system

Abstract

The system according to the embodiment aims to analyze the user's facial expressions and respond in natural language. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions and requests from a user. The analysis unit analyzes the user's facial expression based on the information received by the reception unit. The generation unit generates natural-sounding language based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to respond in natural language while recognizing the user's facial expressions, and there is room for improvement in terms of accurately recognizing emotions.

[0005] The system according to the embodiment aims to analyze the user's facial expressions and respond in natural language. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions and requests from a user. The analysis unit analyzes the user's facial expression based on the information received by the reception unit. The generation unit generates natural-sounding words based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's facial expressions and respond in natural language. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An interactive robot system according to an embodiment of the present invention accepts a user's questions and requests and responds in natural language while recognizing facial expressions. This interactive robot system begins with the user inputting the question or request to the interactive robot. Next, the interactive robot uses image recognition technology to analyze the user's facial expressions and recognize their emotions. A generation AI then generates a response in natural language to the user's question or request. Finally, the interactive robot provides the generated response to the user. This provides services suitable for bank reception, government office reception, and brick-and-mortar service reception. For example, if a user asks the interactive robot, "I'd like to open an account. How do I go about the procedure?", the interactive robot receives the question as speech. Next, the interactive robot uses image recognition technology to analyze the user's facial expressions and recognize a confused expression. The generation AI then generates a response such as, "We'll guide you through the account opening procedure. First, please prepare the necessary documents. Then, we'll complete the procedure at the counter." Finally, the interactive robot provides the generated response to the user. This allows the user to obtain the necessary information through a dialogue with the interactive robot. This service is suitable for bank reception, government office reception, and store-based service reception, and can be provided as an in-house service from proposal to implementation, which has the potential for high profits. This allows the conversational robot system to respond to user questions and requests in natural language while recognizing facial expressions.

[0029] The interactive robot system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question or request. The user's question or request may be, for example, text, audio, or an image, but is not limited to these examples. The reception unit may receive the user's question or request as audio, for example. The analysis unit analyzes the user's facial expression based on the information received by the reception unit. The facial expression analysis may be performed using, for example, facial feature point detection or an emotion classification method, but is not limited to these examples. The analysis unit may analyze the user's facial expression using image recognition technology. The generation unit generates natural-sounding language based on the information analyzed by the analysis unit. The natural-sounding language may be generated using, for example, grammatically correct sentences or expressions appropriate to the context, but is not limited to these examples. The generation unit may generate natural-sounding language using a generation AI. The generation AI may be, for example, a natural language generation model such as GPT-4 (registered trademark) or Gemini, but is not limited to these examples. The provision unit provides the response generated by the generation unit. The providing unit can provide a response in the form of, for example, text, audio, video, etc. This allows the interactive robot system according to the embodiment to respond to a user's questions or requests in natural language while recognizing facial expressions.

[0030] The interactive robot system includes an identification unit that identifies a user's emotion. The identification unit identifies the user's emotion. Emotion identification is performed using, for example, facial expression analysis, voice analysis, text analysis, or other methods, but is not limited to these examples. The identification unit can identify the user's emotion using, for example, facial expression analysis. The facial expression analysis is performed using facial feature point detection, emotion classification methods, or the like. The identification unit can also identify the user's emotion using voice analysis. Voice analysis identifies emotions by analyzing the tone and speed of voice, etc. The identification unit can also identify the user's emotion using text analysis. Text analysis identifies emotions by analyzing the content of the user's speech. This allows the identification unit to identify the user's emotion and provide a more appropriate response.

[0031] The interactive robot system includes an evaluation unit that evaluates the quality of a response. The evaluation unit evaluates the quality of the response. The evaluation of the quality of the response is performed using criteria such as, but not limited to, accuracy, appropriateness, and user satisfaction. The evaluation unit can, for example, evaluate the accuracy of the response. The evaluation of accuracy checks whether the response accurately answers the user's question or request. The evaluation unit can also evaluate the appropriateness of the response. The evaluation of appropriateness checks whether the response is appropriate for the context. The evaluation unit can also evaluate user satisfaction. The evaluation of user satisfaction checks whether the user is satisfied with the response. In this way, the evaluation unit can provide a higher quality response by evaluating the quality of the response.

[0032] The reception unit can receive the user's questions and requests as voice. The reception unit receives the user's questions and requests as voice. Voice input is performed using, for example, voice recognition technology, but is not limited to this example. The reception unit can receive the user's questions and requests as voice using, for example, voice recognition technology. Voice recognition technology is a technology that converts the user's speech into text data and can include processing such as noise filtering. This allows the reception unit to allow the user to naturally convey their questions and requests through voice input.

[0033] The analysis unit can analyze the user's facial expression using image recognition technology. The analysis unit analyzes the user's facial expression using image recognition technology. Image recognition technology is performed using, for example, a face recognition algorithm or deep learning technology, but is not limited to these examples. The analysis unit can analyze the user's facial expression using, for example, a face recognition algorithm. The face recognition algorithm is a technology that detects facial feature points and classifies emotions. The analysis unit can also analyze the user's facial expression using deep learning technology. Deep learning technology is a technology that learns large amounts of data and performs highly accurate facial expression analysis. This allows the analysis unit to accurately analyze the user's facial expression using image recognition technology.

[0034] The generation unit can generate natural language using a generative AI. The generation unit generates natural language using a generative AI. Examples of generative AI include, but are not limited to, natural language generation models such as GPT-4 and Gemini. The generation unit can generate natural language using, for example, GPT-4. GPT-4 is a model that has learned from large amounts of text data and has advanced natural language generation capabilities. The generation unit can also generate natural language using Gemini. Gemini is a model that understands context and generates natural language. This enables the generation unit to respond in natural language using the generative AI.

[0035] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit analyzes the user's past question history and selects the optimal reception method. The analysis of the past question history is performed based on, for example, the type of question, frequency, and answer content, but is not limited to these examples. The reception unit can, for example, analyze the user's past question history and select the optimal reception method. The past question history includes questions the user has asked in the past and the answers to those questions. The reception unit can prioritize receiving questions that the user has frequently asked based on the past question history. The reception unit can also select the optimal reception timing based on the past question history. In this way, the reception unit can select the optimal reception method by analyzing the past question history. For example, it prioritizes receiving questions that the user has frequently asked in the past. The reception unit suggests the optimal reception method based on the user's past question history. The reception unit analyzes the user's past question history and selects the optimal reception timing.

[0036] The reception unit can filter questions and requests based on the user's current situation and areas of interest when receiving the questions and requests. The reception unit can filter questions and requests based on the user's current situation and areas of interest when receiving the questions and requests. Filtering can be performed, for example, based on the user's location information and past behavior history, but is not limited to these examples. The reception unit can, for example, preferentially receive questions and requests related to the user's current situation. The reception unit can also filter related questions and requests based on the user's areas of interest. This allows the reception unit to preferentially receive questions and requests related to the user's current situation and areas of interest. For example, the reception unit preferentially receives questions and requests related to the user's current situation. The reception unit filters related questions and requests based on the user's areas of interest. The reception unit suggests optimal questions and requests based on the user's current situation and areas of interest.

