0037] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can accept only relevant information based on the user's current situation. The reception unit can also prioritize the acceptance of relevant input based on the user's areas of interest. For example, the reception unit can filter out unnecessary information based on the user's current situation and areas of interest. This allows for the priority acceptance of highly relevant information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0038] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving inputs related to that region. The reception unit can also filter relevant information based on the user's geographical location. For example, if the user is on the move, the reception unit will accept the most relevant inputs based on the user's current location. This allows for the priority of receiving highly relevant inputs by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI and have the generating AI identify highly relevant inputs.

[0039] The reception unit can analyze the user's social media activity when receiving input and accept relevant input. For example, the reception unit can identify topics of interest from the user's social media activity and prioritize accepting relevant input. The reception unit can also analyze the user's social media activity and filter relevant information. For example, the reception unit can suggest the optimal input method based on the user's social media activity. This allows for the priority acceptance of relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI identify relevant input.

[0040] The emotion analysis unit can optimize its analysis algorithm by referring to the user's past emotional history during emotion analysis. For example, the emotion analysis unit can optimize its emotion analysis algorithm by referring to the user's past emotional history. The emotion analysis unit can also optimize its analysis algorithm by extracting specific patterns from the user's past emotional history. For example, the emotion analysis unit can improve the accuracy of emotion analysis by analyzing the user's past emotional history. This allows the analysis algorithm to be optimized by referring to the user's past emotional history. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the user's past emotional history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0041] The emotion analysis unit can perform emotion analysis while considering the user's attribute information. For example, the emotion analysis unit performs emotion analysis while considering the user's attribute information such as age, gender, and occupation. The emotion analysis unit can also improve the accuracy of emotion analysis based on the user's attribute information. For example, the emotion analysis unit refers to the user's attribute information and applies the optimal emotion analysis algorithm. This improves the accuracy of emotion analysis by considering the user's attribute information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input user attribute information data into a generating AI and have the generating AI perform the emotion analysis.

[0042] The sentiment analysis unit can perform sentiment analysis while considering the geographical distribution of users. For example, the sentiment analysis unit can improve the accuracy of sentiment analysis based on the geographical distribution of users. The sentiment analysis unit can also apply the optimal sentiment analysis algorithm while considering the geographical distribution of users. For example, the sentiment analysis unit can refer to the geographical distribution of users and adjust the results of the sentiment analysis. This improves the accuracy of sentiment analysis by considering the geographical distribution of users. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the sentiment analysis.

[0043] The sentiment analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during sentiment analysis. For example, the sentiment analysis unit can improve the accuracy of sentiment analysis by referring to the user's relevant literature. The sentiment analysis unit can also extract specific patterns from the user's relevant literature and optimize the sentiment analysis algorithm. For example, the sentiment analysis unit analyzes the user's relevant literature and adjusts the results of the sentiment analysis. This improves the accuracy of sentiment analysis by referring to the user's relevant literature. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the sentiment analysis.

[0044] The response generation unit can adjust the level of detail in the response based on the user's importance when generating a response. For example, if the user is an important customer, the response generation unit will generate a detailed response. If the user is a general customer, the response generation unit can also generate a standard response. For example, if the user is a new customer, the response generation unit will generate a concise response. This allows for an appropriate response by adjusting the level of detail in the response based on the user's importance. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the response.

[0045] The response generation unit can apply different response algorithms depending on the user's category when generating a response. For example, if the user is a business user, the response generation unit can apply a business-oriented response algorithm. If the user is a general consumer, the response generation unit can also apply a consumer-oriented response algorithm. For example, if the user is an educator, the response generation unit can apply an education-oriented response algorithm. This enables appropriate responses by applying different response algorithms depending on the user's category. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user category data into a generation AI and have the generation AI execute the application of the response algorithm.

[0046] The response generation unit can determine the priority of responses based on the user's submission timing when generating responses. For example, if the user is in a hurry, the response generation unit will generate a response quickly. The response generation unit can also generate a standard response if the user is submitting at the normal time. For example, if the user has missed the submission deadline, the response generation unit will take special action. This ensures that responses are provided at the appropriate time by determining the priority of responses based on the user's submission timing. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user submission timing data into a generation AI and have the generation AI determine the priority of responses.

[0047] The response generation unit can adjust the order of responses based on the user's relevance when generating responses. For example, the response generation unit will prioritize generating responses for users who are important customers. The response generation unit can also generate responses in a standard order if the user is a general customer. For example, the response generation unit will postpone generating responses for new customers. This allows important responses to be prioritized by adjusting the order of responses based on the user's relevance. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the response order.

[0048] The Real-Time Data Insights unit can predict current insights by referring to past data when providing data insights. For example, the Real-Time Data Insights unit can refer to the user's past data to predict current insights. The Real-Time Data Insights unit can also extract specific patterns from past data to predict current insights. For example, the Real-Time Data Insights unit can analyze the user's past data to provide optimal insights. By referring to past data to predict current insights, more accurate data insights can be provided. Some or all of the above processing in the Real-Time Data Insights unit may be performed using AI, for example, or without AI. For example, the Real-Time Data Insights unit can input past data into a generating AI and have the generating AI perform the prediction of current insights.

[0049] The Real-Time Data Insights Unit can apply different insight analysis methods to each user category when providing data insights. For example, if the user is a business user, the Real-Time Data Insights Unit can apply a business-oriented insight analysis method. If the user is a general consumer, the Real-Time Data Insights Unit can also apply a consumer-oriented insight analysis method. For example, if the user is an educator, the Real-Time Data Insights Unit can apply an education-oriented insight analysis method. By applying different insight analysis methods to each user category, more appropriate data insights can be provided. Some or all of the above processing in the Real-Time Data Insights Unit may be performed using AI, for example, or without AI. For example, the Real-Time Data Insights Unit can input user category data into a generating AI and have the generating AI execute the application of insight analysis methods.

