system

Abstract

The system according to this embodiment aims to realize natural and personalized interactions with the user. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, an emotion analysis unit, and a dialogue unit. The reception unit receives user input. The analysis unit analyzes the data received by the reception unit. The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. The dialogue unit engages in adaptive dialogue based on the emotion information obtained 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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the interaction with the user is limited and it is difficult to provide a personalized experience.

[0005] The system according to the embodiment aims to realize a natural and personalized dialogue with the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an emotion analysis unit, and a dialogue unit. The reception unit receives user input. The analysis unit analyzes the data received by the reception unit. The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. The dialogue unit engages in adaptive dialogue based on the emotion information obtained by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable natural and personalized 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The live chat system according to an embodiment of the present invention is a system that provides a natural and personalized live chat experience with users by utilizing AI technology to realize natural language processing using a multimodal LLM (Large-Scale Language Model), real-time sentiment analysis, and adaptive dialogue. When a user starts a live chat, the system accepts input from the user. This input can include multimodal data such as text, voice, and images. Next, the multimodal LLM analyzes this input to understand the user's intentions and emotions. For example, if the user inputs "I'm tired today," the system understands that the user is tired and generates an appropriate response. Furthermore, it performs real-time sentiment analysis to grasp the user's emotional state. For example, it determines whether the user is happy or sad from the user's tone of voice and facial expressions. Based on this information, the system provides adaptive dialogue with the user. For example, if the user is sad, the system can offer words of comfort. In this way, the system provides a natural and personalized live chat experience with users. This is expected to improve user satisfaction and expand market share. It is also expected that innovation in AI technology will accelerate and bring about cultural transformation through new forms of entertainment. As a result, the live chat system can improve user satisfaction.

[0029] The live chat system according to this embodiment comprises a reception unit, an analysis unit, an emotion analysis unit, and a dialogue unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone and speech recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera and image recognition technology for receiving image input. For example, the reception unit receives text data entered by the user. The reception unit may also receive voice data spoken by the user via a microphone and convert it into text data using speech recognition technology. Furthermore, the reception unit may also receive image data transmitted by the user via a camera and analyze it using image recognition technology. The analysis unit analyzes the data received by the reception unit. The analysis unit may, for example, analyze text data using natural language processing technology. The analysis unit may also analyze voice data using speech recognition technology. Furthermore, the analysis unit may also analyze image data using image recognition technology. For example, the analysis unit analyzes text data entered by the user to understand the user's intent. The analysis unit can also analyze spoken audio data from the user to understand their intent. Furthermore, the analysis unit can analyze image data sent by the user to understand their intent. The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. For example, the emotion analysis unit can analyze the user's voice tone using voice tone analysis technology. It can also analyze the user's facial expressions using facial expression analysis technology. Furthermore, the emotion analysis unit can analyze the user's text data using text analysis technology. For example, the emotion analysis unit can analyze the user's voice tone to determine whether the user is happy or sad. It can also analyze the user's facial expressions to determine whether the user is happy or sad. Furthermore, the emotion analysis unit can analyze the user's text data to determine whether the user is happy or sad.The dialogue unit performs adaptive dialogue based on emotional information obtained by the emotion analysis unit. For example, if the user is happy, the system generates a response that shares that happiness with the user. The dialogue unit can also generate a response that offers comfort to the user if the user is sad. Furthermore, if the user is angry, the system can generate a response that encourages the user to calm down. For example, if the user inputs "I'm tired today," the system will respond, "You must be tired. Please rest well today." The dialogue unit can also respond if the user inputs "Something good happened today," with the system saying, "That's great. Congratulations." Furthermore, if the user inputs "Something sad happened today," the system can respond, "That must have been tough. Please tell me about it." In this way, the live chat system according to the embodiment can provide a natural and personalized live chat experience with the user.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may be equipped with a keyboard or touchscreen for receiving text input. It may also be equipped with a microphone and voice recognition technology for receiving voice input. Furthermore, it may be equipped with a camera and image recognition technology for receiving image input. For example, the reception unit receives text data entered by the user. It can also receive voice data spoken by the user via a microphone and convert it into text data using voice recognition technology. Furthermore, it can receive image data transmitted by the user via a camera and analyze it using image recognition technology. The reception unit works in conjunction with a server with high-speed data processing capabilities to receive text data entered by the user in real time. This allows information entered by the user to be immediately reflected in the system, enabling smooth dialogue. Regarding voice input, noise cancellation technology can be used to remove ambient noise and obtain clear voice data. This improves the accuracy of voice recognition and allows for accurate understanding of the user's intent. Regarding image input, the use of a high-resolution camera allows for the acquisition of image data with fine detail. Furthermore, the image recognition technology employs advanced algorithms using deep learning, enabling accurate analysis of the user's facial expressions and gestures. As a result, the reception unit can handle a variety of user input formats and receive data quickly and accurately.

[0031] The analysis unit analyzes data received by the reception unit. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data entered by a user to understand the user's intent. It can also analyze audio data spoken by a user to understand the user's intent. Furthermore, the analysis unit can analyze image data sent by a user to understand the user's intent. The analysis unit uses natural language processing technology to grammatically and semantically analyze the user's text data to accurately grasp the user's intent and emotions. For example, if a user enters "I'm tired today," the analysis unit extracts the keyword "tired" and understands that the user is feeling tired. Regarding audio data, speech recognition technology is used to convert the audio to text, and then natural language processing technology is used for analysis. This allows for an accurate understanding of the user's statements and the generation of appropriate responses. Regarding image data, image recognition technology is used to analyze the user's facial expressions and gestures, thereby understanding the user's emotions and intentions. For example, if a user sends an image of themselves smiling, the analysis unit can determine that the user is happy. In this way, the analysis unit can comprehensively analyze the user's diverse input data and accurately understand the user's intentions and emotions.

[0032] The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. For example, the emotion analysis unit analyzes the user's voice tone using voice tone analysis technology. It can also analyze the user's facial expressions using facial expression analysis technology. Furthermore, the emotion analysis unit can analyze the user's text data using text analysis technology. For example, the emotion analysis unit analyzes the user's voice tone to determine whether the user is happy or sad. It can also analyze the user's facial expressions to determine whether the user is happy or sad. Furthermore, the emotion analysis unit can analyze the user's text data to determine whether the user is happy or sad. Using voice tone analysis technology, the emotion analysis unit analyzes features such as the pitch, speed, and volume of the user's voice to determine the user's emotional state. For example, a high-pitched, fast voice may indicate excitement or happiness. On the other hand, a low-pitched, slow voice may indicate sadness or fatigue. Regarding facial expression analysis technology, a deep learning algorithm is employed to analyze the feature points of the user's face and detect subtle changes in facial expression. This allows for highly accurate determination of whether a user is expressing emotions such as smiles, anger, or sadness. Regarding text analysis technology, it uses emotion dictionaries and machine learning models to extract emotions from the user's text data. For example, if a user enters "I'm very happy today," the system extracts the keyword "happy," indicating a positive emotion, and determines that the user is happy. This allows the emotion analysis unit to comprehensively analyze diverse user data and accurately understand the user's emotional state.