[0037] The reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving a question or a request. The reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving a question or a request. Consideration of the geographical location information is performed, for example, using GPS data or an IP address, but is not limited to these examples. The reception unit can prioritize receiving related questions and requests, for example, based on the user's current location. The reception unit can also suggest optimal questions and requests in consideration of the user's geographical location information. In this way, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location information. For example, based on the user's current location, relevant questions and requests are prioritized. The optimal questions and requests are suggested in consideration of the user's geographical location information. The relevant information is filtered based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when receiving a question or request and receive related information. The reception unit analyzes the user's social media activity when receiving a question or request and receive related information. The analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. The reception unit can, for example, analyze the user's social media activity and preferentially receive related questions or requests. The reception unit can also suggest optimal questions or requests based on the user's social media activity. In this way, the reception unit can analyze the user's social media activity and preferentially receive related information. For example, the reception unit analyzes the user's social media activity and preferentially receive related questions or requests. The reception unit suggests optimal questions or requests based on the user's social media activity. The reception unit analyzes the user's social media activity and filters related information.

[0039] The analysis unit can optimize the analysis algorithm by referring to the user's past facial expression data when analyzing facial expressions. The analysis unit can optimize the analysis algorithm by referring to the user's past facial expression data when analyzing facial expressions. The reference to the past facial expression data is made based on, for example, the storage period, the type of data, the analysis method, etc., but is not limited to these examples. The analysis unit can, for example, optimize the analysis algorithm by referring to the user's past facial expression data. The past facial expression data includes facial expressions shown by the user in the past and their analysis results. The analysis unit can improve the accuracy of the facial expression analysis based on the past facial expression data. As a result, the analysis unit can optimize the analysis algorithm by referring to the past facial expression data. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past facial expression data. The analysis unit improves the accuracy of the facial expression analysis based on the user's past facial expression data. The user's past facial expression data is analyzed and an optimal analysis algorithm is selected.

[0040] The analysis unit can perform facial expression analysis while taking into account attribute information of the user. The analysis unit can perform facial expression analysis while taking into account attribute information of the user. Consideration of attribute information is based on, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit can perform facial expression analysis while taking into account attribute information such as the user's age and gender, for example. The analysis unit can also select an optimal facial expression analysis algorithm based on the user's attribute information. This enables the analysis unit to perform more accurate facial expression analysis by taking into account attribute information of the user. For example, the analysis unit performs facial expression analysis while taking into account attribute information such as the user's age and gender. The optimal facial expression analysis algorithm is selected based on the user's attribute information. The accuracy of facial expression analysis is improved by taking into account user attribute information.

[0041] The analysis unit can perform facial expression analysis taking into account the geographical distribution of users. The analysis unit can perform facial expression analysis taking into account the geographical distribution of users. Consideration of the geographical distribution can be based on, for example, user data by region or a method of aggregating location information, but is not limited to these examples. The analysis unit can perform facial expression analysis taking into account, for example, the geographical distribution of users. The analysis unit can also select an optimal facial expression analysis algorithm based on the geographical distribution of users. This enables the analysis unit to perform more accurate facial expression analysis by considering the geographical distribution of users. For example, the analysis unit performs facial expression analysis taking into account the geographical distribution of users. The optimal facial expression analysis algorithm is selected based on the geographical distribution of users. The accuracy of facial expression analysis is improved by considering the geographical distribution of users.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing facial expressions. The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing facial expressions. Reference to related literature is made based on, for example, academic papers, technical reports, patent documents, etc., but is not limited to these examples. The analysis unit can improve the accuracy of the facial expression analysis by referring to, for example, related literature. Related literature includes the latest research results and technical information related to facial expression analysis. The analysis unit can select an optimal facial expression analysis algorithm based on the related literature. As a result, the analysis unit improves the accuracy of the facial expression analysis by referring to the related literature. For example, the analysis unit improves the accuracy of the facial expression analysis by referring to the related literature. The analysis unit selects an optimal facial expression analysis algorithm based on the related literature. The analysis unit optimizes the accuracy of the facial expression analysis by referring to the related literature.

[0043] The generation unit can adjust the level of detail of the response based on the importance of the question when generating a response. The generation unit adjusts the level of detail of the response based on the importance of the question when generating a response. The evaluation of the importance of a question is performed based on, for example, urgency, impact, user intention, etc., but is not limited to these examples. For example, the generation unit can generate a detailed response to an important question. The generation unit can also generate a standard response to an ordinary question. The generation unit can also generate a concise response to a simple question. In this way, the generation unit can adjust the level of detail of the response based on the importance of the question, thereby enabling a more appropriate response. For example, a detailed response is generated for an important question. A standard response is generated for an ordinary question. A concise response is generated for an easy question.

[0044] The generation unit can apply different generation algorithms depending on the category of the question when generating a response. The generation unit applies different generation algorithms depending on the category of the question when generating a response. Questions can be classified into categories such as technical questions, business questions, and general questions, but is not limited to these examples. For example, the generation unit can apply a specialized generation algorithm to a technical question to generate a response. The generation unit can also apply a standard generation algorithm to a general question to generate a response. The generation unit can also apply an emotion-conscious generation algorithm to an emotional question to generate a response. This allows the generation unit to generate an optimal response depending on the category of the question. For example, a specialized generation algorithm can be applied to a technical question to generate a response. A standard generation algorithm can be applied to a general question to generate a response. A emotion-conscious generation algorithm can be applied to an emotional question to generate a response.

[0045] When generating a response, the generation unit can determine the priority of the response based on the time when the question was submitted. When generating a response, the generation unit determines the priority of the response based on the time when the question was submitted. Evaluation of the time when the question was submitted is performed, for example, based on a timestamp or a record of the submission date and time, but is not limited to such examples. For example, the generation unit can quickly generate a response to an urgent question. The generation unit can also generate a standard response to a normal question. The generation unit can also generate a response at an appropriate time for a past question. In this way, the generation unit can determine the priority of the response based on the time when the question was submitted, thereby enabling a more appropriate response. For example, a response is quickly generated for an urgent question. A standard response is generated for a normal question. A response is generated at an appropriate time for a past question.

[0046] The generation unit can adjust the order of responses based on the relevance of the question when generating a response. The generation unit adjusts the order of responses based on the relevance of the question when generating a response. The evaluation of the relevance of a question is performed based on, for example, the degree of topic agreement or the relevance to past questions, but is not limited to these examples. For example, the generation unit can generate responses preferentially to important questions. The generation unit can also generate responses in an appropriate order to highly relevant questions. The generation unit can also postpone generating responses to less relevant questions. In this way, the generation unit can adjust the order of responses based on the relevance of the question, thereby enabling more appropriate responses. For example, responses are generated preferentially to important questions. Responses are generated in an appropriate order to highly relevant questions. Responses are generated postponed to less relevant questions.