[0050] The Real-Time Data Insights unit can analyze changes in insights based on the user's submission timing when providing data insights. For example, if the user is in a hurry, the Real-Time Data Insights unit can quickly analyze changes in insights. The Real-Time Data Insights unit can also analyze standard changes in insights if the user is submitting at a normal time. For example, if the Real-Time Data Insights unit has missed the submission deadline, it can take special action. This enables the provision of insights at the appropriate time by analyzing changes in insights based on the user's submission timing. Some or all of the above processing in the Real-Time Data Insights unit may be performed using AI, for example, or not using AI. For example, the Real-Time Data Insights unit can input user submission timing data into a generating AI and have the generating AI perform the analysis of changes in insights.

[0051] The Real-Time Data Insights Unit can analyze insights by referring to the user's relevant market data when providing data insights. For example, the Real-Time Data Insights Unit can refer to the user's relevant market data and analyze insights. The Real-Time Data Insights Unit can also extract specific patterns from relevant market data and analyze insights. For example, the Real-Time Data Insights Unit analyzes the user's relevant market data and provides optimal insights. This allows for the provision of more accurate data insights by referring to the user's relevant market data and analyzing insights. Some or all of the above processing in the Real-Time Data Insights Unit may be performed using AI, for example, or without AI. For example, the Real-Time Data Insights Unit can input the user's relevant market data into a generating AI and have the generating AI perform the insight analysis.

[0052] The multilingual support unit can select the optimal support method by referring to the user's past language history when providing multilingual support. For example, the multilingual support unit can refer to the user's past language history and select the optimal multilingual support method. The multilingual support unit can also extract specific patterns from the user's past language history and optimize the multilingual support method. For example, the multilingual support unit can analyze the user's past language history and provide the optimal multilingual support. This allows the optimal multilingual support method to be selected by referring to the user's past language history. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's past language history data into a generating AI and have the generating AI select the optimal support method.

[0053] The multilingual support unit can take user attribute information into consideration when providing multilingual support. For example, the multilingual support unit considers user attribute information such as age, gender, and occupation when providing multilingual support. The multilingual support unit can also improve the accuracy of multilingual support based on user attribute information. For example, the multilingual support unit refers to user attribute information and applies the optimal multilingual support method. This improves the accuracy of multilingual support by considering user attribute information. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input user attribute information data into a generating AI and have the generating AI perform the multilingual support.

[0054] The multilingual support unit can take into account the geographical distribution of users when providing multilingual support. For example, the multilingual support unit can improve the accuracy of multilingual support based on the geographical distribution of users. The multilingual support unit can also apply the optimal multilingual support method by taking into account the geographical distribution of users. For example, the multilingual support unit can refer to the geographical distribution of users and adjust the results of multilingual support. This improves the accuracy of multilingual support by taking into account the geographical distribution of users. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit can input user geographical distribution data into a generating AI and have the generating AI perform the multilingual support.

[0055] The multilingual support unit can improve the accuracy of its multilingual support by referring to the user's relevant literature during the multilingual support process. For example, the multilingual support unit can improve the accuracy of its multilingual support by referring to the user's relevant literature. The multilingual support unit can also extract specific patterns from the user's relevant literature and optimize the multilingual support method. For example, the multilingual support unit analyzes the user's relevant literature and provides the optimal multilingual support. This improves the accuracy of its multilingual support by referring to the user's relevant literature. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's relevant literature data into a generating AI and have the generating AI perform the multilingual support.

[0056] The personalization unit can select the optimal personalization method by referring to the user's past behavior history during personalization. For example, the personalization unit can refer to the user's past behavior history and select the optimal personalization method. The personalization unit can also extract specific patterns from the user's past behavior history and optimize the personalization method. For example, the personalization unit can analyze the user's past behavior history and provide optimal personalization. This allows the personalization unit to select the optimal personalization method by referring to the user's past behavior history. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal personalization method.

[0057] The personalization unit can customize the means of personalization based on the user's current situation during the personalization process. For example, the personalization unit can provide the optimal personalization means based on the user's current situation. The personalization unit can also customize the means of personalization by taking the user's current situation into consideration. For example, the personalization unit can adjust the means of personalization according to the user's current situation. This allows for more appropriate personalization by customizing the means of personalization based on the user's current situation. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of personalization.

[0058] The personalization unit can select the optimal personalization method by considering the user's geographical location information during personalization. For example, the personalization unit selects the optimal personalization method based on the user's geographical location information. The personalization unit can also customize the means of personalization by considering the user's geographical location information. For example, the personalization unit refers to the user's geographical location information and provides optimal personalization. This allows the personalization unit to select the optimal personalization method by considering the user's geographical location information. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal personalization method.

[0059] The personalization unit can analyze the user's social media activity and propose personalization methods during the personalization process. For example, the personalization unit can analyze the user's social media activity and propose the optimal personalization method. The personalization unit can also extract specific patterns from the user's social media activity and optimize the personalization method. For example, the personalization unit can refer to the user's social media activity and provide optimal personalization. This allows the personalization unit to propose the optimal personalization method by analyzing the user's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of personalization methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can generate the most appropriate response by referencing the user's past behavior history based on the user's input. For example, if a user has frequently searched for a particular product in the past, the reception desk will prioritize providing information related to that product. Similarly, if a user has made inquiries during specific time periods in the past, the reception desk can provide information related to those times. This allows for the provision of more personalized responses by leveraging the user's past behavior history.