[0033] The dialogue unit performs adaptive dialogue based on emotional information obtained by the emotion analysis unit. For example, if the user is happy, the system generates a response that shares that happiness. If the user is sad, the system can also generate a response offering words of comfort. Furthermore, if the user is angry, the system can generate a response encouraging the user to calm down. For example, if the user inputs "I'm tired today," the system will respond, "You must be tired. Please rest well today." If the user inputs "Something good happened today," the system can respond, "That's great. Congratulations." Furthermore, if the user inputs "Something sad happened today," the system can respond, "That must have been tough. Please tell me about it." The dialogue unit uses natural language generation technology to generate the optimal response for the user based on the emotional information obtained from the emotion analysis unit. Natural language generation technology includes algorithms for generating appropriate words and phrases according to the user's emotional state and intentions. For example, if a user types "I'm very happy today," the dialogue unit will generate a response such as "That's wonderful! What happened?" to share the user's joy. Similarly, if a user types "I'm very sad today," the dialogue unit will generate a response such as "That must have been tough. Can you tell me what happened?" to empathize with the user's feelings. Furthermore, the dialogue unit can provide more personalized responses by considering the user's past conversation history and individual preferences. For example, if a user has previously discussed a particular topic, it will generate a relevant response based on that information. This allows the dialogue unit to achieve natural and personalized conversations with users, thereby improving user satisfaction.

[0034] The reception unit can accept multimodal data such as text, audio, and images. For example, the reception unit may be equipped with a keyboard or touchscreen for receiving text input. It may also be equipped with a microphone and speech recognition technology for receiving voice input. Furthermore, it may be equipped with a camera and image recognition technology for receiving image input. For example, the reception unit can accept text data entered by a user. It can also receive spoken audio data from a user via a microphone and convert it into text data using speech recognition technology. Furthermore, it can receive image data transmitted by a user via a camera and analyze it using image recognition technology. This allows the reception unit to accept data in various formats. Multimodal data includes, but is not limited to, text, audio, images, and videos. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input text data entered by a user into a generating AI and have the generating AI perform the analysis of the text data.

[0035] The analysis unit can analyze user intent using a multimodal LLM. For example, the analysis unit can analyze text data using natural language processing techniques. It can also analyze audio data using speech recognition techniques. Furthermore, it can analyze image data using image recognition techniques. For example, the analysis unit can analyze text data entered by the user to understand the user's intent. It can also analyze audio data spoken by the user to understand the user's intent. Furthermore, it can analyze image data sent by the user to understand the user's intent. This allows for highly accurate analysis of user intent. A multimodal LLM includes, but is not limited to, the names of specific algorithms or models. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user-entered text data into a generating AI and have the generating AI perform the analysis of the text data.

[0036] The emotion analysis unit can determine emotions from the user's voice tone and facial expressions. For example, the emotion analysis unit can analyze the user's voice tone using voice tone analysis technology. The emotion analysis unit can also analyze the user's facial expressions using facial expression analysis technology. Furthermore, the emotion analysis unit can analyze the user's text data using text analysis technology. For example, the emotion analysis unit can analyze the user's voice tone to determine whether the user is happy or sad. The emotion analysis unit can also analyze the user's facial expressions to determine whether the user is happy or sad. Furthermore, the emotion analysis unit can analyze the user's text data to determine whether the user is happy or sad. This allows for accurate determination of the user's emotions. Voice tone includes, but is not limited to, pitch, speed, and intensity of voice. Facial expressions include, but are not limited to, the movement of facial feature points and classification of expressions. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, AI, or not using AI. For example, the emotion analysis unit can input the user's voice tone data into the generating AI and have the generating AI perform voice tone analysis.

[0037] The dialogue unit can generate appropriate responses based on the user's emotions. For example, if the user is happy, the system will generate a response that shares that happiness. If the user is sad, the system can also generate a response offering words of comfort. Furthermore, if the user is angry, the system can generate a response encouraging the user to calm down. For example, if the user inputs "I'm tired today," the system will respond, "You must be tired. Please rest well today." If the user inputs "Something good happened today," the system can respond, "That's great. Congratulations." Furthermore, if the user inputs "Something sad happened today," the system can respond, "That must have been tough. Please tell me about it." This enables dialogue that responds to the user's emotions. Appropriate responses include, but are not limited to, the content, tone, and timing of the response. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input user emotion data into a generating AI, which can then generate responses that correspond to those emotions.

[0038] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can automatically display as suggestions data formats (text, voice, image, etc.) that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest data formats to be used during specific time periods based on the user's past input history. For example, the reception desk can automatically display as suggestions data formats (text, voice, image, etc.) that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest data formats to be used during specific time periods based on the user's past input history. This allows the system to provide the optimal input method based on the user's past input history. The optimal reception method includes, but is not limited to, pattern analysis based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the AI ​​select the optimal reception method.

[0039] The reception unit can filter input data based on the user's current environment and circumstances. For example, if the user is in a noisy environment, the reception unit may suppress voice input and prioritize text input. The reception unit can also provide an interface that allows input with simple tap operations if the user is on the move. Furthermore, if the user is in a quiet environment, the reception unit can prioritize voice input and accept detailed data. For example, if the user is in a noisy environment, the reception unit may suppress voice input and prioritize text input. The reception unit can also provide an interface that allows input with simple tap operations if the user is on the move. Furthermore, if the user is in a quiet environment, the reception unit can prioritize voice input and accept detailed data. This allows for input methods tailored to the user's environment and circumstances. Current environment and circumstances include, but are not limited to, location information and ambient noise. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception desk can input user environment data into the generating AI, and have the generating AI perform filtering of input methods based on the environment.

[0040] The reception unit can prioritize receiving data that is highly relevant to the user, taking into account the user's geographical location information when receiving input data. For example, if the user is in a specific region, the reception unit can prioritize receiving data related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving data related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving data related to their home. This allows the reception unit to prioritize receiving data that is highly relevant based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI determine the priority of data based on geographical location information.