[0047] When providing a response, the providing unit can select the optimal response method by referring to the user's past response history. When providing a response, the providing unit selects the optimal response method by referring to the user's past response history. Referring to the past response history is performed based on, for example, the content of the response, the frequency of responses, user feedback, etc., but is not limited to these examples. For example, the providing unit can select the optimal response method by referring to the user's past response history. The past response history includes responses that the user has received in the past and their evaluations. The providing unit can optimize the response providing method based on the past response history. As a result, the providing unit can select the optimal response method by referring to the past response history. For example, the providing unit selects the optimal response method by referring to the user's past response history. The response providing method is optimized based on the user's past response history. The user's past response history is analyzed and the optimal response method is proposed.

[0048] The providing unit can customize the means of providing a response based on the user's current situation when providing a response. The providing unit customizes the means of providing a response based on the user's current situation when providing a response. Consideration of the current situation is performed, for example, based on real-time location information, a current activity status, etc., but is not limited to these examples. The providing unit can, for example, select an optimal means of providing a response based on the user's current situation. The providing unit can also customize the means of providing a response by considering the user's current situation. In this way, the providing unit can provide a more appropriate response by customizing the means of providing a response based on the user's current situation. For example, the providing unit selects the optimal means of providing a response based on the user's current situation. The providing unit customizes the means of providing a response by considering the user's current situation. The providing unit suggests the optimal means of providing a response based on the user's current situation.

[0049] The providing unit can select the optimal delivery method by taking into consideration the user's geographical location information when providing a response. The providing unit selects the optimal delivery method by taking into consideration the user's geographical location information when providing a response. Consideration of the geographical location information is performed using, for example, GPS data or an IP address, but is not limited to these examples. The providing unit can select the optimal delivery method based on, for example, the user's current location. The providing unit can also optimize the response delivery method by taking into consideration the user's geographical location information. In this way, the providing unit can select the optimal delivery method by taking into consideration the user's geographical location information. For example, the optimal delivery method is selected based on the user's current location. The response delivery method is optimized by taking into consideration the user's geographical location information. The optimal delivery method is proposed based on the user's geographical location information.

[0050] When providing a response, the providing unit can analyze the user's social media activity and suggest a means of delivery. When providing a response, the providing unit can analyze the user's social media activity and suggest a means of delivery. Analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the providing unit can analyze the user's social media activity and suggest an optimal means of delivery. The providing unit can also optimize the means of providing a response based on the user's social media activity. In this way, the providing unit can suggest the optimal means of delivery by analyzing the user's social media activity. For example, the providing unit analyzes the user's social media activity and suggests the optimal means of delivery. The providing unit optimizes the means of providing a response based on the user's social media activity. The providing unit analyzes the user's social media activity and selects the optimal means of delivery.

[0051] The identification unit can optimize the identification algorithm by referring to the user's past emotion data when identifying emotions. The identification unit can optimize the identification algorithm by referring to the user's past emotion data when identifying emotions. Reference to the past emotion data is made based on, for example, a storage period, a data type, an analysis method, etc., but is not limited to these examples. The identification unit can, for example, optimize the identification algorithm by referring to the user's past emotion data. The past emotion data includes emotions previously expressed by the user and analysis results thereof. The identification unit can improve the accuracy of emotion identification based on the past emotion data. This allows the identification unit to optimize the identification algorithm by referring to the past emotion data. For example, the identification algorithm is optimized by referring to the user's past emotion data. The accuracy of emotion identification is improved based on the user's past emotion data. The user's past emotion data is analyzed and an optimal identification algorithm is selected.

[0052] The identification unit can identify emotions taking into consideration the geographical distribution of users when identifying emotions. The identification unit can identify emotions taking into consideration the geographical distribution of users when identifying emotions. Consideration of the geographical distribution can be based on, for example, user data by region or a method of aggregating location information, but is not limited to such examples. The identification unit can identify emotions taking into consideration the geographical distribution of users, for example. The identification unit can also select an optimal emotion identification algorithm based on the geographical distribution of users. In this way, the identification unit can identify emotions more accurately by considering the geographical distribution of users. For example, emotion identification is performed taking into consideration the geographical distribution of users. An optimal emotion identification algorithm is selected based on the geographical distribution of users. The accuracy of emotion identification is improved by considering the geographical distribution of users.

[0053] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating the quality of a response. The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating the quality of a response. The reference to past evaluation data is performed based on, for example, the content of the evaluation, the frequency of the evaluation, user feedback, etc., but is not limited to these examples. The evaluation unit can, for example, optimize the evaluation algorithm by referring to past evaluation data. Past evaluation data includes evaluations made by users in the past and their results. The evaluation unit can improve the response quality evaluation criteria based on the past evaluation data. As a result, the evaluation unit can optimize the evaluation algorithm by referring to the past evaluation data. For example, the evaluation unit optimizes the evaluation algorithm by referring to past evaluation data. The response quality evaluation criteria are improved based on the past evaluation data. The past evaluation data is analyzed and an optimal evaluation algorithm is selected.

[0054] The evaluation unit can evaluate the quality of a response by taking into account the geographical distribution of users. The evaluation unit can evaluate the quality of a response by taking into account the geographical distribution of users. Consideration of the geographical distribution can be based on, for example, user data by region or a method of aggregating location information, but is not limited to such examples. The evaluation unit can evaluate the quality of a response by taking into account the geographical distribution of users, for example. The evaluation unit can also select an optimal evaluation algorithm based on the geographical distribution of users. This enables the evaluation unit to perform a more accurate evaluation by taking into account the geographical distribution of users. For example, the evaluation unit evaluates the quality of a response by taking into account the geographical distribution of users. The optimal evaluation algorithm is selected based on the geographical distribution of users. The response quality evaluation criteria are improved by taking into account the geographical distribution of users.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The conversational robot system may include a learning unit that learns a user's preferences and tendencies by referring to the user's past conversation history. The learning unit can analyze the past conversation history to identify the user's preferred response style and frequently asked questions. For example, if the user has preferred detailed explanations in the past, the system can prioritize providing detailed explanations in future conversations. Also, if the user is interested in a particular topic, the system can prioritize providing information related to that topic. Furthermore, the learning unit can continuously improve the quality of responses based on user feedback.

[0057] The interactive robot system may include an activity detection unit that detects the user's current activity status. The activity detection unit can analyze the user's movements and environmental sounds to estimate what the user is doing. For example, if the user is walking, the interactive robot can provide a short and concise response. If the user is sitting in a quiet environment, the interactive robot can provide a detailed explanation. Furthermore, if the user is driving, the interactive robot can avoid visual information and provide a response only by voice.