[0062] The Real-Time Data Insights unit can analyze the user's current situation in real time based on their input and provide optimal information. For example, if a user is looking for information about the current weather, the Real-Time Data Insights unit will provide that information. Similarly, if a user is looking for information about current traffic conditions, the Real-Time Data Insights unit can provide that information. This allows for a rapid response to the user's current situation.

[0063] The multilingual support unit can select the most appropriate translation method by referencing the user's past language history based on their input. For example, if a user has frequently used English in the past, the multilingual support unit will prioritize providing a response in English. It can also provide a response in a specific language if the user has used that language in the past. This allows the system to leverage the user's past language history to provide more appropriate translations.

[0064] The personalization unit can analyze the user's current situation in real time based on their input and provide optimal, personalized suggestions. For example, if a user is seeking information about their current health status, the personalization unit will provide suggestions based on that status. Similarly, if a user is seeking information based on their current interests, the unit can provide suggestions based on those interests. This allows for a rapid response to the user's current situation.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives user input in natural language. For example, it can accept text input from the user or voice input. In the case of voice input, the reception desk converts the voice to text for reception. The reception desk can also accept input in chat format in real time. Step 2: The emotion analysis unit analyzes the input received by the reception unit. The emotion analysis unit uses natural language processing technology to analyze the user's input and uses emotional intelligence to estimate the user's emotions. For example, the emotion analysis unit classifies emotions such as joy, sadness, and anger from the user's input. Step 3: The response generation unit generates a response based on the input analyzed by the emotion analysis unit. The response generation unit generates a response that corresponds to the user's emotions and provides an empathetic response. For example, if the user is feeling dissatisfied, it generates a response that empathizes with that emotion. It also generates a response that provides appropriate information based on the user's input.

[0067] (Example of form 2) The system according to an embodiment of the present invention is an advanced solution that leverages natural language processing, emotional intelligence, and machine learning to enable robots to interact empathetically with users like humans. This system provides personalized interactive experiences in various industries such as retail, healthcare, education, and business. The system provides real-time data insights, multilingual support, customer service, employee support, and assistance with training tasks. With 24 / 7 availability and seamless integration into existing systems, the system improves engagement, operational efficiency, and user satisfaction. For example, the system accepts user input in natural language. For instance, if a user inputs "I want to check the stock of a product," the system understands the request and generates an appropriate response. Next, the system leverages emotional intelligence to analyze the user's emotions and provide an empathetic response. For example, if a user is dissatisfied, the system recognizes that emotion and takes appropriate action. Furthermore, the system uses machine learning to learn user behavior patterns and provide a personalized experience. For example, based on past conversation history, it can suggest products and services that the user might prefer. The system also provides real-time data insights to quickly respond to user needs. The system is multilingual and can accommodate users who speak different languages. This allows for consistent service delivery to a global user base. In this way, the system can be used for various purposes, such as customer service, employee support, and training task assistance, improving engagement, operational efficiency, and user satisfaction. The system enables empathetic dialogue by receiving user input in natural language, performing sentiment analysis, and generating responses.

[0068] The system according to this embodiment comprises a reception unit, an emotion analysis unit, and a response generation unit. The reception unit receives user input in natural language. For example, the reception unit receives content entered by the user as text. The reception unit can also receive voice input. For example, if a user says "I want to check the stock of the product" by voice, the reception unit converts the voice into text and receives it. Furthermore, the reception unit can receive user input in real time. For example, it can receive input in chat format. The emotion analysis unit analyzes the input received by the reception unit. For example, the emotion analysis unit analyzes the content of the user's input using natural language processing technology. The emotion analysis unit utilizes emotional intelligence to analyze the user's emotions. For example, the emotion analysis unit uses an algorithm to estimate emotions from the content of the user's input. The emotion analysis unit can classify the user's emotions. For example, the emotion analysis unit classifies emotions such as joy, sadness, and anger from the content of the user's input. The response generation unit generates a response based on the input analyzed by the emotion analysis unit. The response generation unit generates a response that corresponds to the user's emotions, for example. Based on the emotions analyzed by the emotion analysis unit, the response generation unit generates an empathetic response. For example, if the user is feeling dissatisfied, the response generation unit generates a response that empathizes with that emotion. The response generation unit generates a response that provides appropriate information based on the user's input. For example, if the user inputs that they want to check the stock of a product, the response generation unit generates a response that provides stock information. Thus, the system according to this embodiment can engage in empathetic dialogue by receiving user input in natural language, performing emotion analysis, and generating a response.

[0069] The reception desk accepts user input in natural language. For example, it accepts text input from users. Specifically, it receives text messages entered by users in a chat window in real time and incorporates them into the system. The reception desk can also accept voice input. For example, if a user says "I want to check the product inventory" by voice, the reception desk converts that voice into text and accepts it. Speech recognition technology is used to process voice input. Speech recognition technology analyzes the user's speech and converts the voice signal into text data. This allows voice input to be treated the same as text input. Furthermore, the reception desk can accept user input in real time. For example, it can accept input in chat format. In chat format, the text entered by the user is immediately sent to the system, and the system processes that input in real time. This allows for smooth interaction with the user and enables quick responses. To accurately accept user input, the reception desk can also check the consistency and grammar of the input content. For example, if the input content contains typos or omissions, the reception desk can have a function to automatically correct them. This ensures that user input is accurately captured by the system, allowing subsequent processing to proceed smoothly.