[0041] The reception unit can analyze the user's social media activity and receive relevant data when receiving input data. For example, the reception unit can prioritize receiving relevant data based on information shared by the user on social media. It can also prioritize receiving relevant data based on information of accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving relevant data based on the user's social media activity history. For example, the reception unit can prioritize receiving relevant data based on information shared by the user on social media. It can also prioritize receiving relevant data based on information of accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving relevant data based on the user's social media activity history. This allows the reception unit to receive relevant data based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of data based on social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the input data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a standard analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on low-importance data. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a standard analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on low-importance data. This makes it possible to perform analysis according to the importance of the input data. The importance of the input data includes, but is not limited to, data content and urgency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance data of the input data into a generating AI and have the generating AI adjust the level of detail of the analysis based on importance.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a speech recognition algorithm to audio data. Furthermore, it can apply an image analysis algorithm to image data. This enables analysis according to the data category. Data categories include, but are not limited to, text data, audio data, and image data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data category data into a generating AI and have the generating AI execute the application of a category-based analysis algorithm.

[0044] The analysis unit can adjust the order of analysis based on the submission date of the input data during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the submission date. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the submission date. This enables analysis based on the submission date of the input data. The submission date includes, but is not limited to, timestamps and submission order. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the input data submission date data into a generating AI and have the generating AI adjust the order of analysis based on the submission date.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the input data during analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables analysis based on the relevance of the input data. The relevance of the input data includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance data of the input data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0046] The sentiment analysis unit can improve the accuracy of its analysis by referring to the user's past emotional data during sentiment analysis. For example, the sentiment analysis unit can more accurately determine the user's current emotions based on the user's past emotional data. The sentiment analysis unit can also analyze the user's past emotional patterns and predict their current emotions. Furthermore, the sentiment analysis unit can adjust its sentiment analysis algorithm by referring to the user's past emotional data. For example, the sentiment analysis unit can more accurately determine the user's current emotions based on the user's past emotional data. The sentiment analysis unit can also analyze the user's past emotional patterns and predict their current emotions. Furthermore, the sentiment analysis unit can adjust its sentiment analysis algorithm by referring to the user's past emotional data. This enables highly accurate sentiment analysis based on the user's past emotional data. Past emotional data includes, but is not limited to, past conversation history and emotional records. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not using AI. For example, the sentiment analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform an improvement in the accuracy of sentiment analysis based on the past emotional data.

[0047] The sentiment analysis unit can perform sentiment analysis while considering the user's attribute information. For example, the sentiment analysis unit can perform sentiment analysis while considering the user's age and gender. It can also perform sentiment analysis while considering the user's occupation and hobbies. Furthermore, the sentiment analysis unit can perform sentiment analysis while considering the user's cultural background. For example, the sentiment analysis unit can perform sentiment analysis while considering the user's age and gender. It can also perform sentiment analysis while considering the user's occupation and hobbies. Furthermore, the sentiment analysis unit can also perform sentiment analysis while considering the user's cultural background. This makes it possible to perform sentiment analysis based on the user's attribute information. Attribute information includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not using AI. For example, the sentiment analysis unit can input user attribute information data into a generating AI and have the generating AI perform sentiment analysis based on the attribute information.

[0048] The sentiment analysis unit can perform sentiment analysis while considering the user's geographical distribution. For example, if the user is in a specific region, the sentiment analysis unit will prioritize analyzing sentiment data related to that region. Furthermore, if the user is traveling, the sentiment analysis unit can prioritize analyzing sentiment data related to the travel destination. Additionally, if the user is at home, the sentiment analysis unit can prioritize analyzing sentiment data related to home. This enables sentiment analysis based on the user's geographical distribution. Geographical distribution includes, but is not limited to, regional data and location information. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not. For example, the sentiment analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform sentiment analysis based on geographical distribution.

[0049] 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 based on literature the user has read in the past. It can also improve the accuracy of sentiment analysis based on literature the user is currently reading. Furthermore, the sentiment analysis unit can improve the accuracy of sentiment analysis based on literature the user is interested in. For example, the sentiment analysis unit can improve the accuracy of sentiment analysis based on literature the user has read in the past. It can also improve the accuracy of sentiment analysis based on literature the user is currently reading. Furthermore, the sentiment analysis unit can improve the accuracy of sentiment analysis based on literature the user is interested in. This enables highly accurate sentiment analysis based on the user's relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not using 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 an improvement in the accuracy of sentiment analysis based on the relevant literature.

[0050] The dialogue unit can generate the optimal response during a conversation by referring to the user's past conversation history. For example, the dialogue unit can generate the optimal response based on response patterns the user has preferred in the past. The dialogue unit can also prioritize generating responses on specific topics from the user's past conversation history. Furthermore, the dialogue unit can analyze the user's past conversation history and generate the most effective response. For example, the dialogue unit can generate the optimal response based on response patterns the user has preferred in the past. The dialogue unit can also prioritize generating responses on specific topics from the user's past conversation history. Furthermore, the dialogue unit can analyze the user's past conversation history and generate the most effective response. This allows the dialogue unit to provide the optimal response based on the user's past conversation history. Past conversation history includes, but is not limited to, past chat logs and conversation records. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input the user's past conversation history data into a generating AI and have the generating AI perform the generation of the optimal response based on the past conversation history.

[0051] The dialogue unit can customize the content of the dialogue based on the user's current situation during the conversation. For example, if the user is at work, the dialogue unit will provide work-related dialogue. It can also provide relaxed dialogue if the user is on vacation. Furthermore, if the user is participating in a specific event, the dialogue unit can provide dialogue related to that event. This allows the dialogue unit to provide dialogue tailored to the user's current situation. Current situation includes, but is not limited to, location information and ambient sound environment. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can input the user's current situation data into a generating AI and have the generating AI customize the dialogue content based on the current situation.

[0052] The dialogue unit can generate an optimal response during a conversation, taking into account the user's geographical location. For example, if the user is in a specific region, the dialogue unit can provide information related to that region. It can also provide information related to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the dialogue unit can provide information related to their home. This allows the dialogue unit to provide an optimal response based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can input the user's geographical location data into a generating AI and have the generating AI generate an optimal response based on the geographical location.

[0053] The dialogue unit can analyze the user's social media activity during a conversation and adjust the content of the conversation accordingly. For example, the dialogue unit can provide relevant conversation content based on information the user has shared on social media. It can also provide relevant conversation content based on information about accounts the user follows on social media. Furthermore, the dialogue unit can provide relevant conversation content based on the user's social media activity history. This allows the dialogue unit to provide conversation content based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI adjust the conversation content based on the social media activity.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Live chat systems can predict user input by referencing the user's past conversation history. For example, they can automatically display frequently used phrases and keywords as suggestions. They can also predict what the user is likely to input next based on their past input and complete the input accordingly. Furthermore, they can prioritize input related to specific topics based on the user's past conversation history. This allows for efficient input support based on the user's past conversation history.