[0058] The interactive robot system may include a location information utilization unit that utilizes the user's geographical location information. The location information utilization unit can analyze the user's current location and provide related information. For example, if the user is in a specific location, the location information utilization unit can provide information about services and events related to that location. If the user is traveling, the location information utilization unit can provide information about tourist spots and restaurants. Furthermore, if the user is facing an emergency, the location information utilization unit can provide information about the nearest emergency contact points and evacuation sites.

[0059] The interactive robot system may include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit analyzes the content of a user's posts, the number of likes, the number of followers, and the like to understand the user's interests and trends. For example, if a user frequently posts about a particular topic, information related to that topic can be provided preferentially. Also, if a user is participating in a particular event, information related to that event can be provided. Furthermore, the system can learn the user's preferences and tendencies based on the user's social media activity and improve the quality of responses.

[0060] The interactive robot system may include a feedback analysis unit that analyzes past user feedback. The feedback analysis unit can analyze feedback provided by the user in the past and improve the quality of responses. For example, if the user previously gave a high rating to a particular response, that response style can be reflected in future interactions. Furthermore, for responses that the user previously expressed dissatisfaction with, areas for improvement can be identified and the quality of the response can be improved. Furthermore, the feedback analysis unit can optimize the learning algorithm of the interactive robot based on the user's feedback.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives a question or request from a user. The question or request from a user may be in the form of text, voice, image, etc. For example, the reception unit may receive the question or request from a user as voice. Step 2: The analysis unit analyzes the user's facial expression based on the information received by the reception unit. The facial expression analysis is performed using methods such as facial feature detection and emotion classification. For example, the user's facial expression can be analyzed using image recognition technology. Step 3: The generator generates natural-sounding language based on the information analyzed by the analyzer. Natural-sounding language is generated using grammatically correct sentences and expressions appropriate to the context. For example, natural-sounding language can be generated using generative AI. Generative AI includes natural language generation models such as GPT-4 and Gemini. Step 4: The providing unit provides the response generated by the generating unit. The providing unit can provide the response in a format such as text, audio, or video.

[0063] (Example 2) An interactive robot system according to an embodiment of the present invention accepts a user's questions and requests and responds in natural language while recognizing facial expressions. This interactive robot system begins with the user inputting the question or request to the interactive robot. Next, the interactive robot uses image recognition technology to analyze the user's facial expressions and recognize their emotions. A generation AI then generates a response in natural language to the user's question or request. Finally, the interactive robot provides the generated response to the user. This provides services suitable for bank reception, government office reception, and brick-and-mortar service reception. For example, if a user asks the interactive robot, "I'd like to open an account. How do I go about the procedure?", the interactive robot receives the question as speech. Next, the interactive robot uses image recognition technology to analyze the user's facial expressions and recognize a confused expression. The generation AI then generates a response such as, "We'll guide you through the account opening procedure. First, please prepare the necessary documents. Then, we'll complete the procedure at the counter." Finally, the interactive robot provides the generated response to the user. This allows the user to obtain the necessary information through a dialogue with the interactive robot. This service is suitable for bank reception, government office reception, and store-based service reception, and can be provided as an in-house service from proposal to implementation, which has the potential for high profits. This allows the conversational robot system to respond to user questions and requests in natural language while recognizing facial expressions.

[0064] The interactive robot system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's questions and requests. The user's questions and requests may include, but are not limited to, text, voice, and images. The reception unit may receive the user's questions and requests as voice, for example. The analysis unit analyzes the user's facial expression based on the information received by the reception unit. The facial expression analysis may be performed using, for example, but not limited to, facial feature point detection or emotion classification methods. The analysis unit may analyze the user's facial expression using image recognition technology. The generation unit generates natural-sounding language based on the information analyzed by the analysis unit. The natural-sounding language may be generated using, for example, grammatically correct sentences or expressions appropriate to the context, for example, but not limited to, examples. The generation unit may generate natural-sounding language using a generation AI. The generation AI may include, for example, but not limited to, natural language generation models such as GPT-4 and Gemini. The provision unit provides the response generated by the generation unit. The providing unit can provide a response in the form of, for example, text, audio, video, etc. This allows the interactive robot system according to the embodiment to respond to a user's questions or requests in natural language while recognizing facial expressions.

[0065] The interactive robot system includes an identification unit that identifies a user's emotion. The identification unit identifies the user's emotion. Emotion identification is performed using, for example, facial expression analysis, voice analysis, text analysis, or other methods, but is not limited to these examples. The identification unit can identify the user's emotion using, for example, facial expression analysis. The facial expression analysis is performed using facial feature point detection, emotion classification methods, or the like. The identification unit can also identify the user's emotion using voice analysis. Voice analysis identifies emotions by analyzing the tone and speed of voice, etc. The identification unit can also identify the user's emotion using text analysis. Text analysis identifies emotions by analyzing the content of the user's speech. This allows the identification unit to identify the user's emotion and provide a more appropriate response.

[0066] The interactive robot system includes an evaluation unit that evaluates the quality of a response. The evaluation unit evaluates the quality of the response. The evaluation of the quality of the response is performed using criteria such as, but not limited to, accuracy, appropriateness, and user satisfaction. The evaluation unit can, for example, evaluate the accuracy of the response. The evaluation of accuracy checks whether the response accurately answers the user's question or request. The evaluation unit can also evaluate the appropriateness of the response. The evaluation of appropriateness checks whether the response is appropriate for the context. The evaluation unit can also evaluate user satisfaction. The evaluation of user satisfaction checks whether the user is satisfied with the response. In this way, the evaluation unit can provide a higher quality response by evaluating the quality of the response.

[0067] The reception unit can receive the user's questions and requests as voice. The reception unit receives the user's questions and requests as voice. Voice input is performed using, for example, voice recognition technology, but is not limited to this example. The reception unit can receive the user's questions and requests as voice using, for example, voice recognition technology. Voice recognition technology is a technology that converts the user's speech into text data and can include processing such as noise filtering. This allows the reception unit to allow the user to naturally convey their questions and requests through voice input.

[0068] The analysis unit can analyze the user's facial expression using image recognition technology. The analysis unit analyzes the user's facial expression using image recognition technology. Image recognition technology is performed using, for example, a face recognition algorithm or deep learning technology, but is not limited to these examples. The analysis unit can analyze the user's facial expression using, for example, a face recognition algorithm. The face recognition algorithm is a technology that detects facial feature points and classifies emotions. The analysis unit can also analyze the user's facial expression using deep learning technology. Deep learning technology is a technology that learns large amounts of data and performs highly accurate facial expression analysis. This allows the analysis unit to accurately analyze the user's facial expression using image recognition technology.