[0070] The sentiment analysis unit analyzes the input received by the reception unit. The sentiment analysis unit analyzes user input using, for example, natural language processing (NLP) technology. NLP is a technology that analyzes text data and understands its grammatical structure and meaning. The sentiment analysis unit utilizes emotional intelligence to analyze the user's emotions. Emotional intelligence is an algorithm that estimates emotions from text data and can classify the user's emotions. For example, the sentiment analysis unit classifies emotions such as joy, sadness, and anger from the user's input. The sentiment analysis unit can use machine learning models for emotion classification. Machine learning models have the ability to learn from large amounts of text data and accurately classify emotions. The sentiment analysis unit can also analyze user input and evaluate the intensity and nuance of emotions. For example, if a user inputs "very happy," the sentiment analysis unit highly values ​​the intensity of that emotion and uses this information to generate an appropriate response. The sentiment analysis unit analyzes the user's emotions in real time and reflects this in the system's overall response generation process. This allows the emotion analysis unit to accurately understand the user's emotions and provide a foundation for empathetic dialogue. Furthermore, the emotion analysis unit can perform more accurate emotion analysis by utilizing past dialogue history and user profile information. As a result, the emotion analysis unit can deeply understand the user's emotions and generate responses that meet their individual needs.

[0071] The response generation unit generates a response based on the input analyzed by the emotion analysis unit. For example, the response generation unit generates a response that corresponds to the user's emotions. Specifically, it generates an empathetic response based on the emotions analyzed by the emotion analysis unit. For example, if the user is feeling dissatisfied, the response generation unit generates a response that empathizes with that emotion. The response generation unit generates a response that provides appropriate information based on the user's input. For example, if the user inputs that they want to check the stock of a product, the response generation unit generates a response that provides stock information. The response generation unit uses natural language generation technology to generate a response to the user in natural language. Natural language generation technology is a technology that generates text data and can provide users with responses that are easy to understand and friendly. The response generation unit can adjust the tone and style of the response according to the user's emotions and input. For example, if the user is angry, the response generation unit will respond in a calm tone and choose words that will soothe the user's emotions. The response generation unit can also search for relevant information based on the user's input and generate an appropriate response. For example, if the user asks a question about a specific product, the response generation unit will search for detailed information about that product and provide it to the user. This allows the response generation unit to quickly generate appropriate responses that meet user needs, facilitating smooth communication with users. Furthermore, the response generation unit can collect user feedback and continuously improve the quality of its responses. As a result, the response generation unit can consistently provide high-quality responses and improve user satisfaction.

[0072] The system includes a Real-Time Data Insights unit that responds quickly to user needs. For example, the Real-Time Data Insights unit analyzes user input in real time to identify user needs. The Real-Time Data Insights unit utilizes user input and behavioral history as data collection methods. For example, it identifies current needs based on keywords the user has previously searched for and purchase history. The Real-Time Data Insights unit uses analytical algorithms to quickly identify user needs. For example, it uses machine learning algorithms to learn user behavior patterns and predict needs. Based on user needs, the Real-Time Data Insights unit provides appropriate information. For example, if a user inputs "I'm looking for a new smartphone," the Real-Time Data Insights unit suggests the optimal smartphone based on the user's past purchase and search history. This improves user satisfaction by responding quickly to user needs. Some or all of the above-described processes in the Real-Time Data Insights unit may be performed using AI, or not. For example, the Real-Time Data Insights unit can input user input into a generating AI and have the generating AI identify user needs.

[0073] The system includes a multilingual support unit that accommodates users who speak different languages. The multilingual support unit, for example, translates user input into different languages. It uses translation algorithms to translate user input in real time. For example, it uses a machine translation algorithm to translate user input from English to Japanese. The multilingual support unit can be configured to support multiple languages, such as English, Japanese, French, and Spanish. Based on the user's language settings, the multilingual support unit generates responses in the appropriate language. For example, if the user selects Japanese, the multilingual support unit generates a response in Japanese. This allows for consistent service to a global user base by accommodating users who speak different languages. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input user input into a generating AI and have the generating AI perform the translation.

[0074] The system includes a personalization unit that learns user behavior patterns and provides a personalized experience. The personalization unit learns, for example, the user's past behavior history. The personalization unit learns user behavior patterns using machine learning algorithms. For example, the personalization unit learns user preferences based on products the user has previously purchased and pages they have viewed. The personalization unit makes personalized suggestions based on the learned behavior patterns. For example, the personalization unit suggests products related to products the user has previously purchased. The personalization unit learns user behavior patterns in real time and provides a personalized experience. For example, the personalization unit provides relevant information based on the page the user is currently viewing. This improves user engagement by learning user behavior patterns and providing a personalized experience. Some or all of the above processing in the personalization unit may be performed using, for example, AI, or not using AI. For example, the personalization unit can input the user's behavior history into a generating AI and have the generating AI generate personalized suggestions.

[0075] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can delay the input acceptance timing to give the user time to relax. If the user is in a hurry, the reception unit can also speed up the input acceptance timing to respond quickly. For example, if the user is feeling anxious, the reception unit can adjust the input acceptance timing to provide reassurance. By adjusting the input acceptance timing according to the user's emotions, it becomes possible to accept input at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk may suggest relevant input methods by referring to content the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI select the optimal reception method.

[0077] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can accept only relevant information based on the user's current situation. The reception unit can also prioritize the acceptance of relevant input based on the user's areas of interest. For example, the reception unit can filter out unnecessary information based on the user's current situation and areas of interest. This allows for the priority acceptance of highly relevant information by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important inputs. If the user is relaxed, the reception unit may also prioritize normal inputs. For example, if the user is in a hurry, the reception unit will prioritize urgent inputs. This ensures that important inputs are received preferentially by prioritizing inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving inputs related to that region. The reception unit can also filter relevant information based on the user's geographical location. For example, if the user is on the move, the reception unit will accept the most relevant inputs based on the user's current location. This allows for the priority of receiving highly relevant inputs by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI and have the generating AI identify highly relevant inputs.

[0080] The reception unit can analyze the user's social media activity when receiving input and accept relevant input. For example, the reception unit can identify topics of interest from the user's social media activity and prioritize accepting relevant input. The reception unit can also analyze the user's social media activity and filter relevant information. For example, the reception unit can suggest the optimal input method based on the user's social media activity. This allows for the priority acceptance of relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI identify relevant input.