[0056] A live chat system can adjust the input method based on the user's current environment when receiving user input data. For example, if the user is in a noisy environment, voice input can be suppressed and text input prioritized. Conversely, if the user is in a quiet environment, voice input can be prioritized and detailed data can be accepted. Furthermore, if the user is on the move, an interface that allows input with simple taps can be provided. This allows the system to provide the optimal input method according to the user's environment.

[0057] The live chat system can prioritize receiving relevant data by considering the user's geographical location when receiving user input data. For example, if the user is in a specific region, it can prioritize receiving data related to that region. Similarly, if the user is traveling, it can prioritize receiving data related to their travel destination. Furthermore, if the user is at home, it can prioritize receiving data related to their home location. This allows the system to prioritize receiving relevant data based on the user's geographical location.

[0058] The live chat system can analyze the user's social media activity and receive relevant data when receiving user input data. For example, it can prioritize receiving relevant data based on information the user has shared on social media. It can also prioritize receiving relevant data based on information about accounts the user follows on social media. Furthermore, it can prioritize receiving relevant data based on the user's social media activity history. In this way, it can receive relevant data based on the user's social media activity.

[0059] A live chat system can customize the input method based on the user's current situation when receiving user input data. For example, if the user is at work, it can provide a work-related input method. If the user is on vacation, it can provide a relaxed input method. Furthermore, if the user is participating in a specific event, it can provide an input method related to that event. This allows the system to provide the most suitable input method according to the user's current situation.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception area receives user input. User input includes text input, voice input, and image input. For example, the reception area may be equipped with a keyboard or touchscreen for receiving text input, a microphone or voice recognition technology for receiving voice input, and a camera or image recognition technology for receiving image input. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes text data using natural language processing technology, analyzes audio data using speech recognition technology, and analyzes image data using image recognition technology. This allows the system to understand the user's intent. Step 3: The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. The emotion analysis unit analyzes the user's voice tone using voice tone analysis technology, analyzes the user's facial expressions using facial expression analysis technology, and analyzes the user's text data using text analysis technology. This determines whether the user is happy or sad. Step 4: The dialogue unit adaptively engages in conversation based on the emotional information obtained by the emotion analysis unit. The dialogue unit generates responses that share the user's joy when the user is happy, responses that offer comfort when the user is sad, and responses that encourage the user to calm down when the user is angry. This provides a natural and personalized live chat experience with the user.

[0062] (Example of form 2) The live chat system according to an embodiment of the present invention is a system that provides a natural and personalized live chat experience with users by utilizing AI technology to realize natural language processing using a multimodal LLM (Large-Scale Language Model), real-time sentiment analysis, and adaptive dialogue. When a user starts a live chat, the system accepts input from the user. This input can include multimodal data such as text, voice, and images. Next, the multimodal LLM analyzes this input to understand the user's intentions and emotions. For example, if the user inputs "I'm tired today," the system understands that the user is tired and generates an appropriate response. Furthermore, it performs real-time sentiment analysis to grasp the user's emotional state. For example, it determines whether the user is happy or sad from the user's tone of voice and facial expressions. Based on this information, the system provides adaptive dialogue with the user. For example, if the user is sad, the system can offer words of comfort. In this way, the system provides a natural and personalized live chat experience with users. This is expected to improve user satisfaction and expand market share. It is also expected that innovation in AI technology will accelerate and bring about cultural transformation through new forms of entertainment. As a result, the live chat system can improve user satisfaction.

[0063] The live chat system according to this embodiment comprises a reception unit, an analysis unit, an emotion analysis unit, and a dialogue unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone and speech recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera and image recognition technology for receiving image input. For example, the reception unit receives text data entered by the user. The reception unit may also receive voice data spoken by the user via a microphone and convert it into text data using speech recognition technology. Furthermore, the reception unit may also receive image data transmitted by the user via a camera and analyze it using image recognition technology. The analysis unit analyzes the data received by the reception unit. The analysis unit may, for example, analyze text data using natural language processing technology. The analysis unit may also analyze voice data using speech recognition technology. Furthermore, the analysis unit may also analyze image data using image recognition technology. For example, the analysis unit analyzes text data entered by the user to understand the user's intent. The analysis unit can also analyze spoken audio data from the user to understand their intent. Furthermore, the analysis unit can analyze image data sent by the user to understand their intent. The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. For example, the emotion analysis unit can analyze the user's voice tone using voice tone analysis technology. It can also analyze the user's facial expressions using facial expression analysis technology. Furthermore, the emotion analysis unit can analyze the user's text data using text analysis technology. For example, the emotion analysis unit can analyze the user's voice tone to determine whether the user is happy or sad. It can also analyze the user's facial expressions to determine whether the user is happy or sad. Furthermore, the emotion analysis unit can analyze the user's text data to determine whether the user is happy or sad.The dialogue unit performs adaptive dialogue based on emotional information obtained by the emotion analysis unit. For example, if the user is happy, the system generates a response that shares that happiness with the user. The dialogue unit can also generate a response that offers comfort to the user if the user is sad. Furthermore, if the user is angry, the system can generate a response that encourages the user to calm down. For example, if the user inputs "I'm tired today," the system will respond, "You must be tired. Please rest well today." The dialogue unit can also respond if the user inputs "Something good happened today," with the system saying, "That's great. Congratulations." Furthermore, if the user inputs "Something sad happened today," the system can respond, "That must have been tough. Please tell me about it." In this way, the live chat system according to the embodiment can provide a natural and personalized live chat experience with the user.

[0064] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may be equipped with a keyboard or touchscreen for receiving text input. It may also be equipped with a microphone and voice recognition technology for receiving voice input. Furthermore, it may be equipped with a camera and image recognition technology for receiving image input. For example, the reception unit receives text data entered by the user. It can also receive voice data spoken by the user via a microphone and convert it into text data using voice recognition technology. Furthermore, it can receive image data transmitted by the user via a camera and analyze it using image recognition technology. The reception unit works in conjunction with a server with high-speed data processing capabilities to receive text data entered by the user in real time. This allows information entered by the user to be immediately reflected in the system, enabling smooth dialogue. Regarding voice input, noise cancellation technology can be used to remove ambient noise and obtain clear voice data. This improves the accuracy of voice recognition and allows for accurate understanding of the user's intent. Regarding image input, the use of a high-resolution camera allows for the acquisition of image data with fine detail. Furthermore, the image recognition technology employs advanced algorithms using deep learning, enabling accurate analysis of the user's facial expressions and gestures. As a result, the reception unit can handle a variety of user input formats and receive data quickly and accurately.