[0069] The generation unit can generate natural language using a generative AI. The generation unit generates natural language using a generative AI. Examples of generative AI include, but are not limited to, natural language generation models such as GPT-4 and Gemini. The generation unit can generate natural language using, for example, GPT-4. GPT-4 is a model that has learned from large amounts of text data and has advanced natural language generation capabilities. The generation unit can also generate natural language using Gemini. Gemini is a model that understands context and generates natural language. This enables the generation unit to respond in natural language using the generative AI.

[0070] The reception unit can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The reception unit can estimate the user's emotions using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial features and classifies emotions. The reception unit can also estimate the user's emotions using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The reception unit can also estimate the user's emotions using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the reception unit to adjust the timing of receiving calls according to the user's emotions, enabling more appropriate responses. For example, if the user is stressed, the reception timing can be delayed to allow the user to relax. If the user is in a hurry, the reception timing can be quickly completed. If the user is excited, the reception timing can be adjusted to calm the user.

[0071] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit analyzes the user's past question history and selects the optimal reception method. The analysis of the past question history is performed based on, for example, the type of question, frequency, and answer content, but is not limited to these examples. The reception unit can, for example, analyze the user's past question history and select the optimal reception method. The past question history includes questions the user has asked in the past and the answers to those questions. The reception unit can prioritize receiving questions that the user has frequently asked based on the past question history. The reception unit can also select the optimal reception timing based on the past question history. In this way, the reception unit can select the optimal reception method by analyzing the past question history. For example, it prioritizes receiving questions that the user has frequently asked in the past. The reception unit suggests the optimal reception method based on the user's past question history. The reception unit analyzes the user's past question history and selects the optimal reception timing.

[0072] The reception unit can filter questions and requests based on the user's current situation and areas of interest when receiving the questions and requests. The reception unit can filter questions and requests based on the user's current situation and areas of interest when receiving the questions and requests. Filtering can be performed, for example, based on the user's location information and past behavior history, but is not limited to these examples. The reception unit can, for example, preferentially receive questions and requests related to the user's current situation. The reception unit can also filter related questions and requests based on the user's areas of interest. This allows the reception unit to preferentially receive questions and requests related to the user's current situation and areas of interest. For example, the reception unit preferentially receives questions and requests related to the user's current situation. The reception unit filters related questions and requests based on the user's areas of interest. The reception unit suggests optimal questions and requests based on the user's current situation and areas of interest.

[0073] The reception unit can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The reception unit can estimate the user's emotions using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial features and classifies emotions. The reception unit can also estimate the user's emotions using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The reception unit can also estimate the user's emotions using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the reception unit to prioritize questions and requests according to the user's emotions, enabling more appropriate responses. For example, if the user is stressed, important questions and requests are received first. If the user is relaxed, they are received with normal priority. If the user is in a hurry, questions or requests that require a quick response are given priority.

[0074] The reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving a question or a request. The reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving a question or a request. Consideration of the geographical location information is performed, for example, using GPS data or an IP address, but is not limited to these examples. The reception unit can prioritize receiving related questions and requests, for example, based on the user's current location. The reception unit can also suggest optimal questions and requests in consideration of the user's geographical location information. In this way, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location information. For example, based on the user's current location, relevant questions and requests are prioritized. The optimal questions and requests are suggested in consideration of the user's geographical location information. The relevant information is filtered based on the user's geographical location information.

[0075] The reception unit can analyze the user's social media activity when receiving a question or request and receive related information. The reception unit analyzes the user's social media activity when receiving a question or request and receive related information. The analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. The reception unit can, for example, analyze the user's social media activity and preferentially receive related questions or requests. The reception unit can also suggest optimal questions or requests based on the user's social media activity. In this way, the reception unit can analyze the user's social media activity and preferentially receive related information. For example, the reception unit analyzes the user's social media activity and preferentially receive related questions or requests. The reception unit suggests optimal questions or requests based on the user's social media activity. The reception unit analyzes the user's social media activity and filters related information.

[0076] The analysis unit can estimate the user's emotion and adjust the accuracy of the facial expression analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the accuracy of the facial expression analysis based on the estimated user's emotion. Emotion estimation is performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The analysis unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The analysis unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The analysis unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the analysis unit to adjust the accuracy of the facial expression analysis according to the user's emotion, enabling more accurate analysis. For example, if the user is stressed, the accuracy of the facial expression analysis is increased. If the user is relaxed, the facial expression analysis is performed with normal accuracy. If the user is excited, the accuracy of the facial expression analysis is adjusted.

[0077] The analysis unit can optimize the analysis algorithm by referring to the user's past facial expression data when analyzing facial expressions. The analysis unit can optimize the analysis algorithm by referring to the user's past facial expression data when analyzing facial expressions. The reference to the past facial expression data is made based on, for example, the storage period, the type of data, the analysis method, etc., but is not limited to these examples. The analysis unit can, for example, optimize the analysis algorithm by referring to the user's past facial expression data. The past facial expression data includes facial expressions shown by the user in the past and their analysis results. The analysis unit can improve the accuracy of the facial expression analysis based on the past facial expression data. As a result, the analysis unit can optimize the analysis algorithm by referring to the past facial expression data. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past facial expression data. The analysis unit improves the accuracy of the facial expression analysis based on the user's past facial expression data. The user's past facial expression data is analyzed and an optimal analysis algorithm is selected.

[0078] The analysis unit can perform facial expression analysis while taking into account attribute information of the user. The analysis unit can perform facial expression analysis while taking into account attribute information of the user. Consideration of attribute information is based on, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit can perform facial expression analysis while taking into account attribute information such as the user's age and gender, for example. The analysis unit can also select an optimal facial expression analysis algorithm based on the user's attribute information. This enables the analysis unit to perform more accurate facial expression analysis by taking into account attribute information of the user. For example, the analysis unit performs facial expression analysis while taking into account attribute information such as the user's age and gender. The optimal facial expression analysis algorithm is selected based on the user's attribute information. The accuracy of facial expression analysis is improved by taking into account user attribute information.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The analysis unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The analysis unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The analysis unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotion, enabling more appropriate display. For example, if the user is stressed, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is excited, a visually stimulating display method is provided.

[0080] The analysis unit can perform facial expression analysis taking into account the geographical distribution of users. The analysis unit can perform facial expression analysis taking into account the geographical distribution of users. Consideration of the geographical distribution can be based on, for example, user data by region or a method of aggregating location information, but is not limited to these examples. The analysis unit can perform facial expression analysis taking into account, for example, the geographical distribution of users. The analysis unit can also select an optimal facial expression analysis algorithm based on the geographical distribution of users. This enables the analysis unit to perform more accurate facial expression analysis by considering the geographical distribution of users. For example, the analysis unit performs facial expression analysis taking into account the geographical distribution of users. The optimal facial expression analysis algorithm is selected based on the geographical distribution of users. The accuracy of facial expression analysis is improved by considering the geographical distribution of users.