[0081] The emotion analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the emotion analysis unit can collect detailed data to improve the accuracy of the emotion analysis. If the user is relaxed, the emotion analysis unit can also refer to past data to improve the accuracy of the emotion analysis. For example, if the user is in a hurry, the emotion analysis unit can use real-time data to improve the accuracy of the emotion analysis. This allows for more accurate emotion analysis by improving the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The emotion analysis unit can optimize its analysis algorithm by referring to the user's past emotional history during emotion analysis. For example, the emotion analysis unit can optimize its emotion analysis algorithm by referring to the user's past emotional history. The emotion analysis unit can also optimize its analysis algorithm by extracting specific patterns from the user's past emotional history. For example, the emotion analysis unit can improve the accuracy of emotion analysis by analyzing the user's past emotional history. This allows the analysis algorithm to be optimized by referring to the user's past emotional history. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the user's past emotional history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0083] The emotion analysis unit can perform emotion analysis while considering the user's attribute information. For example, the emotion analysis unit performs emotion analysis while considering the user's attribute information such as age, gender, and occupation. The emotion analysis unit can also improve the accuracy of emotion analysis based on the user's attribute information. For example, the emotion analysis unit refers to the user's attribute information and applies the optimal emotion analysis algorithm. This improves the accuracy of emotion analysis by considering the user's attribute information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input user attribute information data into a generating AI and have the generating AI perform the emotion analysis.

[0084] The emotion analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the emotion analysis unit can provide a simple and highly visible display method. If the user is relaxed, the emotion analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the emotion analysis unit can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The sentiment analysis unit can perform sentiment analysis while considering the geographical distribution of users. For example, the sentiment analysis unit can improve the accuracy of sentiment analysis based on the geographical distribution of users. The sentiment analysis unit can also apply the optimal sentiment analysis algorithm while considering the geographical distribution of users. For example, the sentiment analysis unit can refer to the geographical distribution of users and adjust the results of the sentiment analysis. This improves the accuracy of sentiment analysis by considering the geographical distribution of users. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the sentiment analysis.

[0086] The sentiment analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during sentiment analysis. For example, the sentiment analysis unit can improve the accuracy of sentiment analysis by referring to the user's relevant literature. The sentiment analysis unit can also extract specific patterns from the user's relevant literature and optimize the sentiment analysis algorithm. For example, the sentiment analysis unit analyzes the user's relevant literature and adjusts the results of the sentiment analysis. This improves the accuracy of sentiment analysis by referring to the user's relevant literature. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the sentiment analysis.

[0087] The response generation unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is stressed, the response generation unit will generate a calm response. If the user is relaxed, the response generation unit can also generate a cheerful response. For example, if the user is in a hurry, the response generation unit will generate a quick and concise response. By adjusting the way it expresses its response based on the user's emotions, a more empathetic response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the response generation unit may be performed using AI, for example, or not using AI. For example, the response generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The response generation unit can adjust the level of detail in the response based on the user's importance when generating a response. For example, if the user is an important customer, the response generation unit will generate a detailed response. If the user is a general customer, the response generation unit can also generate a standard response. For example, if the user is a new customer, the response generation unit will generate a concise response. This allows for an appropriate response by adjusting the level of detail in the response based on the user's importance. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the response.

[0089] The response generation unit can apply different response algorithms depending on the user's category when generating a response. For example, if the user is a business user, the response generation unit can apply a business-oriented response algorithm. If the user is a general consumer, the response generation unit can also apply a consumer-oriented response algorithm. For example, if the user is an educator, the response generation unit can apply an education-oriented response algorithm. This enables appropriate responses by applying different response algorithms depending on the user's category. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user category data into a generation AI and have the generation AI execute the application of the response algorithm.

[0090] The response generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is stressed, the response generation unit will generate a short, to-the-point response. If the user is relaxed, the response generation unit can also generate a longer response that includes detailed explanations. For example, if the user is in a hurry, the response generation unit will generate a quick and concise response. By adjusting the length of the response based on the user's emotions, an appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the response generation unit may be performed using AI or not. For example, the response generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The response generation unit can determine the priority of responses based on the user's submission timing when generating responses. For example, if the user is in a hurry, the response generation unit will generate a response quickly. The response generation unit can also generate a standard response if the user is submitting at the normal time. For example, if the user has missed the submission deadline, the response generation unit will take special action. This ensures that responses are provided at the appropriate time by determining the priority of responses based on the user's submission timing. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user submission timing data into a generation AI and have the generation AI determine the priority of responses.

[0092] The response generation unit can adjust the order of responses based on the user's relevance when generating responses. For example, the response generation unit will prioritize generating responses for users who are important customers. The response generation unit can also generate responses in a standard order if the user is a general customer. For example, the response generation unit will postpone generating responses for new customers. This allows important responses to be prioritized by adjusting the order of responses based on the user's relevance. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the response order.

[0093] The Real-Time Data Insights unit can estimate the user's emotions and adjust how data insights are provided based on the estimated emotions. For example, if the user is stressed, the Real-Time Data Insights unit can provide simple and easy-to-understand data insights. If the user is relaxed, the Real-Time Data Insights unit can also provide detailed data insights. For example, if the user is in a hurry, the Real-Time Data Insights unit can provide concise data insights. By adjusting how data insights are provided based on the user's emotions, more appropriate data insights can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Real-Time Data Insights unit may be performed using AI or not using AI. For example, the Real-Time Data Insights unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The Real-Time Data Insights unit can predict current insights by referring to past data when providing data insights. For example, the Real-Time Data Insights unit can refer to the user's past data to predict current insights. The Real-Time Data Insights unit can also extract specific patterns from past data to predict current insights. For example, the Real-Time Data Insights unit can analyze the user's past data to provide optimal insights. By referring to past data to predict current insights, more accurate data insights can be provided. Some or all of the above processing in the Real-Time Data Insights unit may be performed using AI, for example, or without AI. For example, the Real-Time Data Insights unit can input past data into a generating AI and have the generating AI perform the prediction of current insights.