[0065] The analysis unit analyzes data received by the reception unit. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data entered by a user to understand the user's intent. It can also analyze audio data spoken by a user to understand the user's intent. Furthermore, the analysis unit can analyze image data sent by a user to understand the user's intent. The analysis unit uses natural language processing technology to grammatically and semantically analyze the user's text data to accurately grasp the user's intent and emotions. For example, if a user enters "I'm tired today," the analysis unit extracts the keyword "tired" and understands that the user is feeling tired. Regarding audio data, speech recognition technology is used to convert the audio to text, and then natural language processing technology is used for analysis. This allows for an accurate understanding of the user's statements and the generation of appropriate responses. Regarding image data, image recognition technology is used to analyze the user's facial expressions and gestures, thereby understanding the user's emotions and intentions. For example, if a user sends an image of themselves smiling, the analysis unit can determine that the user is happy. In this way, the analysis unit can comprehensively analyze the user's diverse input data and accurately understand the user's intentions and emotions.

[0066] The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. For example, the emotion analysis unit analyzes the user's voice tone using voice tone analysis technology. It can also analyze the user's facial expressions using facial expression analysis technology. Furthermore, the emotion analysis unit can analyze the user's text data using text analysis technology. For example, the emotion analysis unit analyzes the user's voice tone to determine whether the user is happy or sad. It can also analyze the user's facial expressions to determine whether the user is happy or sad. Furthermore, the emotion analysis unit can analyze the user's text data to determine whether the user is happy or sad. Using voice tone analysis technology, the emotion analysis unit analyzes features such as the pitch, speed, and volume of the user's voice to determine the user's emotional state. For example, a high-pitched, fast voice may indicate excitement or happiness. On the other hand, a low-pitched, slow voice may indicate sadness or fatigue. Regarding facial expression analysis technology, a deep learning algorithm is employed to analyze the feature points of the user's face and detect subtle changes in facial expression. This allows for highly accurate determination of whether a user is expressing emotions such as smiles, anger, or sadness. Regarding text analysis technology, it uses emotion dictionaries and machine learning models to extract emotions from the user's text data. For example, if a user enters "I'm very happy today," the system extracts the keyword "happy," indicating a positive emotion, and determines that the user is happy. This allows the emotion analysis unit to comprehensively analyze diverse user data and accurately understand the user's emotional state.

[0067] The dialogue unit performs adaptive dialogue based on emotional information obtained by the emotion analysis unit. For example, if the user is happy, the system generates a response that shares that happiness. If the user is sad, the system can also generate a response offering words of comfort. Furthermore, if the user is angry, the system can generate a response encouraging the user to calm down. For example, if the user inputs "I'm tired today," the system will respond, "You must be tired. Please rest well today." If the user inputs "Something good happened today," the system can respond, "That's great. Congratulations." Furthermore, if the user inputs "Something sad happened today," the system can respond, "That must have been tough. Please tell me about it." The dialogue unit uses natural language generation technology to generate the optimal response for the user based on the emotional information obtained from the emotion analysis unit. Natural language generation technology includes algorithms for generating appropriate words and phrases according to the user's emotional state and intentions. For example, if a user types "I'm very happy today," the dialogue unit will generate a response such as "That's wonderful! What happened?" to share the user's joy. Similarly, if a user types "I'm very sad today," the dialogue unit will generate a response such as "That must have been tough. Can you tell me what happened?" to empathize with the user's feelings. Furthermore, the dialogue unit can provide more personalized responses by considering the user's past conversation history and individual preferences. For example, if a user has previously discussed a particular topic, it will generate a relevant response based on that information. This allows the dialogue unit to achieve natural and personalized conversations with users, thereby improving user satisfaction.

[0068] The reception unit can accept multimodal data such as text, audio, and images. For example, the reception unit may be equipped with a keyboard or touchscreen for receiving text input. It may also be equipped with a microphone and speech recognition technology for receiving voice input. Furthermore, it may be equipped with a camera and image recognition technology for receiving image input. For example, the reception unit can accept text data entered by a user. It can also receive spoken audio data from a user via a microphone and convert it into text data using speech recognition technology. Furthermore, it can receive image data transmitted by a user via a camera and analyze it using image recognition technology. This allows the reception unit to accept data in various formats. Multimodal data includes, but is not limited to, text, audio, images, and videos. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input text data entered by a user into a generating AI and have the generating AI perform the analysis of the text data.

[0069] The analysis unit can analyze user intent using a multimodal LLM. For example, the analysis unit can analyze text data using natural language processing techniques. It can also analyze audio data using speech recognition techniques. Furthermore, it can analyze image data using image recognition techniques. For example, the analysis unit can analyze text data entered by the user to understand the user's intent. It can also analyze audio data spoken by the user to understand the user's intent. Furthermore, it can analyze image data sent by the user to understand the user's intent. This allows for highly accurate analysis of user intent. A multimodal LLM includes, but is not limited to, the names of specific algorithms or models. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user-entered text data into a generating AI and have the generating AI perform the analysis of the text data.

[0070] The emotion analysis unit can determine emotions from the user's voice tone and facial expressions. For example, the emotion analysis unit can analyze the user's voice tone using voice tone analysis technology. The emotion analysis unit can also analyze the user's facial expressions using facial expression analysis technology. Furthermore, the emotion analysis unit can analyze the user's text data using text analysis technology. For example, the emotion analysis unit can analyze the user's voice tone to determine whether the user is happy or sad. The emotion analysis unit can also analyze the user's facial expressions to determine whether the user is happy or sad. Furthermore, the emotion analysis unit can analyze the user's text data to determine whether the user is happy or sad. This allows for accurate determination of the user's emotions. Voice tone includes, but is not limited to, pitch, speed, and intensity of voice. Facial expressions include, but are not limited to, the movement of facial feature points and classification of expressions. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, AI, or not using AI. For example, the emotion analysis unit can input the user's voice tone data into the generating AI and have the generating AI perform voice tone analysis.

[0071] The dialogue unit can generate appropriate responses based on the user's emotions. For example, if the user is happy, the system will generate a response that shares that happiness. If the user is sad, the system can also generate a response offering words of comfort. Furthermore, if the user is angry, the system can generate a response encouraging the user to calm down. For example, if the user inputs "I'm tired today," the system will respond, "You must be tired. Please rest well today." If the user inputs "Something good happened today," the system can respond, "That's great. Congratulations." Furthermore, if the user inputs "Something sad happened today," the system can respond, "That must have been tough. Please tell me about it." This enables dialogue that responds to the user's emotions. Appropriate responses include, but are not limited to, the content, tone, and timing of the response. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input user emotion data into a generating AI, which can then generate responses that correspond to those emotions.

[0072] The reception desk can estimate the user's emotions and adjust how input data is received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and quickly receive input data. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can also prioritize voice input and quickly receive input data. This allows for the provision of input methods tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI, which can then adjust the input method based on that emotion.