[0081] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing facial expressions. The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing facial expressions. Reference to related literature is made based on, for example, academic papers, technical reports, patent documents, etc., but is not limited to these examples. The analysis unit can improve the accuracy of the facial expression analysis by referring to, for example, related literature. Related literature includes the latest research results and technical information related to facial expression analysis. The analysis unit can select an optimal facial expression analysis algorithm based on the related literature. As a result, the analysis unit improves the accuracy of the facial expression analysis by referring to the related literature. For example, the analysis unit improves the accuracy of the facial expression analysis by referring to the related literature. The analysis unit selects an optimal facial expression analysis algorithm based on the related literature. The analysis unit optimizes the accuracy of the facial expression analysis by referring to the related literature.

[0082] The generation unit can estimate the user's emotion and adjust the response expression method based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the response expression method based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The generation unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The generation unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The generation unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the generation unit to adjust the response expression method according to the user's emotion, thereby enabling a more appropriate response. For example, if the user is stressed, a simple and highly visible response can be generated. If the user is relaxed, a response including detailed information can be generated. If the user is excited, a visually stimulating response can be generated.

[0083] The generation unit can adjust the level of detail of the response based on the importance of the question when generating a response. The generation unit adjusts the level of detail of the response based on the importance of the question when generating a response. The evaluation of the importance of a question is performed based on, for example, urgency, impact, user intention, etc., but is not limited to these examples. For example, the generation unit can generate a detailed response to an important question. The generation unit can also generate a standard response to an ordinary question. The generation unit can also generate a concise response to a simple question. In this way, the generation unit can adjust the level of detail of the response based on the importance of the question, thereby enabling a more appropriate response. For example, a detailed response is generated for an important question. A standard response is generated for an ordinary question. A concise response is generated for an easy question.

[0084] The generation unit can apply different generation algorithms depending on the category of the question when generating a response. The generation unit applies different generation algorithms depending on the category of the question when generating a response. Questions can be classified into categories such as technical questions, business questions, and general questions, but is not limited to these examples. For example, the generation unit can apply a specialized generation algorithm to a technical question to generate a response. The generation unit can also apply a standard generation algorithm to a general question to generate a response. The generation unit can also apply an emotion-conscious generation algorithm to an emotional question to generate a response. This allows the generation unit to generate an optimal response depending on the category of the question. For example, a specialized generation algorithm can be applied to a technical question to generate a response. A standard generation algorithm can be applied to a general question to generate a response. A emotion-conscious generation algorithm can be applied to an emotional question to generate a response.

[0085] The generation unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The generation unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial features and classifies emotions. The generation unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The generation unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the generation unit to adjust the length of the response according to the user's emotion, thereby enabling a more appropriate response. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point response. If the user is relaxed, the generation unit can generate a longer response with detailed explanations. If the user is excited, the generation unit can generate a response with visually stimulating effects.

[0086] When generating a response, the generation unit can determine the priority of the response based on the time when the question was submitted. When generating a response, the generation unit determines the priority of the response based on the time when the question was submitted. Evaluation of the time when the question was submitted is performed, for example, based on a timestamp or a record of the submission date and time, but is not limited to such examples. For example, the generation unit can quickly generate a response to an urgent question. The generation unit can also generate a standard response to a normal question. The generation unit can also generate a response at an appropriate time for a past question. In this way, the generation unit can determine the priority of the response based on the time when the question was submitted, thereby enabling a more appropriate response. For example, a response is quickly generated for an urgent question. A standard response is generated for a normal question. A response is generated at an appropriate time for a past question.

[0087] The generation unit can adjust the order of responses based on the relevance of the question when generating a response. The generation unit adjusts the order of responses based on the relevance of the question when generating a response. The evaluation of the relevance of a question is performed based on, for example, the degree of topic agreement or the relevance to past questions, but is not limited to these examples. For example, the generation unit can generate responses preferentially to important questions. The generation unit can also generate responses in an appropriate order to highly relevant questions. The generation unit can also postpone generating responses to less relevant questions. In this way, the generation unit can adjust the order of responses based on the relevance of the question, thereby enabling more appropriate responses. For example, responses are generated preferentially to important questions. Responses are generated in an appropriate order to highly relevant questions. Responses are generated postponed to less relevant questions.

[0088] The providing unit can estimate the user's emotion and adjust the response providing method based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the response providing method based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The providing unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The providing unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The providing unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the providing unit to adjust the response providing method according to the user's emotion, thereby enabling a more appropriate response. For example, if the user is stressed, the response can be provided in a simple and highly visible manner. If the user is relaxed, the response can be provided in a manner that includes detailed information. If the user is excited, the response can be provided in a visually stimulating manner.

[0089] When providing a response, the providing unit can select the optimal response method by referring to the user's past response history. When providing a response, the providing unit selects the optimal response method by referring to the user's past response history. Referring to the past response history is performed based on, for example, the content of the response, the frequency of responses, user feedback, etc., but is not limited to these examples. For example, the providing unit can select the optimal response method by referring to the user's past response history. The past response history includes responses that the user has received in the past and their evaluations. The providing unit can optimize the response providing method based on the past response history. As a result, the providing unit can select the optimal response method by referring to the past response history. For example, the providing unit selects the optimal response method by referring to the user's past response history. The response providing method is optimized based on the user's past response history. The user's past response history is analyzed and the optimal response method is proposed.

[0090] The providing unit can customize the means of providing a response based on the user's current situation when providing a response. The providing unit customizes the means of providing a response based on the user's current situation when providing a response. Consideration of the current situation is performed, for example, based on real-time location information, a current activity status, etc., but is not limited to these examples. The providing unit can, for example, select an optimal means of providing a response based on the user's current situation. The providing unit can also customize the means of providing a response by considering the user's current situation. In this way, the providing unit can provide a more appropriate response by customizing the means of providing a response based on the user's current situation. For example, the providing unit selects the optimal means of providing a response based on the user's current situation. The providing unit customizes the means of providing a response by considering the user's current situation. The providing unit suggests the optimal means of providing a response based on the user's current situation.

[0091] The providing unit can estimate the user's emotion and determine the priority of providing responses based on the estimated user's emotion. The providing unit can estimate the user's emotion and determine the priority of providing responses based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The providing unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial features and classifies emotions. The providing unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The providing unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the providing unit to determine the priority of providing responses based on the user's emotion, thereby enabling more appropriate responses. For example, if the user is stressed, important responses can be provided with priority. If the user is relaxed, responses can be provided with normal priority. If the user is in a hurry, responses requiring a quick response can be provided with priority.