[0095] The Real-Time Data Insights Unit can apply different insight analysis methods to each user category when providing data insights. For example, if the user is a business user, the Real-Time Data Insights Unit can apply a business-oriented insight analysis method. If the user is a general consumer, the Real-Time Data Insights Unit can also apply a consumer-oriented insight analysis method. For example, if the user is an educator, the Real-Time Data Insights Unit can apply an education-oriented insight analysis method. By applying different insight analysis methods to each user category, more appropriate data insights can be provided. Some or all of the above processing in the Real-Time Data Insights Unit may be performed using AI, for example, or without AI. For example, the Real-Time Data Insights Unit can input user category data into a generating AI and have the generating AI execute the application of insight analysis methods.

[0096] The Real-Time Data Insights unit can estimate the user's emotions and adjust the importance of insights based on the estimated user emotions. For example, if the user is stressed, the Real-Time Data Insights unit will prioritize providing important insights. If the user is relaxed, the Real-Time Data Insights unit can also provide normal insights. For example, if the user is in a hurry, the Real-Time Data Insights unit will prioritize providing urgent insights. This allows for the prioritization of important insights by adjusting their importance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Real-Time Data Insights unit may be performed using AI or not using AI. For example, the Real-Time Data Insights unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The Real-Time Data Insights unit can analyze changes in insights based on the user's submission timing when providing data insights. For example, if the user is in a hurry, the Real-Time Data Insights unit can quickly analyze changes in insights. The Real-Time Data Insights unit can also analyze standard changes in insights if the user is submitting at a normal time. For example, if the Real-Time Data Insights unit has missed the submission deadline, it can take special action. This enables the provision of insights at the appropriate time by analyzing changes in insights based on the user's submission timing. Some or all of the above processing in the Real-Time Data Insights unit may be performed using AI, for example, or not using AI. For example, the Real-Time Data Insights unit can input user submission timing data into a generating AI and have the generating AI perform the analysis of changes in insights.

[0098] The Real-Time Data Insights Unit can analyze insights by referring to the user's relevant market data when providing data insights. For example, the Real-Time Data Insights Unit can refer to the user's relevant market data and analyze insights. The Real-Time Data Insights Unit can also extract specific patterns from relevant market data and analyze insights. For example, the Real-Time Data Insights Unit analyzes the user's relevant market data and provides optimal insights. This allows for the provision of more accurate data insights by referring to the user's relevant market data and analyzing insights. Some or all of the above processing in the Real-Time Data Insights Unit may be performed using AI, for example, or without AI. For example, the Real-Time Data Insights Unit can input the user's relevant market data into a generating AI and have the generating AI perform the insight analysis.

[0099] The multilingual support unit can estimate the user's emotions and adjust its multilingual support methods based on the estimated emotions. For example, if the user is stressed, the multilingual support unit can provide simple and highly visible multilingual support. If the user is relaxed, the multilingual support unit can also provide detailed multilingual support. For example, if the user is in a hurry, the multilingual support unit can provide concise multilingual support. By adjusting the multilingual support methods based on the user's emotions, more appropriate multilingual support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or not using AI. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The multilingual support unit can select the optimal support method by referring to the user's past language history when providing multilingual support. For example, the multilingual support unit can refer to the user's past language history and select the optimal multilingual support method. The multilingual support unit can also extract specific patterns from the user's past language history and optimize the multilingual support method. For example, the multilingual support unit can analyze the user's past language history and provide the optimal multilingual support. This allows the optimal multilingual support method to be selected by referring to the user's past language history. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's past language history data into a generating AI and have the generating AI select the optimal support method.

[0101] The multilingual support unit can take user attribute information into consideration when providing multilingual support. For example, the multilingual support unit considers user attribute information such as age, gender, and occupation when providing multilingual support. The multilingual support unit can also improve the accuracy of multilingual support based on user attribute information. For example, the multilingual support unit refers to user attribute information and applies the optimal multilingual support method. This improves the accuracy of multilingual support by considering user attribute information. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input user attribute information data into a generating AI and have the generating AI perform the multilingual support.

[0102] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the user is stressed, the multilingual support unit will prioritize providing important multilingual support. If the user is relaxed, the multilingual support unit can also provide normal multilingual support. For example, if the user is in a hurry, the multilingual support unit will prioritize providing urgent multilingual support. This ensures that important multilingual support is prioritized by determining the priority of multilingual support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI or not using AI. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The multilingual support unit can take into account the geographical distribution of users when providing multilingual support. For example, the multilingual support unit can improve the accuracy of multilingual support based on the geographical distribution of users. The multilingual support unit can also apply the optimal multilingual support method by taking into account the geographical distribution of users. For example, the multilingual support unit can refer to the geographical distribution of users and adjust the results of multilingual support. This improves the accuracy of multilingual support by taking into account the geographical distribution of users. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit can input user geographical distribution data into a generating AI and have the generating AI perform the multilingual support.

[0104] The multilingual support unit can improve the accuracy of its multilingual support by referring to the user's relevant literature during the multilingual support process. For example, the multilingual support unit can improve the accuracy of its multilingual support by referring to the user's relevant literature. The multilingual support unit can also extract specific patterns from the user's relevant literature and optimize the multilingual support method. For example, the multilingual support unit analyzes the user's relevant literature and provides the optimal multilingual support. This improves the accuracy of its multilingual support by referring to the user's relevant literature. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's relevant literature data into a generating AI and have the generating AI perform the multilingual support.