[0073] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can automatically display as suggestions data formats (text, voice, image, etc.) that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest data formats to be used during specific time periods based on the user's past input history. For example, the reception desk can automatically display as suggestions data formats (text, voice, image, etc.) that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest data formats to be used during specific time periods based on the user's past input history. This allows the system to provide the optimal input method based on the user's past input history. The optimal reception method includes, but is not limited to, pattern analysis based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the AI ​​select the optimal reception method.

[0074] The reception unit can filter input data based on the user's current environment and circumstances. For example, if the user is in a noisy environment, the reception unit may suppress voice input and prioritize text input. The reception unit can also provide an interface that allows input with simple tap operations if the user is on the move. Furthermore, if the user is in a quiet environment, the reception unit can prioritize voice input and accept detailed data. For example, if the user is in a noisy environment, the reception unit may suppress voice input and prioritize text input. The reception unit can also provide an interface that allows input with simple tap operations if the user is on the move. Furthermore, if the user is in a quiet environment, the reception unit can prioritize voice input and accept detailed data. This allows for input methods tailored to the user's environment and circumstances. Current environment and circumstances include, but are not limited to, location information and ambient noise. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception desk can input user environment data into the generating AI, and have the generating AI perform filtering of input methods based on the environment.

[0075] The reception desk can estimate the user's emotions and determine the priority of data to receive based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize receiving important data. If the user is relaxed, the reception desk may also prioritize receiving detailed data. Furthermore, if the user is in a hurry, the reception desk may also prioritize receiving data that needs to be processed quickly. For example, if the user is nervous, the reception desk will prioritize receiving important data. If the user is relaxed, the reception desk may also prioritize receiving detailed data. Furthermore, if the user is in a hurry, the reception desk may also prioritize receiving data that needs to be processed quickly. This allows for the determination of data prioritization according to the user's emotions. Data prioritization includes, but is not limited to, the intensity of emotions and urgency. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, 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 desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input user emotion data into a generating AI, which can then prioritize the data based on those emotions.

[0076] The reception unit can prioritize receiving data that is highly relevant to the user, taking into account the user's geographical location information when receiving input data. For example, if the user is in a specific region, the reception unit can prioritize receiving data related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving data related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving data related to their home. This allows the reception unit to prioritize receiving data that is highly relevant based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI determine the priority of data based on geographical location information.

[0077] The reception unit can analyze the user's social media activity and receive relevant data when receiving input data. For example, the reception unit can prioritize receiving relevant data based on information shared by the user on social media. It can also prioritize receiving relevant data based on information of accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving relevant data based on the user's social media activity history. For example, the reception unit can prioritize receiving relevant data based on information shared by the user on social media. It can also prioritize receiving relevant data based on information of accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving relevant data based on the user's social media activity history. This allows the reception unit to receive relevant data based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of data based on social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. The analysis unit can also apply an algorithm that provides quick results if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can apply an algorithm that provides visually stimulating analysis results. This allows for the application of an analysis algorithm tailored to the user's emotions. Adjusting the analysis algorithm includes, but is not limited to, changing parameters or selecting algorithms. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the analysis algorithm based on that emotion.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the input data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a standard analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on low-importance data. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a standard analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on low-importance data. This makes it possible to perform analysis according to the importance of the input data. The importance of the input data includes, but is not limited to, data content and urgency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance data of the input data into a generating AI and have the generating AI adjust the level of detail of the analysis based on importance.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a speech recognition algorithm to audio data. Furthermore, it can apply an image analysis algorithm to image data. This enables analysis according to the data category. Data categories include, but are not limited to, text data, audio data, and image data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data category data into a generating AI and have the generating AI execute the application of a category-based analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is tense, the analysis unit will prioritize the analysis of important data. If the user is relaxed, the analysis unit may also prioritize the analysis of detailed data. Furthermore, if the user is in a hurry, the analysis unit may also prioritize the analysis of data that provides results quickly. For example, if the user is tense, the analysis unit will prioritize the analysis of important data. If the user is relaxed, the analysis unit may also prioritize the analysis of detailed data. Furthermore, if the user is in a hurry, the analysis unit may also prioritize the analysis of data that provides results quickly. This allows for the determination of analysis priorities according to the user's emotions. The analysis priorities include, but are not limited to, the intensity and urgency of emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI determine the priority of analysis based on emotion.

[0082] The analysis unit can adjust the order of analysis based on the submission date of the input data during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the submission date. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the submission date. This enables analysis based on the submission date of the input data. The submission date includes, but is not limited to, timestamps and submission order. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the input data submission date data into a generating AI and have the generating AI adjust the order of analysis based on the submission date.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the input data during analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables analysis based on the relevance of the input data. The relevance of the input data includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance data of the input data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0084] The sentiment analysis unit can estimate the user's emotions and adjust the criteria for sentiment analysis based on the estimated user emotions. For example, if the user is relaxed, the sentiment analysis unit can perform a detailed sentiment analysis. It can also apply criteria for quickly determining emotions if the user is in a hurry. Furthermore, if the user is excited, the sentiment analysis unit can apply criteria that provide visually stimulating sentiment analysis results. For example, if the user is relaxed, the sentiment analysis unit can perform a detailed sentiment analysis. It can also apply criteria for quickly determining emotions if the user is in a hurry. Furthermore, if the user is excited, the sentiment analysis unit can apply criteria that provide visually stimulating sentiment analysis results. This allows for the application of sentiment analysis criteria tailored to the user's emotions. Criteria for sentiment analysis include, for example, the intensity of emotion and the type of emotion. Emotion estimation is achieved using a sentiment estimation function, for example, an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes 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 generating AI and have the generating AI perform adjustments to the emotion analysis criteria based on that data.

[0085] The sentiment analysis unit can improve the accuracy of its analysis by referring to the user's past emotional data during sentiment analysis. For example, the sentiment analysis unit can more accurately determine the user's current emotions based on the user's past emotional data. The sentiment analysis unit can also analyze the user's past emotional patterns and predict their current emotions. Furthermore, the sentiment analysis unit can adjust its sentiment analysis algorithm by referring to the user's past emotional data. For example, the sentiment analysis unit can more accurately determine the user's current emotions based on the user's past emotional data. The sentiment analysis unit can also analyze the user's past emotional patterns and predict their current emotions. Furthermore, the sentiment analysis unit can adjust its sentiment analysis algorithm by referring to the user's past emotional data. This enables highly accurate sentiment analysis based on the user's past emotional data. Past emotional data includes, but is not limited to, past conversation history and emotional records. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not using AI. For example, the sentiment analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform an improvement in the accuracy of sentiment analysis based on the past emotional data.