[0092] The providing unit can select the optimal delivery method by taking into consideration the user's geographical location information when providing a response. The providing unit selects the optimal delivery method by taking into consideration the user's geographical location information when providing a response. Consideration of the geographical location information is performed using, for example, GPS data or an IP address, but is not limited to these examples. The providing unit can select the optimal delivery method based on, for example, the user's current location. The providing unit can also optimize the response delivery method by taking into consideration the user's geographical location information. In this way, the providing unit can select the optimal delivery method by taking into consideration the user's geographical location information. For example, the optimal delivery method is selected based on the user's current location. The response delivery method is optimized by taking into consideration the user's geographical location information. The optimal delivery method is proposed based on the user's geographical location information.

[0093] When providing a response, the providing unit can analyze the user's social media activity and suggest a means of delivery. When providing a response, the providing unit can analyze the user's social media activity and suggest a means of delivery. Analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the providing unit can analyze the user's social media activity and suggest an optimal means of delivery. The providing unit can also optimize the means of providing a response based on the user's social media activity. In this way, the providing unit can suggest the optimal means of delivery by analyzing the user's social media activity. For example, the providing unit analyzes the user's social media activity and suggests the optimal means of delivery. The providing unit optimizes the means of providing a response based on the user's social media activity. The providing unit analyzes the user's social media activity and selects the optimal means of delivery.

[0094] The identification unit can estimate the user's emotion and adjust the accuracy of emotion identification based on the estimated user's emotion. The identification unit can estimate the user's emotion and adjust the accuracy of emotion identification based on the estimated user's emotion. Emotion estimation is performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The identification unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The identification unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The identification unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the identification unit to adjust the accuracy of emotion identification according to the user's emotion, thereby enabling more accurate emotion identification. For example, if the user is stressed, the accuracy of emotion identification is increased. If the user is relaxed, emotion identification is performed with normal accuracy. If the user is excited, the accuracy of emotion identification is adjusted.

[0095] The identification unit can optimize the identification algorithm by referring to the user's past emotion data when identifying emotions. The identification unit can optimize the identification algorithm by referring to the user's past emotion data when identifying emotions. Reference to the past emotion data is made based on, for example, a storage period, a data type, an analysis method, etc., but is not limited to these examples. The identification unit can, for example, optimize the identification algorithm by referring to the user's past emotion data. The past emotion data includes emotions previously expressed by the user and analysis results thereof. The identification unit can improve the accuracy of emotion identification based on the past emotion data. This allows the identification unit to optimize the identification algorithm by referring to the past emotion data. For example, the identification algorithm is optimized by referring to the user's past emotion data. The accuracy of emotion identification is improved based on the user's past emotion data. The user's past emotion data is analyzed and an optimal identification algorithm is selected.

[0096] The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The identification unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The identification unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The identification unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the identification unit to adjust the display method of the identification result according to the user's emotion, thereby enabling more appropriate display. For example, if the user is stressed, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is excited, a visually stimulating display method is provided.

[0097] The identification unit can identify emotions taking into consideration the geographical distribution of users when identifying emotions. The identification unit can identify emotions taking into consideration the geographical distribution of users when identifying emotions. Consideration of the geographical distribution can be based on, for example, user data by region or a method of aggregating location information, but is not limited to such examples. The identification unit can identify emotions taking into consideration the geographical distribution of users, for example. The identification unit can also select an optimal emotion identification algorithm based on the geographical distribution of users. In this way, the identification unit can identify emotions more accurately by considering the geographical distribution of users. For example, emotion identification is performed taking into consideration the geographical distribution of users. An optimal emotion identification algorithm is selected based on the geographical distribution of users. The accuracy of emotion identification is improved by considering the geographical distribution of users.

[0098] The evaluation unit can estimate the user's emotion and adjust the quality evaluation criteria of the response based on the estimated user's emotion. The evaluation unit can estimate the user's emotion and adjust the quality evaluation criteria of the response based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The evaluation unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The evaluation unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The evaluation unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the evaluation unit to adjust the quality evaluation criteria of the response according to the user's emotion, enabling more appropriate evaluation. For example, if the user is stressed, the quality evaluation criteria of the response can be made stricter. If the user is relaxed, the response can be evaluated using normal evaluation criteria. If the user is excited, the quality evaluation criteria of the response can be adjusted.

[0099] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating the quality of a response. The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating the quality of a response. The reference to past evaluation data is performed based on, for example, the content of the evaluation, the frequency of the evaluation, user feedback, etc., but is not limited to these examples. The evaluation unit can, for example, optimize the evaluation algorithm by referring to past evaluation data. Past evaluation data includes evaluations made by users in the past and their results. The evaluation unit can improve the response quality evaluation criteria based on the past evaluation data. As a result, the evaluation unit can optimize the evaluation algorithm by referring to the past evaluation data. For example, the evaluation unit optimizes the evaluation algorithm by referring to past evaluation data. The response quality evaluation criteria are improved based on the past evaluation data. The past evaluation data is analyzed and an optimal evaluation algorithm is selected.

[0100] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The evaluation unit can estimate the user's emotion using, for example, facial expression analysis. Facial expression analysis is a technology that detects facial feature points and classifies emotions. The evaluation unit can also estimate the user's emotion using voice analysis. Voice analysis estimates emotions by analyzing the tone and speed of voice. The evaluation unit can also estimate the user's emotion using text analysis. Text analysis estimates emotions by analyzing the content of the user's speech. This allows the evaluation unit to adjust the display method of the evaluation result according to the user's emotion, enabling more appropriate display. For example, if the user is stressed, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is excited, a visually stimulating display method is provided.

[0101] The evaluation unit can evaluate the quality of a response by taking into account the geographical distribution of users. The evaluation unit can evaluate the quality of a response by taking into account the geographical distribution of users. Consideration of the geographical distribution can be based on, for example, user data by region or a method of aggregating location information, but is not limited to such examples. The evaluation unit can evaluate the quality of a response by taking into account the geographical distribution of users, for example. The evaluation unit can also select an optimal evaluation algorithm based on the geographical distribution of users. This enables the evaluation unit to perform a more accurate evaluation by taking into account the geographical distribution of users. For example, the evaluation unit evaluates the quality of a response by taking into account the geographical distribution of users. The optimal evaluation algorithm is selected based on the geographical distribution of users. The response quality evaluation criteria are improved by taking into account the geographical distribution of users. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's question or request as voice using the microphone 38B of the smart device 14. The analysis unit analyzes the user's facial expression using the camera 42 of the smart device 14. The generation unit generates natural language using the identification processing unit 290 of the data processing device 12. The provision unit provides the generated response to the user using the speaker 40B of the smart device 14. The identification unit identifies the user's emotion using the identification processing unit 290 of the data processing device 12. The evaluation unit evaluates the quality of the response using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's question or request as voice using the microphone 238 of the smart glasses 214. The analysis unit analyzes the user's facial expression using the camera 42 of the smart glasses 214. The generation unit generates natural language using the identification processing unit 290 of the data processing device 12. The provision unit provides the generated response to the user using the speaker 240 of the smart glasses 214. The identification unit identifies the user's emotion using the identification processing unit 290 of the data processing device 12. The evaluation unit evaluates the quality of the response using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's question or request as voice using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the user's facial expression using the camera 42 of the headset type terminal 314. The generation unit generates natural language using the identification processing unit 290 of the data processing device 12. The provision unit provides the generated response to the user using the speaker 240 of the headset type terminal 314. The identification unit identifies the user's emotion using the identification processing unit 290 of the data processing device 12. The evaluation unit evaluates the quality of the response using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the user's questions or requests as voice using the microphone 238 of the robot 414. The analysis unit analyzes the user's facial expressions using the camera 42 of the robot 414. The generation unit generates natural language using the identification processing unit 290 of the data processing device 12. The provision unit provides the generated response to the user using the speaker 240 of the robot 414. The identification unit identifies the user's emotions using the identification processing unit 290 of the data processing device 12. The evaluation unit evaluates the quality of the response using the identification processing unit 290 of the data processing device 12.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The interactive robot system may further include a voice analysis unit that analyzes the tone and speed of the user's voice. The voice analysis unit can analyze the tone and speed of the user's voice to estimate the user's emotions and level of urgency. For example, if the user's voice is high-pitched and fast, it can estimate that the level of urgency is high and provide a prompt response. Alternatively, if the user's voice is low and slow, it can estimate that the user is relaxed and provide a detailed explanation. Furthermore, the voice analysis unit can monitor changes in the user's voice in real time and adjust responses according to the progress of the dialogue.