[0105] The personalization unit can estimate the user's emotions and adjust the personalization method based on the estimated emotions. For example, if the user is stressed, the personalization unit can provide simple and highly visible personalization. If the user is relaxed, the personalization unit can also provide detailed personalization. For example, if the user is in a hurry, the personalization unit can provide concise personalization. This allows for more appropriate personalization by adjusting the personalization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personalization unit may be performed using AI or not using AI. For example, the personalization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The personalization unit can select the optimal personalization method by referring to the user's past behavior history during personalization. For example, the personalization unit can refer to the user's past behavior history and select the optimal personalization method. The personalization unit can also extract specific patterns from the user's past behavior history and optimize the personalization method. For example, the personalization unit can analyze the user's past behavior history and provide optimal personalization. This allows the personalization unit to select the optimal personalization method by referring to the user's past behavior history. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal personalization method.

[0107] The personalization unit can customize the means of personalization based on the user's current situation during the personalization process. For example, the personalization unit can provide the optimal personalization means based on the user's current situation. The personalization unit can also customize the means of personalization by taking the user's current situation into consideration. For example, the personalization unit can adjust the means of personalization according to the user's current situation. This allows for more appropriate personalization by customizing the means of personalization based on the user's current situation. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of personalization.

[0108] The personalization unit can estimate the user's emotions and determine the priority of personalization based on the estimated emotions. For example, if the user is stressed, the personalization unit will prioritize important personalization. If the user is relaxed, the personalization unit can also provide normal personalization. For example, if the user is in a hurry, the personalization unit will prioritize urgent personalization. This allows for the priority of important personalization by determining the priority of personalization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personalization unit may be performed using AI or not using AI. For example, the personalization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The personalization unit can select the optimal personalization method by considering the user's geographical location information during personalization. For example, the personalization unit selects the optimal personalization method based on the user's geographical location information. The personalization unit can also customize the means of personalization by considering the user's geographical location information. For example, the personalization unit refers to the user's geographical location information and provides optimal personalization. This allows the personalization unit to select the optimal personalization method by considering the user's geographical location information. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal personalization method.

[0110] The personalization unit can analyze the user's social media activity and propose personalization methods during the personalization process. For example, the personalization unit can analyze the user's social media activity and propose the optimal personalization method. The personalization unit can also extract specific patterns from the user's social media activity and optimize the personalization method. For example, the personalization unit can refer to the user's social media activity and provide optimal personalization. This allows the personalization unit to propose the optimal personalization method by analyzing the user's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of personalization methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can generate the most appropriate response by referencing the user's past behavior history based on the user's input. For example, if a user has frequently searched for a particular product in the past, the reception desk will prioritize providing information related to that product. Similarly, if a user has made inquiries during specific time periods in the past, the reception desk can provide information related to those times. This allows for the provision of more personalized responses by leveraging the user's past behavior history.

[0113] The Real-Time Data Insights unit can analyze the user's current situation in real time based on their input and provide optimal information. For example, if a user is looking for information about the current weather, the Real-Time Data Insights unit will provide that information. Similarly, if a user is looking for information about current traffic conditions, the Real-Time Data Insights unit can provide that information. This allows for a rapid response to the user's current situation.

[0114] The multilingual support unit can select the most appropriate translation method by referencing the user's past language history based on their input. For example, if a user has frequently used English in the past, the multilingual support unit will prioritize providing a response in English. It can also provide a response in a specific language if the user has used that language in the past. This allows the system to leverage the user's past language history to provide more appropriate translations.

[0115] The personalization unit can analyze the user's current situation in real time based on their input and provide optimal, personalized suggestions. For example, if a user is seeking information about their current health status, the personalization unit will provide suggestions based on that status. Similarly, if a user is seeking information based on their current interests, the unit can provide suggestions based on those interests. This allows for a rapid response to the user's current situation.

[0116] The reception system can estimate the user's emotions and adjust the input process based on those estimates. For example, if the user is stressed, the reception system can simplify the input process to help the user relax. Conversely, if the user is relaxed, it can provide a more detailed input method. By adjusting the input process according to the user's emotions, more appropriate input processing becomes possible.

[0117] The emotion analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the emotion analysis unit collects detailed data to improve the accuracy of the analysis. Similarly, if the user is relaxed, it can refer to past data to improve the accuracy of the analysis. This allows for more accurate emotion analysis by improving the accuracy of the analysis based on the user's emotions.

[0118] The response generation unit can estimate the user's emotions and adjust the way the response is expressed based on those emotions. For example, if the user is stressed, the response generation unit will generate a response using a calm expression. Conversely, if the user is relaxed, it can generate a response using a cheerful expression. By adjusting the way the response is expressed based on the user's emotions, a more empathetic response becomes possible.

[0119] The real-time data insights unit can estimate the user's emotions and adjust how data insights are delivered based on those emotions. For example, if a user is stressed, the real-time data insights unit provides simple and easy-to-understand data insights. Conversely, if a user is relaxed, it can provide more detailed data insights. By adjusting how data insights are delivered based on the user's emotions, more relevant data insights can be provided.

[0120] The multilingual support unit can estimate the user's emotions and adjust its multilingual support methods based on those estimates. For example, if the user is stressed, the unit provides simple and highly visible multilingual support. Conversely, if the user is relaxed, it can provide more detailed multilingual support. By adjusting the multilingual support methods based on the user's emotions, more appropriate multilingual support becomes possible.