[0086] The sentiment analysis unit can perform sentiment analysis while considering the user's attribute information. For example, the sentiment analysis unit can perform sentiment analysis while considering the user's age and gender. It can also perform sentiment analysis while considering the user's occupation and hobbies. Furthermore, the sentiment analysis unit can perform sentiment analysis while considering the user's cultural background. For example, the sentiment analysis unit can perform sentiment analysis while considering the user's age and gender. It can also perform sentiment analysis while considering the user's occupation and hobbies. Furthermore, the sentiment analysis unit can also perform sentiment analysis while considering the user's cultural background. This makes it possible to perform sentiment analysis based on the user's attribute information. Attribute information includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not using AI. For example, the sentiment analysis unit can input user attribute information data into a generating AI and have the generating AI perform sentiment analysis based on the attribute information.

[0087] The emotion analysis unit can estimate the user's emotions and adjust the order in which the emotion analysis results are displayed based on the estimated emotions. For example, if the user is nervous, the emotion analysis unit will prioritize displaying important emotion analysis results. It can also prioritize displaying detailed emotion analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the emotion analysis unit can prioritize displaying emotion analysis results that provide quick results. This enables the display of emotion analysis results tailored to the user's emotions. The order in which emotion analysis results are displayed may include, but is not limited to, emotion intensity and urgency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input user emotion data into a generating AI and have the generating AI adjust the display order of the emotion analysis results based on those emotions.

[0088] The sentiment analysis unit can perform sentiment analysis while considering the user's geographical distribution. For example, if the user is in a specific region, the sentiment analysis unit will prioritize analyzing sentiment data related to that region. Furthermore, if the user is traveling, the sentiment analysis unit can prioritize analyzing sentiment data related to the travel destination. Additionally, if the user is at home, the sentiment analysis unit can prioritize analyzing sentiment data related to home. This enables sentiment analysis based on the user's geographical distribution. Geographical distribution includes, but is not limited to, regional data and location information. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not. For example, the sentiment analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform sentiment analysis based on geographical distribution.

[0089] 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 based on literature the user has read in the past. It can also improve the accuracy of sentiment analysis based on literature the user is currently reading. Furthermore, the sentiment analysis unit can improve the accuracy of sentiment analysis based on literature the user is interested in. For example, the sentiment analysis unit can improve the accuracy of sentiment analysis based on literature the user has read in the past. It can also improve the accuracy of sentiment analysis based on literature the user is currently reading. Furthermore, the sentiment analysis unit can improve the accuracy of sentiment analysis based on literature the user is interested in. This enables highly accurate sentiment analysis based on the user's relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, AI, or not using 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 an improvement in the accuracy of sentiment analysis based on the relevant literature.

[0090] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, if the user is nervous, the dialogue unit will speak in a calm tone. If the user is relaxed, the dialogue unit can also speak in a bright tone. Furthermore, if the user is in a hurry, the dialogue unit can also speak quickly and concisely. This allows for the expression of dialogue to be tailored to the user's emotions. The expression of dialogue includes, but is not limited to, word choice, tone, and response timing. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input user emotion data into a generating AI, which can then adjust the way the dialogue is expressed based on those emotions.

[0091] The dialogue unit can generate the optimal response during a conversation by referring to the user's past conversation history. For example, the dialogue unit can generate the optimal response based on response patterns the user has preferred in the past. The dialogue unit can also prioritize generating responses on specific topics from the user's past conversation history. Furthermore, the dialogue unit can analyze the user's past conversation history and generate the most effective response. For example, the dialogue unit can generate the optimal response based on response patterns the user has preferred in the past. The dialogue unit can also prioritize generating responses on specific topics from the user's past conversation history. Furthermore, the dialogue unit can analyze the user's past conversation history and generate the most effective response. This allows the dialogue unit to provide the optimal response based on the user's past conversation history. Past conversation history includes, but is not limited to, past chat logs and conversation records. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input the user's past conversation history data into a generating AI and have the generating AI perform the generation of the optimal response based on the past conversation history.

[0092] The dialogue unit can customize the content of the dialogue based on the user's current situation during the conversation. For example, if the user is at work, the dialogue unit will provide work-related dialogue. It can also provide relaxed dialogue if the user is on vacation. Furthermore, if the user is participating in a specific event, the dialogue unit can provide dialogue related to that event. This allows the dialogue unit to provide dialogue tailored to the user's current situation. Current situation includes, but is not limited to, location information and ambient sound environment. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can input the user's current situation data into a generating AI and have the generating AI customize the dialogue content based on the current situation.

[0093] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue unit will prioritize important dialogue. If the user is relaxed, the dialogue unit can also prioritize detailed dialogue. Furthermore, if the user is in a hurry, the dialogue unit can conduct the dialogue quickly. For example, if the dialogue unit is nervous, it will prioritize important dialogue. If the user is relaxed, the dialogue unit can also prioritize detailed dialogue. Furthermore, if the user is in a hurry, the dialogue unit can conduct the dialogue quickly. This allows for the determination of dialogue priorities according to the user's emotions. Dialogue priorities include, but are not limited to, the intensity of emotions and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user emotion data into a generating AI, which can then perform the task of determining dialogue priorities based on those emotions.

[0094] The dialogue unit can generate an optimal response during a conversation, taking into account the user's geographical location. For example, if the user is in a specific region, the dialogue unit can provide information related to that region. It can also provide information related to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the dialogue unit can provide information related to their home. This allows the dialogue unit to provide an optimal response based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not. For example, the dialogue unit can input the user's geographical location data into a generating AI and have the generating AI generate an optimal response based on the geographical location.

[0095] The dialogue unit can analyze the user's social media activity during a conversation and adjust the content of the conversation accordingly. For example, the dialogue unit can provide relevant conversation content based on information the user has shared on social media. It can also provide relevant conversation content based on information about accounts the user follows on social media. Furthermore, the dialogue unit can provide relevant conversation content based on the user's social media activity history. This allows the dialogue unit to provide conversation content based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the above processing in the dialogue unit may be performed using, for example, AI, or not using AI. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI adjust the conversation content based on the social media activity.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The live chat system can estimate the user's emotions and monitor their stress level based on those estimates. For example, if a user is experiencing high stress, the system can offer advice on how to relax. If the user is experiencing low stress, the system can suggest new topics that might interest them. Furthermore, if the user is experiencing moderate stress, the system can introduce them to stress management techniques. This allows for the provision of appropriate support tailored to the user's stress level.

[0098] Live chat systems can predict user input by referencing the user's past conversation history. For example, they can automatically display frequently used phrases and keywords as suggestions. They can also predict what the user is likely to input next based on their past input and complete the input accordingly. Furthermore, they can prioritize input related to specific topics based on the user's past conversation history. This allows for efficient input support based on the user's past conversation history.