[0104] The conversational robot system may include a learning unit that learns a user's preferences and tendencies by referring to the user's past conversation history. The learning unit can analyze the past conversation history to identify the user's preferred response style and frequently asked questions. For example, if the user has preferred detailed explanations in the past, the system can prioritize providing detailed explanations in future conversations. Also, if the user is interested in a particular topic, the system can prioritize providing information related to that topic. Furthermore, the learning unit can continuously improve the quality of responses based on user feedback.

[0105] The interactive robot system may include a tone adjustment unit that estimates the user's emotions and adjusts the tone of the conversation based on the estimated emotions. The tone adjustment unit can analyze the user's emotions and provide a response in an appropriate tone. For example, if the user is angry, the tone adjustment unit can respond in a calm tone to reassure the user. If the user is sad, the tone adjustment unit can respond in a gentle tone to encourage the user. Furthermore, if the user is excited, the tone adjustment unit can respond in a cheerful tone to liven up the conversation.

[0106] The interactive robot system may include an activity detection unit that detects the user's current activity status. The activity detection unit can analyze the user's movements and environmental sounds to estimate what the user is doing. For example, if the user is walking, the interactive robot can provide a short and concise response. If the user is sitting in a quiet environment, the interactive robot can provide a detailed explanation. Furthermore, if the user is driving, the interactive robot can avoid visual information and provide a response only by voice.

[0107] The interactive robot system can include a content customization unit that estimates the user's emotions and customizes the content of the response based on the estimated emotions. The content customization unit can analyze the user's emotions and provide a response with appropriate content. For example, if the user is feeling stressed, the content customization unit can provide information or advice to help the user relax. If the user is happy, the content customization unit can provide positive information or a congratulatory message. Furthermore, if the user is feeling anxious, the content customization unit can provide reassuring information or support.

[0108] The interactive robot system may include a location information utilization unit that utilizes the user's geographical location information. The location information utilization unit can analyze the user's current location and provide related information. For example, if the user is in a specific location, the location information utilization unit can provide information about services and events related to that location. If the user is traveling, the location information utilization unit can provide information about tourist spots and restaurants. Furthermore, if the user is facing an emergency, the location information utilization unit can provide information about the nearest emergency contact points and evacuation sites.

[0109] The interactive robot system may include a timing adjustment unit that estimates the user's emotions and adjusts the timing of a response based on the estimated emotions. The timing adjustment unit can analyze the user's emotions and provide a response at an appropriate time. For example, if the user is impatient, the timing adjustment unit can provide a response quickly. If the user is relaxed, the timing adjustment unit can provide a response at a slower pace. Furthermore, if the user is confused, the timing adjustment unit can provide a response at an appropriate time to calm the user.

[0110] The interactive robot system may include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit analyzes the content of a user's posts, the number of likes, the number of followers, and the like to understand the user's interests and trends. For example, if a user frequently posts about a particular topic, information related to that topic can be provided preferentially. Also, if a user is participating in a particular event, information related to that event can be provided. Furthermore, the system can learn the user's preferences and tendencies based on the user's social media activity and improve the quality of responses.

[0111] The interactive robot system may include a format adjustment unit that estimates a user's emotion and adjusts the format of a response based on the estimated emotion. The format adjustment unit can analyze the user's emotion and provide a response in an appropriate format. For example, if the user is feeling stressed, the response can be provided in a simple, highly visible format. If the user is relaxed, the response can be provided in a format that includes detailed information. Furthermore, if the user is excited, the response can be provided in a visually stimulating format.

[0112] The interactive robot system may include a feedback analysis unit that analyzes past user feedback. The feedback analysis unit can analyze feedback provided by the user in the past and improve the quality of responses. For example, if the user previously gave a high rating to a particular response, that response style can be reflected in future interactions. Furthermore, for responses that the user previously expressed dissatisfaction with, areas for improvement can be identified and the quality of the response can be improved. Furthermore, the feedback analysis unit can optimize the learning algorithm of the interactive robot based on the user's feedback.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives a question or request from a user. The question or request from a user may be in the form of text, voice, image, etc. For example, the reception unit may receive the question or request from a user as voice. Step 2: The analysis unit analyzes the user's facial expression based on the information received by the reception unit. The facial expression analysis is performed using methods such as facial feature detection and emotion classification. For example, the user's facial expression can be analyzed using image recognition technology. Step 3: The generator generates natural-sounding language based on the information analyzed by the analyzer. Natural-sounding language is generated using grammatically correct sentences and expressions appropriate to the context. For example, natural-sounding language can be generated using generative AI. Generative AI includes natural language generation models such as GPT-4 and Gemini. Step 4: The providing unit provides the response generated by the generating unit. The providing unit can provide the response in a format such as text, audio, or video.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions and requests from users; an analysis unit that analyzes a facial expression of a user based on the information received by the reception unit; a generation unit that generates natural language based on the information analyzed by the analysis unit; a providing unit that provides the response generated by the generating unit; A system comprising:

2. Equipped with an identification unit that identifies the user's emotion 2. The system of claim 1.

3. Equipped with an evaluation unit that evaluates the quality of the response 2. The system of claim 1.

4. The reception unit Accepting user questions and requests via voice 2. The system of claim 1.

5. The analysis unit Analyzing the user's facial expressions using image recognition technology 2. The system of claim 1.

6. The generation unit Generate natural language using generative AI 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of accepting questions or requests based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1.

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