[0121] The personalization unit can estimate the user's emotions and adjust the personalization method based on those emotions. For example, if the user is stressed, the personalization unit will provide simple and highly visible personalization. Conversely, if the user is relaxed, it can provide more detailed personalization. This allows for more appropriate personalization by adjusting the personalization method based on the user's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk receives user input in natural language. For example, it can accept text input from the user or voice input. In the case of voice input, the reception desk converts the voice to text for reception. The reception desk can also accept input in chat format in real time. Step 2: The emotion analysis unit analyzes the input received by the reception unit. The emotion analysis unit uses natural language processing technology to analyze the user's input and uses emotional intelligence to estimate the user's emotions. For example, the emotion analysis unit classifies emotions such as joy, sadness, and anger from the user's input. Step 3: The response generation unit generates a response based on the input analyzed by the emotion analysis unit. The response generation unit generates a response that corresponds to the user's emotions and provides an empathetic response. For example, if the user is feeling dissatisfied, it generates a response that empathizes with that emotion. It also generates a response that provides appropriate information based on the user's input.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, emotion analysis unit, response generation unit, real-time data insight unit, multilingual support unit, and personalization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input in natural language. The emotion analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's emotions. The response generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates empathetic responses. The real-time data insight unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's needs in real time. The multilingual support unit is implemented by the control unit 46A of the smart device 14 and translates the user's input into different languages. The personalization unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the user's behavior patterns and makes personalized suggestions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, emotion analysis unit, response generation unit, real-time data insight unit, multilingual support unit, and personalization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input in natural language. The emotion analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's emotions. The response generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates empathetic responses. The real-time data insight unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's needs in real time. The multilingual support unit is implemented by the control unit 46A of the smart glasses 214 and translates the user's input into different languages. The personalization unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the user's behavior patterns and makes personalized suggestions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, emotion analysis unit, response generation unit, real-time data insight unit, multilingual support unit, and personalization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input in natural language. The emotion analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's emotions. The response generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates empathetic responses. The real-time data insight unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's needs in real time. The multilingual support unit is implemented by the control unit 46A of the headset terminal 314 and translates the user's input into different languages. The personalization unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the user's behavior patterns and makes personalized suggestions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, emotion analysis unit, response generation unit, real-time data insight unit, multilingual support unit, and personalization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input in natural language. The emotion analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's emotions. The response generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates empathetic responses. The real-time data insight unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the user's needs in real time. The multilingual support unit is implemented by the control unit 46A of the robot 414 and translates the user's input into different languages. The personalization unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the user's behavior patterns and makes personalized suggestions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A reception desk that accepts user input in natural language, An emotion analysis unit analyzes the input received by the aforementioned reception unit, A response generation unit that generates a response based on the input analyzed by the emotion analysis unit, Equipped with A system characterized by the following features. (Note 2) Equipped with a real-time data insights unit to quickly respond to user needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a multilingual support section to accommodate users who speak different languages. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a personalization unit that learns user behavior patterns and provides personalized experiences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system prioritizes accepting input that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned emotion analysis unit, It estimates user emotions and improves the accuracy of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned emotion analysis unit, During sentiment analysis, the analysis algorithm is optimized by referring to the user's past sentiment history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned emotion analysis unit, When performing sentiment analysis, the analysis takes into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned emotion analysis unit, When performing sentiment analysis, the analysis takes into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned emotion analysis unit, During sentiment analysis, we improve the accuracy of the analysis by referring to the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 17) The response generation unit, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The response generation unit, When generating a response, adjust the level of detail in the response based on the user's importance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The response generation unit, When generating a response, different response algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The response generation unit, It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The response generation unit, When generating a response, the system prioritizes responses based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 22) The response generation unit, When generating responses, the order of responses is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned real-time data insight unit is: We estimate user sentiment and adjust how data insights are delivered based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned real-time data insight unit is: When providing data insights, we refer to historical data to predict current insights. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned real-time data insight unit is: When providing data insights, different insight analysis methods are applied to each user category. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned real-time data insight unit is: It estimates user sentiment and adjusts the importance of insights based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned real-time data insight unit is: When providing data insights, we analyze how those insights change based on when users submit them. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned real-time data insight unit is: When providing data insights, we analyze those insights by referring to relevant market data from the user. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the multilingual support method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned multilingual support unit is When implementing multilingual support, the system selects the optimal support method by referring to the user's past language history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned multilingual support unit is When implementing multilingual support, user attribute information should be taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned multilingual support unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned multilingual support unit is When implementing multilingual support, the geographical distribution of users should be taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned multilingual support unit is When providing multilingual support, we improve the accuracy of the support by referring to relevant user literature. The system described in Appendix 3, characterized by the features described herein. (Note 35) The personalization unit described above is It estimates the user's emotions and adjusts the personalization method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The personalization unit described above is During personalization, the system selects the optimal personalization method by referring to the user's past behavior history. The system described in Appendix 4, characterized by the features described herein. (Note 37) The personalization unit described above is During personalization, the means of personalization are customized based on the user's current situation. The system described in Appendix 4, characterized by the features described herein. (Note 38) The personalization unit described above is It estimates the user's emotions and determines personalization priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The personalization unit described above is When personalizing, the system selects the optimal personalization method by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 40) The personalization unit described above is During personalization, the system analyzes the user's social media activity and suggests ways to personalize their experience. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user input in natural language, An emotion analysis unit analyzes the input received by the aforementioned reception unit, A response generation unit that generates a response based on the input analyzed by the emotion analysis unit, Equipped with A system characterized by the following features.

2. Equipped with a real-time data insights unit to quickly respond to user needs. The system according to feature 1.

3. It features a multilingual support section to accommodate users who speak different languages. The system according to feature 1.

4. It features a personalization unit that learns user behavior patterns and provides personalized experiences. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

7. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

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