[0099] Live chat systems can estimate a user's emotions and adjust the tone of the conversation based on those estimates. For example, if a user is angry, the system will engage in conversation in a calm and composed tone. If a user is happy, the system can engage in conversation in a bright and friendly tone. Furthermore, if a user is sad, the system can engage in conversation in a gentle and comforting tone. This allows for conversations to be conducted in an appropriate tone according to the user's emotions.

[0100] A live chat system can adjust the input method based on the user's current environment when receiving user input data. For example, if the user is in a noisy environment, voice input can be suppressed and text input prioritized. Conversely, if the user is in a quiet environment, voice input can be prioritized and detailed data can be accepted. Furthermore, if the user is on the move, an interface that allows input with simple taps can be provided. This allows the system to provide the optimal input method according to the user's environment.

[0101] Live chat systems can estimate a user's emotions and customize the conversation based on those estimates. For example, if a user is stressed, the system can offer topics to help them relax. If a user is excited, the system can offer interesting new information. Furthermore, if a user is feeling down, the system can offer words of encouragement. This allows for personalized conversations tailored to the user's emotions.

[0102] The live chat system can prioritize receiving relevant data by considering the user's geographical location when receiving user input data. For example, if the user is in a specific region, it can prioritize receiving data related to that region. Similarly, if the user is traveling, it can prioritize receiving data related to their travel destination. Furthermore, if the user is at home, it can prioritize receiving data related to their home location. This allows the system to prioritize receiving relevant data based on the user's geographical location.

[0103] A live chat system can estimate a user's emotions and prioritize conversations based on those emotions. For example, if a user is nervous, important conversations can be prioritized. If a user is relaxed, detailed conversations can be prioritized. Furthermore, if a user is in a hurry, the conversation can be conducted quickly. This allows for prioritizing conversations according to the user's emotions.

[0104] The live chat system can analyze the user's social media activity and receive relevant data when receiving user input data. For example, it can prioritize receiving relevant data based on information the user has shared on social media. It can also prioritize receiving relevant data based on information about accounts the user follows on social media. Furthermore, it can prioritize receiving relevant data based on the user's social media activity history. In this way, it can receive relevant data based on the user's social media activity.

[0105] The live chat system can estimate the user's emotions and adjust the criteria for emotion analysis based on those estimates. For example, if the user is relaxed, a detailed emotion analysis can be performed. If the user is in a hurry, criteria for quickly determining emotions can be applied. Furthermore, if the user is excited, criteria that provide visually stimulating emotion analysis results can be applied. This allows for the application of emotion analysis criteria tailored to the user's emotions.

[0106] A live chat system can customize the input method based on the user's current situation when receiving user input data. For example, if the user is at work, it can provide a work-related input method. If the user is on vacation, it can provide a relaxed input method. Furthermore, if the user is participating in a specific event, it can provide an input method related to that event. This allows the system to provide the most suitable input method according to the user's current situation.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception area receives user input. User input includes text input, voice input, and image input. For example, the reception area may be equipped with a keyboard or touchscreen for receiving text input, a microphone or voice recognition technology for receiving voice input, and a camera or image recognition technology for receiving image input. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes text data using natural language processing technology, analyzes audio data using speech recognition technology, and analyzes image data using image recognition technology. This allows the system to understand the user's intent. Step 3: The emotion analysis unit analyzes the user's emotions based on the data analyzed by the analysis unit. The emotion analysis unit analyzes the user's voice tone using voice tone analysis technology, analyzes the user's facial expressions using facial expression analysis technology, and analyzes the user's text data using text analysis technology. This determines whether the user is happy or sad. Step 4: The dialogue unit adaptively engages in conversation based on the emotional information obtained by the emotion analysis unit. The dialogue unit generates responses that share the user's joy when the user is happy, responses that offer comfort when the user is sad, and responses that encourage the user to calm down when the user is angry. This provides a natural and personalized live chat experience with the user.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, emotion analysis unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user input using the touchscreen or microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's intentions using natural language processing technology. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions using voice tone analysis technology and facial expression analysis technology. The dialogue unit is implemented by the control unit 46A of the smart device 14 and engages in adaptive dialogue based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, emotion analysis unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's intentions using natural language processing technology. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions using voice tone analysis technology and facial expression analysis technology. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and conducts adaptive dialogue based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, emotion analysis unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 and camera 42 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's intentions using natural language processing technology. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions using voice tone analysis technology and facial expression analysis technology. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and conducts adaptive dialogue based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, emotion analysis unit, and dialogue unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 and camera 42 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's intentions using natural language processing technology. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's emotions using voice tone analysis technology and facial expression analysis technology. The dialogue unit is implemented by the control unit 46A of the robot 414 and engages in adaptive dialogue based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception area that receives user input, An analysis unit that analyzes the data received by the reception unit, An emotion analysis unit analyzes the user's emotions based on the data analyzed by the aforementioned analysis unit, The system includes a dialogue unit that performs adaptive dialogue based on emotional information obtained by the emotion analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts multimodal data such as text, audio, and images. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze user intent using multimodal LLM. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned emotion analysis unit, Determining emotions from the user's voice tone and facial expressions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, Generate appropriate responses based on the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts how input data is accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving input data, filtering is performed based on the user's current environment and situation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the data to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving input data, the system prioritizes accepting data 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 11) The aforementioned reception unit is When receiving input data, the system analyzes the user's social media activity and accepts relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the input data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on when the input data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the input data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the criteria for sentiment analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned emotion analysis unit, During sentiment analysis, we improve the accuracy of the analysis by referencing the user's past sentiment data. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the order in which the sentiment analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned emotion analysis unit, When performing 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 24) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, During interaction, the system generates the optimal response by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During an interaction, the content of the conversation is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, During interaction, the system generates the optimal response by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity to adjust the content of the conversation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 area that receives user input, An analysis unit that analyzes the data received by the reception unit, An emotion analysis unit analyzes the user's emotions based on the data analyzed by the aforementioned analysis unit, The system includes a dialogue unit that performs adaptive dialogue based on emotional information obtained by the emotion analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is It accepts multimodal data such as text, audio, and images. The system according to feature 1.

3. The aforementioned analysis unit, Analyze user intent using multimodal LLM. The system according to feature 1.

4. The aforementioned emotion analysis unit, Determining emotions from the user's voice tone and facial expressions. The system according to feature 1.

5. The aforementioned dialogue unit, Generate appropriate responses based on the user's emotions. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts how input data is accepted based on those estimated emotions. The system according to feature 1.

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

8. The aforementioned reception unit is When receiving input data, filtering is performed based on the user's current environment and situation. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes the data to accept based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving input data, the system prioritizes accepting data that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

Images